Workflow for assigning putative sources to de novo peptide sequences

Abstract

Methods and systems are described for optimizing search results by querying multiple databases with false discoveries, and random hit rates are presented herein. Methods and systems adapted for assigning putative sources to de novo peptide sequences and / or creating workflows for making said assignments are presented herein. Generally, embodiments of methods, systems and devices directed to assigning putative sources to de novo peptide sequences and / or creating workflows for making said assignments are described herein.

Description

[Technical field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims priority to U.S. Provisional Application No. 63 / 159,879, filed March 11, 2021, and U.S. Provisional Application No. 63 / 159,880, filed March 11, 2021, the contents of each of which are incorporated by reference herein in their entirety.

[0002] Technical Field In some embodiments, the present invention relates to a computer method / system for optimizing search results by querying multiple databases according to false discovery rate, random hit rate. [Background technology]

[0003] background Many data sources are produced and maintained throughout the world. The large number of data sources almost guarantees that a query can be answered in part or in whole using one of these data sources. The mere task of performing a query can be difficult, whether the scope of the query is within a local computer system, a private network, a local area network, or the World Wide Web. The process of querying these data sources is made even more difficult as users must decide which data sources are reliable enough to yield meaningful search results. For example, users must consider both the relative accuracy of the sources and the timeliness of the data contained within the sources. These and other shortcomings are addressed herein.

[0004] In the field of bioinformatics, there have been attempts to assign putative sources to de novo peptide sequences (e.g., translated from RNA, synthesized from DNA, etc.) By definition, putative sources have not been fully experimentally confirmed and are therefore flawed. Summary of the Invention [Means for solving the problem]

[0005] A brief summary Generally, described herein are method, system and device embodiments directed to assigning putative sources to de novo peptide sequences and / or creating workflows for making said assignments. In some embodiments, the invention includes workflows that increase the confidence of an assigned putative source in the absence of experimental confirmation of the source assignment.

[0006] In one embodiment, a putative source of a peptide sequence can be determined based at least in part on one or more searches of the peptide sequence in one or more databases, where the one or more searches are performed in order of increasing random hit rate until a putative source is determined. The random hit rate for each individual search can be determined based at least in part on the number of random peptide sequences found by the individual searches. The one or more databases can include, but are not limited to, an extended human proteome database, a human genome database, a non-endogenous proteome database, additional databases, and combinations thereof. The one or more searches can include, but are not limited to, a linear human proteome search for peptide sequences in the extended human proteome database, a linear human genome search of a translation of the human genome database, a linear mismatch search for peptides with mismatches to peptide sequences in the extended human proteome database, a linear non-endogenous search for peptide sequences in the non-endogenous proteome database, a cis-splice search in the extended human proteome database, and a trans-splice search in the extended human proteome database. Each of the searches may represent a distinct potential source of the searched peptide sequence if the distinct source finds a match. The probable source determined for the searched peptide sequence may be the potential source identified by the search step having the lowest random hit rate at which a match for the searched peptide was found.

[0007] In one embodiment, the peptide source search step is adapted to generate a peptide source assignment workflow. OrderingA plurality of random peptide sequences may be generated, and each of the random peptide sequences may be searched by a respective peptide source searching step. A random hit rate may be determined for each peptide source searching step based at least in part on the number of the plurality of random peptide sequences found by the peptide source searching step. The peptide source searching steps may be ranked in the workflow from lowest random hit rate to highest random hit rate. Ordering The random hit rate may increase as the number of random peptide sequences found increases. The peptide source search step may include, but is not limited to, a linear human proteome search for peptide sequences in the extended human proteome database, a linear human genome search of the translation of the human genome database, a linear mismatch search for peptides with mismatches to the peptide sequence, a linear non-endogenous search for peptide sequences in the non-endogenous proteome database, a cis-splice search in the extended human proteome database, and a trans-splice search in the extended human proteome database.

[0008] Described herein are embodiments of a method of the present invention that include creating a plurality of simulated random queries, determining a number of matches associated with each source based on applying the plurality of simulated random queries to each source of a plurality of sources, determining a false discovery rate associated with each source based on the number of matches associated with each source, and creating a query support data structure configured to facilitate application of new queries to the plurality of sources based on the false discovery rates.

[0009] Also described are method embodiments that include receiving a query, applying the query to one or more sources of the multiple sources based on a query support data structure, determining an indicator associated with a source of the multiple sources associated with the query result based on the query results, and applying the indicator to the query.

[0010] Various steps of the methods disclosed herein, or steps performed by the systems disclosed herein, may be performed at the same or different times, in the same or different geographic locations, e.g., countries, and / or by the same or different persons.

[0011] Additional advantages of the method and system embodiments will be set forth in part in the description which follows and in part will be understood from the description or may be learned by practice of the method and system embodiments. The advantages of the disclosed embodiments of the method and system will be realized and attained by means of the elements and combinations particularly pointed out in the appended claims. It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention as claimed.

[0012] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate several embodiments of the disclosed methods and systems and, together with the description, serve to explain the principles of the disclosed methods and systems. The present invention provides, for example, the following items. (Item 1) 1. A method for determining a putative source of a peptide sequence of a peptide, comprising: receiving a peptide sequence; and determining the putative sources associated with the peptide sequence based at least in part on one or more searches of the peptide sequence in one or more databases; each individual search of the one or more searches has a random hit rate based at least in part on the number of random sequences found by the individual search; the one or more searches are performed in order of increasing random hit rate until the putative source is determined. method. (Item 2) the one or more databases comprising an extended human proteome database; The expanded human proteome database includes a computer-readable representation of translations from messenger ribonucleic acid (RNA) and non-coding RNA; The method according to item 1. (Item 3) 3. The method of claim 2, wherein the extended human proteome database comprises a computer-readable representation of translation from microRNA. (Item 4) 3. The method of claim 2, wherein the extended human proteome database comprises a computer-readable representation of translation from long non-coding RNA. (Item 5) 3. The method of claim 2, wherein the extended human proteome database comprises a computer-readable representation of the translation of a human endogenous retrovirus. (Item 6) the one or more searches comprise a linear human proteome search for the peptide sequence in the extended human proteome database; if the linear human proteome search for the peptide sequence in the extended human proteome database finds the peptide sequence in the extended human proteome database, the putative source is a linear extended human proteome source; The method according to item 2. (Item 7) and identifying whether said peptides are putatively translated from messenger RNA or non-coding RNA when said source is said linear extended human proteome source. The method according to item 6, further comprising: (Item 8) the one or more databases include a human genome database; the one or more searches comprise a linear human genome search of translations of the human genome database; If the linear human genome search finds a human genome sequence from which the peptide is putatively synthesized, then the putative source is a linear genome source. The method according to item 2. (Item 9) said linear human genome search excluding portions of said human genome into which said messenger RNA and said non-coding RNA are transcribed and including the remaining portions of said human genome in said extended human proteome database; the linear human genome search comprises a search of six frame translations of the human genome; The method according to item 8. (Item 10) the one or more searches include a linear mismatch search for peptides having mismatches to the peptide sequence in the extended human proteome database; if the linear mismatch search finds a peptide sequence having a mismatch to the peptide sequence in the extended human proteome database, the putative source is a linear mismatch in the extended human proteome. The method according to item 2. (Item 11) 11. The method of claim 10, wherein the linear mismatch search is a search for peptide sequences having only a single mismatch to the peptide sequence. (Item 12) the one or more databases include a non-endogenous proteome database that includes computer-readable representations of proteins translated from RNA from the non-endogenous organism and / or synthesized by the non-endogenous organism; said one or more searches comprising a linear non-endogenous search for said peptide sequence in said non-endogenous proteome database; If the linear non-endogenous search finds the peptide sequence in the non-endogenous proteome database, the putative source is a linear non-endogenous proteome source. The method according to item 1. (Item 13) 13. The method of claim 12, wherein the non-endogenous proteome database comprises a basic local alignment search tool (BLAST) database. (Item 14) the one or more searches include a cis-splice search for peptide fragments that may be cis-spliced ​​to match the peptide sequence in the extended human proteome database; if the cis-splice search finds a peptide fragment in the extended human proteome database that can be cis-spliced ​​to match the peptide sequence, the source is a cis-spliced ​​human proteome source; The method according to item 2. (Item 15) wherein the one or more searches comprise a trans-splice search for computer readable representations of peptide fragments that can be trans-spliced ​​to match the peptide sequence in the extended human proteome database; if the trans-splice search finds a computer-readable representation of a peptide fragment that can be trans-spliced ​​to match the peptide sequence in the extended human proteome database, the source is a trans-spliced ​​human proteome source. The method according to item 2. (Item 16) 16. The method of claim 15, wherein if the trans-splice search does not find a computer-readable representation of a peptide fragment that can be trans-spliced ​​to match the peptide sequence, the putative source is determined to be unidentified. (Item 17) the one or more databases include a human genome database; The one or more searches are sequentially ordered in a workflow as follows: a linear human proteome search for said peptide sequence in said extended human proteome database; a linear human genome search of translations of said human genome database; a linear mismatch search for peptides having mismatches to the peptide sequence in the extended human proteome database; and a cis-splice search for peptide fragments that can be cis-spliced ​​to match the peptide sequence in the extended human proteome database; Including, The method according to item 2. (Item 18) the one or more databases include a non-endogenous proteome database that includes computer-readable representations of proteins translated from RNA from the non-endogenous organism and / or synthesized by the non-endogenous organism; the one or more searches further comprise a linear non-endogenous search for the peptide sequence in the non-endogenous proteome database; the linear non-endogenous search is sequentially ordered in the workflow after the linear mismatch search and before the cis-splice search. Item 18. The method according to item 17. (Item 19) the one or more searches further comprising a trans-splice search for peptide fragments that can be trans-spliced ​​to match the peptide sequence in the extended human proteome database; the trans-splice search is sequentially ordered in the workflow after the cis-splice search. Item 18. The method according to item 17. (Item 20) stopping the progression of the workflow to subsequent searches of the one or more searches if the putative source is determined for the peptide sequence. 18. The method of claim 17, further comprising: (Item 21) the peptide sequence comprises at least one ambiguous residue, generating a plurality of permuted peptide sequences each including a possible residue for each of said at least one ambiguous residue; determining a distinct potential source for each of said plurality of permuted peptide sequences; and determining the putative sources of the peptide sequences such that the putative sources are distinct possible sources; Further comprising: The method according to item 1. (Item 22) 22. The method of claim 21, wherein the possible residues for each of the at least one ambiguous residue include leucine and isoleucine. (Item 23) determining an individual random hit rate for each of said individual possible sources, such that said random hit rate increases as a number of random sequences are found by individual searches of said one or more searches; and determining the putative sources such that the individual random hit rate of the putative sources is the lowest of the individual random hit rates for each of the possible sources; 22. The method of claim 21, further comprising: (Item 24) identifying one or more likely permuted peptide sequences of said plurality of permuted peptide sequences, such that each of said one or more likely permuted peptide sequences is associated with said putative source. 22. The method of claim 21, further comprising: (Item 25) 2. The method of claim 1, wherein the peptide sequence is a de novo peptide sequence determined by mass spectrometry. (Item 26) A non-transitory computer-readable medium configured to be in communication with one or more processors of a computing device, the non-transitory computer-readable medium including instructions thereon that, when executed by the processor, cause the computing device to: As an input, a peptide sequence is received, determining a putative source associated with the peptide sequence based at least in part on one or more searches of the peptide sequence in one or more databases; wherein each individual search of the one or more searches has a random hit rate based at least in part on the number of random sequences found by the individual search; the one or more searches are performed in order of increasing random hit rate until the putative source is determined; and A non-transitory computer-readable medium that provides, as an output, the inferred source. (Item 27) the one or more databases comprising an extended human proteome database; The expanded human proteome database includes a computer-readable representation of translations from messenger ribonucleic acid (RNA) and non-coding RNA; Item 27. The non-transitory computer-readable medium of item 26. (Item 28) 28. The non-transitory computer readable medium of claim 27, wherein the extended human proteome database comprises a computer readable representation of translation from microRNA. (Item 29) 28. The non-transitory computer readable medium of claim 27, wherein the extended human proteome database comprises a computer readable representation of a translation from long non-coding RNA. (Item 30) 28. The non-transitory computer readable medium of claim 27, wherein the extended human proteome database comprises a computer readable representation of the translation of a human endogenous retrovirus. (Item 31) the one or more searches comprise a linear human proteome search for the peptide sequence in the extended human proteome database; if the linear human proteome search for the peptide sequence in the extended human proteome database finds the peptide sequence in the extended human proteome database, the putative source is a linear extended human proteome source; Item 28. The non-transitory computer-readable medium of item 27. (Item 32) The instructions, when executed by the processor, cause the computing device to: 32. The non-transitory computer readable medium of claim 31, wherein when the source is the linear extended human proteome source, the medium identifies whether the peptide is putatively translated from messenger RNA or non-coding RNA. (Item 33) the one or more databases include a human genome database; the one or more searches comprise a linear human genome search of translations of the human genome database; If the linear human genome search finds a human genome sequence from which a peptide is putatively synthesized, then the putative source is a linear genome source. Item 28. The non-transitory computer-readable medium of item 27. (Item 34) the linear human genome search excludes portions of the human genome from which the messenger RNA and the non-coding RNA are transcribed, and includes the remaining portions of the human genome in the extended human proteome database; Item 34. The non-transitory computer-readable medium of item 33. (Item 35) the one or more searches include a linear mismatch search for peptides having mismatches to the peptide sequence in the extended human proteome database; if the linear mismatch search finds a peptide sequence having a mismatch to the peptide sequence in the extended human proteome database, the putative source is a linear mismatch in the extended human proteome. Item 28. The non-transitory computer-readable medium of item 27. (Item 36) 36. The non-transitory computer-readable medium of claim 35, wherein the linear mismatch search is a search for peptide sequences having only a single mismatch to the peptide sequence. (Item 37) the one or more databases include a non-endogenous proteome database that includes computer-readable representations of proteins translated from RNA from the non-endogenous organism and / or synthesized by the non-endogenous organism; said one or more searches comprising a linear non-endogenous search for said peptide sequence in said non-endogenous proteome database; If the linear non-endogenous search finds the peptide sequence in the non-endogenous proteome database, the putative source is a linear non-endogenous proteome source. Item 27. The non-transitory computer-readable medium of item 26. (Item 38) 38. The non-transitory computer-readable medium of claim 37, wherein the non-endogenous proteome database comprises a basic local alignment search tool (BLAST) database. (Item 39) the one or more searches include a cis-splice search for peptide fragments that may be cis-spliced ​​to match the peptide sequence in the extended human proteome database; if the cis-splice search finds a peptide fragment in the extended human proteome database that can be cis-spliced ​​to match the peptide sequence, the source is a cis-spliced ​​human proteome source; Item 28. The non-transitory computer-readable medium of item 27. (Item 40) wherein the one or more searches comprise a trans-splice search for computer readable representations of peptide fragments that can be trans-spliced ​​to match the peptide sequence in the extended human proteome database; if the trans-splice search finds a computer-readable representation of a peptide fragment that can be trans-spliced ​​to match the peptide sequence in the extended human proteome database, the source is a trans-spliced ​​human proteome source. Item 28. The non-transitory computer-readable medium of item 27. (Item 41) 41. The non-transitory computer-readable medium of claim 40, wherein if the trans-splice search does not find a computer-readable representation of a peptide fragment that can be trans-spliced ​​to match the peptide sequence, the putative source is determined to be unidentified. (Item 42) the one or more databases include a human genome database; The one or more searches are sequentially ordered in a workflow as follows: a linear human proteome search for said peptide sequence in said extended human proteome database; a linear human genome search of translations of said human genome database; a linear mismatch search for peptides having mismatches to the peptide sequence in the extended human proteome database; and a cis-splice search for peptide fragments that can be cis-spliced ​​to match the peptide sequence in the extended human proteome database; Including, Item 28. The non-transitory computer-readable medium of item 27. (Item 43) the one or more databases include a non-endogenous proteome database that includes computer-readable representations of proteins translated from RNA from the non-endogenous organism and / or synthesized by the non-endogenous organism; the one or more searches further comprise a linear non-endogenous search for the peptide sequence in the non-endogenous proteome database; the linear non-endogenous search is sequentially ordered in the workflow after the linear mismatch search and before the cis-splice search. Item 43. The non-transitory computer-readable medium of item 42. (Item 44) the one or more searches further comprising a trans-splice search for peptide fragments that can be trans-spliced ​​to match the peptide sequence in the extended human proteome database; the trans-splice search is sequentially ordered in the workflow after the cis-splice search. Item 43. The method according to item 42. (Item 45) The instructions, when executed by the processor, cause the computing device to: if the putative source is determined for the peptide sequence, stopping the workflow from proceeding further with the one or more searches. Item 43. The non-transitory computer-readable medium of item 42. (Item 46) the peptide sequence comprises at least one ambiguous residue, The instructions, when executed by the processor, cause the computing device to: generating a plurality of permuted peptide sequences each including a possible residue for each of said at least one ambiguous residue; determining a distinct possible source for each of said plurality of permuted peptide sequences; and determining the putative sources of the peptide sequences such that the putative sources are distinct possible sources; Item 27. The non-transitory computer-readable medium of item 26. (Item 47) 47. The non-transitory computer-readable medium of claim 46, wherein the possible residues for each of the at least one ambiguous residue include leucine and isoleucine. (Item 48) The instructions, when executed by the processor, cause the computing device to: determining an individual random hit rate for each of said individual possible sources, such that said random hit rate increases as a number of random sequences are found by individual searches of said one or more searches; and determining the putative sources such that the individual random hit rate of the putative sources is the lowest of the individual random hit rates for each of the possible sources; Item 47. The non-transitory computer-readable medium of item 46. (Item 49) The instructions, when executed by the processor, cause the computing device to: identifying one or more likely permuted peptide sequences of said plurality of permuted peptide sequences, such that each of said one or more likely permuted peptide sequences is associated with said putative source. Item 47. The non-transitory computer-readable medium of item 46. (Item 50) 27. The non-transitory computer readable medium of claim 26, wherein the peptide sequence is a de novo peptide sequence determined by mass spectrometry. (Item 51) 1. A method for sequencing a peptide source assignment workflow, comprising: generating a plurality of random peptide sequences; Determining a plurality of peptide source search steps; searching for each of the plurality of random peptide sequences by each of the plurality of peptide source searching steps; determining, for each of the plurality of peptide source searching steps, a random hit rate for an individual search step of the plurality of peptide source searching steps based at least in part on a number of the plurality of random peptide sequences found by the individual search step; and ordering said peptide source search steps in said peptide source assignment workflow from lowest random hit rate to highest random hit rate; A method comprising: (Item 52) 52. The method of claim 51, wherein the random peptide sequence comprises a random sequence that uniformly samples all amino acids. (Item 53) 52. The method of claim 51, wherein the random peptide sequences include sequences having amino acid frequencies that match the frequencies of amino acids found in vertebrates. (Item 54) 52. The method according to item 51, wherein each peptide of the random peptide sequence comprises a length of 8 to 14 amino acids. (Item 55) 52. The method according to item 51, wherein each peptide of the random peptide sequence comprises a length of 9 to 14 amino acids, 10 to 14 amino acids, 11 to 14 amino acids, 12 to 14 amino acids, 13 to 14 amino acids, 8 to 13 amino acids, 8 to 12 amino acids, 8 to 11 amino acids, 8 to 10 amino acids, 8 to 9 amino acids, 9 to 13 amino acids, 9 to 12 amino acids, 9 to 11 amino acids, 9 to 10 amino acids, 10 to 13 amino acids, 10 to 12 amino acids, 10 to 11 amino acids, 11 to 13 amino acids, 11 to 12 amino acids, or 12 to 13 amino acids. (Item 56) said step of searching multiple peptide sources comprises a linear human proteome search for peptide sequences in an extended human proteome database; The expanded human proteome database includes a computer-readable representation of translations from messenger ribonucleic acid (RNA) and non-coding RNA; Item 52. The method according to item 51. (Item 57) 57. The method of claim 56, wherein the extended human proteome database comprises a computer-readable representation of translation from microRNA. (Item 58) 57. The method of claim 56, wherein the extended human proteome database comprises a computer-readable representation of translation from long non-coding RNA. (Item 59) 57. The method of claim 56, wherein the extended human proteome database comprises a computer-readable representation of the translation of human endogenous retroviruses. (Item 60) 57. The method of claim 56, wherein the step of searching multiple peptide sources comprises a linear human genome search of a translation of a human genome database. (Item 61) the linear human genome search excludes portions of the human genome from which the messenger RNA and the non-coding RNA are transcribed, and includes the remaining portions of the human genome in the extended human proteome database; The method according to item 60. (Item 62) said step of searching multiple peptide sources comprises a linear mismatch search for peptides having mismatches to said peptide sequence in an extended human proteome database; The expanded human proteome database includes a computer-readable representation of translations from messenger ribonucleic acid (RNA) and non-coding RNA; Item 52. The method according to item 51. (Item 63) the step of searching multiple peptide sources comprises a linear non-endogenous search for the peptide sequence in a non-endogenous proteome database; the non-endogenous proteome database comprises computer-readable representations of proteins translated from RNA from the non-endogenous organism and / or synthesized by the non-endogenous organism; Item 52. The method according to item 51. (Item 64) Item 64. The method of item 63, wherein the non-endogenous proteome database comprises a basic local alignment search tool (BLAST) database. (Item 65) said step of searching a plurality of peptide sources comprises a cis-splice search for peptide fragments that may be cis-spliced ​​to match said peptide sequence in an extended human proteome database; The expanded human proteome database includes a computer-readable representation of translations from messenger ribonucleic acid (RNA) and non-coding RNA; Item 52. The method according to item 51. (Item 66) said step of searching multiple peptide sources comprises a trans-splice search in an extended human proteome database for peptide fragments that can be trans-spliced ​​to match said peptide sequence; The expanded human proteome database includes a computer-readable representation of translations from messenger ribonucleic acid (RNA) and non-coding RNA; Item 52. The method according to item 51. (Item 67) 52. The method of claim 51, wherein if a peptide cannot be assigned a peptide source by any of the multiple peptide source searching steps, the peptide source assignment workflow ends with the unassigned peptide. (Item 68) The peptide source assignment workflow comprises the following searches, ordered sequentially as follows: a linear human proteome search for said peptide sequence in said extended human proteome database; Linear human genome search of translations of the human genome database; a linear mismatch search for peptides having mismatches to the peptide sequence in the extended human proteome database; and a cis-splice search for peptide fragments that can be cis-spliced ​​to match the peptide sequence in the extended human proteome database; 52. The method according to item 51, comprising: (Item 69) the peptide source assignment workflow comprises a linear non-endogenous search for the peptide sequence in a non-endogenous proteome database; the linear non-endogenous search is sequentially ordered within the peptide assignment workflow after the linear mismatch search and before the cis-splice search; Item 69. The method according to item 68. (Item 70) the peptide source assignment workflow comprises a trans-splice search in the extended human proteome database for peptide fragments that can be trans-spliced ​​to match the peptide sequence; the trans-splice search is sequentially ordered after the cis-splice search within the peptide assignment workflow; Item 69. The method according to item 68. (Item 71) A non-transitory computer-readable medium configured to be in communication with one or more processors of a computing device, the non-transitory computer-readable medium including instructions thereon that, when executed by the processor, cause the computing device to: It takes as input multiple peptide source search steps, Generate multiple random peptide sequences, searching each of the plurality of random peptide sequences by each of the plurality of peptide source searching steps; determining, for each of the plurality of peptide source searching steps, a random hit rate for an individual search step of the plurality of peptide source searching steps based at least in part on a number of the plurality of random peptide sequences found by the individual search step; ordering the peptide source search steps in a peptide source assignment workflow from lowest random hit rate to highest random hit rate; and A non-transitory computer readable medium providing, as an output, said peptide source assignment workflow. (Item 72) 72. The non-transitory computer-readable medium of claim 71, wherein the random peptide sequences comprise random sequences that uniformly sample all amino acids. (Item 73) 72. The non-transitory computer readable medium of claim 71, wherein the random peptide sequences comprise sequences having amino acid frequencies that match amino acid frequencies found in vertebrates. (Item 74) 72. The non-transitory computer-readable medium of claim 71, wherein each peptide of the random peptide sequence comprises a length of 8 to 14 amino acids. (Item 75) 72. The non-transitory computer readable medium according to item 71, wherein each peptide of the random peptide sequence comprises a length of 9 to 14 amino acids, 10 to 14 amino acids, 11 to 14 amino acids, 12 to 14 amino acids, 13 to 14 amino acids, 8 to 13 amino acids, 8 to 12 amino acids, 8 to 11 amino acids, 8 to 10 amino acids, 8 to 9 amino acids, 9 to 13 amino acids, 9 to 12 amino acids, 9 to 11 amino acids, 9 to 10 amino acids, 10 to 13 amino acids, 10 to 12 amino acids, 10 to 11 amino acids, 11 to 13 amino acids, 11 to 12 amino acids, or 12 to 13 amino acids. (Item 76) said step of searching multiple peptide sources comprises a linear human proteome search for peptide sequences in an extended human proteome database; The expanded human proteome database includes a computer-readable representation of translations from messenger ribonucleic acid (RNA) and non-coding RNA; Item 72. The non-transitory computer-readable medium of item 71. (Item 77) 77. The non-transitory computer readable medium of claim 76, wherein the extended human proteome database comprises a computer readable representation of translation from microRNA. (Item 78) 77. The non-transitory computer readable medium of claim 76, wherein the extended human proteome database comprises a computer readable representation of a translation from long non-coding RNA. (Item 79) 77. The non-transitory computer readable medium of claim 76, wherein the extended human proteome database comprises a computer readable representation of the translation of a human endogenous retrovirus. (Item 80) 77. The non-transitory computer readable medium of claim 76, wherein the step of searching multiple peptide sources comprises a linear human genome search of a translation of a human genome database. (Item 81) 81. The non-transitory computer readable medium of claim 80, wherein the linear human genome search excludes portions of the human genome into which the messenger RNA and the non-coding RNA of the extended human proteome database are transcribed and includes remaining portions of the human genome. (Item 82) said step of searching multiple peptide sources comprises a linear mismatch search for peptides having mismatches to said peptide sequence in an extended human proteome database; The expanded human proteome database includes a computer-readable representation of translations from messenger ribonucleic acid (RNA) and non-coding RNA; Item 72. The non-transitory computer-readable medium of item 71. (Item 83) the step of searching multiple peptide sources comprises a linear non-endogenous search for the peptide sequence in a non-endogenous proteome database; the non-endogenous proteome database comprises computer-readable representations of proteins translated from RNA from the non-endogenous organism and / or synthesized by the non-endogenous organism; Item 72. The non-transitory computer-readable medium of item 71. (Item 84) 84. The non-transitory computer-readable medium of claim 83, wherein the non-endogenous proteome database comprises a basic local alignment search tool (BLAST) database. (Item 85) said step of searching a plurality of peptide sources comprises a cis-splice search for peptide fragments that may be cis-spliced ​​to match said peptide sequence in an extended human proteome database; The expanded human proteome database includes a computer-readable representation of translations from messenger ribonucleic acid (RNA) and non-coding RNA; Item 72. The non-transitory computer-readable medium of item 71. (Item 86) said step of searching multiple peptide sources comprises a trans-splice search in an extended human proteome database for peptide fragments that can be trans-spliced ​​to match said peptide sequence; The expanded human proteome database includes a computer-readable representation of translations from messenger ribonucleic acid (RNA) and non-coding RNA; Item 72. The non-transitory computer-readable medium of item 71. (Item 87) 72. The non-transitory computer-readable medium of claim 71, wherein if a peptide cannot be assigned a peptide source by any of the multiple peptide source searching steps, the peptide source assignment workflow ends with the unassigned peptide. (Item 88) The peptide source assignment workflow comprises the following searches, ordered sequentially as follows: a linear human proteome search for said peptide sequence in said extended human proteome database; Linear human genome search of translations of the human genome database; a linear mismatch search for peptides having mismatches to the peptide sequence in the extended human proteome database; and a cis-splice search for peptide fragments that can be cis-spliced ​​to match the peptide sequence in the extended human proteome database; 72. The non-transitory computer-readable medium of item 71, comprising: (Item 89) the peptide source assignment workflow comprises a linear non-endogenous search for the peptide sequence in a non-endogenous proteome database; the linear non-endogenous search is sequentially ordered within the peptide assignment workflow after the linear mismatch search and before the cis-splice search; Item 89. The non-transitory computer-readable medium of item 88. (Item 90) the peptide source assignment workflow comprises a trans-splice search in the extended human proteome database for peptide fragments that can be trans-spliced ​​to match the peptide sequence; the trans-splice search is sequentially ordered after the cis-splice search within the peptide assignment workflow; Item 89. The non-transitory computer-readable medium of item 88. (Item 91) generating a plurality of simulated random queries; determining a number of matches associated with each source based on applying the plurality of simulated random queries to each source of a plurality of sources; determining a false discovery rate associated with each source based on the number of matches associated with each source; and creating a query support data structure configured to facilitate application of new queries to the plurality of sources based on the false discovery rate; A method comprising: (Item 92) generating the plurality of simulated random queries, Creating multiple uniform random queries; or Creating multiple weighted random queries Item 92. The method according to item 91, comprising at least one of the following: (Item 93) Item 92. The method of item 91, wherein the plurality of simulated random queries includes a plurality of simulated random text strings. (Item 94) Item 92. The method of item 91, wherein the plurality of simulated random queries comprises a plurality of simulated random peptide sequences. (Item 95) 92. The method of claim 91, wherein determining the false discovery rate associated with each source based on the number of matches associated with each source comprises a function of the number of matches and a number of the plurality of simulated random queries. (Item 96) 92. The method of claim 91, wherein determining the false discovery rate associated with each source based on the number of matches associated with each source comprises dividing the number of matches by a number of the plurality of simulated random queries. (Item 97) one or more processors; and When executed by the one or more processors, the apparatus Create multiple simulated random queries, determining a number of matches associated with each source based on applying the plurality of simulated random queries to each source of a plurality of sources; determining a false discovery rate associated with each source based on the number of matches associated with each source; and generating a query support data structure configured to facilitate application of new queries to the plurality of sources based on the false discovery rate; Memory Stored Processor Executable Instructions 13. An apparatus comprising: (Item 98) The processor-executable instructions for causing the apparatus to generate the plurality of simulated random queries further include causing the apparatus to: Have multiple uniform random queries generated, or Generate multiple weighted random queries At least one of the following must be performed: 98. The apparatus according to item 97. (Item 99) Item 98. The apparatus of item 97, wherein the plurality of simulated random queries includes a plurality of simulated random text strings. (Item 100) 98. The apparatus of claim 97, wherein the plurality of simulated random queries comprises a plurality of simulated random peptide sequences. (Item 101) 98. The apparatus of claim 97, wherein the processor-executable instructions that cause the apparatus to determine the false discovery rate associated with each source based on the number of matches associated with each source further cause the apparatus to determine the false discovery rate as a function of the number of matches and a number of the plurality of simulated random queries. (Item 102) 98. The apparatus of claim 97, wherein the processor-executable instructions further cause the apparatus to determine the false discovery rate by dividing the number of matches by the number of the plurality of simulated random queries. (Item 103) When executed by a processor, the processor: Create multiple simulated random queries, determining a number of matches associated with each source based on applying the plurality of simulated random queries to each source of a plurality of sources; determining a false discovery rate associated with each source based on the number of matches associated with each source; and generating a query support data structure configured to facilitate application of new queries to the plurality of sources based on the false discovery rate; One or more non-transitory computer-readable media having processor-executable instructions stored thereon. (Item 104) The processor executable instructions for causing the processor to generate the plurality of simulated random queries further include causing the processor to: Have multiple uniform random queries generated, or Generate multiple weighted random queries At least one of the following must be performed: Item 104. One or more non-transitory computer-readable media according to item 103. (Item 105) Item 104. The one or more non-transitory computer-readable media of item 103, wherein the plurality of simulated random queries comprises a plurality of simulated random text strings. (Item 106) Item 104. The one or more non-transitory computer-readable media of item 103, wherein the plurality of simulated random queries comprises a plurality of simulated random peptide sequences. (Item 107) Item 104. The one or more non-transitory computer-readable media of item 103, wherein the processor-executable instructions that cause the processor to determine the false discovery rate associated with each source based on the number of matches associated with each source further cause the processor to determine the false discovery rate as a function of the number of matches and a number of the plurality of simulated random queries. (Item 108) Item 104. The one or more non-transitory computer-readable media of item 103, wherein the processor-executable instructions that cause the processor to determine the false discovery rate associated with each source based on the number of matches associated with each source further cause the processor to determine the false discovery rate by dividing the number of matches by a number of the plurality of simulated random queries. (Item 109) Create multiple simulated random queries, determining a number of matches associated with each source based on applying the plurality of simulated random queries to each source of the plurality of sources; determining a false discovery rate associated with each source based on the number of matches associated with each source; and creating a query support data structure configured to facilitate application of new queries to the plurality of sources based on the false discovery rate; A computing device configured to receiving the plurality of simulated random queries; determining whether there are a number of matches for the plurality of simulated random queries; and Output a result indicating the number of matches. The plurality of sources configured to Including, the system. (Item 110) The computing device configured to generate the plurality of simulated random queries may further include: Have multiple uniform random queries generated, or Generate multiple weighted random queries At least one of

[0033] Item 109. The system of item 109. (Item 111) Item 110. The system of item 109, wherein the plurality of simulated random queries includes a plurality of simulated random text strings. (Item 112) Item 109. The system of item 109, wherein the plurality of simulated random queries comprises a plurality of simulated random peptide sequences. (Item 113) Item 109. The system of item 109, wherein the computer device configured to determine the false discovery rate associated with each source based on the number of matches associated with each source is further configured to determine the false discovery rate as a function of the number of matches and a number of the plurality of simulated random queries. (Item 114) Item 110. The system of item 109, wherein the computer device configured to determine the false discovery rate associated with each source based on the number of matches associated with each source is further configured to determine the false discovery rate by dividing the number of matches by a number of the plurality of simulated random queries. (Item 115) Receiving queries, applying the query to one or more sources of the plurality of sources based on a query support data structure; determining, based on a query result, an indication associated with a source of the plurality of sources associated with the query result; and applying said indicator to said query; A method comprising: (Item 116) Item 116. The method of item 115, wherein the query includes a text string. (Item 117) 116. The method of claim 115, wherein the query comprises a peptide sequence. (Item 118) Item 118. The method of item 117, wherein receiving the query comprises receiving the peptide sequence from a mass spectrometry system. (Item 119) 116. The method of claim 115, further comprising determining one or more amino acids of the peptide sequence by a mass spectrometry system. (Item 120) 116. The method of claim 115, wherein the query support data structure indicates an ordering of the plurality of sources for applying the query, the ordering being based on a false discovery rate associated with each source of the plurality of sources. (Item 121) Item 116. The method of item 115, further comprising determining one or more permutations of the query. (Item 122) applying the query to the one or more sources of the plurality of sources based on the query support data structure; applying each permutation of the one or more permutations of the query to the one or more sources of the plurality of sources; if an identical match for the one or more permutations of the query is found in a first source of the plurality of sources, stopping further searching and applying a linear indicator to the one or more permutations of the query associated with the identical match; and assigning the one or more permutations of the query associated with the same match as a correct query. Item 122. The method according to item 121, comprising: (Item 123) applying the query to one or more sources of a plurality of sources; searching for an identical match to the query in a first source of the plurality of sources; and stopping further searches if a same match for the query is found in the first source of the plurality of sources. Item 116. The method according to item 115, comprising: (Item 124) Item 124. The method of item 123, wherein the query result includes the same match and the indicator associated with a source of the plurality of sources associated with the query result includes a linear indicator. (Item 125) applying the query to one or more sources of a plurality of sources; searching for an identical match to the query in a first source of the plurality of sources; and stopping further searches if an identical match to the one or more permutations of the query is found in the first source of the plurality of sources. Item 116. The method according to item 115, comprising: (Item 126) Item 126. The method of item 125, wherein the query result includes the same match and the indicator associated with a source of the plurality of sources associated with the query result includes a linear indicator. (Item 127) applying the query to one or more sources of a plurality of sources; searching for an identical match to the query in any frame of a plurality of frames of a second source of the plurality of sources; and stopping further searches if an identical match to the query is found in any frame of a plurality of frames of the second source of the plurality of sources. Item 126. The method according to item 125, comprising: (Item 128) Item 128. The method of item 127, wherein the query result includes the same match and the indicator associated with a source of the plurality of sources associated with the query result includes a linear indicator. (Item 129) applying the query to one or more sources of a plurality of sources; searching for a non-identical match to the query in a third source of the plurality of sources; and stopping further searches if a non-identical match to the query is found in the third source of the plurality of sources. Item 128. The method according to item 127, comprising: (Item 130) Item 130. The method of item 129, wherein the query result includes the non-identical matches and the indicator associated with a source of the plurality of sources associated with the query result includes a mismatch indicator. (Item 131) applying the query to one or more sources of a plurality of sources; searching for a homologous match to the query in a fourth source of the plurality of sources; and stopping further searching if a homologous match to the query is found in the fourth source of the plurality of sources. Item 130. The method according to item 129, comprising: (Item 132) Item 132. The method of item 131, wherein the query result comprises the homologous match and the labels associated with sources of the plurality of sources associated with the query result comprise homologous labels. (Item 133) applying the query to one or more sources of a plurality of sources; Dividing the query into a set of fragments; searching for a respective set of fragments in a fifth source of said plurality of sources; stopping further searches if a match for the set of fragments is found in the fifth source of the plurality of sources; and stopping further searching if a first match for a first fragment of the set of fragments and a second match for a second fragment of the set of fragments are found in the fifth source of the plurality of sources. Item 132. The method according to item 131, comprising: (Item 134) Item 134. The method of item 133, wherein the query result includes the matches for the set of fragments, and the tags associated with sources of the plurality of sources associated with the query result include cis splice tags. (Item 135) Item 134. The method of item 133, wherein the query result includes the first match for the first fragment of the set of fragments and the second match for the second fragment of the set of fragments, and the indicators associated with a source of the plurality of sources associated with the query result include a trans-splice indicator. (Item 136) Item 116. The method of item 115, further comprising determining a source of the query based on the indication. (Item 137) Item 137. The method of item 136, further comprising validating an output of a mass spectrometry system based on the source of the query. (Item 138) one or more processors; and When executed by the one or more processors, the apparatus Receive queries, applying the query to one or more sources of the plurality of sources based on a query support data structure; determining, based on a query result, an indicator associated with a source of the plurality of sources associated with the query result; and applying said indicator to said query; Memory Stored Processor Executable Instructions 13. An apparatus comprising: (Item 139) Item 139. The apparatus of item 138, wherein the query comprises a text string. (Item 140) Item 139. The apparatus of item 138, wherein the query comprises a peptide sequence. (Item 141) Item 139. The apparatus of item 138, wherein the processor executable instructions that cause the apparatus to receive the query further cause the apparatus to receive a peptide sequence from a mass spectrometry system. (Item 142) Item 139. The apparatus of item 138, wherein the processor executable instructions further cause the apparatus to determine one or more amino acids of a peptide sequence by the mass spectrometry system. (Item 143) Item 139. The apparatus of item 138, wherein the query support data structure indicates an ordering of the plurality of sources for applying the query, the ordering being based on a false discovery rate associated with each source of the plurality of sources. (Item 144) 19. The apparatus of claim 138, wherein the processor-executable instructions further cause the apparatus to determine one or more permutations of the query. (Item 145) The processor-executable instructions for causing the apparatus to apply the query to the one or more sources of the plurality of sources based on the query support data structure further include: applying each permutation of the one or more permutations of the query to the one or more sources of the plurality of sources; if an identical match to the one or more permutations of the query is found in a first source of the plurality of sources, stopping further searches and applying linear indicators to the one or more permutations of the query associated with the identical match; and assigning the one or more permutations of the query associated with the same match as a correct query. Item 145. The apparatus according to item 144. (Item 146) The processor-executable instructions for causing the apparatus to apply the query to one or more sources of a plurality of sources further include causing the apparatus to: searching for an identical match to the query in a first source of the plurality of sources; and stopping further searches if a same match for the query is found in the first source of the plurality of sources. Item 139. The apparatus according to item 138. (Item 147) Item 147. The apparatus of item 146, wherein the query result includes the same match and the indicator associated with a source of the plurality of sources associated with the query result includes a linear indicator. (Item 148) The processor-executable instructions for causing the apparatus to apply the query to one or more sources of a plurality of sources further include causing the apparatus to: searching for an identical match to the query in a first source of the plurality of sources; and stopping further searches if an identical match to the one or more permutations of the query is found in the first source of the plurality of sources. Item 139. The apparatus according to item 138. (Item 149) Item 149. The apparatus of item 148, wherein the query result includes the same match and the indicator associated with a source of the plurality of sources associated with the query result includes a linear indicator. (Item 150) The processor-executable instructions for causing the apparatus to apply the query to one or more sources of a plurality of sources further include causing the apparatus to: searching for an identical match to the query in any frame of a plurality of frames of a second source of the plurality of sources; and stopping further searches if an identical match to the query is found in any frame of a plurality of frames of the second source of the plurality of sources. Item 149. The apparatus according to item 148. (Item 151) Item 151. The apparatus of item 150, wherein the query result includes the same match and the indicator associated with a source of the plurality of sources associated with the query result includes a linear indicator. (Item 152) The processor-executable instructions for causing the apparatus to apply the query to one or more sources of a plurality of sources further include causing the apparatus to: searching for non-identical matches to the query in a third source of the plurality of sources; and stopping further searches if a non-identical match to the query is found in the third source of the plurality of sources. Item 151. The apparatus according to item 150. (Item 153) Item 153. The apparatus of item 152, wherein the query result includes the non-identical matches and the indicator associated with a source of the plurality of sources associated with the query result includes a mismatch indicator. (Item 154) The processor-executable instructions for causing the apparatus to apply the query to one or more sources of a plurality of sources further include causing the apparatus to: searching for a homologous match to the query in a fourth source of the plurality of sources; and stopping further searches if a homologous match to the query is found in the fourth source of the plurality of sources. Item 153. The apparatus according to item 152. (Item 155) Item 155. The apparatus of item 154, wherein the query result includes the homologous match and the labels associated with sources of the plurality of sources associated with the query result include homology labels. (Item 156) The processor-executable instructions for causing the apparatus to apply the query to one or more sources of a plurality of sources further include causing the apparatus to: Dividing the query into a set of fragments; searching for each set of fragments in a fifth source of the plurality of sources; stopping further searches if a match for the set of fragments is found in the fifth source of the plurality of sources; and stopping further searching if a first match for a first fragment of the set of fragments and a second match for a second fragment of the set of fragments are found in the fifth source of the plurality of sources. Item 155. The apparatus according to item 154. (Item 157) Item 157. The apparatus of item 156, wherein the query result includes the matches for the set of fragments, and the indicators associated with sources of the plurality of sources associated with the query result include cis splice indicators. (Item 158) Item 157. The apparatus of item 156, wherein the query result includes the first match for the first fragment of the set of fragments and the second match for the second fragment of the set of fragments, and the indicators associated with a source of the plurality of sources associated with the query result include a trans-splice indicator. (Item 159) 19. The apparatus of claim 138, wherein the processor executable instructions further cause the apparatus to determine a source of the query based on the indication. (Item 160) Item 160. The apparatus of item 159, wherein the processor executable instructions further cause the apparatus to validate an output of a mass spectrometry system based on the source of the query. (Item 161) When executed by a processor, the processor: Receive queries, applying the query to one or more sources of the plurality of sources based on a query support data structure; determining, based on a query result, an indicator associated with a source of the plurality of sources associated with the query result; and applying said indicator to said query; One or more non-transitory computer-readable media having processor-executable instructions stored thereon. (Item 162) Item 162. The one or more non-transitory computer-readable media of item 161, wherein the query comprises a text string. (Item 163) Item 162. The one or more non-transitory computer-readable media of Item 161, wherein the query comprises a peptide sequence. (Item 164) Item 162. The one or more non-transitory computer-readable media described in item 161, wherein the processor-executable instructions that cause the processor to receive the query further cause the processor to receive a peptide sequence from a mass spectrometry system. (Item 165) Item 162. The one or more non-transitory computer readable media of item 161, wherein the processor executable instructions further cause the processor to determine one or more amino acids of a peptide sequence by the mass spectrometry system. (Item 166) Item 162. The one or more non-transitory computer-readable media of item 161, wherein the query support data structure indicates an ordering of the multiple sources for applying the query, the ordering being based on a false discovery rate associated with each source of the multiple sources. (Item 167) Item 162. The one or more non-transitory computer-readable media of item 161, wherein the processor-executable instructions further cause the processor to determine one or more permutations of the query. (Item 168) The processor-executable instructions for causing the processor to apply the query to the one or more sources of the plurality of sources based on the query support data structure further include: applying each permutation of the one or more permutations of the query to the one or more sources of the plurality of sources; if an identical match to the one or more permutations of the query is found in a first source of the plurality of sources, stopping further searches and applying linear indicators to the one or more permutations of the query associated with the identical match; and assigning the one or more permutations of the query associated with the same match as a correct query. Item 168. One or more non-transitory computer-readable media according to item 167. (Item 169) The processor-executable instructions for causing the processor to apply the query to one or more sources of a plurality of sources further include causing the processor to: searching for an identical match to the query in a first source of the plurality of sources; and stopping further searches if a same match for the query is found in the first source of the plurality of sources. Item 162. One or more non-transitory computer-readable media according to item 161. (Item 170) Item 169. The one or more non-transitory computer-readable media described in Item 169, wherein the query result includes the same match and the indicator associated with a source of the multiple sources associated with the query result includes a linear indicator. (Item 171) The processor-executable instructions for causing the processor to apply the query to one or more sources of a plurality of sources further include causing the processor to: searching for an identical match to the query in a first source of the plurality of sources; and stopping further searches if an identical match to the one or more permutations of the query is found in the first source of the plurality of sources. Item 162. One or more non-transitory computer-readable media according to item 161. (Item 172) Item 172. The one or more non-transitory computer-readable media of item 171, wherein the query result includes the same match and the indicator associated with a source of the plurality of sources associated with the query result includes a linear indicator. (Item 173) The processor-executable instructions for causing the processor to apply the query to one or more sources of a plurality of sources further include causing the processor to: searching for an identical match to the query in any frame of a plurality of frames of a second source of the plurality of sources; and stopping further searches if an identical match to the query is found in any frame of a plurality of frames of the second source of the plurality of sources. Item 172. One or more non-transitory computer-readable media according to item 171. (Item 174) Item 174. The one or more non-transitory computer-readable media described in Item 173, wherein the query result includes the same match and the indicator associated with a source of the multiple sources associated with the query result includes a linear indicator. (Item 175) The processor-executable instructions for causing the processor to apply the query to one or more sources of a plurality of sources further include causing the processor to: searching for non-identical matches to the query in a third source of the plurality of sources; and stopping further searches if a non-identical match to the query is found in the third source of the plurality of sources. Item 174. One or more non-transitory computer-readable media according to item 173. (Item 176) Item 176. The one or more non-transitory computer-readable media described in Item 175, wherein the query result includes the non-identical match and the indicator associated with a source of the plurality of sources associated with the query result includes a mismatch indicator. (Item 177) The processor-executable instructions for causing the processor to apply the query to one or more sources of a plurality of sources further include causing the processor to: searching for a homologous match to the query in a fourth source of the plurality of sources; and stopping further searches if a homologous match to the query is found in the fourth source of the plurality of sources. Item 177. One or more non-transitory computer-readable media according to item 176. (Item 178) Item 178. The one or more non-transitory computer-readable media described in Item 177, wherein the query result includes the homologous match and the label associated with a source of the plurality of sources associated with the query result includes a homology label. (Item 179) The processor-executable instructions for causing the processor to apply the query to one or more sources of a plurality of sources further include causing the processor to: Dividing the query into a set of fragments; searching for each set of fragments in a fifth source of the plurality of sources; stopping further searches if a match for the set of fragments is found in the fifth source of the plurality of sources; and stopping further searching if a first match for a first fragment of the set of fragments and a second match for a second fragment of the set of fragments are found in the fifth source of the plurality of sources. Item 178. One or more non-transitory computer-readable media according to item 177. (Item 180) Item 179. The one or more non-transitory computer-readable media described in Item 179, wherein the query result includes the matches for the set of fragments, and the tags associated with a source of the plurality of sources associated with the query result include a cis splice tag. (Item 181) Item 179. The one or more non-transitory computer-readable media of Item 179, wherein the query result includes the first match for the first fragment of the set of fragments and the second match for the second fragment of the set of fragments, and the indicator associated with a source of the multiple sources associated with the query result includes a trans-splice indicator. (Item 182) Item 162. The one or more non-transitory computer-readable media of item 161, wherein the processor-executable instructions further cause the processor to determine a source of the query based on the indication. (Item 183) Item 183. The one or more non-transitory computer-readable media of item 182, wherein the processor-executable instructions further cause the processor to validate an output of a mass spectrometry system based on the source of the query. (Item 184) Receives a query, applying the query to one or more sources of the plurality of sources based on a query support data structure; determining, based on the query result, an indication associated with a source of the plurality of sources associated with the query result; and Applying the indicator to the query A computing device configured to receiving the query; and the one or more sources of the plurality of sources configured to determine the query results; Including, the system. (Item 185) Item 185. The system of item 184, wherein the query includes a text string. (Item 186) Item 185. The system of item 184, wherein the query comprises a peptide sequence. (Item 187) Item 185. The system of item 184, wherein the computer device configured to receive the query is further configured to receive a peptide sequence from a mass spectrometry system. (Item 188) Item 185. The system of item 184, wherein the computer device is further configured to cause the processor to determine one or more amino acids of a peptide sequence by the mass spectrometry system. (Item 189) Item 185. The system of item 184, wherein the query support data structure indicates an ordering of the plurality of sources for applying the query, the ordering being based on a false discovery rate associated with each source of the plurality of sources. (Item 190) Item 185. The system of item 184, wherein the computing device is further configured to determine one or more permutations of the query. (Item 191) the computing device configured to apply the query to the one or more sources of the plurality of sources based on the query support data structure; applying each permutation of the one or more permutations of the query to the one or more sources of the plurality of sources; if an identical match for the one or more permutations of the query is found in a first source of the plurality of sources, stopping further searching and applying a linear indicator to the one or more permutations of the query associated with the identical match; and assigning the one or more permutations of the query associated with the same match as a correct query; Item 185. The system of item 184, further configured as follows: (Item 192) the computing device configured to cause the processor to apply the query to one or more sources of a plurality of sources; searching for an identical match to the query in a first source of the plurality of sources; and Stopping further searches if a same match for the query is found in the first source of the plurality of sources. Item 185. The system of item 184, further configured as follows: (Item 193) Item 193. The system of item 192, wherein the query result includes the same match and the indicator associated with a source of the plurality of sources associated with the query result includes a linear indicator. (Item 194) the computing device configured to apply the query to one or more sources of a plurality of sources; searching for an identical match to the query in a first source of the plurality of sources; and stopping further searches if an identical match for the one or more permutations of the query is found in the first source of the plurality of sources. Item 185. The system of item 184, further configured as follows: (Item 195) Item 195. The system of item 194, wherein the query result includes the same match and the indicator associated with a source of the plurality of sources associated with the query result includes a linear indicator. (Item 196) the computing device configured to apply the query to one or more sources of a plurality of sources; searching for an identical match to the query in any frame of a plurality of frames of a second source of the plurality of sources; and stopping further searches if a same match for the query is found in any frame of a plurality of frames of the second source of the plurality of sources. Item 195. The system of item 194, further configured as follows: (Item 197) 197. The system of claim 196, wherein the query result includes the same match and the indicator associated with a source of the plurality of sources associated with the query result includes a linear indicator. (Item 198) the computing device configured to apply the query to one or more sources of a plurality of sources; searching for a non-identical match to the query in a third source of the plurality of sources; and stopping further searches if a non-identical match to the query is found in the third source of the plurality of sources. 197. The system of claim 196, further configured as follows: (Item 199) 200. The system of claim 198, wherein the query result includes the non-identical matches and the indicator associated with a source of the plurality of sources associated with the query result includes a mismatch indicator. (Item 200) the computing device configured to apply the query to one or more sources of a plurality of sources; searching for a homologous match to the query in a fourth source of the plurality of sources; and stopping further searches if a homologous match to the query is found in the fourth source of the plurality of sources. 201. The system of item 198, further configured as follows: (Item 201) Item 201. The system of item 200, wherein the query result includes the homologous match and the labels associated with sources of the plurality of sources associated with the query result include homology labels. (Item 202) the computing device configured to apply the query to one or more sources of a plurality of sources; Dividing the query into a set of fragments; searching for each set of fragments in a fifth source of the plurality of sources; stopping further searches if a match for the set of fragments is found in the fifth source of the plurality of sources; and stopping further searching if a first match for a first fragment of the set of fragments and a second match for a second fragment of the set of fragments are found in the fifth source of the plurality of sources. The system of item 200, further configured as follows: (Item 203) Item 203. The system of item 202, wherein the query result includes the matches for the set of fragments, and the indicators associated with sources of the plurality of sources associated with the query result include cis splice indicators. (Item 204) Item 203. The system of item 202, wherein the query result includes the first match for the first fragment of the set of fragments and the second match for the second fragment of the set of fragments, and the indicator associated with a source of the plurality of sources associated with the query result includes a trans-splice indicator. (Item 205) 185. The system of claim 184, wherein the processor executable instructions further cause the processor to determine a source of the query based on the indication. (Item 206) Item 206. The system of item 205, wherein the computer device is further configured to validate an output of a mass spectrometry system based on the source of the query. [Brief description of the drawings]

[0013] [Figure 1] FIG. 1 illustrates an exemplary embodiment of the system.

[0014] [Diagram 2] FIG. 2 illustrates an exemplary embodiment of a query support data structure.

[0015] [Diagram 3] FIG. 3 illustrates an exemplary embodiment of the method.

[0016] [Figure 4] FIG. 4 is a schematic diagram of linear, cis- and trans-spliced ​​peptides generated by proteasome-catalyzed peptide splicing / ligation.

[0017] [Diagram 5] FIG. 5 illustrates an exemplary embodiment of the system.

[0018] [Figure 6] FIG. 6 illustrates an exemplary embodiment of a query support data structure.

[0019] [Figure 7]FIG. 7 illustrates an exemplary embodiment of the method.

[0020] [Figure 8] FIG. 8 shows an example of estimating the random hit rate for each putative source of peptides by means of a bar graph showing the percentage of 5,000 randomly generated peptide sequences that could be individually matched at each step.

[0021] [Figure 9] FIG. 9 illustrates an exemplary embodiment of a schematic diagram of a hybrid finder workflow.

[0022] [Figure 10] FIG. 10 shows an exemplary diagram illustrating an estimation of the random hit rate.

[0023] [Figure 11] FIG. 11 shows an exemplary embodiment of a pattern of potential peptide sources identified for randomly generated peptides.

[0024] [Figure 12] FIG. 12 shows an exemplary embodiment of the percentage of concordance between the search and the average local confidence scores given during de novo sequencing of an embodiment of a putative peptide source assignment workflow.

[0025] [Figure 13] FIG. 13 shows an exemplary embodiment of using the subject system to provide identification of peptide sources for cell lines expressing HLA-A02:04 by which peptides have switched annotations (center) using Hybrid Finder (left) and the disclosed method (right).

[0026] [Figure 14]FIG. 14 shows an exemplary embodiment of using the subject system to provide peptide source identification for all HLA monoallelic cell lines by which peptides have switched annotations (center) using Hybrid Finder (left) and the disclosed method (right).

[0027] [Figure 15] FIG. 15 shows an exemplary embodiment using the subject system to provide a Fisher's exact test p-value that measures the enrichment of how many peptides could be assigned to a source at each step compared to how many would be expected based on how many of the simulated random sequences were assigned.

[0028] [Figure 16] FIG. 16 illustrates an exemplary embodiment using the subject system to provide stacked bar plots showing the proportion of peptides mapped to different genomic regions in step 1 of the peptide source assignment workflow applied to an HLA monoallelic cell line.

[0029] [Figure 17] Figure 17 shows a heatmap illustrating the enrichment of genomic annotations for mapped peptide locations in step 1 of an exemplary embodiment of the peptide source assignment workflow. Signed log10 Fisher's exact test p-values ​​of enrichment of peptides identified exclusively in the OpenProt database for HLA monoallelic cell lines versus the distribution of all proteins in the OpenProt database.

[0030] [Figure 18] FIG. 18 shows results using an exemplary embodiment of the system showing stacked bar plots indicating the percentage of peptides mapped to different genomic regions in step 2 of the peptide source assignment workflow applied to an HLA monoallelic cell line.

[0031] [Figure 19] Figure 19 shows a heatmap illustrating the enrichment of genome annotations for mapped peptide locations in step 2 of an exemplary embodiment of the peptide source assignment workflow. Signed log10 p-values ​​were calculated by HOMER, which calculates the enrichment of assigned peptide locations.

[0032] [Figure 20] FIG. 20 shows results using an exemplary embodiment of the system showing stacked bar plots indicating the percentage of peptides mapped to different genomic regions in step 3 of the peptide source assignment workflow applied to an HLA monoallelic cell line.

[0033] [Figure 21] Figure 21 shows a heatmap showing the enrichment of genome annotations for mapped peptide locations in step 3 of an exemplary embodiment of the workflow. Signed log10 p-values ​​were calculated by HOMER, which calculates the enrichment of assigned peptide locations.

[0034] [Figure 22] FIG. 22 shows a block diagram of an exemplary embodiment of a computing device for performing the example methods described herein.

[0035] [Diagram 23] 23 and 24 show a flow chart of an exemplary embodiment of the method. [Figure 24] 23 and 24 show a flow chart of an exemplary embodiment of the method. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0036] Detailed Description The disclosed methods and systems may be understood more readily by reference to the following detailed description of specific embodiments and examples contained herein, as well as the drawings and accompanying description.

[0037] It is understood that the disclosed methods and systems are not limited to the particular methodology, protocols, and reagents described, as these may vary. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only, and is not intended to limit the scope of the invention, which is limited only by the appended claims.

[0038] It must be noted that as used in this specification and the appended claims, the singular forms "a," "an," and "the" include plural referents unless the context clearly dictates otherwise. So, for example, a reference to "a peptide" includes a plurality of such peptides, a reference to "the peptide" is a reference to one or more peptides and equivalents thereof known to those skilled in the art, and so forth.

[0039] The term "peptide" may be used interchangeably with "polypeptide" and refers to a polymeric form of amino acids of any length, which may include genetically encoded and non-genetically encoded amino acids, chemically or biochemically modified or derivatized amino acids, and peptides with modified peptide backbones. In some aspects, the term peptide refers to a string of two or more naturally occurring amino acids.

[0040] "Optionally" or "as needed" means that the subsequently described event, circumstance, or material may or may not occur or may not exist, and that the description includes instances where the event, circumstance, or material occurs or exists, as well as instances where it does not occur or does not exist.

[0041] Throughout the description and claims of this specification, the word "comprise" and variations of that word, such as "comprising" and "comprises," mean "including, but not limited to," and are not intended to exclude, for example, other additives, components, integers, or steps. In particular, in a method described as including one or more steps or operations, it is specifically intended that each step includes what is listed (unless the step includes a limiting term such as "consisting of"), and each step is not intended to exclude, for example, other additives, components, integers, or steps not listed in the step.

[0042] As used herein, the term "computer-readable representation of a protein sequence" may include the protein itself, a gene sequence (e.g., DNA, RNA) from which the protein sequence may be derived through processes (e.g., transcription, translation) understood by those of skill in the relevant art, and / or sequence listings of portions thereof. Similarly, as used herein, the term "computer-readable representation of a translation from ribonucleic acid (RNA)" may include a protein or peptide that may (at least in theory) be translated from RNA as understood by those of skill in the relevant art, the gene sequence of the RNA, the gene sequence of the DNA from which the RNA may (at least in theory) be transcribed as understood by those of skill in the relevant art, and / or sequence listings of portions thereof. As used herein, the term "computer-readable representation of a translation from RNA" may include messenger RNA (mRNA), Non-coding RNA, long chain Non-codingIt may refer to a specific type of RNA, including RNA, microRNA, and other types of RNA as understood by those skilled in the art in the relevant field.The computer-readable representation of the translation from a specific type of RNA may include the protein or peptide that can be translated (at least theoretically) from the specific type of RNA as understood by those skilled in the art in the relevant field, the gene sequence of the specific type of RNA, the gene sequence of the DNA that can be transcribed from the specific type of RNA as understood by those skilled in the art in the relevant field, and / or the sequence list of their parts.

[0043] The terms "random hit rate" and "false discovery rate" are used interchangeably herein and are understood to mean the frequency with which a randomly generated input is found by a search of a database.

[0044] "Individual" or "subject" or "animal" refers to humans, veterinary animals (e.g., cats, dogs, cows, horses, sheep, pigs, etc.) and experimental animal models of disease (e.g., mice, rats). In some embodiments, the subject is a human.

[0045] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which the disclosed method belongs. Any methods and materials similar or equivalent to those described herein can be used in the practice or testing of the present methods and compositions, but particularly useful methods, devices and materials are as described. Publications cited herein and materials cited therein are specifically incorporated herein by reference. Nothing herein should be construed as an admission that the invention is not entitled to antedate such disclosure by prior invention. No admission is made that any reference constitutes prior art. The discussion of references states what their authors assert, and applicants reserve the right to challenge the accuracy and pertinence of the cited documents. Although many publications are referenced herein, it will be clearly understood that such references do not constitute an admission that any of these documents form part of the common general knowledge in the art.

[0046] 1 illustrates an example of a system 100. The system 100 may be used to analyze one or more portions of data / information, such as, for example, query information, as well as to determine / identify data sources, for example, optimal data sources and / or devices, for analyzing the complete data / information and / or for receiving / obtaining additional data / information associated with the data / information.

[0047] The devices and / or components of the system 100 may be connected to and / or communicate with each other via a network 106. The network 106 may be a public network, a private network, and / or a combination thereof. The network 106 may support any wired and / or wireless communication technology and / or technique. For example, the network 106 may include and / or support a cellular network, a data network, a content delivery network, a fiber optic network, and / or any other type of network.

[0048] The system 100 may include a user device 102 (e.g., a computing device, a client device, a smart device, etc.). The user device 102 may include a communication element 103 for providing an interface to a user and interacting with the user device 102 and / or any other devices / components of the system 100. The communication element 103 may be any interface for presenting and / or receiving information to / from a user, such as user feedback. The interface may include a display and / or an interactive interface (e.g., a keyboard, a touch screen, a mouse, an audio controller, etc.). The interface may include a communication interface, such as a web browser (e.g., Internet Explorer®, Mozilla Firefox®, Google Chrome®, Safari®). Other software, hardware, and / or interfaces may be used to provide communication between a user and one or more of the user devices 102 and / or any other devices / components of the system 100. The communications element 103 may request or query various files from local and / or remote sources, such as computing devices 107-112 and / or any other devices / components of the system 100. The computing devices 107-112 may be located locally or remotely with respect to the user device 102.

[0049] The communication element 103 may transmit / send data to local or remote devices, such as computing devices 107-112, and / or any other devices / components of the system 100, via wired and / or wireless communication techniques. For example, the communication element 103 may utilize any suitable wired communication technique, such as Ethernet, coaxial cable, fiber optics, etc. The communication element 103 may utilize any suitable long-range communication technique, such as Wi-Fi (IEEE 802.11), BLUETOOTH, cellular, satellite, infrared, etc. The communication element 103 may utilize any suitable short-range communication technique, such as BLUETOOTH, near field communication, infrared, etc.

[0050] The user device 102 may receive and / or analyze data / information, e.g., query information, etc. For example, the user device 102 may receive the data / information, query information, etc. via the communication element 103. The data / information, query information, etc. may include any type of information, e.g., statistical queries, analytical queries, industry-specific queries (e.g., immunopeptidomics-related queries, bioinformatics-related queries, biotechnology-related queries, healthcare-related queries, business-related queries, chemistry-based queries, mathematics-based queries, etc.).

[0051] The user device 102 may include a query module 105 that may analyze data / information, such as, for example, query information. The query module 105 may be software, hardware, and / or a combination of software and hardware. The query module 105 may be configured for natural language processing, syntax determination / analysis, query language (coding) processing / analysis, and the like.

[0052] The user device 102 (e.g., the query module 105) may receive / create a query. For example, the user device may receive / create a query such as, "Was the health inspection score for XYZ restaurant the same in 2020 as it was in 2019?". In another example, the user device may receive / create a query such as, "What was the health inspection score for XYZ restaurant in 2020?". The query module 105 may determine / identify portions / components of the query using, for example, natural language processing, syntax determination / analysis, query language (coding) processing / analysis, etc. The portions / components of the query may include one or more data constraints, predicates, text strings, syntactic elements, semantic divisions, etc. The query module 105 may combine portions / components of the query to, for example, determine / create a boilerplate.

[0053] The query-based set expressions may be applied to data / information sources and / or systems to determine results and / or accuracy of the results. The results may be an aggregate value / amount of data records, e.g., number / quantity of matches, hits, correspondences, etc. between parts / components of the query, as well as an indication of one or more data records stored by and / or associated with the source and / or system. The number / quantity of matches, hits, correspondences, etc. may be evaluated and / or compared against a threshold, e.g., a data discovery threshold. If the number / quantity of matches, hits, correspondences, etc. meets / exceeds the discovery threshold, the query module 105 may create a data record, provide an indication, and / or assign an indication to the source and / or system. The indication may indicate, for example, the type and / or quantity of matches, hits, correspondences, etc. associated with the source and / or system. The indication may indicate any data / information related to the query applied to the source and / or system and / or the corresponding results.

[0054] The user device 102 may evaluate the effectiveness of any source and / or system to output the results of a query. For example, the user device 102 (e.g., query module 105, etc.) may send a query to one or more data sources and / or process the query based thereon. For simplicity and example, the computing devices 107-112 may represent one or more data sources and / or one or more search engines. Although not shown, the computing devices 107-112 may each represent multiple related data sources, systems, devices, repositories, etc. For example, the computing devices 107-112 may each include and / or be associated with a database (e.g., data store, data repository, etc.). The database may include any type of database, e.g., internet, in-memory / centralized database, distributed database, computational database, relational database, cloud-based database, object-oriented database, query language-based database (e.g., NoSQL, etc.), graph database, etc. The database may include any data / information. In one embodiment, each of computing devices 107-112 may represent a different search engine configured to search the same database (eg, the Internet).

[0055] To evaluate the effectiveness of the computing devices 107-112 for outputting query results, the user device 102 (e.g., query module 105, etc.) may apply one or more queries to one or more of the computing devices 107-112 and determine a false discovery rate (FDR) associated with the computing devices 107-112. For example, the user device 102 (e.g., query module 105, etc.) may determine / create multiple random queries. The multiple random queries may be, for example, uniform random queries, weighted random queries, and / or any other type of queries. The multiple simulated queries may be, for example, immunopeptidomics related queries and / or bioinformatics / biotechnology related queries, for example queries associated with multiple simulated random peptide sequences. The multiple simulated random queries may be created by any known technique. For example, one that creates random numbers / letters / words may be used to create the multiple simulated random queries and / or test queries / cases. The quantity of simulated random queries may affect the types of queries, e.g., the combination of simulated queries and / or ReplacementThe number of simulation queries may vary based on the number of queries. For example, the number of simulation queries for restaurants, airfares, etc. may vary from the number of simulation queries for DNA, RNA, and / or amino acid sequences. In an embodiment, the number of simulation queries may be constrained by the particular length of the simulation query. For example, the simulation queries may be limited to the number of characters and / or words. In some embodiments, the number of simulation queries may range anywhere between, including, 10 queries and 10,000,000 queries. In some embodiments, the number of simulation queries may be, but is not limited to, 10 queries to 1,000 queries. In some embodiments, the number of simulation queries may be, but is not limited to, 10 queries to 10,000 queries. In some embodiments, the number of simulation queries may be, but is not limited to, 10 queries to 100,000 queries. In some embodiments, the number of simulation queries may be, but is not limited to, 10 queries to 1,000,000 queries. In some embodiments, the number of simulation queries may be, but is not limited to, 100 queries to 1,000 queries. In some embodiments, the number of simulation queries may be, but is not limited to, 100 queries to 10,000 queries. In some embodiments, the number of simulation queries may be, but is not limited to, 100 queries to 100,000 queries. In some embodiments, the number of simulation queries may be, but is not limited to, 100 queries to 1,000,000 queries. In some embodiments, the number of simulation queries may be, but is not limited to, 1,000 queries to 100,000 queries. In some embodiments, the number of simulation queries may be, but is not limited to, 10,000 queries to 100,000 queries. In some embodiments, the number of simulation queries may be, but is not limited to, 100,000 or more queries.In some embodiments, the number of simulation queries may be, but is not limited to, 1,000,000 or more queries. In some embodiments, the number of simulation queries may be at least 100,000 queries. In some embodiments, the number of simulation queries may be at least 1,000,000 queries.

[0056] For example, the query module 105 may create multiple (e.g., tens, hundreds, millions, etc.) simulated random queries using an application, such as MySQL, and / or test queries / cases with a suitable grammar / format. The suitable grammar may be any grammar, language, syntax, coding, etc. that is understood / executable by the query module 105. The query module 105 may create queries of any suitable grammar / format using a query template. The query template may be created according to a scripting language. The query template may be mapped to and / or correspond to a particular test case. The query module 105 may determine results and / or expected results for the query determined from the query template by applying the query to sources and / or systems, such as computing devices 107-112.

[0057] The query module 105 may create / determine a random query based on the determined query, e.g., from a query template. The query module 105 may apply the random query to each of the computing devices 107-112 and determine which computing devices 107-112 output positive and / or expected results. The output and / or expected results may be based, for example, on the computing devices 107-112's ability to process the meaning and / or syntax of any given query and retrieve data / information associated with the meaning and / or syntax. The user device 102 may determine / create a false discovery rate associated with each of the computing devices 107-112, for example, based on the output of each of the computing devices 107-112.

[0058] In an aspect, the randomly generated queries may be false, nonsensical, and / or illogical queries designed to assess the false discovery rate of any source and / or system, e.g., computing devices 107-112. For example, a query template may be used to generate queries such as "What is the price of an airplane ticket to Dubai?". The query module 105 may generate false, nonsensical, and / or illogical versions of queries such as "What is the price of apples by midday," "When is the price to develop an airplane," "Dubai airplane ticket currency," "Airplane ticket when price is low," and / or "Airplane ticket when price is low." Replacement False, nonsensical, and / or illogical versions of the query and / or Replacement may be determined based on, for example, synonyms, phonetic relationships, etc. of the elements of the query (predicates, constraints, conditions, indices, parts, etc.). Incorrect, nonsensical, and / or illogical versions of the query and / or Replacement may be determined by repositioning elements of the query. Incorrect, nonsensical, and / or illogical versions of the query and / or Replacement may be determined by any method.

[0059] The query module 105 may be configured to determine how frequently the computing devices 107-112 make queries, e.g., incorrect, nonsensical, and / or illogical versions of multiple random queries and / or Replacement How often the computing devices 107-112 may output results for incorrect, nonsensical, and / or illogical versions of the query and / or Replacement may indicate whether the computer device 107-112 outputs results for a given number of matches and / or this may correspond to the number of matches associated with each of the computer devices 107-112. A false discovery rate (FDR) for any given computer device 107-112 may be determined as a function of the number of matches and the number of the plurality of random queries. Determining the FDR for the computer device 107-112 based on the number of matches associated with each computer device 107-112 may include dividing the number of matches by the number of the plurality of random queries. In an embodiment, determining the FDR may take into account a relevance score associated with the matches provided by the computer device 107-112. For example, the search engine may identify a match and assign a relevance score to the match indicating how relevant the match is to the query. Each search engine may use a proprietary relevance scoring technique. A match may be counted toward the FDR determination if the simulated query returns a match with a relevance score above a threshold.

[0060] The user device 102 may determine / create a query support data structure configured to facilitate application of new queries to the computing devices 107-112 based on the false discovery rates associated with each of the computing devices 107-112. For example, in an embodiment, the computing devices 107-112 may include and / or be associated with search engines (e.g., Google®, Yahoo®, Bing®, Firefox®, etc.) and / or similar data sources, data repositories, and / or data access systems.

[0061] 2 illustrates an example of a data structure 200 that may be used to facilitate application of a query to the computing devices 107-112. The query support data structure 200 may indicate an ordering of the computing devices 107-112 (e.g., data sources and / or search engines).

[0062] The order of the data sources may be based on the false discovery rate associated with each source. The query support data structure 200 may indicate one or more search techniques for one or more of the data sources 107-112. The query support data structure 200 may indicate multiple search techniques for a single data source (e.g., data source 107, etc.), for example, in column 202, or the query support data structure 200 may indicate a single search technique for the data sources 107-112 and combinations thereof. The query support data structure 200 may include data source identifiers for the data sources 107-112, in column 201, ordered according to false discovery rate. The false discovery rate may be optionally indicated, for example, in column 203. Data sources associated with a lower false discovery rate may be searched before data sources having a higher false discovery rate are searched. Additional data may be included for each data source indicated in the query support data structure 200. The additional data may include one or more of a data source location, a query syntax, one or more query parameters, combinations thereof, and the like.

[0063] The query may be labeled based on which data sources return query results. The indicator may be an indication of the source data / information associated with the query. For example, the indicator may indicate one or more levels of precision of the results returned by the source based on the query. As another example, the indicator may indicate one or more of textual data, multimedia data, statistical data, historical data, private / secure data, public data, and / or any other indication of the type of data returned by the source based on the query.

[0064] As an example shown in FIG. 3, the query 300 may be applied to one or more of a number of data sources 307-309 (e.g., search engines, data sources 107-112, computing devices 107-112, etc.). ReplacementAnd / or versions may also be applied to multiple data sources 307-309. Query 300 may be, for example, "How much does an airplane ticket to Dubai cost?" Replacement and / or versions may be, for example, "how much is airfare to Dubai," "how much is a flight to Dubai," "airfare to Dubai," etc. The order in which the query 300 is applied to the multiple data sources 307-309 may be indicated by a query support data structure, as described herein, based on the false discovery rate associated with each of the multiple data sources 307-309. In an embodiment, as shown in FIG. 3, the data sources are ordered according to FDR. For example, the FDR for data source 307 may be about 1%, the FDR for data source 308 may be about 10%, and the FDR for data source 309 may be about 68%. The data source with the lowest false discovery rate may be searched first and the data source with the highest false discovery rate may be searched last. In an embodiment, the query 300 may be stopped at any point in returning search results. In one embodiment, the query 300 may be applied to each of multiple data sources 307-309, and after the query 300 is completed, the search results may be presented along with an indication of the associated data sources and FDRs. In this manner, the user may decide that they have more confidence in the search results, and whether they wish to apply any FDR-based filters (e.g., remove search results associated with data sources that have high FDR values).

[0065] Additionally, each data source of the plurality of data sources 307-309 may be associated with a threshold, e.g., a data discovery threshold, that is applied to the relevance score of a match. The data discovery threshold may be a system-defined threshold and / or a user-defined threshold. In one embodiment, a data source associated with a low false discovery rate may be associated with a low data discovery threshold because the data source is generally associated with "good" results and any matches from the data source must be subject to less strict relevance requirements. A data source associated with a high false discovery rate may be associated with a high data discovery threshold because the data source is generally associated with "good" results and any matches from the data source must be subject to more strict relevance requirements. In another embodiment, a data source associated with a low false discovery rate may be associated with a high data discovery threshold because the data source is generally associated with "good" results and is more likely to contain relevant results. A data source associated with a high false discovery rate may be associated with a low data discovery threshold because the data source is associated with less "good" results and a low data discovery threshold may be necessary to determine relevant results. In an embodiment, the data discovery threshold may be determined and / or set by a user, for example, via a user interface.

[0066] In an embodiment, each data source of the plurality of data sources 307-309 may be associated with the same or different data discovery thresholds. For example, when a query is applied to a first data source, the first data discovery threshold may determine that a match exists only if the match has a relevance score greater than the first data discovery threshold (e.g., 85%), and if the match does not satisfy the first data discovery threshold, the query may be applied to a second data source associated with a second data discovery threshold that determines that a match exists only if the match has a relevance score greater than a second data discovery threshold (e.g., 90%). If the match does not satisfy the second data discovery threshold, the query may be applied to a third data source associated with a third data discovery threshold that determines that a match exists only if the match has a relevance score greater than a third data discovery threshold (e.g., 95%), and if the match does not satisfy the third data discovery threshold, no results are output.

[0067] When applying the query 300 to the data sources, if a match is found in and / or through the data source 307 that satisfies a data discovery threshold (e.g., system determined, user configurable, etc.) for the query 300, the result may receive a first indication (highly accurate result) at 312 and all relevant and / or possible results may be included in the output. Otherwise, the query 300 may be applied to the next data source 308. If a match is found that satisfies a data discovery threshold (e.g., system determined, user configurable, etc.), the result may receive a second indication (likely accurate result) at 313 and all relevant and / or possible results may be included in the output. Otherwise, the query 300 may be applied to the next data source 309. If a match is found that satisfies a data discovery threshold (e.g., system determined, user configurable, etc.), the result may receive a third indication (exact result) at 314 and all relevant and / or possible results may be included in the output. If no match is determined / identified, then No Results may receive a fourth indicator (No Results) at 316 .

[0068] Next, applying an exemplary embodiment of the disclosed method and system to de novo peptide sequencing, Figure 4 shows a schematic diagram of how linear, cis, and trans spliced ​​peptides are produced. For example, linear peptide sequences are similarly matched to their parent proteins, cis spliced ​​peptide fragments are from the same protein, and trans spliced ​​peptide fragments are from different proteins.

[0069] FIG. 5 shows an example of a system 500. The example system 500 can be configured for mass spectrometry. The mass spectrometer 504 allows for accurate determination of the molecular mass of peptides and their sequences. For example, the mass spectrometer 504 can output data / information, such as mass spectrometry data, which can be used for protein identification, de novo sequencing, and identification of post-translational modifications. The system 500 can be configured to assign the source of de novo sequenced peptides.

[0070] Tandem mass spectrometry (MS / MS) has become an excellent high-throughput technique for protein identification. A tandem mass spectrometer 504 may be configured to ionize a mixture of peptides in a sample 502 having different peptide sequences and measure their individual parent mass / charge ratios, selectively fragment each peptide into pieces, and measure the mass / charge ratios of the fragment ions. The tandem mass spectrometer 504 may be, as a non-limiting example, a linear ion trap mass spectrometer (LTQ) coupled with a Fourier transform ion cyclotron resonance mass spectrometer (LTQ-FT). Thus, a tandem mass spectrum can be viewed as a collection of fragment masses from a single peptide. This collection or set of fragment masses or fragment mass values ​​is a "fingerprint" that identifies the peptide. The challenge of peptide sequencing is therefore to derive the sequence of peptides given their MS / MS spectra. For an ideal fragmentation process and an ideal mass spectrometer, the sequence of a peptide can be easily determined by converting the differences in masses of successive ions in the spectrum to the corresponding amino acids. This ideal situation would occur if the fragmentation process could be controlled so that each peptide was cleaved between every two consecutive amino acids and a single charge was retained only on the N-terminal piece. In reality, however, the fragmentation process in a mass spectrometer is not ideal.

[0071] The challenge for tandem mass spectrometry peptide sequencing is to find a peptide of mass m that has a maximum match to spectrum S, given a spectrum S, an ion type Δ, and a mass m. Peptide fragmentation in a tandem mass spectrometer is performed by dividing the peptide into a set of numbers Δ={δ l ,...,δ k}. The δ-ion P' ⊂ P of a partial peptide is a modification of P' with mass m(P')-δ. For tandem mass spectrometry, the theoretical spectrum of peptide P is the sum of all possible ion species {δ l ,...,δ k} from the masses of all the peptide fragments of P (i.e., every peptide fragment gives rise to a mass k in the theoretical spectrum). l ,...,s m} are the masses of the set of fragment ions. The match between a spectrum S and a peptide P is the number of masses that the experimental and theoretical spectra have in common.

[0072] LTQ-FT mass spectrometers can produce on the order of 100,000 spectra per instrument per day. Software is a critical and limiting factor in mass spectrometry proteomics analysis, and typical large data sets can require days or weeks of computation time on expensive computers or grids. Most peptide identification algorithms use database search methods that match spectra against protein databases. FIG. 5 illustrates an exemplary process for a spectrum matching technique for peptide identification. Specifically, a sample 502 is provided to a mass spectrometer 504. The mass spectrometer 504 may include any number of mass spectrometers, for example, two mass spectrometers, in a tandem arrangement. A two-step process is illustrated, however, single-step processes are also known. In the first mass spectrometer 504A, peptide ions are selected such that a targeted component of a specific mass is separated from the remainder of the sample. The targeted component is then activated or decomposed in 504B. In the case of peptides, the result will be a mixture of ionized parent peptides ("precursor ions") and lower mass component peptides that are ionized to various states. Several activation methods can be used, including collision with a neutral gas (also called collision-induced dissolution). The parent peptide and its fragments are then provided to a second mass analyzer 504C, which outputs the intensity and m / z for each of the multiple fragments in the fragment mixture. This information can be output as a fragment mass spectrum 506. In the fragment mass spectrum 506, each fragment ion is represented as a bar graph, with the horizontal axis value indicating the mass-to-charge ratio (m / z) and the vertical axis value representing the intensity. The fragment mass spectrum 506 may take the form of mass spectrometry data.

[0073] The computing device 512 may be configured to analyze mass spectrometry data (e.g., fragment mass spectrum 506) generated by the mass spectrometer 504 to identify one or more amino acids based on a comparison of information derived from the mass spectrometry data to information contained in the protein sequence library 508. In some implementations, a user operating the computing device 512 may access a mass spectrometry data analyzer 514 running on the computing device 512. In some implementations, the user provides the mass spectrometry data generated by the mass spectrometer 504 to the mass spectrometry data analyzer 514. In other implementations, the user selects mass spectrometry data from available mass spectrometry data (e.g., data previously downloaded, transferred, or otherwise made available to the computing device 512 by the mass spectrometer 504). In some implementations, the mass spectrometer 504 includes the computing device 512. For example, the computing device 512 may be implemented as one or more computer processors functioning within a mass spectrometer system. It is understood that each implementation describes an additional embodiment of the methods and systems described herein.

[0074] In some implementations, the mass spectrometry data analyzer 514 calculates additional data from the mass spectrometry data, such as the mass-to-charge ratios of ions (e.g., calculated as the centroids of the peaks in a so-called "profile" spectrum), the relative intensities and / or charges of the peaks, based on experimental information contained within the mass spectrometry data.

[0075] In an embodiment, the subsequences contained in the protein sequence library 508 are used as a basis for predicting a plurality of mass spectra 510. The predicted mass spectra 510 of the subsequences may be compared to the experimentally derived fragment spectra 506 using a mass spectrometry data analyzer 514 of a computing device 512 to identify one or more of the predicted mass spectra that most closely match the experimentally derived fragment spectra 506.

[0076] In an embodiment, de novo peptide sequencing may be performed, for example, using a spectral graph approach, where the spectrum is represented as a graph with peaks as vertices connected by edges where their mass differences correspond to the masses of amino acids. The vertices of the spectrum graph are further scored based on peak intensity and neutral loss, and the peptide sequence is obtained by finding the longest path in the graph. De novo peptide sequencing can be viewed as a search in a database of all possible peptides. For a typical spectrum identified in the database search, there may be hundreds, even thousands, of very different peptide sequences that match the spectrum. As a result, de novo peptide sequencing algorithms output multiple peptide reconstructions rather than a single reconstruction.

[0077] In an embodiment, the protein sequence library 508 may include a spectral dictionary that can be used to generate full-length peptide reconstructions with a high probability of containing the correct peptide. However, an open question is how many reconstructions must be generated to avoid losing the correct peptide. Generating too few peptides results in false negative errors, while generating too many peptides results in false positive errors. Some de novo algorithms output a single or fixed number (determined before the search) of peptides. For some spectra, generating only one reconstruction may be sufficient to guarantee finding the correct peptide, while in other cases (equivalent to the same parent mass), even thousands of reconstructions may be insufficient. The challenge of generating a variable number of reconstructions for each spectrum becomes especially important for long peptides due to the increased complexity of the search space.

[0078] The predicted peptide sequences resulting from the comparison of the mass spectrometry data by the mass spectrometry data analyzer 514 to the protein sequence library 508 may be provided to a query module 505. The query module 505 may be configured to identify sources of peptide sequences using a plurality of data sources 518A-518N in communication with the query module via a network 520. The plurality of data sources 518A-518N may include any number and any type of data sources. The plurality of data sources 518A-518N may each include and / or be associated with a database (e.g., a data store, a data repository, etc.). The database may include any type of database, e.g., an in-memory / centralized database, a distributed database, a computational database, a relational database, a cloud-based database, an object-oriented database, a query language-based database (e.g., NoSQL, etc.), a graph database, etc. The database may include any data / information, e.g., data / information associated with a peptide, etc.

[0079] In an embodiment, the data sources 518A-518N may include an extended human proteome database. The extended human proteome database may include computer readable representations of protein sequences. The extended human proteome database may include: Non-coding The extended human proteome database can include a computer readable representation of the translation of RNA. Non-coding The extended human proteome database can include RNA (lncRNA). Non-coding The extended human proteome database may include RNA transcribed from human endogenous retroviruses (HERVs). The extended human proteome database may further include messenger RNA (mRNA) that classically codes for proteins. In some embodiments, at least a portion of the computer-readable representations of protein sequences in the extended human proteome database may be associated with a specific subject, such that the workflow may assign subject-specific putative sources to de novo peptide sequences derived from the subject.

[0080] The Extended Human Proteome Database includes peptides derived from non-classically translated regions of the human genome, i.e. Non-codingThe extended human proteome database can include peptides from regions annotated as: . The extended human proteome database can include one or more databases including part or all of OpenProt, and / or data similar to part or all of OpenProt, as will be understood by those skilled in the relevant art. OpenProt is disclosed, for example, in BrunetM. A., Brunelle M., Lucier J.-F., Delcourt V., Levesque M.,Grenier F., et al.(2019). OpenProt: A More Comprehensive Guide to ExploreEukaryotic CodingPotential and Proteomes. Nucleic Acids Res. 47, D403-D410.10.1093 / nar / gky936, which is incorporated herein by reference in its entirety ... Non-coding By including one or more databases containing RNA sequences and / or their translations, Non-coding It can include a computer readable representation of a protein sequence representing the translation of the RNA. OpenProt is a polycistronic model of eukaryotic genomes and includes all open reading frames (ORFs) of at least 30 codons in length.

[0081] The Extended Human Proteome Database can include translations of lncRNAs, i.e., non-classically translated regions of the human genome. lncRNAs are mRNA-like in that they undergo splicing and have features such as poly(A) signals / tails. Non-codingAlthough initially characterized as an RNA, the optional criterion of "transcripts longer than 200 nucleotides" was later added to its "definition". The extended human proteome database can include one or more databases containing part or all of NONCODE and / or data similar to part or all of NONCODE, as will be understood by those skilled in the relevant art. NONCODE is disclosed, for example, in Bu, D. et al. NONCODE v3.0: Integrative annotation of long noncodingRNAs. NucleicAcids Res. 40, D210-5 (2012), which is incorporated herein by reference in its entirety. The extended human proteome database can include computer-readable representations of protein sequences representing translations of lncRNAs by including part or all of NONCODE and / or one or more databases containing lncRNA sequences and / or their translations.

[0082] The extended human proteome database has a length of approximately 22 bases. Non-codingThe extended human proteome database can include translation of miRNA, a type of RNA. Typically, miRNA regulates gene expression by blocking translation of specific mRNAs, causing their degradation. The extended human proteome database can include one or more databases including part or all of the miRBase and / or data similar to part or all of the miRBase, as will be understood by those skilled in the relevant art. The miRBase is disclosed, for example, in Kozomara, A., Birgaoanu, M. & Griffiths-Jones, S. miRBase: frommicroRNA sequences tofunction. Nucleic Acids Res. 47, D155-D162 (2019), which is incorporated herein by reference in its entirety. The extended human proteome database can include computer-readable representations of protein sequences representing translation of miRNAs by including part or all of the miRNAs and / or one or more databases including miRNA sequences and / or their translations.

[0083] The extended human proteome database can include transcription of HERVs, which are human genome sequences corresponding to endogenous viral elements. The extended human proteome database can include one or more databases including part or all of gEVE and / or data similar to part or all of gEVE, as will be understood by those skilled in the relevant art. gEVE is disclosed, for example, in Nakagawa, S. & Takahashi, MU gEVE: a genome-based endogenous viral element database provides comprehensive viral protein-coding sequences inmammalian genomes.Database (Oxford). (2016) doi:10.1093 / database / baw087, which is incorporated herein by reference in its entirety. The extended human proteome database can include computer-readable representations of protein sequences representing translations of HERVs by including part or all of gEVE and / or one or more databases including HERV sequences and / or their translations.

[0084] The extended human proteome database can include mRNA by including one or more databases that include part or all of UniProt and / or data similar to part or all of UniProt, as understood by those skilled in the relevant art. The extended human proteome database can include UniProt by including OpenProt, to the extent that OpenProt utilizes UniProt. Additionally or alternatively, UniProt or a portion thereof can be included separately from OpenProt in the extended human proteome database. In a preferred embodiment, the extended human proteome database includes one or more databases that include reviewed UniProt and / or data similar to part or all of reviewed UniProt, as understood by those skilled in the relevant art. In some embodiments, the extended human proteome database includes one or more databases that include unreviewed UniProt and / or data similar to part or all of unreviewed UniProt, as understood by those skilled in the relevant art.

[0085] The extended proteome database may be stored in a single memory or may be distributed across multiple memories. The extended proteome database may include multiple completely different databases that can be queried as one database by a single query of a workflow, such as, but not limited to, the workflow illustrated in FIG. 7 and modifications thereof, as well as other workflows of embodiments disclosed herein.

[0086] In one embodiment, the data sources 518A-518N can include a human genome database that includes all or a portion of the human genome from which computer-readable representations of proteins can be synthesized by computer. The human genome includes approximately 3 billion base pairs of deoxyribonucleic acid (DNA) that make up the entire set of chromosomes of a human organism. The human genome includes the open reading frames of DNA that code for all genes (20,000-25,000) of a human organism, as well as the open reading frames of DNA that are not coded for by any genes. Non-coding In some embodiments, the human genome database includes translated regions of DNA and Non-coding In some embodiments, the human genome database can include the entire human genome, including the mRNA and Non-coding of the human genome, excluding the portion of the human genome from which RNA is transcribed and / or the frame read Non-coding In some embodiments, proteins can be synthesized in silico based on one, two, three, four, five and / or six frame translations of all or a portion of the human genome, such that a portion of the human genome may or may not be translated using the same number of frame reads as other portions of the human genome.

[0087] In an embodiment, the data sources 518A-518N can include a non-endogenous proteome database that includes computer-readable representations of proteins and / or peptides originating from sources non-endogenous to humans, including, but not limited to, bacterial sources, viral sources, and other organisms. In an embodiment, the non-endogenous proteome database can include the NCBI BLAST database and / or one or more databases that include data similar to part or all of NCBI BLAST, as would be understood by one of ordinary skill in the relevant art. NCBI BLAST is disclosed, for example, in Johnson, M. et al. NCBI BLAST: a better web interface. NucleicAcidsRes. 36, W5-9 (2008), which is incorporated by reference herein in its entirety. Data sources 518A-518N can include computer readable representations of protein sequences representing translations of sources non-endogenous to humans, by including part or all of NCBI BLAST and / or one or more databases containing such sequences and / or their translations.

[0088] In an embodiment, data sources 518A-518N can include computer-readable representations of proteins and / or peptides associated with individual subjects in a subject-specific manner. These subject-specific data can be incorporated into one or more databases disclosed herein (e.g., an extended human proteome database, a human genome database, a non-endogenous proteome database, etc.) and / or can be included in a separate subject-specific database.

[0089] The query module 505 may utilize a query support data structure 516 to guide the identification process. The query support data structure 516 may indicate an order of search steps of multiple data sources to be applied to the query. The order may be based on a random hit rate associated with each search step. The query support data structure 516 may indicate one or more search techniques for one or more of the multiple data sources 518A-518N. The query support data structure 516 may indicate multiple search techniques for a single data source, and the query support data structure 516 may indicate a single search technique for the multiple data sources 518A-518N and combinations thereof.

[0090] The query support data structure 516 can include a peptide source assignment workflow to assign putative sources to peptide sequence inputs to the workflow, where the putative sources indicate the most likely origins of the peptide sequence. Each search step of the query support data structure 516 can include a peptide source search step that indicates distinct possible sources of the peptide sequence if the peptide source search step finds a match. A linear extended human proteome source can be indicated by a linear human proteome search for peptide sequences in the extended human proteome database. A linear genome source can be indicated by a linear human genome search of a translation of the human genome database. A linear mismatch can be indicated by a linear mismatch search for peptides with mismatches to peptide sequences in the extended human proteome database, a linear mismatch search for peptides with mismatches to peptides derived from a translation of the human genome, and / or a linear mismatch search of a subject-specific database. A linear non-endogenous proteome source can be indicated by a linear non-endogenous search for peptide sequences in the non-endogenous proteome database. Cis-spliced ​​human proteome sources can be identified by a cis-splice search of the Extended Human Proteome Database. Trans-spliced ​​human proteome sources can be indicated by a trans-splice search of the Extended Human Proteome Database. The putative source assigned to a peptide sequence can be the first likely source found in the workflow, i.e., the search step with the lowest random hit rate.

[0091] 6 illustrates an example of a query support data structure 600. The query support data structure 600 may include search steps for searching the data sources of a plurality of data sources 518A-518N, which are shown in order by random hit rate. Search steps associated with lower random hit rates may be searched before performing a search step having a higher random hit rate. For each search step shown in the query support data structure 600, additional search steps may be included or search steps may be omitted.

[0092] In an embodiment, the query support data structure 600 may have been created previously or may be created as needed. The query support data structure 600 may be created, for example, by creating a plurality of simulated random queries, determining a number of matches associated with each search step based on applying the plurality of simulated random queries to each search step, determining a random hit rate associated with each search step based on the number of matches associated with each search step, and creating a query support data structure configured to facilitate application of new queries to the plurality of sources based on the random hit rate. The plurality of simulated random queries may include at least one of a plurality of uniform random queries or a plurality of weighted random queries. A uniform random query (e.g., peptide sequence) may be created by uniformly randomly sampling all amino acids. A weighted random query (e.g., peptide sequence) may be created by randomly sampling amino acids having amino acid frequencies that match the frequencies of amino acids found in vertebrates. Determining a random hit rate associated with each search step based on the number of matches associated with each source may include a function of the number of matches and the number of the plurality of simulated random queries. As a non-limiting example, the random hit rate associated with each source may be determined by dividing the number of matches by the number of simulated random queries. The random hit rate may further depend on the size and / or complexity of the data sources being searched.

[0093] In an embodiment, the mass spectrometry data may be used or processed and then used as a query that is applied to one or more of the plurality of data sources 518A-518N according to the query support data structure 600. The query may be further processed before being applied to one or more of the plurality of data sources 518A-518N. In an embodiment, one or more of the queries may be Replacement For example, one or more of the peptide sequences may be determined. Replacement may be determined, and one or more Replacement may be used as a query in addition to the original query. For example, a peptide sequence provided as a query to the workflow of query support data structure 600 may contain one or more ambiguous residues. For example, leucine (L) and isoleucine (I) have the same mass and therefore it is not possible to distinguish them in de novo search sequencing. To account for this, for a given peptide containing I / L, all of the I and L residues are Replacement is associated with Replacement The peptide sequence can be considered to be provided as a query to the workflow of the query support data structure 600. For example, for the peptide "ATTSLLHN (SEQ ID NO: 1)", there are four possible Replacement There are: ATTSLLHN (SEQ ID NO: 1), ATTSLIHN (SEQ ID NO: 2), ATTSILHN (SEQ ID NO: 3), and ATTSIIHN (SEQ ID NO: 4). Replacement The peptide sequence obtained may be used as a query. Replacement The resulting peptide sequences can then be assigned individual putative sources according to the peptide source assignment workflow of the query module 505. Replacement The assigned putative source of is a possible source for the provided peptide sequence with ambiguous residues. The possible source indicated by the peptide source step having the lowest random hit rate can be assigned as the putative source of the provided peptide sequence with ambiguous residues. Replacement is not assigned a probable source Replacement can be filtered to remove

[0094] 7 is a flow diagram outlining the steps of an example of a peptide assignment workflow. A de novo sequenced peptide sequence 701 is used to assign one or more of the de novo sequenced peptide sequences. Replacement 702 may be created.

[0095] In a first peptide source search step 703, the query (701 and 702) may be applied to the extended human proteome database to identify an identity match. Replacement If a peptide sequence is found for a peptide sequence in the extended human proteome database 703, the peptide sequence may be labeled "Linear" at 704 and all possible protein sources of the peptide may be included in the output of the workflow. Replacement 702 can assign a linear extended human proteome source. The assigned source can be included in the output of the workflow. Replacement may be included in the output of the workflow.

[0096] In a second peptide source search step 705, the query (701 and 702) may be applied to the translated human genome frame using BLAT or a similar algorithmic tool. BLAT is disclosed, for example, in GenomeRes. 2002 Apr; 12(4): 656-664. BLAT-The BLAST-Like Alignment Tool, which is incorporated herein by reference in its entirety. An example BLAT command may be, as a non-limiting example, "blat -t=dnax -q=prot -minScore=7 -stepSize=1 hg38.2bit Fasta_query output.psl psl2bed < output.psl > perfect_match.bed". If an identical match is found, the peptide sequence may be labeled "linear" in 706 and the possible source sequence may be included in the output. The peptide sequences 701 and their corresponding sequences found by the linear human genome search 705 may be identified as "linear" in 706 and the possible source sequences may be included in the output. Replacement 702 can assign a linear genome source. The assigned source can be included in an output of the workflow.

[0097] In a third peptide source search step 707, the peptide sequence of the query (701 and 702) may be mapped to the extended human proteome database 703, which allows for some mismatches (as non-limiting examples, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, etc. mismatches). In an embodiment, the number of mismatches may be 1. An example of a BLAT command may be, for example, "blat -t=prot -q=prot -minScore=7 -stepSize=1 combined_DB.processed.fasta Fasta_query output_blat_hits.psl". If a peptide sequence with mismatches is found (as an example, 1 mismatch), the peptide sequence may be labeled as "1 mismatch" in 708. The peptide sequences 701 and their corresponding peptides found by the linear mismatch search for peptides with mismatches against peptide sequences in the extended human proteome database 707 may be labeled as "1 mismatch" in 708. Replacement 702 assigns linear mismatches in the extended human proteome as sources. The assigned sources can be included in the output of the workflow.

[0098] In a fourth peptide source search step 709, the peptide sequences of the queries (701 and 702) may be mapped to other organisms at 709, for example, by using the BLAST NCBI tool. If any identical matches (e.g., homologous matches) are found, the results may be annotated as "Linear BLAST" at 710. The peptide sequences 701 and their corresponding peptides found by the linear non-endogenous search for peptide sequences in the non-endogenous proteome database 709 may be annotated as "Linear BLAST" at 710. Replacement 702, a linear non-endogenous proteome source is assigned. The assigned source may be included in the output of the workflow. In some embodiments, the fourth peptide source search step 709 may be omitted, and the workflow illustrated in FIG. 7 may be modified to omit block 709 and output 710 associated with this step. In such an embodiment, the workflow may proceed from the third peptide source search step 707 to the fifth peptide source search step 711.

[0099] In a fifth peptide source search step 711, the peptide sequence of the query (701 and 702) may be fragmented into two or more fragments (if each fragment is more than one amino acid). The fragments may be used as a query applied to the Extended Human Proteome Database. If there are matches for both fragments in the same protein, the peptide sequence may be labeled "cis splice" in 712. The peptide sequences 701 and Replacement At 702, a cis-spliced ​​human proteome source is assigned. The assigned source can be included in the output of the workflow.

[0100] In the sixth peptide source search step 713, if there are hits for both fragments in two different proteins, the peptide sequence may be labeled "trans-spliced" at 714. Replacement 702, a trans-spliced ​​human proteome source is assigned. The assigned source may be included in the output of the workflow. In some embodiments, the sixth peptide source search step 713 may be omitted, and the workflow illustrated in FIG. 7 may be modified to omit block 713 and output 714 associated with this step. In such an embodiment, the workflow may proceed from the fifth peptide source search step 711 to block 715.

[0101] Any remaining peptide sequences may be marked as not assigned (N / A) at 715. The workflow may stop progressing to subsequent peptide source search steps upon assigning a putative source to a peptide sequence of a query 701, 702.

[0102] Returning to FIG. 5, in an embodiment, the computing device 512 may validate the data / information received from the mass spectrometer 504 based on the peptide sequence labels determined according to the query support data structure 600. EXAMPLES

[0103] The examples presented herein generally include peptide source assignment workflows with search steps sequenced in order of increasing random hit rate, as well as methods and systems for using and creating peptide source assignment workflows. The examples presented below are specific to peptide labeling, but other applications, including those disclosed above, can be performed following similar methodologies. The examples presented below can reduce false labeling of peptides as cis-spliced ​​and trans-spliced ​​compared to previous systems and methodologies.

[0104] Antigen-presenting cells use major histocompatibility (MHC) complexes I or II to present peptides to CD8+ or CD4+ T cells, respectively. Characterization of peptides presented to T cells, known as the immune peptidome, has been studied in the fields of infectious diseases, autoimmunity, and cancer immunotherapy. Cancer-associated MHC-presented peptides that induce immune responses are potential safe and effective targets for cancer immunotherapy. Discovery and characterization of the immune peptidome can be achieved using a number of techniques, such as whole-exome sequencing, RNA sequencing, ribosome profiling, and peptide sequencing based on tandem mass spectrometry (MS / MS). Next-generation sequencing approaches can characterize the possible endogenous immune peptidome, but only direct detection of peptides, such as by MS / MS, can provide experimental evidence for the presence of peptides presented by MHC complexes. Notably, besides using peptides bound to MHC complexes for characterization of the immune peptidome, peptides can also originate from multiple other genetic and transcription-based abnormalities. Examples of additional means for identifying aberrant peptides include cancer-specific gene and transposon overexpression (e.g., but not limited to, cancer-testis genes, transposons, and human endogenous retroviruses (HERVs)), alternative splicing, stop codon read-through, or alternative open reading frame translation.

[0105] Immunopeptidomics using peptide-MHC elution followed by MS / MS traditionally requires a reference database of peptides that may be detected. Recent advances in peptide spectrum matching software omit the reference database search to perform de novo sequencing, thereby allowing the software to identify sequences of unknown peptides, post-translational modifications (PTMs) and amino acid substitutions directly from MS / MS spectra. These methods can be used to understand the diversity of peptides that may bind to MHC complexes, rather than their protein source. For MHC I, the classical mechanism of presenting peptides begins with proteasomal cleavage of proteins in the cytoplasm, resulting in fragments 8-12 amino acids long. These peptides then bind to the MHC I complex before their translocation to the cell membrane. However, some studies suggest that in addition to cleavage, the proteasome may catalyze the reverse reaction, ligating small peptides together in a process called proteasome-catalyzed peptide splicing (PCPS). Classical cleavage results in peptides whose sequence is identical to the parent protein (referred to herein as linear), but the spliced ​​peptide pieces can be from the same protein (referred to herein as cis-spliced) or, in theory, from different proteins (referred to herein as trans-spliced).

[0106] Prior to attempting to use de novo sequencing approaches to identify peptides of unknown origin ("hidden peptides"), many of these hidden peptides have been identified as likely to arise by post-translational splicing. However, the abundance and even existence of spliced ​​peptides is a matter of debate in the field. A strategy to identify spliced ​​peptides in the MHC-I immunopeptidome by mass spectrometry was previously developed. A database containing all possible cis-spliced ​​peptides was created, allowing MS / MS spectra to be queried for cis-spliced ​​peptides. It was reported that approximately 30% of p-HLA are short-distance cis-spliced ​​peptides. The same group also developed a pipeline to map the spliced ​​immunopeptidome of MHC class I of cancer cells. This study suggested that a substantial (approximately 25%) portion of peptides could be mapped to cis-spliced ​​sequences in HCT116 and HCC1143 cell lines derived from colon and breast cancer, respectively. Trans-spliced ​​peptides were excluded from the analysis because their presence in vivo has raised debate and their addition to the database would greatly increase its complexity. A bioinformatics workflow was then developed to identify linear, cis, and trans-spliced ​​peptides, called HybridFinder. HybridFinder first searches for an exact match of a peptide in the UniProt human protein sequence database and then searches for all possible cis-, then trans-spliced ​​forms of that peptide in the human proteome. HybridFinder was used to analyze MS / MS data containing peptides eluted from MHC I complexes purified from 17 HLA monoallelic cell lines. Cis- and trans-spliced ​​peptides were found to represent up to 45% of MHC-bound peptides. A. Results 1. Expanding the search for sources of non-classical human peptides

[0107] Disclosed herein is a strategy for determining the order of putative sources when assigning sources to de novo sequence peptides. Using this strategy, a peptide source assignment workflow is developed that searches for sources of peptides among multiple sources in a specific order with an order optimized to minimize assignment of peptides to the wrong source. For example, assignment of de novo peptides to post-translational cis or trans splicing occurs very often by chance, and most peptides can be attributed to other sources that are less likely to occur by chance. As disclosed herein, a rigorous derivation of the optimal order of peptide source assignment is presented, and the usefulness of the workflow in identifying the most plausible sources of de novo peptides is presented, thus facilitating understanding of the immunopeptidome.

[0108] Previous studies have shown that up to 45% of MHC-binding peptides are not mapped as well in the UniProt human proteome. The workflow disclosed herein includes a developed database of several other possible sources that may also result from unmapped peptides. Peptides from non-classically translated regions of the human genome, e.g. Non-coding For this source, we used OpenProt, which contains all open reading frames (ORFs) of at least 30 codons in length, supplemented with the remainder of the human genome translated into six frames. Translations of known transcribed elements are also included, which is consistent with the long sequence. Non-codingThe sources include RNA (lncRNA), microRNA (miRNA), and HERVs that may be spliced ​​and therefore contain sequences that are not found through translation of genomic DNA. Below, this combination of sources is referred to as the extended human proteome database. In addition, unknown SNPs, missense mutations, or recurrent errors in either transcription, translation, or MS amino acid identification may result in peptides with single mismatches to sequences encoded in the human proteome. Mismatched peptides were searched for using BLAT to align de novo peptide sequences with the extended human proteome database with single mismatches allowed. Finally, some peptides may originate from other organisms, particularly bacterial or viral sources. For these sources, de novo peptide sequences were searched in the BLAST database (see Methods). 2. Optimal ordering of estimation sources by estimating random hit rates

[0109] For each potential source (e.g., peptide sources identified by computer devices 107-112 of FIG. 1, search steps 703, 705, 707, 709, 711, 713 of FIG. 7, etc.) of peptides described above, including cis and trans splicing of peptides, the chance of randomly finding a match was determined. To estimate the random hit rate associated with each potential putative source of peptides, it was determined how many randomly generated sequences were found in each source (e.g., the number of randomly generated sequences found in each search step 703, 705, 707, 709, 711, 713 of FIG. 7). The random hit rate associated with a source and / or search step may further depend on the size and / or complexity of the database. Peptide sequences of 8-12 mers (1,000 per length) were generated in two ways: random sequences that uniformly sample all amino acids (hereinafter referred to as uniform random) or sequences with amino acid frequencies that match the frequencies of amino acids found in vertebrates (hereinafter referred to as weighted random); see Table 1. However, peptides of 8-14 amino acids in length may be used (such as, but not limited to, 8-14 amino acids, 9-14 amino acids, 10-14 amino acids, 11-14 amino acids, 12-14 amino acids, 13-14 amino acids, 8-13 amino acids, 8-12 amino acids, 8-11 amino acids, 8-10 amino acids, 8-9 amino acids, 9-13 amino acids, 9-12 amino acids, 9-11 amino acids, 9-10 amino acids, 10-13 amino acids, 10-12 amino acids, 10-11 amino acids, 11-13 amino acids, 11-12 amino acids, 12-13 amino acids).

[0110] Random sequences were used to estimate the random hit rate for each putative source of peptides (Figure 8). Using uniform random sequences, 14 of the 5,000 peptides (0.28%) were found in the extended human proteome (e.g., but not limited to, UniProt, OpenProt, lncRNA, miRNA, and HERV). BLAT was used to find the classical sequence of the human genome. Non-coding When searching for regions, 178 out of 5,000 (3.3%) of the random peptides could be mapped. We also determined an estimate of the random hit rate when searching for peptides mapping to the human proteome with a single mismatch and found that 192 out of 5,000 peptides (3.9%) could be mapped. When searching for peptides that may be derived from non-human organisms in the BLAST database, 604 out of 5,000 sequences (12.1%) could be mapped. Finally, when searching for cis-spliced ​​peptides in the extended human proteome, 1,936 / 5,000 (38%) could be mapped, and for trans-spliced ​​peptides, 3,598 / 5,000 (71%) could be mapped (Figure 8). For weighted random peptides, 50% and 68% of the peptides could be assigned as cis or trans-spliced, respectively (Figure 8).

[0111] We designed an enhanced peptide mapping pipeline that assigns sources for peptides in order of decreasing random hit rate. When using either set of simulation data to order peptide sources by random hit rate, the enhanced pipeline searches for peptide sources in the following order: 1) the extended human proteome database (assigned as linear), 2) the human genome database using BLAT. Non-codingregion (assigned as linear), 3) single mismatch peptides in the extended human proteome (assigned as linear), 4) BLAST database (assigned as linear), 5) cis-spliced ​​peptides in the extended human proteome, and 6) trans-spliced ​​peptides. When ordered sequentially, 4,495 / 5,000 (90%) of uniform random sequences and 4,847 / 5,000 (97%) of weighted random sequences were found by this pipeline (Figure 10), and although this random hit rate is high, researchers can choose an appropriate threshold to eliminate mapped peptides from sources with high random hit rates. Indeed, previous studies have excluded searches for trans-spliced ​​peptides due to the presumed high random hit rate and assumed rarity of occurrence. 3. Identifying peptides whose sequences can be assigned with higher confidence during de novo sequencing in the first part of the assignment workflow

[0112] Tests were performed to determine whether the percentage of peptides found in the actual experiment matched the final order. A peptide source assignment workflow applied to six novel immunopeptidomic data sets from IM9 and Raji cell lines (see Methods). During de novo sequencing, amino acid calls are given a local confidence score and the quality of sequencing across a peptide can be quantified by an average local confidence (ALC%) score. The ALC% score is generated by MS / MS and associated with each de novo peptide sequence 701 (Figure 7).

[0113] We hypothesized that peptides with higher ALC% would be more likely to be assigned to more reliable sources with lower random hit rates, i.e., earlier sources in our workflow. Indeed, across six experiments, the majority of peptides with the highest ALC% were found in the first source of the pipeline (the Linear Extended Human Proteome source), in stark contrast to the pattern of sources found for randomly generated peptides (Figure 11). As the ALC% decreases, more peptides can be found in later sources in the pipeline, with the most significant increase for cis-spliced ​​peptides in both cell lines, and blast and trans-spliced ​​peptides for samples from IM9 and Raji cell lines, respectively (Figure 11). Depending on the true composition of a particular sample, different sources in the pipeline may be differentially enriched in the final call.

[0114] Secondary fragmentation in MS / MS experiments (MS2 scans) may be prominent due to poor fragmentation or ionization of certain peptides. To assess the percentage of ambiguous de novo calls as a function of ALC%, a series of MS2 scans were acquired from the IM9 cell line, for which both de novo identified peptides and conventional database calls were available. As hypothesized, the de novo ALC% declines, and consequently the percentage of matching peptide calls between de novo and conventional database searches also declines (Figure 12). Taken together, this indicates that de novo peptide sequences with low ALC% and their sources should be placed under additional scrutiny. 4. Reanalysis of Single-allelic Cell Line Data Using the Peptide Source Assignment Workflow

[0115] Peptide identification by the peptide source assignment workflow was compared against HybridFinder in peptides eluted from MHC complexes in a dataset from HybridFinder Exposure: Immunopeptidomics from a collection of cell lines engineered to express a single HLA allele. See Figure 9 for the HybridFinder workflow. We found that the majority of peptides identified by HybridFinder as cis or trans spliced ​​could also be mapped to sources with even lower random hit rates. For example, HLA-A * For cell lines expressing 02:04, we found that of 1,075 peptides classified as spliced ​​by HybridFinder, 215 were classified as linear from the extended human database, 120 were classified as linear with 1 mismatch, and 301 were classified as linear from the BLAST database, resulting in an overall 636 / 1,075 (60%) of putative spliced ​​peptides being reclassified as linear. In addition, 133 of the peptides classified as trans-spliced ​​could be reclassified as cis-spliced ​​using the extended human proteome (Figure 10). Across all cell lines, 36% of putative cis-spliced ​​peptides could be reclassified as linear, and 45.9% of putative trans-spliced ​​peptides could be reclassified as linear or cis-spliced ​​(Figure 13).

[0116] The peptide source assignment workflow presented herein shows that the putative spliced ​​peptides may be peptides resulting from mutant DNA sequences, non-classically spliced ​​RNA sequences, non-classically translated regions of the human genome, mismatched human sequences, or bacterial proteins. In summary, 20% of peptides are assigned as spliced ​​peptides by the workflow presented herein, down from 29% using Hybrid Finder (FIG. 13). This overall reduction in the identification of putative spliced ​​peptides is noteworthy, as it is part of the subject exemplary method of providing a workflow that results in higher peptide assignment confidence due to assigning peptides to the putative source with the lowest random hit rate. Since spliced ​​peptides have the highest random hit rate compared to the other possible putative sources presented herein, a significant portion of peptides assigned as splices by Hybrid Finder may be inappropriately assigned. Thus, the workflow presented herein is an improvement over Hybrid Finder because of the overall reduction in the identification of putative spliced ​​peptides compared to Hybrid Finder. In some embodiments, the methods of the present invention reduce the identification of spliced ​​peptides by 5-60%. In some embodiments, the methods of the present invention reduce the identification of spliced ​​peptides by 5-50%. In some embodiments, the methods of the present invention reduce the identification of spliced ​​peptides by 5-40%. In some embodiments, the methods of the present invention reduce the identification of spliced ​​peptides by 5-30%. In some embodiments, the methods of the present invention reduce the identification of spliced ​​peptides by 5-20%. In some embodiments, the methods of the present invention reduce the identification of spliced ​​peptides by 5-10%. In some embodiments, the methods of the present invention reduce the identification of spliced ​​peptides by 10-60%. In some embodiments, the methods of the present invention reduce the identification of spliced ​​peptides by 10-50%. In some embodiments, the methods of the present invention reduce the identification of spliced ​​peptides by 10-40%.In some embodiments, the methods of the invention reduce the identification of spliced ​​peptides by 10-30%. In some embodiments, the methods of the invention reduce the identification of spliced ​​peptides by 10-20%.

[0117] In some embodiments, the methods of the present invention reduce the identification of spliced ​​peptides by 20-60%. In some embodiments, the methods of the present invention reduce the identification of spliced ​​peptides by 30-60%. In some embodiments, the methods of the present invention reduce the identification of spliced ​​peptides by 40-60%. In some embodiments, the methods of the present invention reduce the identification of spliced ​​peptides by 50-60%. In some embodiments, the methods of the present invention reduce the identification of spliced ​​peptides by 20-50%. In some embodiments, the methods of the present invention reduce the identification of spliced ​​peptides by 30-40%.

[0118] In some embodiments, the methods of the invention reduce the identification of spliced ​​peptides by 5-70%. In some embodiments, the methods of the invention reduce the identification of spliced ​​peptides by 14-60%.

[0119] At each step, the random peptide mapping results were used to estimate how many peptides could have been found by chance. When compared to the weighted random peptides, more peptides detected in the cell lines were assigned as linear (P<1e-308, two-sided Fisher's exact test), linear with a single mismatch (P=3.16e-05), linear from the BLAST database (P=0.00126), cis-spliced ​​(P=9.9e-92) and trans-spliced ​​(P=3.29e-73) (Figure 14). More peptides than expected were found by searching for random peptides, indicating that due to the optimal ordering of source assignments, each source contributes to the immunopeptidome found in each cell line. 5. Enrich peptides mapping throughout the human genome for expressed regions

[0120] We investigated where peptides mapped within the human genome but outside the UniProt proteome fall in terms of genome annotation. The first three steps of the pipeline allow mapping peptides to regions of the human genome. In the first step, peptides that map exclusively in the OpenProt database fall into ORFs that are not present in the UniProt human proteome. The location of these peptides in the human genome is analyzed (Figure 16), and compared to the location of all proteins in OpenProt, these peptides are enriched in exons, promoters and 5'UTRs (Figure 17). The exon enrichment may be due to out-of-frame translation. In a subsequent step, peptides are mapped to the translation of six frames of the human genome, even though OpenProt includes all proteins originating from ORFs longer than 30 amino acids and these peptides must originate from ORFs shorter than 30 amino acids. The genome annotation distribution of peptides mapped in this step is closer to that of the human genome, i.e., the majority of peptides are mapped to intergenic or intronic regions (Figure 18), indicating that the assignment of these peptides is contaminated with random matching. However, peptides from the proteome dataset were more enriched for exons, promoters and 5'UTRs than peptides from the random uniform or weighted simulations (Figure 19). In the third step of the enrichment pipeline, peptides can be mapped to the extended human proteome with a single mismatch (Figure 20). Peptides mapped in this step show stronger enrichment in exons, introns and promoters than the enrichment found from peptides in the weighted or uniform simulation datasets (Figure 21). At each step, there is a consistent depletion of intergenic regions and enrichment of transcribed regions, as has been found in other studies focusing on unidentified peptides in the immunopeptidome.The enrichment of transcribed sequences supports the idea that peptides assigned in these steps of the pipeline are accurately assigned even if they do not map to proteins in the UniProt database. 6. Peptides identified by BLAST are not enriched for any bacterial genus

[0121] Searching within the BLAST database has the highest random hit rate for linear peptides in the peptide source assignment workflow. Although peptides from cell lines had somewhat more matches in the BLAST database than would be expected based on uniform or weighted random data (Figure 14), it was determined whether the BLAST assignments indicated enrichment for specific microorganisms that could be contaminating. To calculate enrichment, peptides that could not be uniquely mapped to a single species were removed, and then Fisher's exact test was applied to the counts of peptide mapping to each genus in each and every cell line. After correcting for multiple hypothesis testing, no genera were significantly enriched in any cell line or when all cell lines were considered together. There were three possible, non-mutually exclusive causes for the observed lack of enrichment. First, there were no contaminating organisms in the immunopeptidomics preparations. Second, the presence of the organisms was not represented in the BLAST database. Third, peptides resulting from contaminating organisms could not be uniformly mapped to a single organism and were therefore excluded from the above analysis. In the first two possibilities, it is unclear why a cell line would have more BLAST matches than would be expected. Taken together, these results do not support a biological basis for the peptides assigned in the BLAST step; rather, the matches found in this step may be random and spurious. 7. Novel repeat peptides

[0122] To identify common peptides that are reclassified across multiple data sets, peptides common to more than three cell lines were selected. For example, QSPVALRPL (SEQ ID NO: 5) is highly repetitive and was identified as trans-spliced ​​by the Hybridfinder algorithm, but was reclassified as linear using the disclosed pipeline. The same peptide is listed in the immune epitope database as being part of an unidentified peptide. Upon further examination, it is an out-of-frame peptide in the FAM96A gene, which is a proapoptotic tumor suppressor in gastrointestinal stromal tumors (see, for example, Schwamb et al. Int. J. Cancer (2015) Sept 15; 137(6):1318-29, the entirety of which is incorporated herein by reference). If the out-of-frame translation is specific to cancer samples, this peptide can be a target for cancer immunotherapy. B. Discussion

[0123] As the number of peptides identified in immunopeptidomics experiments using de novo sequencing increases, the need for better characterization of the immunopeptidome is more pressing than ever. Previous studies have attributed PCPS as the primary source of peptides of unknown protein identity. Described herein is a peptide source assignment workflow that assigns parent proteins of de novo sequenced peptides from several sources with a lower random hit rate than the set of all possible PCPS peptides. We found that 32% of the putative PCPS peptides could be explained by a single mismatch with a known protein, translation of a likely untranslated part of the human genome, or a bacterial and viral peptide. Not surprisingly, the majority of peptides are encoded by known expressed regions. Finally, repetitive out-of-frame peptides were identified in the tumor suppressor gene FAM96A, which may be of interest as a target for cancer immunotherapy. C. Method 1. Dataset i. Simulation random peptides

[0124] Two sets of peptide sequences were simulated for an estimate of the random hit rate. The "random" built-in python library was used to create sets of amino acid sequences of lengths 8-12, with 1,000 peptides for each length, for a total of 5,000 random peptides in each set. For the first set of simulated peptide sequences, all amino acids have an equal probability of being incorporated into the sequence, and this set is referred to as "uniform random." In the second set, amino acids have a probability of being incorporated that matches their frequency in vertebrates, and this set is referred to as "weighted random." The two sets of peptide sequences are included in Table 1. ii.IM9 and Raji cell line immunopeptidomics

[0125] Three replicates of IM9 and Raji cell lines were processed by MS / MS: 210210_IM-9_1_IFN_cl1, 210210_IM-9_2_IFN_cl1, 210210_IM-9_3_IFN_cl1, 180316_RAJI_NoIFN, 180323_Raji_IFN, and 180323_Raji. All replicates of the IM9 cell line were simulated with IFNγ, while only two replicates of the Raji cell line received the same treatment.

[0126] IFNγ can enhance the expression of surface major histocompatibility complex (HLA) molecules and increase the processing and presentation of tumor-specific antigens, promoting T cell recognition and cytotoxicity. IFNγ also upregulates many components of the antigen presentation pathway, as well as induces a shift between constitutive immunoproteasome subunits with different catalytic activities in the proteosome, creating distinct populations of HLA-associated peptides. We use IFNγ treatment with cell lines to increase the opportunity to expand the immunopeptidome we can detect by mass spectrometry. Immunoprecipitation

[0127] An HLA-Pan class I (W6 / 32) column was prepared using NHS-activated Sepharose 4 beads (GE Healthcare 17090601) and a coupling buffer of 0.2 M sodium bicarbonate and 0.5 M sodium chloride, which was washed with 0.1 M Tris-HCl and 0.1 M acetate buffer at pH 8.5. Affinity purification was performed under gravity and the flow-through was captured for further analysis. Bound HLA molecules were eluted under gravity using 0.1 M glycine (Sigma), pH 2.7 (Figure 1). 0.1% trifluoroacetic acid (catalog number: LC485-1 Honeywell) was added to the glycine elution. HLA-associated peptides were eluted in a two-step elution using Sep-Pak (catalog number: WAT054960 Waters). HLA-specific peptides were eluted using 30% acetonitrile (catalog number: LC34967 Honeywell) / 0.1% trifluoroacetic acid, and HLA molecules were eluted using 70% acetonitrile / 0.1% trifluoroacetic acid. Aliquots of lysate, flow-through, glycine, 30% acetonitrile / 0.1% trifluoroacetic acid, and 70% acetonitrile / 0.1% trifluoroacetic acid eluates were collected throughout the process.

[0128] The peptide and HLA fractions were placed on a SpeedVac Vacuum Concentrator (Thermo) for 2 hours. Each sample was resuspended in 0.1% trifluoroacetic acid after the SpeedVac. The peptide fractions were further purified using a C-18 ZipTip® (Cat. No.: ZTC185096 Millipore). All samples were then analyzed on an Orbitrap Fusion™ Lumos™ Tribrid™ mass spectrometer (Thermo) for peptide sequencing. b. Data Analysis

[0129] Raw data files from the Orbitrap Fusion™ Lumos™ (Thermo) LC / MS were searched with PEAKS® Studio X (BSI) proteomics software against the human Uniprot database, custom databases for proteins of interest and de novo. iii. HLA single allele immunopeptidomics

[0130] For MS / MS data from HLA-I monoallelic cell lines, peptides were downloaded from the supplemental table of Faridi, P. et al. Sci. Immunol. Vol 3, issue 28, pg 3947, Oct. 12 (2018), which is incorporated by reference in its entirety. Data were collected from eight H including expression of HLA-A alleles (A0101, A0203, A0204, A0207, A0301, A3101, A6802, A2402) and nine different HLA-B alleles (B5801, B5703, B5701, B4402, B5101, B0801, B1502, B2705, B0702). In total, there were over 51,000 unique peptides. 2. Reproduction of the hybrid viewfinder

[0131] To facilitate comparison with the described peptide source assignment workflow, the workflow was compared with the Hybrid Finder workflow described in Faridi, P. et al. Sci. Immunol. Vol 3, issue 28, pg 3947, Oct. 12 (2018), which is incorporated by reference in its entirety. Reproduced from . First, each peptide was searched in the UniProt human reference proteome database. Peptides with identical matches were annotated as linear. For peptides without a linear match, all possible splits of that peptide were generated where the length of the smaller piece was greater than one amino acid. Then, the possible matches for each fragment were searched through the database. If identical matches of both fragments were found in a single protein, the peptide was annotated as cis-spliced. Matches could be reversed ordered. Otherwise, if matches were available in two separate proteins, the peptide was annotated as trans-spliced. Peptides whose split pairs did not match any protein sequence were annotated as not applicable (N / A). 3. Expanded Human Proteome Database

[0132] We combined FASTA files of reviewed and unreviewed human sequences from OpenProt (www.openprot.org), UniProt (www.uniprot.org), which also includes protein sequences from some viruses that use humans as hosts (UniProt proteome version UP0000056430, downloaded May 2020). We extended this database to include translated protein sequences from lncRNAs (NONCODE version v5.19, downloaded May 2020), miRNAs (last updated 3 / 10 / 18, downloaded May 2020), and endogenous viral elements (gEVE database ORFs21, downloaded May 2020). We use this database when the workflow searches for linear and single mismatched human peptides (steps 1 and 3), as well as in searches for cis- and trans-spliced ​​peptides. 4. Peptide Source Assignment Workflow

[0133] The inherent random hit rate was measured for each source from which peptides in an immunopeptidomics experiment could be found using the simulated random data set described above. A workflow was constructed in which the steps were ordered in order of increasing random hit rate. The steps applied to each de novo sequenced peptide are as follows:

[0134] Step 1: Search for identical sequence matches in the extended human proteome database (described above). Leucine (L) and isoleucine (I) have the same mass and therefore it is not possible to distinguish them in de novo search sequencing. To account for this, for a given peptide containing I / L, search for all I and L residues. Replacement For example, for the peptide "ATTSLLHN (SEQ ID NO: 1)" there are four possibilities: Replacement There are: ATTSLLHN (SEQ ID NO: 1), ATTSLIHN (SEQ ID NO: 2), ATTSILHN (SEQ ID NO: 3), and ATTSIIHN (SEQ ID NO: 4). Replacement If an identical match (e.g., 100% identical) is found for , the peptide is annotated as "linear" and all possible protein sources of the peptide are included in the output. Because a match has been identified, the algorithm does not need to proceed to additional steps, e.g., continue to step 2. Otherwise, if no match is identified, the algorithm proceeds to step 2.

[0135] Step 2: Search for identical matches in any of the six frames of the translated human genome using BLAT32. Use the following command:

[0136] blat -t=dnax -q=prot -minScore=7 -stepSize=1 hg38.2bit Fasta_query output.psl

[0137] psl2bed < output.psl > perfect_match.bed

[0138] If an identical match is found, the peptide is annotated as "linear" and the potential source sequence is included in the output, otherwise the peptide moves to step 3.

[0139] Step 3: Map peptides to the extended human proteome database, allowing 1 mismatch at the genomic location of the BLAT hit analysis, using this code: "blat -t=prot -q=prot -minScore=7 -stepSize=1 combined_DB.processed.fasta Fasta_query output_blat_hits.psl".

[0140] If a sequence with a single mismatch is found, the peptide is annotated as "1 mismatch", otherwise the peptide moves to step 4.

[0141] Step 4: The sequence is mapped to other organisms using the BLAST NCBI tool. If any identical matches are found, the result is annotated as "Linear BLAST".

[0142] Step 5: For the remaining peptides, the algorithm creates all possible splits of the peptide where the length of the smaller piece is greater than 1. It then looks for matches of both fragments in all human sequence databases. If there is a match for both chunks in the same protein, the tool annotates the peptide as "cis-splice". Otherwise, if there is a hit for both fragments in two different proteins, the tool annotates the peptide as "trans-splice". The rest of the peptides that do not have any matches are assigned as not applicable (N / A). 5. Genomic location of BLAT hit analysis

[0143] Analysis of the genomic location of the BLAT hits was performed using the annotatepeaks.pl script from the HOMER suite. Specifically:

[0144] annotatePeaks.pl ${file} hg38 -annStats ${file}.summary.txt

[0145] Only basic annotations were considered for further analysis. To calculate the enrichment of genomic locations of peptides found in the OpenProt database with either an identical match (step 1) or a single mismatch (step 3), Fisher's exact test was performed to compare the number of peptides in each genome annotation in the sample against the entire OpenProt database. For peptides mapping to any translated region in the human genome (step 2), the enrichment p-value calculated by HOMER was used for over- or under-representation of each genome annotation. 6. Tools

[0146] Python, bedops, psl2bed, BLAT, BLAST, HOMER.

[0147] FIG. 22 illustrates a system 2200 for performing the methods described herein. In an embodiment, the system 2200 can be configured to execute the workflow illustrated in FIG. 7. In an embodiment, the system 2200 can include some or all of the databases utilized by the workflow illustrated in FIG. 7. In an embodiment, the system 2200 can be configured to communicate with one or more of the databases utilized by the workflow illustrated in FIG. 7. In an embodiment, the system 2200 can include some or all of the data sources 518A-518N illustrated in FIG. 5. In an embodiment, the system 2200 can be configured to communicate with one or more of the data sources 518A-518N illustrated in FIG. 5.

[0148] The devices / components described herein may include a computer 2201, as shown in Figure 22. The computer 2201 may include one or more processors 2203, a system memory 2212, and a bus 2213 that connects various components of the computer 2201, including the one or more processors 2203 to the system memory 2212. In the case of multiple processors 2203, the computer 2201 may utilize parallel computing.

[0149] The bus 2213 may include one or more of several possible types of bus structures, such as a memory bus, a memory controller, a bus for peripherals, an accelerated graphics port, and a processor or local bus using any of a variety of bus configurations.

[0150] The computer 2201 may operate on and / or include various computer readable media (e.g., non-transitory). The computer readable media may be any available media accessible to the computer 2201, including non-transitory, volatile and / or non-volatile media, removable and non-removable media. The system memory 2212 has computer readable media in the form of volatile memory, such as random access memory (RAM), and / or non-volatile memory, such as read only memory (ROM). The system memory 2212 may store data, such as mass spectrometry data 2207, and / or program modules, such as operating system 2205 and query analysis software 2206, accessible to and / or operated on one or more processors 2203. The system memory 2212 may further include some or all of the databases utilized by the workflow illustrated in FIG. 7, and / or some or all of the data sources 518A-518N illustrated in FIG. 5.

[0151] The computer 2201 may also include other removable / non-removable volatile / non-volatile computer storage media. The mass storage device 2204 may provide non-volatile storage of computer code, computer readable instructions, data structures, program modules, and other data for the computer 2201. The mass storage device 2204 may be, but is not limited to, a hard disk, a removable magnetic disk, a removable optical disk, a magnetic cassette or other magnetic storage device, a flash memory card, a CD-ROM, a digital versatile disk (DVD) or other optical storage, a random access memory (RAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), or the like.

[0152] Any number of program modules may be stored on the mass storage device 2204. An operating system 2205 and a query analysis software 2206 may be stored on the mass storage device 2204. One or more of the operating system 2205 and the query analysis software 2206 (or some combination thereof) may include the program modules and the query analysis software 2206. Mass spectrometry data 2207 may also be stored on the mass storage device 2204. The mass spectrometry data 2207 may be stored in any of one or more databases known in the art. The databases may be centralized or distributed across multiple locations within the network 2215. The mass storage device 2204 may further include some or all of the databases utilized by the workflow illustrated in FIG. 7 and / or some or all of the data sources 518A-518N illustrated in FIG. 5.

[0153] A user may enter commands and information into the computer 2201 through input devices (not shown). Such input devices include, but are not limited to, keyboards, pointing devices (e.g., computer mice, remote controls), microphones, joysticks, scanners, tactile input devices such as gloves and other body coverings, motion sensors, etc. These and other input devices may be connected to the one or more processors 2203 through a human-machine interface 2202 coupled to a bus 2213, but may also be connected by other interface and bus structures, such as parallel ports, game ports, IEEE 1394 ports (also known as Firewire® ports), serial ports, network adapters 2208, and / or universal serial buses (USB).

[0154] A display device 2211 may also be connected to the bus 2213 via an interface such as a display adapter 2209. It is contemplated that the computer 2201 may have more than one display adapter 2209 and that the computer 2201 may have more than one display device 2211. The display device 2211 may be a monitor, an LCD (liquid crystal display), a light emitting diode (LED) display, a television, a smart lens, smart glasses, and / or a projector. In addition to the display device 2211, other output peripheral devices may include components such as a speaker (not shown) and a printer (not shown) that may be connected to the computer 2201 via the input / output interface 2210. Any step and / or result of the method may be output (or may be caused to be output) to an output device in any form. Such output may be any form of visual representation, including but not limited to text, graphics, animation, audio, tactile, etc. The display 2211 and the computer 2201 may be part of one device or separate devices.

[0155] The computer 2201 may operate in a network environment using logical connections to one or more remote computer devices 2214a, b, c. The remote computer devices 2214a, b, c may be personal computers, computer stations (e.g., workstations), portable computers (e.g., laptops, mobile phones, tablet devices), smart devices (e.g., smartphones, smart watches, activity trackers, smart apparel, smart accessories), security and / or monitoring devices, servers, routers, network computers, peer devices, edge devices, or other common network nodes, etc. The logical connections between the computer 2201 and the remote computer devices 2214a, b, c may be made over a network 2215, such as a local area network (LAN) and / or a common wide area network (WAN). Such network connections may be through a network adapter 2208. The network adapter 2208 may be implemented in both wired and wireless environments. Such network environments are conventional and common in homes, offices, enterprise-wide computer networks, intranets, and the Internet.

[0156] Although application programs and other executable program components, such as the operating system 2205, are illustrated as separate blocks herein, it is recognized that such programs and components may reside at various times in different storage components of the computing device 2201 and are executed by one or more processors 2203 of the computing device 2201. An implementation of the query analysis software 2206 may be stored on or transmitted over some form of computer readable medium. Any of the disclosed methods may be performed by processor executable instructions embodied on a computer readable medium.

[0157] In one embodiment, the query analysis software 2206 may be configured to perform some or all of the search steps 703, 705, 707, 709, 711, 713 illustrated in FIG.

[0158] In an embodiment, the query analysis software 2206 may be configured to perform a method 2300 shown in FIG. 23. The method 2300 may be performed in whole or in part by a single computing device, multiple electronic devices, etc. The method 2300 may include, at 2302, generating a plurality of simulated random queries. Generating the plurality of simulated random queries may include at least one of generating a plurality of uniform random queries or generating a plurality of weighted random queries. The plurality of simulated random queries may include a plurality of simulated random text strings. The plurality of simulated random queries may include a plurality of simulated random peptide sequences.

[0159] The method 2300 may include, at 2304, determining a number of matches associated with each source based on applying a plurality of simulated random queries to each source of the plurality of sources.

[0160] In an embodiment, the method 2300 may include, at 2306, determining a false discovery rate associated with each source based on a number of matches associated with the respective source. In an embodiment, a function of the number of matches and the number of the plurality of simulated random queries may be determined. In an embodiment, the determining may be made by dividing the number of matches by the number of the plurality of simulated random queries. In an embodiment, determining a false discovery rate associated with each source based on a number of matches associated with the respective source may include a function of the number of matches and the number of the plurality of simulated random queries. In an embodiment, determining a false discovery rate associated with each source based on a number of matches associated with the respective source may include dividing the number of matches by the number of the plurality of simulated random queries.

[0161] The method 2300 may include, at 2308, creating a query support data structure configured to facilitate application of new queries to multiple sources based on the false discovery rate.

[0162] In an embodiment, the query analysis software 2206 may be configured to perform the method 2400 shown in FIG. 24. The method 2400 may be performed, in whole or in part, by a single computing device, multiple electronic devices, etc. The method 2400 may include, at 2402, receiving a query. The query may include a text string. The query may include a peptide sequence. Receiving the query may include receiving the peptide sequence from a mass spectrometry system. The method 2400 may include determining, by the mass spectrometry system, one or more amino acids of the peptide sequence.

[0163] The method 2400 may include, at 2404, applying the query to one or more of the multiple sources based on the query support data structure. The query support data structure may indicate an order in which the multiple sources are applied to the query. The order may be based on a false discovery rate associated with each source of the multiple sources. The method 2400 also includes applying the query to one or more of the sources based on the query support data structure. Replacement Applying the query to one or more of the multiple sources based on the query support data structure may include determining one or more of the query support data structures. Replacement Each of Replacement applying the multiple source to one or more of the multiple sources; Replacement If an identical match for is found in a first source of the multiple sources, then stop further searching and assign a linear indicator to one or more of the queries associated with the identical match. Replacement and one or more of the queries associated with the same match. Replacement as the correct query.

[0164] Applying the query to one or more sources of the plurality of sources may include searching for an identical match to the query in a first source of the plurality of sources and stopping further searching if an identical match to the query is found in the first source of the plurality of sources. The query results may include the identical match, and an indicator associated with the source of the plurality of sources associated with the query results may include a linear indicator.

[0165] Applying the query to one or more of the multiple sources includes searching for an identical match to the query in a first source of the multiple sources and applying the query to one or more of the multiple sources. Replacementand stopping further searches if an identical match for is found in a first source of the multiple sources. The query result may include the identical match, and an indicator associated with a source of the multiple sources associated with the query result may include a linear indicator.

[0166] Applying the query to one or more sources of the multiple sources may include searching for an identical match to the query in any frame of the multiple frames of a second source of the multiple sources, and stopping further searching if an identical match to the query is found in any frame of the multiple frames of the second source of the multiple sources. The query results may include the identical match, and an indicator associated with the source of the multiple sources associated with the query results may include a linear indicator.

[0167] Applying the query to one or more sources of the plurality of sources may include searching for a non-identical match to the query in a third source of the plurality of sources, and stopping further searching if a non-identical match to the query is found in the third source of the plurality of sources. The query results may include the non-identical matches, and an indicator associated with the source of the plurality of sources associated with the query results may include a mismatch indicator.

[0168] Applying the query to one or more sources of the plurality of sources may include searching for a homologous match to the query in a fourth source of the plurality of sources, and stopping further searching if a homologous match to the query is found in the fourth source of the plurality of sources. The query results may include the homologous matches, and the labels associated with the sources of the plurality of sources associated with the query results may include homology labels.

[0169] Applying the query to one or more sources of the multiple sources may include splitting the query into a plurality of sets of fragments, searching for each set of fragments in a fifth source of the multiple sources, stopping further searching if a match for the set of fragments is found in the fifth source of the multiple sources, and stopping further searching if a first match for a first fragment of the set of fragments and a second match for a second fragment of the set of fragments are found in the fifth source of the multiple sources. The query result may include a match for the set of fragments, and the indicator associated with the source of the multiple sources associated with the query result may include a cis splice indicator. The query result may include a first match for the first fragment of the set of fragments and a second match for the second fragment of the set of fragments, and the indicator associated with the source of the multiple sources associated with the query result may include a trans splice indicator.

[0170] The method 2400 may include, at 2406, determining, based on the query result, an indication associated with a source of the multiple sources associated with the query result.

[0171] The method 2400 may include applying the label to the query, at 2408. The method 2400 may also include determining a source of the query based on the label. The method 2400 may also include validating an output of the mass spectrometry system based on the source of the query.

[0172] In view of the described apparatus, systems, and methods, and variations thereof, certain more specifically described embodiments of the invention are described herein below. These specifically recited embodiments, however, should not be construed as having any limiting effect on any different claims that include different or more general teachings described herein, or as the "specific" embodiments being limited in any way other than the inherent meaning of the language literally used herein.

[0173] Embodiment 1: A method for determining a putative source of a peptide sequence of a peptide, comprising receiving the peptide sequence and determining a putative source associated with the peptide sequence based at least in part on one or more searches of the peptide sequence in one or more databases, each individual search of the one or more searches having a random hit rate based at least in part on a number of random sequences found by the individual search, and the one or more searches being performed in order of increasing random hit rate until a putative source is determined.

[0174]

[0023] Embodiment 2: The one or more databases include an extended human proteome database, the extended human proteome database comprising messenger ribonucleic acid (RNA) and Non-coding An embodiment as in embodiment 1, comprising a computer readable representation of the translation from the RNA.

[0175] Embodiment 3: An embodiment as in embodiment 2, wherein the extended human proteome database comprises a computer-readable representation of translations from microRNAs.

[0176]

[0036] Embodiment 4: The extended human proteome database is Non-coding The embodiment of any of embodiments 2-3, comprising a computer readable representation of the translation from the RNA.

[0177] Embodiment 5: The embodiment of any of embodiments 2-4, wherein the extended human proteome database comprises a computer-readable representation of the translation of a human endogenous retrovirus.

[0178] Embodiment 6: Any of embodiments 2 to 5, wherein the one or more searches include a linear human proteome search for a peptide sequence in the extended human proteome database, and if the linear human proteome search for a peptide sequence in the extended human proteome database finds the peptide sequence in the extended human proteome database, the putative source is a linear extended human proteome source.

[0179]

[0046] Embodiment 7: When the source is a linear expanded human proteome source, the peptides are derived from messenger RNA or Non-coding The embodiment as in embodiment 6, further comprising identifying whether the RNA is putatively translated.

[0180] Embodiment 8: Any of the embodiments 2-7, wherein the one or more databases include a human genome database, and the one or more searches include a linear human genome search of a translation of the human genome database, and if the linear human genome search finds a human genome sequence from which the peptide is putatively synthesized, then the putative source is a linear genome source.

[0181]

[0046] Example 9: A linear human genome search is performed on messenger RNA and Non-coding An embodiment as in embodiment 8, wherein the portion of the human genome from which the RNA is transcribed is excluded and the remainder of the human genome is included, and the linear human genome search includes a search of six frame translations of the human genome.

[0182] Embodiment 10: Any of the embodiments of embodiments 2 to 9, wherein the one or more searches include a linear mismatch search for peptides having mismatches to peptide sequences in the extended human proteome database, and if the linear mismatch search finds a peptide sequence having a mismatch to a peptide sequence in the extended human proteome database, the putative source is a linear mismatch in the extended human proteome.

[0183] Embodiment 11: An embodiment as in embodiment 10, wherein the linear mismatch search is a search for peptide sequences having only a single mismatch to the peptide sequence.

[0184] Embodiment 12: The embodiment of any of embodiments 1-11, wherein the one or more databases include a non-endogenous proteome database that includes computer-readable representations of proteins translated from RNA from a non-endogenous organism and / or proteins synthesized by a non-endogenous organism, and the one or more searches include a linear non-endogenous search for a peptide sequence in the non-endogenous proteome database, and if the linear non-endogenous search finds the peptide sequence in the non-endogenous proteome database, then the putative source is a linear non-endogenous proteome source.

[0185] Embodiment 13: An embodiment as in embodiment 12, wherein the non-endogenous proteome database comprises a basic local alignment search tool (BLAST) database.

[0186] Embodiment 14: Any of the embodiments of embodiments 2 to 13, wherein the one or more searches include a cis-splice search for peptide fragments that can be cis-spliced ​​to match the peptide sequence in the extended human proteome database, and if the cis-splice search finds peptide fragments that can be cis-spliced ​​to match the peptide sequence in the extended human proteome database, the source is a cis-spliced ​​human proteome source.

[0187] Embodiment 15: Any of the embodiments of embodiments 2 to 14, wherein the one or more searches include a trans-splice search for computer-readable representations of peptide fragments that can be trans-spliced ​​to match the peptide sequence in the extended human proteome database, and if the trans-splice search finds computer-readable representations of peptide fragments that can be trans-spliced ​​to match the peptide sequence in the extended human proteome database, the source is a trans-spliced ​​human proteome source.

[0188] Embodiment 16: An embodiment as in embodiment 15, wherein if the trans-splice search does not find a computer-readable representation of a peptide fragment that can be trans-spliced ​​to match the peptide sequence, the putative source is determined to be unidentified.

[0189]

[0081] Embodiment 17: The one or more databases include a human genome database, and the one or more searches are performed sequentially in a workflow as follows: Ordering 17. Any of the embodiments of embodiments 2-16, wherein the following searches are performed: a linear human proteome search for the peptide sequence in the extended human proteome database, a linear human genome search of a translation of the human genome database, a linear mismatch search for peptides that have mismatches to the peptide sequence in the extended human proteome database, and a cis-splice search for peptide fragments that can be cis-spliced ​​to match the peptide sequence in the extended human proteome database.

[0190]

[0041] Embodiment 18: The one or more databases include a non-endogenous proteome database comprising computer-readable representations of proteins translated from RNA from a non-endogenous organism and / or proteins synthesized by a non-endogenous organism, and the one or more searches further include a linear non-endogenous search for peptide sequences in the non-endogenous proteome database, the linear non-endogenous search being sequentially performed in the workflow after the linear mismatch search and before the cis-splice search. Ordering An embodiment such as embodiment 17.

[0191]

[0041] Embodiment 19: The one or more searches further comprise a trans-splice search for peptide fragments that can be trans-spliced ​​to match the peptide sequence in an extended human proteome database, the trans-splice search being sequentially performed in the workflow after the cis-splice search. Ordering The embodiment of any one of embodiments 17 to 18.

[0192] Embodiment 20: The embodiment of any of embodiments 17-19, further comprising stopping the progression of the workflow to subsequent searches of the one or more searches if a putative source is determined for the peptide sequence.

[0193]

[0081] Embodiment 21: The peptide sequence comprises at least one ambiguous residue, and the method comprises generating a plurality of peptide sequences each comprising a possible residue for each of the at least one ambiguous residue. Replacement creating a peptide sequence that is Replacement 21. The embodiment of any of embodiments 1-20, further comprising determining a distinct likely source for each of the determined peptide sequences, and determining a probable source of the peptide sequences such that the probable sources are distinct likely sources.

[0194] Embodiment 22: An embodiment as in embodiment 21, wherein the possible residues for each of the at least one ambiguous residue include leucine and isoleucine.

[0195] Embodiment 23: Any of the embodiments of embodiments 21-22, further comprising determining an individual random hit rate for each of the individual possible sources such that the random hit rate increases as the number of random sequences is found by the individual searches of one or more searches, and determining the putative source such that the individual random hit rate of the putative source is the lowest individual random hit rate for each of the possible sources.

[0196] Embodiment 24: One or more possibilities Replacement Multiple peptide sequences are generated so that each of the peptide sequences is associated with a putative source. Replacement There are one or more possible peptide sequences Replacement The embodiment of any of embodiments 21-23, further comprising identifying the peptide sequence identified.

[0197] Embodiment 25: Any of embodiments 1 to 24, wherein the peptide sequence is a de novo peptide sequence determined by mass spectrometry.

[0198]

[0081] Embodiment 26: A non-transitory computer-readable medium configured to communicate with one or more processors of a computing device, comprising: The non-transitory computer readable medium includes instructions thereon, the instructions comprising: A non-transitory computer-readable medium that, when executed by a processor, causes a computing device to receive as input a peptide sequence, determine a putative source associated with the peptide sequence based at least in part on one or more searches of the peptide sequence in one or more databases, where each individual search of the one or more searches has a random hit rate based at least in part on the number of random sequences found by the individual search, the one or more searches being performed in order of increasing random hit rate until a putative source is determined, and provide as output the putative source.

[0199]

[0081] Embodiment 27: The one or more databases include an extended human proteome database, the extended human proteome database comprising messenger ribonucleic acid (RNA) and Non-coding An embodiment as in embodiment 26, comprising a computer readable representation of the translation from the RNA.

[0200] Embodiment 28: An embodiment as in embodiment 27, wherein the extended human proteome database comprises a computer-readable representation of translations from microRNAs.

[0201] Embodiment 29: The extended human proteome database comprises: Non-coding The embodiment of any of embodiments 27-28, comprising a computer readable representation of the translation from the RNA.

[0202] Embodiment 30: Any of the embodiments 27-29, wherein the extended human proteome database comprises a computer-readable representation of the translation of a human endogenous retrovirus.

[0203] Embodiment 31: Any of the embodiments 27 to 29, wherein the one or more searches include a linear human proteome search for a peptide sequence in the extended human proteome database, and if the linear human proteome search for a peptide sequence in the extended human proteome database finds the peptide sequence in the extended human proteome database, the putative source is a linear extended human proteome source.

[0204]

[0081] Embodiment 32: The instructions, when executed by a processor, cause a computer device to: Non-coding An embodiment as in embodiment 31, which identifies whether the RNA is putatively translated.

[0205] Embodiment 33: Any of the embodiments of embodiments 27 to 32, wherein the one or more databases include a human genome database, and the one or more searches include a linear human genome search of a translation of the human genome database, and if the linear human genome search finds a human genome sequence from which the peptide is putatively synthesized, then the putative source is a linear genome source.

[0206]

[0081] Embodiment 34: A linear human genome search is performed using an extended human proteome database of messenger RNA and Non-coding An embodiment as in embodiment 33, excluding the portion of the human genome from which the RNA is transcribed and including the remainder of the human genome.

[0207] Embodiment 35: Any of the embodiments of embodiments 27 to 34, wherein the one or more searches include a linear mismatch search for peptides having mismatches to peptide sequences in the extended human proteome database, and if the linear mismatch search finds a peptide sequence having a mismatch to a peptide sequence in the extended human proteome database, the putative source is a linear mismatch in the extended human proteome.

[0208] Embodiment 36: An embodiment as in embodiment 35, wherein the linear mismatch search is a search for peptide sequences having only a single mismatch to the peptide sequence.

[0209] Embodiment 37: The embodiment of any of embodiments 26 to 36, wherein the one or more databases include a non-endogenous proteome database comprising computer-readable representations of proteins translated from RNA from a non-endogenous organism and / or proteins synthesized by a non-endogenous organism, and the one or more searches include a linear non-endogenous search for a peptide sequence in the non-endogenous proteome database, and if the linear non-endogenous search finds the peptide sequence in the non-endogenous proteome database, then the putative source is a linear non-endogenous proteome source.

[0210] Embodiment 38: An embodiment as in embodiment 37, wherein the non-endogenous proteome database comprises a basic local alignment search tool (BLAST) database.

[0211] Embodiment 39: Any of the embodiments of embodiments 27 to 38, wherein the one or more searches include a cis-splice search for peptide fragments that can be cis-spliced ​​to match the peptide sequence in the extended human proteome database, and if the cis-splice search finds peptide fragments that can be cis-spliced ​​to match the peptide sequence in the extended human proteome database, the source is a cis-spliced ​​human proteome source.

[0212] Embodiment 40: Any of the embodiments of embodiments 27 to 39, wherein the one or more searches include a trans-splice search for computer-readable representations of peptide fragments that can be trans-spliced ​​to match the peptide sequence in the extended human proteome database, and if the trans-splice search finds computer-readable representations of peptide fragments that can be trans-spliced ​​to match the peptide sequence in the extended human proteome database, the source is a trans-spliced ​​human proteome source.

[0213] Embodiment 41: An embodiment as in embodiment 40, wherein if the trans-splice search does not find a computer-readable representation of a peptide fragment that can be trans-spliced ​​to match the peptide sequence, the putative source is determined to be unidentified.

[0214]

[0081] Embodiment 42: The one or more databases include the human genome, and the one or more searches are performed sequentially in a workflow as follows: OrderingAny of the embodiments of embodiments 27-41, wherein the following searches are performed: a linear human proteome search for the peptide sequence in the extended human proteome database, a linear human genome search of a translation of the human genome database, a linear mismatch search for peptides that have mismatches to the peptide sequence in the extended human proteome database, and a cis-splice search for peptide fragments that can be cis-spliced ​​to match the peptide sequence in the extended human proteome database.

[0215]

[0081] Embodiment 43: The one or more databases include a non-endogenous proteome database that includes computer-readable representations of proteins translated from RNA from a non-endogenous organism and / or proteins synthesized by a non-endogenous organism, and the one or more searches further include a linear non-endogenous search for peptide sequences in the non-endogenous proteome database, the linear non-endogenous search being performed sequentially in the workflow after the linear mismatch search and before the cis-splice search. Ordering An embodiment such as embodiment 42.

[0216]

[0081] Embodiment 44: The one or more searches further comprise a trans-splice search for peptide fragments that can be trans-spliced ​​to match the peptide sequence in an extended human proteome database, the trans-splice search being sequentially performed in the workflow after the cis-splice search. Ordering The embodiment of any one of embodiments 42 to 43.

[0217] Embodiment 45: Any of the embodiments of embodiments 42-44, wherein the instructions, when executed by the processor, cause the computer device to stop advancing the workflow to subsequent searches of one or more searches if a putative source is determined for a peptide sequence.

[0218]

[0081] Embodiment 46: A peptide sequence comprising at least one ambiguous residue, the instructions, when executed by a processor, causing a computer device to generate a plurality of sequences each including a possible residue for each of the at least one ambiguous residue. Replacement Generate a peptide sequence based on the Replacement Any of embodiments 26-45, wherein for each of the peptide sequences obtained, a distinct possible source is determined, and a putative source of the peptide sequence is determined such that the putative source is a distinct possible source.

[0219] Embodiment 47: An embodiment as in embodiment 46, wherein the possible residues for each of the at least one ambiguous residue include leucine and isoleucine.

[0220] Embodiment 48: Any of the embodiments of embodiments 46-47, wherein the instructions, when executed by the processor, cause the computer device to determine an individual random hit rate for each of the individual possible sources such that the random hit rate increases as the number of random sequences is found by the individual searches of one or more searches, and to determine a putative source such that the individual random hit rate of the putative source is the lowest individual random hit rate for each of the possible sources.

[0221]

[0081] Embodiment 49: The instructions, when executed by a processor, cause a computing device to have one or more Replacement Multiple peptide sequences are generated so that each of the peptide sequences is associated with a putative source. Replacement There are one or more possible peptide sequences Replacement The embodiment of any of embodiments 46 to 48, wherein the determined peptide sequence is identified.

[0222] Embodiment 50: Any of embodiments 26 to 49, wherein the peptide sequence is a de novo peptide sequence determined by mass spectrometry.

[0223] Embodiment 51: Peptide source assignment workflow Ordering 11. A method for generating a plurality of random peptide sequences; determining a plurality of peptide source search steps; searching for each of the plurality of random peptide sequences by each of the plurality of peptide source search steps; for each of the plurality of peptide source search steps, determining a random hit rate for each individual search step of the plurality of peptide source search steps based at least in part on a number of the plurality of random peptide sequences found by the individual search step; and ranking the peptide source search steps from lowest random hit rate to highest random hit rate in a peptide source allocation workflow. Ordering The method includes:

[0224] Embodiment 52: An embodiment as in embodiment 51, wherein the random peptide sequence comprises a random sequence that uniformly samples all amino acids.

[0225] Embodiment 53: Any of embodiments 51-52, wherein the random peptide sequences comprise sequences having amino acid frequencies that match the frequencies of amino acids found in vertebrates.

[0226] Embodiment 54: Any of embodiments 51 to 53, wherein each peptide of the random peptide sequence comprises a length of 8 to 14 amino acids.

[0227] Embodiment 55: The embodiment of any of embodiments 51-54, wherein each peptide of the random peptide sequence comprises a length of 9-14 amino acids, 10-14 amino acids, 11-14 amino acids, 12-14 amino acids, 13-14 amino acids, 8-13 amino acids, 8-12 amino acids, 8-11 amino acids, 8-10 amino acids, 8-9 amino acids, 9-13 amino acids, 9-12 amino acids, 9-11 amino acids, 9-10 amino acids, 10-13 amino acids, 10-12 amino acids, 10-11 amino acids, 11-13 amino acids, 11-12 amino acids, or 12-13 amino acids.

[0228]

[0081] Embodiment 56: The step of searching multiple peptide sources comprises a linear human proteome search for peptide sequences in an extended human proteome database, the extended human proteome database comprising messenger ribonucleic acid (RNA) and Non-coding The embodiment of any of embodiments 51-55, comprising a computer readable representation of the translation from the RNA.

[0229] Embodiment 57: An embodiment as in embodiment 56, wherein the extended human proteome database comprises a computer-readable representation of translations from microRNAs.

[0230]

[0046] Embodiment 58: The extended human proteome database comprises: Non-coding The embodiment of any of embodiments 56-57, comprising a computer readable representation of the translation from the RNA.

[0231] Embodiment 59: Any of embodiments 56-58, wherein the expanded human proteome database comprises a computer-readable representation of the translation of a human endogenous retrovirus.

[0232] Embodiment 60: The embodiment of any of embodiments 56-59, wherein the multiple peptide source searching step comprises a linear human genome search of a translation of a human genome database.

[0233]

[0081] Embodiment 61: A linear human genome search is performed using an extended human proteome database of messenger RNA and Non-coding An embodiment as in embodiment 60, which excludes the portion of the human genome from which the RNA is transcribed and includes the remainder of the human genome.

[0234]

[0081] Embodiment 62: The method of claim 62, wherein the step of searching multiple peptide sources comprises a linear mismatch search for peptides having mismatches to peptide sequences in an extended human proteome database, the extended human proteome database comprising messenger ribonucleic acid (RNA) and Non-codingThe embodiment of any of embodiments 51-61, comprising a computer readable representation of the translation from RNA.

[0235] Embodiment 63: Any of the embodiments of embodiments 51-62, wherein the multiple peptide source searching step comprises a linear non-endogenous search for peptide sequences in a non-endogenous proteome database, the non-endogenous proteome database comprising computer-readable representations of proteins translated from RNA derived from the non-endogenous organism and / or proteins synthesized by the non-endogenous organism.

[0236] Embodiment 64: An embodiment as in embodiment 63, wherein the non-endogenous proteome database comprises a basic local alignment search tool (BLAST) database.

[0237]

[0081] Embodiment 65: The step of searching multiple peptide sources comprises a cis-splice search for peptide fragments that can be cis-spliced ​​to match the peptide sequence in an extended human proteome database, the extended human proteome database comprising messenger ribonucleic acid (RNA) and Non-coding The embodiment of any of embodiments 51-64, comprising a computer readable representation of the translation from the RNA.

[0238]

[0081] Embodiment 66: The step of searching multiple peptide sources comprises a trans-splicing search for peptide fragments that can be trans-spliced ​​to match the peptide sequence in an extended human proteome database, the extended human proteome database comprising messenger ribonucleic acid (RNA) and Non-coding The embodiment of any of embodiments 51-65, comprising a computer readable representation of the translation from the RNA.

[0239] Embodiment 67: Any of embodiments 51 to 66, wherein if a peptide cannot be assigned a peptide source by any of the multiple peptide source search steps, the peptide source assignment workflow ends with an unassigned peptide.

[0240]

[0081] Embodiment 68: A peptide source allocation workflow, comprising the steps of: Ordering Any of the embodiments of embodiments 51-67, wherein the following searches are performed: a linear human proteome search for the peptide sequence in the extended human proteome database, a linear human genome search of a translation of the human genome database, a linear mismatch search for peptides that have mismatches to the peptide sequence in the extended human proteome database, and a cis-splice search for peptide fragments that can be cis-spliced ​​to match the peptide sequence in the extended human proteome database.

[0241]

[0081] Embodiment 69: The peptide source assignment workflow comprises a linear non-endogenous search for peptide sequences in a non-endogenous proteome database, the linear non-endogenous search being performed sequentially in the peptide assignment workflow after the linear mismatch search and before the cis-splice search. Ordering An embodiment such as embodiment 68.

[0242]

[0081] Embodiment 70: The peptide source assignment workflow includes a trans-splice search for peptide fragments that can be trans-spliced ​​to match a peptide sequence in the extended human proteome database, the trans-splice search being performed sequentially after the cis-splice search in the peptide assignment workflow. Ordering The embodiment of any one of embodiments 68 to 69.

[0243]

[0081] Embodiment 71: A non-transitory computer-readable medium configured to communicate with one or more processors of a computing device, comprising: The non-transitory computer readable medium includes instructions thereon, the instructions comprising:When executed by a processor, the method causes a computer device to receive as input a plurality of peptide source searching steps, generate a plurality of random peptide sequences, search for each of the plurality of random peptide sequences by each of the plurality of peptide source searching steps, determine, for each of the plurality of peptide source searching steps, a random hit rate for a respective search step of the plurality of peptide source searching steps based at least in part on a number of the plurality of random peptide sequences found by the respective search step, and rank the peptide source searching steps in the peptide source allocation workflow from the lowest random hit rate to the highest random hit rate. Ordering and providing as output a peptide source assignment workflow.

[0244] Embodiment 72: An embodiment as in embodiment 71, wherein the random peptide sequence comprises a random sequence that uniformly samples all amino acids.

[0245] Embodiment 73: Any of embodiments 71-72, wherein the random peptide sequences comprise sequences having amino acid frequencies that match the frequencies of amino acids found in vertebrates.

[0246] Embodiment 74: Any of embodiments 71 to 73, wherein each peptide of the random peptide sequence comprises a length of 8 to 14 amino acids.

[0247] Embodiment 75: The embodiment of any of embodiments 71-74, wherein each peptide of the random peptide sequence comprises a length of 9-14 amino acids, 10-14 amino acids, 11-14 amino acids, 12-14 amino acids, 13-14 amino acids, 8-13 amino acids, 8-12 amino acids, 8-11 amino acids, 8-10 amino acids, 8-9 amino acids, 9-13 amino acids, 9-12 amino acids, 9-11 amino acids, 9-10 amino acids, 10-13 amino acids, 10-12 amino acids, 10-11 amino acids, 11-13 amino acids, 11-12 amino acids, or 12-13 amino acids.

[0248]

[0081] Embodiment 76: The step of searching multiple peptide sources comprises a linear human proteome search for peptide sequences in an extended human proteome database, the extended human proteome database comprising messenger ribonucleic acid (RNA) and Non-coding The embodiment of any of embodiments 71-75, comprising a computer readable representation of the translation from the RNA.

[0249] Embodiment 77: An embodiment as in embodiment 76, wherein the extended human proteome database comprises a computer-readable representation of translations from microRNAs.

[0250]

[0081] Embodiment 78: The extended human proteome database comprises: Non-coding The embodiment of any of embodiments 76-77, comprising a computer readable representation of the translation from RNA.

[0251] Embodiment 79: Any of the embodiments of embodiments 76-78, wherein the expanded human proteome database comprises a computer-readable representation of the translation of a human endogenous retrovirus.

[0252] Embodiment 80: The embodiment of any of embodiments 76-79, wherein the multiple peptide source searching step comprises a linear human genome search of a translation of a human genome database.

[0253]

[0082] Embodiment 81: A linear human genome search is performed using messenger RNA and Non-coding An embodiment as in embodiment 80, which excludes the portion of the human genome from which the RNA is transcribed and includes the remainder of the human genome.

[0254]

[0082] Embodiment 82: The method of claim 82, wherein the step of searching multiple peptide sources comprises a linear mismatch search for peptides having mismatches to peptide sequences in an extended human proteome database, the extended human proteome database comprising messenger ribonucleic acid (RNA) and Non-codingThe embodiment of any of embodiments 71-81, comprising a computer readable representation of the translation from RNA.

[0255] Embodiment 83: Any of the embodiments of embodiments 71-82, wherein the multiple peptide source searching step comprises a linear non-endogenous search for peptide sequences in a non-endogenous proteome database, the non-endogenous proteome database comprising computer-readable representations of proteins translated from RNA derived from the non-endogenous organism and / or proteins synthesized by the non-endogenous organism.

[0256] Embodiment 84: An embodiment as in embodiment 83, wherein the non-endogenous proteome database comprises a basic local alignment search tool (BLAST) database.

[0257]

[0082] Embodiment 85: The step of searching multiple peptide sources comprises a cis-splice search for peptide fragments that can be cis-spliced ​​to match the peptide sequence in an extended human proteome database, the extended human proteome database comprising messenger ribonucleic acid (RNA) and Non-coding The embodiment of any of embodiments 71-84, comprising a computer readable representation of the translation from the RNA.

[0258]

[0082] Embodiment 86: The step of searching multiple peptide sources comprises a trans-splice search for peptide fragments that can be trans-spliced ​​to match the peptide sequence in an extended human proteome database, the extended human proteome database comprising messenger ribonucleic acid (RNA) and Non-coding The embodiment of any of embodiments 71-85, comprising a computer readable representation of the translation from the RNA.

[0259] Embodiment 87: Any of the embodiments of embodiments 71 to 86, wherein if a peptide cannot be assigned a peptide source by any of the multiple peptide source search steps, the peptide source assignment workflow ends with an unassigned peptide.

[0260]

[0081] Embodiment 88: A peptide source allocation workflow is provided, comprising the steps of: Ordering Any of the embodiments of embodiments 71-87, comprising the following searches performed: a linear human proteome search for a peptide sequence in the extended human proteome database, a linear human genome search of a translation of the human genome database, a linear mismatch search for peptides having mismatches to a peptide sequence in the extended human proteome database, a linear non-endogenous search for a peptide sequence in the non-endogenous proteome database, a cis-splice search for peptide fragments that can be cis-spliced ​​to match the peptide sequence in the extended human proteome database, and a trans-splice search for peptide fragments that can be trans-spliced ​​to match the peptide sequence in the extended human proteome database.

[0261]

[0081] Embodiment 89: The peptide source assignment workflow comprises a linear non-endogenous search for peptide sequences in a non-endogenous proteome database, the linear non-endogenous search being performed sequentially in the peptide assignment workflow after the linear mismatch search and before the cis-splice search. Ordering An embodiment such as embodiment 88.

[0262]

[0089] Embodiment 90: A peptide source assignment workflow includes a trans-splice search for peptide fragments that can be trans-spliced ​​to match a peptide sequence in an extended human proteome database, the trans-splice search being performed sequentially after the cis-splice search in the peptide assignment workflow. Ordering Any one of embodiments 88 to 89, wherein

[0263] Embodiment 91: A method including: generating a plurality of simulated random queries; determining a number of matches associated with each source based on applying the plurality of simulated random queries to each source of a plurality of sources; determining a false discovery rate associated with each source based on the number of matches associated with each source; and creating a query support data structure configured to facilitate application of new queries to the plurality of sources based on the false discovery rate.

[0264] Embodiment 92: An embodiment as in embodiment 91, wherein generating a plurality of simulated random queries includes at least one of generating a plurality of uniform random queries or generating a plurality of weighted random queries.

[0265] Embodiment 93: Any of embodiments 91-92, wherein the multiple simulated random queries include multiple simulated random text strings.

[0266] Embodiment 94: The embodiment of any of embodiments 91-93, wherein the plurality of simulated random queries comprises a plurality of simulated random peptide sequences.

[0267] Embodiment 95: Any of the embodiments of embodiments 91 to 94, wherein determining a false discovery rate associated with each source based on the number of matches associated with each source includes a function of the number of matches and the number of multiple simulated random queries.

[0268] Embodiment 96: Any of the embodiments of embodiments 91 to 95, wherein determining a false discovery rate associated with each source based on the number of matches associated with each source includes dividing the number of matches by the number of multiple simulated random queries.

[0269] Embodiment 97: A method including receiving a query, applying the query to one or more sources of a plurality of sources based on a query support data structure, determining an indicator associated with a source of the plurality of sources associated with the query result based on the query results, and applying the indicator to the query.

[0270] Embodiment 98: An embodiment such as embodiment 97, in which the query includes a text string.

[0271] Embodiment 99: Any of embodiments 97-98, wherein the query comprises a peptide sequence.

[0272] Embodiment 100: An embodiment such as embodiment 99, wherein receiving the query includes receiving a peptide sequence from a mass spectrometry system.

[0273] Embodiment 101: The embodiment of any of embodiments 97-100, further comprising determining one or more amino acids of the peptide sequence by a mass spectrometry system.

[0274] Embodiment 102: Any of embodiments 97 to 101, wherein the query support data structure indicates an ordering of multiple sources for applying a query, the ordering being based on a false discovery rate associated with each source of the multiple sources.

[0275] 103: One or more of the queries Replacement The embodiment of any of embodiments 97-102, further comprising determining:

[0276]

[0046] Embodiment 104: Applying a query to one or more of the multiple sources based on a query support data structure includes: Replacement Each of Replacement applying the multiple source to one or more of the multiple sources; ReplacementIf an identical match for is found in a first source of the multiple sources, then stop further searching and assign a linear indicator to one or more of the queries associated with the identical match. Replacement and one or more of the queries associated with the same match. Replacement as the correct query.

[0277] Embodiment 105: Any of the embodiments of embodiments 97 to 104, wherein applying the query to one or more sources of the plurality of sources includes searching for an identical match to the query in a first source of the plurality of sources, and stopping further searches if an identical match to the query is found in the first source of the plurality of sources.

[0278] Embodiment 106: An embodiment such as embodiment 105, in which the query results include identical matches and the indicators associated with the sources of the plurality of sources associated with the query results include linear indicators.

[0279]

[0081] Embodiment 107: Applying a query to one or more of the multiple sources includes searching for identical matches to the query in a first source of the multiple sources, and searching one or more of the multiple sources for identical matches to the query. Replacement 107. The embodiment of any of embodiments 97-106, comprising stopping further searches if an identical match for is found in a first source of the multiple sources.

[0280] Embodiment 108: An embodiment such as embodiment 107, in which the query results include identical matches and the indicators associated with the sources of the multiple sources associated with the query results include linear indicators.

[0281] Embodiment 109: An embodiment such as embodiment 107, in which applying the query to one or more sources of the multiple sources includes searching for an identical match to the query in any frame of the multiple frames of a second source of the multiple sources, and stopping further searches if an identical match to the query is found in any frame of the multiple frames of the second source of the multiple sources.

[0282] Embodiment 110: An embodiment such as embodiment 109, in which the query results include identical matches and the indicators associated with the sources of the multiple sources associated with the query results include linear indicators.

[0283] Embodiment 111: An embodiment such as embodiment 109, in which applying the query to one or more sources of the plurality of sources includes searching for non-identical matches to the query in a third source of the plurality of sources, and stopping further searching if a non-identical match to the query is found in the third source of the plurality of sources.

[0284] Embodiment 112: An embodiment such as embodiment 111, in which the query result includes non-identical matches and the indicator associated with a source of the plurality of sources associated with the query result includes a mismatch indicator.

[0285] Embodiment 113: An embodiment as in embodiment 111, in which applying the query to one or more sources of the plurality of sources includes searching for a homologous match to the query in a fourth source of the plurality of sources, and stopping further searching if a homologous match to the query is found in the fourth source of the plurality of sources.

[0286] Embodiment 114: An embodiment such as embodiment 113, in which the query results include homologous matches and the labels associated with sources of the plurality of sources associated with the query results include homologous labels.

[0287] Embodiment 115: An embodiment such as embodiment 113, in which applying the query to one or more sources of the multiple sources includes dividing the query into a plurality of sets of fragments, searching for each set of fragments in a fifth source of the multiple sources, stopping further searching if a match for the set of fragments is found in the fifth source of the multiple sources, and stopping further searching if a first match for a first fragment of the set of fragments and a second match for a second fragment of the set of fragments are found in the fifth source of the multiple sources.

[0288] Embodiment 116: An embodiment such as embodiment 115, in which the query result includes matches for a set of fragments and the tags associated with a source of the multiple sources associated with the query result include cis splice tags.

[0289] Embodiment 117: An embodiment such as embodiment 115, wherein the query result includes a first match for a first fragment of the set of fragments and a second match for a second fragment of the set of fragments, and the label associated with a source of the multiple sources associated with the query result includes a trans-splice label.

[0290] Embodiment 118: Any of embodiments 97 to 117, further comprising determining a source of the query based on the indication.

[0291] Embodiment 119: An embodiment such as embodiment 118, further comprising validating the output of the mass spectrometry system based on the source of the query.

[0292] Embodiment 120: An apparatus including one or more processors and memory stored processor-executable instructions that, when executed by the one or more processors, cause the apparatus to perform any of embodiments 91 to 119.

[0293] Embodiment 121: One or more non-transitory computer-readable media storing processor-executable instructions which, when executed by a processor, cause the processor to perform any of embodiments 91 to 119. Embodiment 122: A system including a computing device and a plurality of sources configured to perform any of embodiments 91 to 119.

[0294] Although specific configurations have been described, the scope is not intended to be limited to the specific configurations shown, as the configurations herein are intended in all respects to be potential configurations rather than limiting.

[0295] Unless expressly stated otherwise, it is in no way intended that any method set forth herein be construed as requiring that its steps be performed in a particular order. Thus, unless a method claim actually limits the order its steps must follow or the claim or description specifically states otherwise that the steps are limited to a particular order, no order is intended to be inferred in any respect. This preserves any possible non-express basis for interpretation, including questions of logic regarding the sequence of steps or operational flow, the plain meaning derived from grammatical constructions or punctuation, the number or type of constructions described herein.

[0296] Those skilled in the art will recognize, or be able to ascertain using no more than routine experimentation, many equivalents to the specific embodiments of the methods and compositions described herein which equivalents are intended to be encompassed by the following claims. Table 1 [Table 1-1] [Table 1-2] [Table 1-3]

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Claims

1. 1. A computer-implemented method for determining a putative source of a peptide sequence of a peptide, comprising: receiving said peptide sequence; conducting one or more searches of a plurality of searches of said peptide sequence in one or more databases, each individual search of said plurality of searches having a random hit rate based at least in part on the number of random sequences found by said individual search; determining the putative source associated with the peptide sequence, the putative source being identified by the individual search of the plurality of searches having a lowest random hit rate for finding a match for the search peptide sequence; Including, method.

2. the one or more databases comprising an extended human proteome database; the expanded human proteome database includes a computer-readable representation of translations from messenger ribonucleic acid (RNA) and non-coding RNA; The method of claim 1.

3. The method of claim 2, wherein the extended human proteome database comprises a computer-readable representation of translations from microRNAs.

4. The method of claim 2 , wherein the extended human proteome database comprises a computer-readable representation of translations from long non-coding RNA.

5. The method of claim 2 , wherein the extended human proteome database comprises computer-readable representations of translations of human endogenous retroviruses.

6. the plurality of searches comprises a linear human proteome search for the peptide sequence in the extended human proteome database; the putative source is a linear extended human proteome source if the linear human proteome search for the peptide sequence in the extended human proteome database finds the peptide sequence in the extended human proteome database, and the linear human proteome search is the individual search of the multiple searches that has the lowest random hit rate at which a match is found for the search peptide sequence; The method of claim 2.

7. and identifying whether the peptide is putatively translated from messenger RNA or non-coding RNA, when the putative source is the linear extended human proteome source. The method of claim 6 further comprising:

8. the one or more databases include a human genome database; the plurality of searches includes a linear human genome search of translations of the human genome database; The putative source is a linear genome source if the linear human genome search finds a human genome sequence from which the peptide is putatively synthesized, and the linear human genome search is the individual search of the plurality of searches that has the lowest random hit rate at which a match is found for the search peptide sequence. The method of claim 2.

9. said linear human genome search excluding portions of said human genome into which said messenger RNA and said non-coding RNA are transcribed and including the remaining portions of said human genome in said extended human proteome database; the linear human genome search comprises a search of six frame translations of the human genome; The method according to claim 8.

10. the one or more searches include a linear mismatch search for peptides having mismatches to the peptide sequence in the extended human proteome database; If the linear mismatch search finds a peptide sequence having a mismatch to the peptide sequence in the extended human proteome database, and the linear mismatch search is the individual search of the multiple searches that has the lowest random hit rate for finding a match for the search peptide sequence, the putative source is a linear mismatch in the extended human proteome. The method of claim 2.

11. The method of claim 10, wherein the linear mismatch search is a search for peptide sequences that have only a single mismatch to the peptide sequence.

12. the one or more databases include a non-endogenous proteome database that includes computer readable representations of proteins translated from RNA from the non-endogenous organism and / or proteins synthesized by the non-endogenous organism; the plurality of searches comprises a linear non-endogenous search for the peptide sequence in the non-endogenous proteome database; the putative source is a linear non-endogenous proteome source if the linear non-endogenous search finds the peptide sequence in the non-endogenous proteome database, and the linear non-endogenous search is the individual search of the multiple searches that has the lowest random hit rate at which a match is found for the search peptide sequence; The method of claim 1.

13. The method of claim 12, wherein the non-endogenous proteome database comprises a basic local alignment search tool (BLAST).

14. the one or more searches include a cis-splice search in the extended human proteome database for peptide fragments that can be cis-spliced ​​to match the peptide sequence; the putative source is a cis-spliced ​​human proteome source if the cis-splice search finds peptide fragments in the extended human proteome database that can be cis-spliced ​​to match the peptide sequence, and the cis-splice search is the individual search of the multiple searches that has the lowest random hit rate for finding a match for the search peptide sequence; The method of claim 2.

15. the one or more searches comprising a trans-splice search in the extended human proteome database for computer readable representations of peptide fragments that can be trans-spliced ​​to match the peptide sequence; the putative source is a trans-spliced ​​human proteome source if the trans-splice search finds computer-readable representations of peptide fragments in the extended human proteome database that can be trans-spliced ​​to match the peptide sequence, and the trans-splice search is the individual search of the plurality of searches that has the lowest random hit rate at which a match is found for the search peptide sequence; The method of claim 2.

16. The method of claim 1, wherein the one or more searches are performed in order of increasing random hit rate, such that each search is performed before any search in the plurality of searches associated with a higher random hit rate.

17. The method of claim 16, wherein the method includes stopping progress to a subsequent peptide source search step upon determining the source of the peptide sequence.

18. the one or more databases include a human genome database; The plurality of searches are sequentially ordered in a workflow as follows: a linear human proteome search for said peptide sequence in said extended human proteome database; a linear human genome search of translations of said human genome database; a linear mismatch search for peptides having mismatches to the peptide sequence in the extended human proteome database; and a cis-splice search in the extended human proteome database for peptide fragments that can be cis-spliced ​​to match the peptide sequence. Including, 17. The method of claim 16.

19. the one or more databases include a non-endogenous proteome database that includes computer readable representations of proteins translated from RNA from the non-endogenous organism and / or proteins synthesized by the non-endogenous organism; the plurality of searches further comprising a linear non-endogenous search for the peptide sequence in the non-endogenous proteome database; the linear non-endogenous search is sequentially ordered in the workflow after the linear mismatch search and before the cis-splice search; 20. The method of claim 18.

20. the plurality of searches further comprises a trans-splice search in the extended human proteome database for peptide fragments that can be trans-spliced ​​to match the peptide sequence; the trans-splice search is sequentially ordered in the workflow after the cis-splice search.

20. The method of claim 18.

21. The method of claim 18, wherein the putative source is determined to be unidentified if the trans-splice search does not find a computer-readable representation of a peptide fragment that can be trans-spliced ​​to match the peptide sequence.

22. The peptide sequence includes at least one ambiguous residue, the ambiguous residue of the peptide sequence being associated with a plurality of possible residues, and the method comprises: generating a plurality of permuted peptide sequences each including a possible residue for each of said at least one ambiguous residue, such that said plurality of permuted peptide sequences includes leucine and isoleucine as possible residues; determining the putative source of the peptide sequence such that the putative source is the distinct potential source identified by a search of the plurality of searches having the lowest random hit rate for finding a match for any of the plurality of permuted peptide sequences. Further comprising: The method according to any one of claims 1 to 16.

23. identifying one or more likely permuted peptide sequences of said plurality of permuted peptide sequences, such that each of said one or more likely permuted peptide sequences is associated with said putative source.

23. The method of claim 22, further comprising:

24. The method of claim 1 , wherein the peptide sequence is a de novo peptide sequence determined by mass spectrometry.

25. A non-transitory computer-readable medium configured to be in communication with one or more processors of a computing device, the non-transitory computer-readable medium including instructions thereon that, when executed by the processor, cause the computing device to: As an input, a peptide sequence is received, performing one or more searches of a plurality of searches of said peptide sequence in one or more databases, wherein each individual search of said one or more searches has a random hit rate based at least in part on the number of random sequences found by said individual search; determining a putative source associated with the peptide sequence, wherein the putative source is identified by the individual search of the plurality of searches having the lowest random hit rate for finding a match for the search peptide sequence; and A non-transitory computer-readable medium that provides, as an output, the inferred source.

26. the one or more databases comprising an extended human proteome database; the expanded human proteome database comprises a computer readable representation of translations from messenger ribonucleic acid (RNA) and non-coding RNA; the plurality of searches comprises a linear human proteome search for the peptide sequence in the extended human proteome database; 26. The non-transitory computer readable medium of claim 25.

27. 27. The non-transitory computer readable medium of claim 26, wherein the extended human proteome database comprises computer readable representations of translations from microRNAs, computer readable representations of translations from long non-coding RNAs, and / or computer readable representations of translations of human endogenous retroviruses.

28. The instructions, when executed by the processor, cause the computing device to:

27. The non-transitory computer readable medium of claim 26, wherein the peptide sequence is identified as being putatively translated from messenger RNA or non-coding RNA.

29. the one or more databases include a human genome database; the plurality of searches includes a linear human genome search of translations of the human genome database; 27. The non-transitory computer readable medium of claim 26.

30. the linear human genome search excludes portions of the human genome from which the messenger RNA and the non-coding RNA are transcribed, and includes the remaining portions of the human genome in the extended human proteome database; 30. The non-transitory computer readable medium of claim 29.

31. the plurality of searches includes a linear mismatch search for peptides having mismatches to the peptide sequence in the extended human proteome database; 27. The non-transitory computer readable medium of claim 26.

32. 32. The non-transitory computer readable medium of claim 31 , wherein the linear mismatch search is a search for peptide sequences having only a single mismatch to the peptide sequence.

33. the one or more databases include a non-endogenous proteome database that includes computer readable representations of proteins translated from RNA from the non-endogenous organism and / or proteins synthesized by the non-endogenous organism; the plurality of searches comprises a linear non-endogenous search for the peptide sequence in the non-endogenous proteome database; 26. The non-transitory computer readable medium of claim 25.

34. 34. The non-transitory computer-readable medium of claim 33, wherein the non-endogenous proteome database comprises sequences of non-endogenous organisms that have been mapped using basic local alignment search tool (BLAST).

35. 27. The non-transitory computer readable medium of claim 26, wherein the multiple searches comprise a cis-splice search in the extended human proteome database for peptide fragments that can be cis-spliced ​​to match the peptide sequence.

36. the plurality of searches includes a trans-splice search for computer readable representations of peptide fragments that can be trans-spliced ​​to match the peptide sequence in the extended human proteome database; 27. The non-transitory computer readable medium of claim 26.

37. The non-transitory computer-readable medium of claim 26, wherein the instructions, when executed by the processor, cause the computing device to perform the one or more searches of the multiple searches in order of increasing random hit rate.

38. the one or more databases include a human genome database; The plurality of searches are sequentially ordered in a workflow as follows: a linear human proteome search for said peptide sequence in said extended human proteome database; a linear human genome search of translations of said human genome database; a linear mismatch search for peptides having mismatches to the peptide sequence in the extended human proteome database; and a cis-splice search in the extended human proteome database for peptide fragments that can be cis-spliced ​​to match the peptide sequence. Including, 38. The non-transitory computer readable medium of claim 37.

39. the one or more databases include a non-endogenous proteome database that includes computer readable representations of proteins translated from RNA from the non-endogenous organism and / or proteins synthesized by the non-endogenous organism; the plurality of searches further comprising a linear non-endogenous search for the peptide sequence in the non-endogenous proteome database; the linear non-endogenous search is sequentially ordered in the workflow after the linear mismatch search and before the cis-splice search; 40. The non-transitory computer readable medium of claim 38.

40. the plurality of searches further comprises a trans-splice search in the extended human proteome database for peptide fragments that can be trans-spliced ​​to match the peptide sequence; the trans-splice search is sequentially ordered in the workflow after the cis-splice search.

40. The non-transitory computer readable medium of claim 38.

41. The non-transitory computer-readable medium of claim 40, wherein if the trans-splice search does not find a computer-readable representation of a peptide fragment that can be trans-spliced ​​to match the peptide sequence, the putative source is determined to be unidentified.

42. The instructions, when executed by the processor, cause the computing device to: when the putative source is determined for the peptide sequence, stopping the workflow from proceeding to a subsequent search of the plurality of searches.

40. The non-transitory computer readable medium of claim 38.

43. the peptide sequence comprises at least one ambiguous residue; The instructions, when executed by the processor, cause the computing device to: generating a plurality of permuted peptide sequences each including a possible residue for each of said at least one ambiguous residue, said plurality of permuted peptide sequences including leucine and isoleucine as possible residues; and determining the putative source of the peptide sequence such that the putative source is identified by a search among the plurality of searches having the lowest random hit rate for finding a match of any of the plurality of permuted peptide sequences.

26. The non-transitory computer readable medium of claim 25.

44. 26. The non-transitory computer readable medium of claim 25, wherein the peptide sequence is a de novo peptide sequence determined by mass spectrometry.

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