Handling of biological information

Abstract

In a first aspect, the present invention relates to a computer-implemented method for obtaining information about a biological entity based on at least one biological sequence, the computer-implemented method comprising: (a) providing a repository of fingerprint data strings for a biological sequence database, wherein each fingerprint data string represents a characteristic biological subsequence composed of sequence units, and each characteristic biological subsequence has a combination number in the biological sequence database that is less than the total number of different sequence units available to it, the combination number of a biological subsequence being defined as the number of different sequence units that appear as consecutive sequence units of the biological subsequence in the biological sequence database; (b) determining one or more fingerprint data strings that represent the biological entity; (c) searching the repository containing information associated with the fingerprint data strings for information associated with one or more representative fingerprint data strings; and (d) processing the information.

Description

[Technical field]

[0001] The present invention relates to the handling of biological information, and more particularly to retrieving and / or correlating said biological information. [Background technology]

[0002] Biological sequencing has evolved with dizzying speed over the past decades, making possible the Human Genome Project, which achieved the complete sequencing of the human genome already more than 15 years ago. To facilitate this evolution, ample technological advances have been required, ranging from advances in sample preparation and sequencing methods to data acquisition, processing, and analysis. At the same time, new scientific fields such as genomics, proteomics, and bioinformatics have emerged and developed.

[0003] With the emphasis on data acquisition in the post-genomic era, this evolution has led to the accumulation of vast amounts of biological (e.g., sequence) data. However, the ability to organize, analyze, interpret, and extract biologically relevant information from this sequence has lagged behind. This problem is further complicated by the scale of new sequence information that is still being generated on a daily basis. Muir et al. observe that this is causing a paradigm shift, commenting on the resulting changing cost structure for sequencing and other associated hurdles (MUIR, Paul, et al. The real cost of sequencing: scaling computation to keep pace with data generation. Genome biology, 2016, 17.1:53.).

[0004] To access, analyze, or utilize sequence information in a meaningful way generally requires the need for some form of sequence alignment and similarity search. A wealth of computer software (e.g., BLAST, PSI-BLAST, SSEARCH, FASTA, HMMER3) is commercially available to perform such alignments and sequence similarity searches. Nevertheless, known algorithms lack the speed or practical ability to process the vast amount of existing data. Hardware optimizations such as those disclosed in US2006 / 020397A1 have also been attempted, but have not provided the necessary breakthrough. The crux of this effort is that the problems being addressed are NP-hard or NP-complete in nature (NP=nondeterministic polynomial time). Therefore, the required resources scale exponentially as the difficulty of the task increases (e.g., the length of the sequence increases or the number of sequences to be compared increases).

[0005] Structural variants play an important role in the development of cancer and other diseases, but are less well studied than single nucleotide variations due to the lack of reliable identification from read data. When using k-mer techniques, the detection window for variations is, by definition, smaller than the total length of the k-mer. Using algorithms to overcome the k-mer window problem, structural variances cannot be effectively identified. High coverage is required to find evidence of even a single structural variation. Thus, the use of k-mers requires large pools before real variation can be effectively identified from noise and read errors. The lack of dynamic algorithms to align k-mers leads to a difficult computational problem with many k-mers. This indicates the need for heuristics or parameterization to reduce the search space. Nevertheless, the latter results in inevitable error accumulation, which indicates that k-mers are not effective for integrated spatial patterns. At present, this has only been solved in a syntactic way, which is strictly one-dimensional.

[0006] It is widely recognized that the vast store of biological data holds many secrets to be discovered, but currently available tools do not allow for combing through said data in a sufficiently convenient manner, for example to identify targets for treating specific pathologies. Current efforts to do so therefore generally amount to searching for the proverbial "needle in a haystack." New and unique methods that allow for linking biological data from different sources, thereby providing new insights and revealing hidden patterns herein, are highly desirable and sought after.

[0007] Thus, there remains a need in the art for further improvements in the handling of biological information. Summary of the Invention

[0008] It is an object of the present invention to provide an improved method for handling biological information. This object is achieved by the method, the device and the data structure according to the present invention.

[0009] In a first aspect, the present invention relates to a computer-implemented method for obtaining information about a biological entity based on at least one biological sequence, the computer-implemented method comprising: (a) providing a repository of fingerprint data strings for a biological sequence database, each fingerprint data string representing a characteristic biological subsequence composed of sequence units, each characteristic biological subsequence having a combination number less than the total number of different sequence units available to it in the biological sequence database, the combination number of a biological subsequence being defined as the number of different sequence units that appear as consecutive sequence units of the biological subsequence in the biological sequence database; (b) determining one or more fingerprint data strings that represent the biological entity; (c) searching the repository containing information associated with the fingerprint data strings for information associated with one or more representative fingerprint data strings; and (d) processing the information.

[0010] An advantage of embodiments of the present invention is that a system and method are obtained that provides reduced complexity.

[0011] An advantage of embodiments of the present invention is that different pieces of common information, e.g., originating from different sources, may be linked together via common anchor points. A further advantage of embodiments of the present invention is that the common anchor points may be collected into a repository of fingerprint data strings, which in itself has a number of advantages (see below).

[0012] An advantage of embodiments of the present invention is that the same approaches that can be used to improve sequencing of biopolymers and biopolymer fragments can be improved (e.g., by reducing the likelihood of error or by speeding up the process) by relying on information contained in a repository of fingerprint data strings.

[0013] An advantage of embodiments of the present invention is that tentatively proposed biological sequences can be verified or rejected.An advantage of embodiments of the present invention is that errors occurring during sequencing can be reduced.

[0014] An advantage of embodiments of the present invention is that the speed of sequencing can be improved by predicting the next unit in a sequence, or by limiting the number of choices therefor.

[0015] An advantage of embodiments of the present invention is that the systems and methods have a deterministic character, i.e., the methods and systems result in the determination of a specific solution for the identification / characterization of a biopolymer or biopolymer fragment sequence.

[0016] An advantage of embodiments of the present invention is that the system and method allows for keeping track of lead IDs. The system and method allows for backtracking, for example backtracking of errors or uncertainties on a lead.

[0017] An advantage of embodiments of the present invention is that in embodiments of the present invention, in contrast to at least most of the state-of-the-art systems, fast and deterministic sequence generation can be obtained.

[0018] An advantage of embodiments of the present invention is that high speed data analysis systems and methods can be formulated.

[0019] In a second aspect, the present invention relates to a computer-implemented method for associating information with one or more fingerprint data strings as defined in any of the preceding claims, the computer-implemented method comprising: (a) providing a biological sequence of a biological entity, the biological entity sharing equivalent information; (b) searching the biological sequence for equivalent characteristic biological subsequences; and (c) associating the equivalent information with the fingerprint data strings representing the equivalent characteristic biological subsequences.

[0020] An advantage of embodiments of the present invention is that links between different pieces of biological information can be sought and found in ways not previously explored.

[0021] An advantage of embodiments of the present invention is that repositories of fingerprint data strings and / or repositories of processed biological sequences can be annotated with biological information.

[0022] An advantage of embodiments of the present invention is that information can be obtained from different sources, including public databases, proprietary databases, clinical records, and / or scientific literature. A further advantage of embodiments of the present invention is that these different sources can be linked together through a central repository.

[0023] In a third aspect, the present invention relates to a data processing system adapted to perform a computer-implemented method according to any embodiment of the first or second aspect.

[0024] An advantage of embodiments of the present invention is that, depending on the application, the steps of the method may be implemented by a variety of systems and devices, such as computer-based systems or sequencers, etc. A further advantage of embodiments of the present invention is that the method may be implemented by computer-based systems, including cloud-based systems.

[0025] In a fourth aspect, the present invention relates to a computer program comprising instructions which, when the program is executed by a computer, cause the computer to carry out a method according to any embodiment of the first or second aspect.

[0026] In a fifth aspect, the present invention relates to a computer readable medium comprising instructions which, when executed by a computer, cause the computer to perform a method according to any embodiment of the first or second aspects.

[0027] Certain preferred aspects of the invention are set out in the accompanying independent and dependent claims. Features from a dependent claim may be combined with features of the independent claim and with features of other dependent claims and not merely as explicitly set out in the claims.

[0028] While improvements, modifications, and evolutions of devices are constantly occurring in this field, the present concepts are believed to represent substantially new and novel improvements, including departures from conventional practice, resulting in the provision of more efficient, stable, and reliable devices of this nature.

[0029] These and other characteristics, features and advantages of the present invention will become apparent from the following detailed description, taken in conjunction with the accompanying drawings illustrating the principles of the invention. This description is given for the purposes of example only, without limiting the scope of the invention. The reference figures quoted below refer to the attached drawings. [Brief description of the drawings]

[0030] [Figure 1-2] 1 is a graph illustrating expected advances enabled by embodiments of the present invention. [Diagram 3-6] FIG. 1 illustrates a system according to an embodiment of the present invention. [Figure 7] 1 illustrates diagrammatically the results observed in a proof-of-concept according to the present invention. [Figure 8] FIG. 1 illustrates a schematic diagram of processing steps that may be performed in a method for sequencing according to an embodiment of the present invention. [Figure 9-12] 2 is a schematic representation of some steps that may be used in an embodiment according to the invention; [Figure 13-17] 1 is a chart showing various metrics for a processed Protein Data Bank (PDB) analysis according to an embodiment of the present invention. [Figure 18] 1 is a chart plotting the number of HYFT™ matches found in the PDB database using two different matching strategies against each other. [Figure 19] 1 is a graph comparing the total length of search results using, on the one hand, a prior art method (dotted line) and, on the other hand, a method according to an exemplary embodiment of the present invention (solid line). [Figure 20] 1 is a graph comparing the Levenshtein distance of search results using, on the one hand, a prior art method (dotted line) and, on the other hand, a method according to an exemplary embodiment of the present invention (solid line). [Figure 21] 1 is a graph comparing the longest common substrings of search results using, on the one hand, a prior art method (dotted line) and, on the other hand, a method according to an exemplary embodiment of the present invention (solid line). [Figure 22] 1 is a graph comparing the total length of search results using, on the one hand, a prior art method (dotted line) and, on the other hand, a method according to an exemplary embodiment of the present invention (solid line). [Figure 23] 1 is a graph comparing the Levenshtein distance of search results using, on the one hand, a prior art method (dotted line) and, on the other hand, a method according to an exemplary embodiment of the present invention (solid line). [Figure 24] 1 is a graph comparing the longest common substrings of search results using, on the one hand, a prior art method (dotted line) and, on the other hand, a method according to an exemplary embodiment of the present invention (solid line).

[0031] In the different figures, the same reference signs refer to the same or similar elements. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0032] The present invention will be described with respect to certain embodiments and with reference to certain drawings, but the invention is not limited thereto and is illustrated only by the claims. The drawings described are only schematic and non-limiting. In the drawings, the size of some of the elements may be exaggerated and are not drawn to scale for illustrative purposes. The dimensions and relative dimensions do not correspond to actual reductions to the practice of the invention.

[0033] Moreover, terms such as first, second, third, etc. in the description and claims are used to distinguish between similar elements and are not necessarily used to describe arrangements temporally, spatially, ranked, or in any other manner, It is to be understood that the terms so used are interchangeable under appropriate circumstances and that the embodiments of the invention described herein are capable of operation with arrangements other than those described or illustrated herein.

[0034] Moreover, terms such as "front," "back," and the like in the description and claims are used for purposes of explanation and not necessarily to describe relative positions. Terms so used are interchangeable with their antonyms under appropriate circumstances, with the understanding that the embodiments of the invention described herein are capable of operation in orientations other than those described or illustrated herein.

[0035] It should be noted that the term "comprising" used in the claims should not be interpreted as being limited to the means recited thereafter, and does not exclude other elements or steps. It should therefore be interpreted as specifying the presence of the recited features, integers, steps or components, but not excluding the presence or addition of one or more other features, integers, steps or components, or groups thereof. The term "comprising" therefore encompasses situations where only the recited features are present, as well as situations where these features and one or more other features are present. Thus, the scope of the expression "a device comprising means A and B" should not be interpreted as being limited to a device consisting only of components A and B. This means that in the context of the present invention, the relevant components of the device are only A and B.

[0036] Throughout this specification, a reference to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic described in connection with that embodiment is included in at least one embodiment of the invention. Thus, the appearances of the phrases "in one embodiment" or "in an embodiment" in various places throughout this specification do not necessarily all refer to the same embodiment, although they may. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments, as would be apparent to one of ordinary skill in the art from this disclosure.

[0037] Similarly, in describing exemplary embodiments of the invention, it should be understood that various features of the invention may sometimes be grouped together in a single embodiment, drawing, or description thereof for the purpose of streamlining the disclosure and aiding in understanding one or more of the various inventive aspects. However, this method of disclosure should not be interpreted as reflecting an intention that the claimed invention requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of the invention.

[0038] Furthermore, while some embodiments described herein include some (but not all) features that are included in other embodiments, as will be understood by those skilled in the art, combinations of features of different embodiments are meant to form different embodiments within the scope of the invention. For example, in the following claims, any of the claimed embodiments may be used in any combination.

[0039] Furthermore, some of the embodiments are described herein as a method or combination of method elements and may be implemented by a processor of a computer system or other means for performing that function. Thus, a processor with the necessary instructions for performing such a method or method element forms a means for performing the method or method element. Furthermore, the elements described herein of the apparatus embodiments are examples of means for performing functions performed by the elements for the purpose of implementing the invention.

[0040] In the description provided herein, numerous specific details are set forth. However, it should be understood that embodiments of the present invention may be practiced without these specific details. In other instances, well-known methods, structures, and techniques have not been shown in detail in order not to obscure an understanding of this description.

[0041] The following terms are provided solely to aid in the understanding of the invention.

[0042] As used herein, a biological sequence is a sequence of a biopolymer that defines at least the primary structure of the biopolymer. A biopolymer can be, for example, deoxyribonucleic acid (DNA), ribonucleic acid (RNA), or a protein. A biopolymer is typically a polymer of biomonomers (e.g., nucleotides or amino acids), but can optionally further include one or more synthetic monomers.

[0043] As used herein, a "sequence unit" in a biological sequence is an amino acid when the biological sequence relates to a protein, or a codon when the biological sequence relates to DNA or RNA.

[0044] As used herein, a biological subsequence is a portion of a biological sequence that is smaller than the full-length biological sequence. A biological subsequence may have a total length of, for example, 100 sequence units or less, preferably 50 or less, and even more preferably 20 or less.

[0045] As used herein, a distinction is made between a "characteristic biological subsequence" (or "(HYFT™) Fingerprint"), a "(HYFT™) Fingerprint Data String" and a "(HYFT™) Fingerprint Marker". The first is a subsequence with specific characteristics, which are described in more detail below. The second is a data representation of such a HYFT™ Fingerprint, optionally combined with additional data (see below), which may be stored, for example, in a corresponding repository. In some embodiments, one HYFT™ Fingerprint Data String may simultaneously represent multiple equivalent HYFT™ Fingerprints (e.g., equivalent through encoding of the same result, such as in the case of multiple codons encoding the same amino acid, or equivalent through translation, see below). The third may be a pointer to a HYFT™ fingerprint, such as a memory address where the HYFT™ fingerprint may be located, or a reference that allows the HYFT™ fingerprint to be found in a repository of fingerprint data strings. Nevertheless, given their close relationship (where a strict distinction between these three terms need not be drawn or where the meaning is clear in the context), they may be referred to herein simply as "HYFT™."

[0046] As used herein, a distinction is made between "biological sequences" and "processed biological sequences." The former are biological sequences commonly known in the art, while the latter are reconstructed / translated biological sequences that include fingerprint markers associated with the HYFT™ fingerprints of the present invention.

[0047] It will be clear that neither the HYFT™ fingerprint data strings, nor the processed biological sequences, nor the repository storing them, can be considered as cognitive data, and are not intended for (human) users. Instead, they are intended and configured to be used by a computer (or similar technical system) as functional data in various computer-implemented methods. For example, the repository can be structured as a relational database (e.g., based on SQL) or a NoSQL database (e.g., a document-oriented database such as an XML database). Similarly, the HYFT™ fingerprint data strings and / or the processed biological sequences can be configured as suitable entries in such a database.

[0048] As used herein, some concepts are illustrated with examples relating to proteins, where the monomeric sequence units considered are assumed to be the 20 canonical (or "standard") amino acids. However, this is merely for ease of illustration, and it is clear that similar embodiments can be formulated with an extended number of amino acids (e.g., adding non-canonical amino acids, or even synthetic compounds) or in relation to DNA or RNA. In the case of DNA or RNA, the association between DNA or RNA and proteins can be easily made through the correspondence between codons and amino acids.

[0049] As used herein, "secondary / tertiary / quaternary" refers to "secondary, and / or tertiary, and / or quaternary."

[0050] Previously, the primary structure of a biological sequence consisted of a selection of essentially independent sequence units, so that, for example, a biological sequence of length n based on m possible sequence units would have m n (e.g., based on 20 canonical amino acids, 20 nAlthough it was assumed that in principle, a number of amino acids (e.g., "MCMHNQ" in the above example) exist, it was surprisingly noticed in the present invention that this is practically not observed. Indeed, it was discovered that, from a certain length onwards, not all theoretical combinations are found. To take just one example, the protein subsequence "MCMHNQA" is not found in any protein in the public database. It has been thought that this absence has a physical and / or chemical origin, rather than being a simple omission in the database. Without being bound by theory, to take one possible effect, steric hindrance of adjacent amino acids (e.g., "MCMHNQ" in the above example) may prohibit binding with one or more other amino acids (e.g., "A" in the above example). Thus, once an absent subsequence is identified, computational studies can be used to verify whether this subsequence could potentially occur or whether its presence is physically impossible (or unlikely, e.g., due to chemical instability). The "particular length" referred to above depends on the data set under consideration, but for example, in the case of publicly available protein sequence databases, it corresponds to about 5 or 6 amino acids (which substantially reflects the total diversity found in nature). For more restricted sets (e.g., sets filtered based on certain criteria, or sets formulated for a particular biological sequence database, e.g., a particular domain), m n Less than the theoretical maximum of combinations has already been found for lengths of about four or five.

[0051] At the same time, since the partial sequence "MCMHNQA" does not exist, the partial sequence "MCMHNQ" acquires additional significance rather than simply being a random combination of five amino acids, and such partial sequence will be further referred to as a "characteristic biological partial sequence" or "(HYFT™) fingerprint". Due to the additional significance or meaning of these HYFT™ fingerprints, the present invention can be considered to handle biological sequence information in a more semantic manner. In general, a characteristic is characterized by having fewer options (i.e., fewer combinations) for a sequence unit that can directly follow (or precede) it than the maximum number of sequence units (i.e., the total number of different sequence units available, e.g., less than 20 regular amino acids), in other words, at least one of the sequence units cannot follow (or precede) it. However, it is possible to choose a more strict definition (e.g., only partial sequences with 15 or fewer sequence units that can possibly follow it, or 10 or fewer, 5 or fewer, 3, 2, or even 1 sequence unit). Furthermore, one may choose to consider each such subsequence as a HYFT™ fingerprint, or to consider only those subsequences as HYFT™ fingerprints that do not already contain another HYFT™ fingerprint (i.e., are non-redundant). For example, if "MCMHNQ" is taken as a HYFT™ fingerprint, the subsequence that contains "MCMHNQ" will be longer than the number of theoretical sequence units that can follow (or precede) it. In that case, there is a choice between considering both the longer sequence and "MCMHNQ" as a HYFT™ fingerprint, or only "MCMHNQ" as a HYFT™ fingerprint. The latter approach may typically be preferred to speed up the methods associated therewith while preserving the size of the repository of HYFT™ data strings. Indeed, searching for biological sequences that match a string typically becomes more resource intensive and slower as the length of the string increases.Furthermore, as the size of a repository of HYFT™ data strings increases, searching and retrieving a particular HYFT™ data string typically takes longer. In this non-redundant approach, longer subsequences with limited combination possibilities can still be identified, but then identified as HYFT™ patterns (with or without spacing). Thus, the advantages provided by this approach do not necessarily come with a corresponding loss of information. Notwithstanding the above, it is noted that the former approach is still possible and, as such, remains advantageous over the prior art.

[0052] Surprisingly, it is then found that a limited set of characteristic biological subsequences can be identified.Furthermore, it is observed that these characteristic biological subsequences strike a balance between, on the one hand, being sufficiently specific so that no characteristic biological subsequences are found in any biological sequence, and, on the other hand, being sufficiently common so that known biological sequences typically contain at least one of these HYFT fingerprints.

[0053] Of the above accounts, a protocol can be formulated for identifying HYFT™ fingerprints and building a repository of corresponding HYFT™ data strings (or "HYFT™ repository"). Indeed, since the objective is to identify those subsequences with limited combinatorial possibilities in a biological sequence database, it is sufficient to mine said biological sequence database for subsequences that do not appear therein. Once such a non-occurring subsequence (e.g., "MCMHNQA") is identified, a subsequence that is one sequence unit shorter (e.g., "MCMHNQ") corresponds to the HYFT™ fingerprint (provided that the shorter subsequence appears). Once identified, additional data on the HYFT™ fingerprint can be obtained. For example, the number of combinations can be obtained by searching the biological sequence database for combinations of the identified HYFT™ fingerprint with other sequence units (e.g., replacing the "A" in "MCMHNQA" each time with one of the other possible amino acids) and counting the number of combinations found to occur. Optionally, the missing combinations may also be stored separately and used, for example, for error detection. Furthermore, since the correspondence between DNA, RNA, and proteins is typically known through applicable codon tables, once a particular type of HYFT™ fingerprint is identified (e.g., protein HYFT™), it can be translated into a corresponding HYFT™ fingerprint of a different type (e.g., DNA and / or RNA HYFT™). By repeating the above process and storing at least the identified HYFT™ in a suitable format, a repository of translated HYFT™-HYFT™ fingerprint data strings, optionally with additional data, can be optimally constructed.Alternatively, or complementary thereto, at least some HYFT™ fingerprints may be found by experimental or computational methods, for example, through synthesizing or modeling various subsequences and then identifying subsequences that may not or are highly unlikely to occur in the context of the biological sequence database under consideration.

[0054] In the above, the biological sequence database may be a publicly available database, such as the Protein Data Bank (PDB), or a proprietary database. In an embodiment, the biological sequence database may be a combination of multiple individual databases. For example, a repository of HYFT™ fingerprint data strings can be formulated from a biological sequence database that combines as many (trusted) biological sequence databases as possible, thereby seeking to become a general repository of HYFT™ fingerprint data strings that substantially represents all biological sequences found in nature. Conversely, in a specific domain, it may prove fruitful to build a specific repository of HYFT™ fingerprint data strings based on a biological sequence database that represents that specific domain. Such a specific repository may, in an embodiment, include HYFT™, which do not exist in a general repository because they occur in nature but do not occur in this specific domain. Similarly, a repository of HYFT™ fingerprint data strings may be built for synthetic sequences, with its own specific content.

[0055] Based on the above findings, new approaches can be formulated to handle biological sequence information in all its different but interrelated stages. These approaches can be considered as similar to a more lexical analysis of sequences. The results are illustrated diagrammatically in Figure 1, which shows the scaling of the complexity of biological sequence information with an increasing number of sequence units (n). This complexity can be the total number of possible combinations of sequence units, which in turn is also related to the amount of computation (e.g., time and memory) required to handle it (e.g., to perform a similarity search). The solid curves represent the theoretical number of combinations, m, assuming that all sequence units are selected independently. n The dashed curve shows the scaling as m n Starting from and leveling off asymptotically for large n, the dotted line indicates the number of sequences that correspond for the first time to a characteristic sequence whose number of sequence units that can follow it is equal to 1, where "for the first time" means that a longer sequence is not counted if it contains a HYFT™ fingerprint that has already been counted. The latter thus corresponds to the number of HYFT™ fingerprints of length n (as observed in the present invention), where the definition is then chosen as a subsequence that has only one sequence unit that can potentially follow it and that does not already contain another (shorter) HYFT™ fingerprint (see above).

[0056] Figure 2 shows the projected benefits in time of using a repository of fingerprint data strings as described herein, with the marks on the horizontal axis indicating the present. Curve 1 refers to Moore's Law. Curve 2 shows the total amount of sequencing data obtained. Curve 3 shows the total cost of processing and maintaining said sequencing data. By handling biological sequence information as described herein, the total storage capacity required for sequencing data and the total cost of data processing and maintenance are expected to decrease as shown by curves 4 and 5, respectively.

[0057] It should be noted that although a repository of HYFT™ fingerprint data strings is typically constructed with respect to a particular biological sequence database (or combination thereof), this does not mean that the HYFT™ fingerprint data strings are only suitable for handling biological sequences in that particular biological sequence database. Indeed, a general repository of HYFT™ fingerprint data strings may be used, for example, for processing more specific biological sequences. In other cases, a specific repository of HYFT™ fingerprint data strings may be used in the context of biological sequences that are outside the scope of the database used to formulate the repository. In either case, advantageous results may still be obtained. In either case, it can always be determined by trial and error whether an existing repository of HYFT™ fingerprint data strings can be used for a particular application, or whether better results can be obtained with a repository of HYFT™ fingerprint data strings dedicated to it. Similarly, a repository of HYFT™ fingerprint data strings does not need to be strictly inclusive of all HYFT™ fingerprints that can be found in a biological sequence database. Indeed, partial repositories have already produced useful results: such partial repositories may, for example, be associated with HYFT™ fingerprints of selected length (i.e., as opposed to HYFT™ fingerprints of any length).

[0058] The present invention utilizes a repository of fingerprint data strings. Thus, a repository of fingerprint data strings for a biological sequence database is described, where each fingerprint data string represents a characteristic biological subsequence composed of sequence units, and each characteristic biological subsequence has a combination number in the biological sequence database that is less than the total number of different sequence units available to it, and the combination number of a biological subsequence is defined as the number of different sequence units that appear as consecutive sequence units of the biological subsequence in the biological sequence database. A repository (e.g., a database) of fingerprint data strings 100 is shown generally in FIG. 4 and will be described in more detail below.

[0059] An advantage of embodiments of the present invention is that they can provide a repository of fingerprint data strings that correspond to characteristic biological subsequences.A further advantage of embodiments of the present invention is that the biological subsequences do not need to be of a single length (e.g., as in the case of k-mers).

[0060] An advantage of embodiments of the present invention is that the repository can include additional data (e.g., metadata), such as data regarding sequence units that may be contiguous to (i.e., immediately following or immediately following) the characteristic biological subsequence, data regarding the secondary / tertiary / quaternary structure of the characteristic biological subsequence (e.g., if the characteristic biological subsequence is present within a biopolymer), data regarding relationships between fingerprints (e.g., data relating to relationships between the characteristic biological subsequence and one or more further characteristic biological subsequences), etc.

[0061] In an embodiment, the repository may include at least a first fingerprint data string representing a first characteristic biological subsequence of a first length and a second fingerprint data string representing a second characteristic biological subsequence of a second length, the first length and the second length being equal to 4 or more, and the first length and the second length being different from each other.

[0062] In an embodiment, the length may correspond to the number of sequence units. In an embodiment, the length may be up to 1000 or less, for example up to 100 or less, preferably up to 50 or less, and even more preferably up to 20 or less. In an embodiment, the first and second lengths may be 5 or more, preferably 6 or more. In an embodiment, the characteristic biological partial sequence may have a length of 4 to 20, preferably 5 to 15, and even more preferably 6 to 12.

[0063] In an embodiment, the repository of fingerprint data strings may include at least three fingerprint data strings, preferably at least four, even more preferably at least five, and most preferably at least six fingerprint data strings, which are different in length from each other. Since the characteristic biological subsequences are not defined by their length, but by the number of possible sequence units that follow (or precede) them, the set of characteristic biological subsequences typically advantageously includes subsequences of various lengths. The repository of fingerprint data strings in the present invention differs from, for example, a collection of k-mers in that it includes biological subsequences of various lengths (as known in the art). Moreover, a collection of k-mers typically includes all permutations (i.e., all possible combinations of sequence units) of a fixed length k, which is not the case for the present repository of fingerprint data strings.

[0064] In embodiments, the fingerprint data strings may be protein fingerprint data strings, DNA fingerprint data strings, RNA fingerprint data strings, or combinations thereof. In embodiments, the characteristic biological subsequences may be characteristic protein subsequences, characteristic DNA subsequences, or characteristic RNA subsequences. In embodiments, the repository of fingerprint data strings may include (e.g., consist of) protein fingerprint data strings, DNA fingerprint data strings, RNA fingerprint data strings, or a combination of one or more of these. A characteristic protein subsequence may in embodiments be translated into a characteristic DNA or RNA subsequence, or vice versa. This translation may be based on known DNA and RNA codon tables. Similarly, a protein fingerprint data string may be translated into a DNA or RNA fingerprint data string. In embodiments, the repository of DNA or RNA fingerprint data strings may include information about equivalent codons (i.e., codons that code for the same amino acid). This information about equivalent codons may be included in the fingerprint data strings as such, or may be stored in the repository separately therefrom. In certain embodiments, the fingerprint data strings may be in a format that is sequence independent, meaning that the fingerprint data strings and surrounding systems and processes are in a format such that they can be rapidly compared to DNA, RNA, and protein sequences. This may be accomplished, for example, by having the methods that use the fingerprint data strings perform the necessary translations on the fly. Such fingerprint data strings advantageously allow for the formulation of a single repository of data strings that are universally applicable across sequence types.

[0065] In an embodiment, the repository of fingerprint data strings may further include additional data for at least one of the fingerprint data strings. In a preferred embodiment, the data may be included in the fingerprint data string. In an alternative embodiment, the data may be stored separately from the fingerprint data string. In an embodiment, the additional data may include one or more of combination data, structural data, relationship data, location data, and orientation data.

[0066] In an embodiment, the combination data may be data related to one or more sequence units that may be contiguous to the characteristic biological subsequence (e.g., may appear immediately before or after it in practice, such that their combination is stable) when the characteristic biological subsequence is present in the biological sequence. In an embodiment, the combination data may include the number of possible sequence units, such possible sequence units, the likelihood (e.g., probability) of each sequence unit, etc.

[0067] In embodiments, the structural data may be structural and / or spatial shape information embedded in the fingerprint data string (e.g., data related to the secondary / tertiary / quaternary structure of the characteristic biological subsequence, if the characteristic biological subsequence is present in the biopolymer). In embodiments, the structural data may include the number of possible structures, such possible structures, the likelihood (e.g., probability) of each structure, etc. In the case of multiple possible secondary / tertiary / quaternary structures for a given characteristic biological subsequence, the repository may, in embodiments, include a separate entry for each combination of the characteristic biological subsequence and the associated secondary / tertiary / quaternary structure. In alternative embodiments, the repository may include one entry that includes the characteristic biological subsequence and its associated multiple secondary / tertiary / quaternary structures. In embodiments, the secondary / tertiary / quaternary structure (particularly quaternary structure) may be more relevant to proteins than to DNA and RNA.

[0068] In an embodiment, the relationship data may be data related to the relationship between the characteristic biological subsequence and one or more further characteristic biological subsequences. In an embodiment, the relationship data may include the further characteristic biological subsequences that usually appear in its vicinity, the likelihood of the further characteristic biological subsequences appearing in its vicinity, the specific significance of these characteristic biological subsequences appearing in close proximity to each other (e.g., biologically relevant meaning such as trait or secondary / tertiary / quaternary structure), etc. In an embodiment, the relationship may be expressed in the form of a path between two or more characteristic biological subsequences. In an embodiment, the relationship may include the order of the characteristic biological subsequences and / or their interdistance. In an embodiment, the additional data may also include metadata useful for constructing the path.

[0069] In an embodiment, the location data may be data relating to intervals with respect to the fingerprint data strings (eg, between the characteristic biological sequences that they represent).

[0070] In an embodiment, the orientation data may be data relating to the orientation (eg, the unique orientation) of the fingerprint data strings (eg, the characteristic biological sequences that they represent).

[0071] In some embodiments, the additional data may be obtained from known data sets, for example, the secondary / tertiary / quaternary structures of some biological sequences are available in the art. In other embodiments, the additional data may be extracted from the processed biological sequences, as described below, or from a repository of processed biological sequences, as described below. For example, after processing the biological sequences as described below (or building a repository of processed biological sequences, as described below), relationships (e.g., pathways) between characteristic biological subsequences may be extracted and added to the repository of fingerprint data strings, which is shown diagrammatically in FIG. 4 by dashed arrows pointing from the processed biological sequences 210 and the repository of processed biological sequences 220 to the repository of fingerprint data strings 100.

[0072] In an embodiment, the fingerprint data string may be inherently oriented. In an embodiment, the fingerprint data string may include a direction (i.e., may explicitly include a direction). Since the HYFT™ fingerprint is defined based on the actual fragments occurring in the biopolymer or biopolymer fragment, the inherent physical, chemical, and structural limitations occurring in nature on the combinatorial possibilities occurring in the biopolymer are inherently present in HYFT™. Here, under "essentially present", it is understood that such information is implicitly associated (or at least possible) with HYFT™ even if it is not explicitly included in the repository as additional data. Thus, since such biological sequences usually have an inherent directionality (i.e., following the 5' to 3' direction in DNA / RNA and the N-terminus to C-terminus direction in proteins), this same directionality is inherently present in HYFT™. This association with the actual fragments further defines a limit on the maximum amount of biopolymer fragments that may follow the last character or before the first character of HYFT™. The latter can also be explicitly expressed by a parameter (i.e., the number of combinations) that represents the total amount of possible next or previous combinations, which gives HYFT™ a unique (strict) directionality.

[0073] In an embodiment, the fingerprint data string may include position information. HYFT™ and characters between HYFT™ are related to each other at a syntactic level, so that intervals between them or between different HYFT™ can be defined. Such positions or intervals belong to the position information that may be inherently present in HYFT™.

[0074] In an embodiment, the fingerprint data string may also include structural and / or spatial shape information. Also, the possible structures and / or spatial shapes of a particular HYFT™ or combination of HYFT™ are limited due to inherent physical, chemical, and structural constraints. Such information is also inherently present in a HYFT™ or set of interrelated HYFT™s.

[0075] In a first aspect, the present invention relates to a computer-implemented method for obtaining information about a biological entity based on at least one biological sequence, the computer-implemented method comprising: (a) providing a repository of fingerprint data strings for a biological sequence database, each fingerprint data string representing a characteristic biological subsequence composed of sequence units, each characteristic biological subsequence having a combination number less than the total number of different sequence units available to it in the biological sequence database, the combination number of a biological subsequence being defined as the number of different sequence units that appear as consecutive sequence units of the biological subsequence in the biological sequence database; (b) determining one or more fingerprint data strings representative of the biological entity; (c) searching the repository containing information associated with the fingerprint data strings for information associated with the one or more representative fingerprint data strings; and (d) processing the information.

[0076] Surprisingly, it has been found that HYFT™ can also be effectively used to link different biological information pieces (e.g., from different sources) together, with HYFT™ acting as an anchor point between the different pieces. Thus, a practical method for obtaining biological information would be to select an entry point (e.g., entered by a user), determine (e.g., automatically by a computer) a HYFT™ that represents the entry point, and then obtain information associated with the representative HYFT™. Here, the obtained information can be substantially all information associated with the HYFT™ contained in the repository, or a selection thereof (e.g., filtered based on input by the user about what type of information he or she wants to obtain). Optionally, the processing in step d can go beyond simple retrieval, as outlined below.

[0077] In embodiments, the repository containing information associated with fingerprint data strings may be a repository of fingerprint data strings as described herein, a repository of processed biological sequences as described herein, or any other repository containing information associated with fingerprint data strings.

[0078] In an embodiment, one or more of the fingerprint data strings representing the biological entity comprises a fingerprint data string representing the longest characteristic biological subsequence found in at least one biological sequence, or, if two or more longest characteristic biological subsequences are found, comprises a characteristic biological subsequence of the longest characteristic biological subsequence with the fewest number of combinations.Surprisingly, it has been found that representative HYTF™ often comprises the most strict HYTF™ (i.e., the longest HYTF™ with the fewest number of combinations present in at least one biological sequence).Moreover, in many cases, very useful information can be obtained by selecting the most strict HYTF™ as representative and performing a search based on it.

[0079] In embodiments, the biological entity based on at least one biological sequence, including but not limited to a protein, a protein active site or domain, a gene, a genome, a membrane, an organelle, a cell, a bacterium, a virus, an organism, etc., may be anything from a biological sequence (e.g., a sequencing read) to an organism or species, so long as a representative HYFT™ can be associated with the entity.

[0080] In embodiments, the information may include one or more of medical conditions, biological functions, spatial structures, combination information, or relationship information. A variety of different "exit points" (i.e., resulting category information) are advantageously accessible.

[0081] In embodiments, processing the information may include obtaining such information or using the information for different effects. For example, the information may be used to improve processing of sequencing reads, as described below. Other examples include identifying targets or biomarkers, relating variances (e.g., structural variances and / or indels) across multiple genes and / or proteins to specific outcomes (e.g., disease mechanisms), etc.

[0082] In a particular set of embodiments, the method may be for processing sequencing reads of a biopolymer or biopolymer fragment taking into account information contained in a repository of fingerprint data strings. In an embodiment, the information associated with the fingerprint data strings contained in the repository may include combined data representing different sequence units that appear in a biological sequence database as consecutive sequence units of a corresponding characteristic biological subsequence. In some embodiments, step b may include searching the read for one or more occurrences of the characteristic biological subsequences represented by the fingerprint data strings, and step d may include validating or rejecting the read by determining, for each occurrence, whether a sequence unit consecutive to the characteristic biological subsequence matches the combined data in the repository. In an alternative or complementary embodiment, step b may include searching the head and / or tail of the read for one occurrence of the characteristic biological subsequences represented by the fingerprint data strings, and step d includes predicting one or more consecutive sequence units to the read from the combined data of the repository. FIG. 3 illustrates generally a sequencer 350 that sequences a biopolymer (fragment) 500 using information contained in a repository of fingerprint data strings 100 .

[0083] In embodiments, the reads may be initial (e.g., provisional or partial) biological sequences. In embodiments, the method may be performed on a batch of reads. In embodiments, the reads may be obtained using a sequencer (e.g., a sequencing system). In embodiments, the method may be initiated after a batch of reads has been obtained.

[0084] In an embodiment, the searching in step b may be as described for step b of the method for processing a biological sequence described below.

[0085] Regarding the first type of embodiment, since the repository contains combined data on sequence units that may appear (e.g., before or after) in a subsequent HYFT™ fingerprint, this information can be advantageously used to verify whether the read matches it. If not, the tentative biological sequence can be rejected and redone. Alternatively, the same can be achieved by not matching the read with the HYFT™ fingerprint, but by directly matching it with the biological sequence that was not found (see above). Furthermore, this verification of the match can be combined with the use of additional data such as structural, relationship, positional and / or orientation data (see above). Such a combination can, for example, make it possible to reject reads that match a known HYFT™ fingerprint, but do not match in the context set by the additional data.

[0086] Regarding the second type of embodiment, based on the same combination data, some HYFT™ fingerprints (or combinations of HYFT™ fingerprints) will be known to have very limited combination possibilities (i.e., corresponding to a low number of combinations). For example, for a HYFT™ fingerprint with combination number 1, the next sequence unit is known. This information can be advantageously used to speed up sequencing by attaching that sequence unit directly to the read, thereby allowing the actual sequencing to skip past that sequence unit. In an embodiment, the repository may contain data on a series of two, three or more sequence units that are the only possible options to appear after a particular HYFT™ fingerprint. In this case, the entire series can advantageously be attached directly to the read, thereby allowing the actual sequencing to skip these units. Similarly, if the repository indicates that for an observed HYFT™ fingerprint, a limited number (but more than one) of alternatives are possible as further sequence units (e.g., two or three alternatives), this information can still allow the sequencer to more quickly identify the specific sequence unit in this instance. Furthermore, for such HYFT™ fingerprints with a small number of combinations, the number of possibilities in this case can be reduced to one (or, therefore, at least the possibilities can exceed a predefined threshold) by combining the data of the combination with the use of additional data. Similarly, such combinations can set a context that allows rejecting some of the combination possibilities, thereby, for example, reducing the remaining number to one, thereby revealing subsequent sequence units.

[0087] In an embodiment, the information associated with the fingerprint data strings included in the repository may further include one or more of structural data, relational data, spatial data, and directional data, as described above with respect to the repository of fingerprint data strings, and in an embodiment the information processed in step d may include one or more thereof.

[0088] In embodiments, the methods (e.g., steps b-d) may include analyzing the reads (e.g., using information in the repository of fingerprint data strings), e.g., according to the methods for processing biological sequences described below. In embodiments, the methods (e.g., steps b-d) may include analyzing a batch of reads (e.g., after obtaining a batch of reads).

[0089] In embodiments, the method may include a further step (e.g., included in step d) of aligning (e.g., matching) the processed reads, for example by aligning and / or constructing according to the method for comparing biological sequences described below. In embodiments, the alignment may include using the characteristic biological subsequences identified in step b. In embodiments, the fingerprint data string may be inherently oriented or may include position information. In embodiments, the alignment may include aligning the processed reads with a directed graph. In embodiments, the method may include aligning the batch of processed reads after obtaining the batch of reads. In at least some embodiments, the alignment may be aligning the processed reads with a directed acyclic graph.

[0090] In some embodiments, the alignment may be performed using Navarro-Levenshtein matching. A more detailed description of Navarro-Levenshtein matching can be found, for example, in Navarro, Theoretical Computer Science 237 (2000) 455-463. Based on the results of one or more of the above data processing steps, feedback information may be generated regarding identifying one or more reads as erroneous and ignoring them in further data processing, ...

[0091] In embodiments, the method (eg, aligning) may further include identifying variations such as indels, deletions, insertions, and / or repeats.

[0092] In an embodiment, the method may further include collapsing the processed reads by sorting them. It is noted that the collapsing step in an embodiment of the present invention is not based on dynamic programming. Every HYFT™ has a certain amount of bits that can be reduced / optimized via Shannon entropy. HYFT™ and attached reads can be ranked or classified by the amount of information (bits) they carry. Since this is not equal for every HYFT™, there will be HYFT™ and corresponding read patterns with very little amount of bits and HYFT™ and read patterns that require a larger amount of bits, since the next number of combinations can be up to n-1. Thus, the sorting mechanism can set a global bit threshold to optimize the amount of bits used at any point during the computation process. The hardware that needs to be used to perform these given tasks can be maximized through parallelization. In this way, parallelization can be performed, resulting in acceleration and true optimization. In some embodiments, sorting can be performed based on length. In an embodiment, classification can be performed based on the position of the HYFT™ in the read.

[0093] In an embodiment, the method may further comprise converting the plurality of processed reads into a sub-read graph and / or a read graph.

[0094] In embodiments, the method may further include any of removing dead ends and / or loops.

[0095] In an embodiment, the method may include obtaining feedback regarding reads that are discarded as erroneous reads based on information obtained from the processing and / or alignment and / or other processing of the reads.

[0096] In an embodiment, the method may include backtracking toward or to the lead. In an embodiment, the method may further include capturing metadata, such as a lead ID, and retaining this throughout the process. This may advantageously facilitate backtracking, e.g., backtracking errors or uncertainties to the lead.

[0097] According to an embodiment of the present invention, the construction of the subgraph and the corresponding processing can be performed in a separate thread. This can be further facilitated, for example, by an auto-complete feature that can be inherently introduced in an embodiment according to the present invention. If a certain confidence threshold is reached in the construction of the graph or subgraph (comparable to sufficient coverage), no further lead information is required to complete the original string reconstruction.

[0098] According to an embodiment of the present invention, the method may include generating feedback information regarding the discarded leads.

[0099] In a second aspect, the present invention relates to a computer-implemented method for associating information with one or more fingerprint data strings as defined in any of the preceding claims, the computer-implemented method comprising: (a) providing a biological sequence of a biological entity, the biological entity sharing equivalent information; (b) searching the biological sequence for equivalent characteristic biological subsequences; and (c) associating the equivalent information with the fingerprint data strings representing the equivalent characteristic biological subsequences.

[0100] In embodiments, equivalent information may be information that is common between biological entities, provided that the information is suitably transformed (e.g., translated, transcribed, transposed, etc.) as necessary. For example, a DNA string and a protein string (or a DNA string and an RNA string) may have sequences that are equivalent upon translation (or transcription). Similarly, two species may have traits in common, mutatis mutandis, when making a comparison. In embodiments, equivalent information may include one or more of a medical condition, a biological function, a spatial structure, or combination information.

[0101] In an embodiment, the method may further include a step a' prior to step a, of searching a data pool for biological entities that share equivalent information. In an embodiment, the data pool may include sequencing data or a biological sequence database. The sequencing data may include, for example, reads and / or assembled sequences (e.g., obtained through alignment of reads). In an embodiment, the data pool may be from a public database, a proprietary database, clinical records, and / or scientific literature. In an embodiment, step a' may include the use of machine learning to identify equivalent information.

[0102] In embodiments, step c may include annotating a repository of fingerprint data strings as described herein, a repository of processed biological sequences as described herein, or any other repository with equivalent information.

[0103] In a third aspect, the present invention relates to a data processing system adapted to perform a computer-implemented method according to any embodiment of the first or second aspect.

[0104] Such a system may for example include a data processing means or processor for processing a batch of biopolymer or biopolymer fragment reads.

[0105] In an embodiment, the data processing system may be comprised of or in a distributed computing environment (e.g., a cloud-based system). The distributed computing environment may include, for example, a server device (e.g., a data processing system) and a networked client device. In this specification, the server device may perform most of one of the described methods. On the other hand, the networked client device may communicate instructions (e.g., inputs such as queries and settings such as search preferences) with the server device and may receive the output of the method. In an embodiment, the data processing system may be located on-site (e.g., in the same building) or off-site (e.g., in the cloud) with respect to the client device.

[0106] In a fourth aspect, the present invention relates to a computer program comprising instructions which, when the program is executed by a computer, cause the computer to carry out a method according to any embodiment of the first or second aspect.

[0107] In a fifth aspect, the present invention relates to a computer readable medium comprising instructions which, when executed by a computer, cause the computer to perform a method according to any embodiment of the first or second aspects.

[0108] Also described is a computer-implemented method for constructing and / or updating a repository of fingerprint data strings as described above, comprising: (a) identifying characteristic biological subsequences in a biological sequence database, the characteristic biological subsequences having a combination number that is less than the total number of distinct sequence units available, the combination number of a biological subsequence being defined as the number of distinct sequence units that appear as consecutive sequence units of a biological sequence in the biological sequence database; (b) optionally translating the identified characteristic biological subsequences into one or more further characteristic biological subsequences; and (c) inputting into the repository one or more fingerprint data strings representing the identified characteristic biological subsequences and / or the one or more further characteristic biological subsequences.

[0109] Also described is a computer-implemented method for processing a biological sequence, comprising: (a) obtaining one or more fingerprint data strings from a repository of fingerprint data strings as described above; (b) searching the biological sequence for occurrences of characteristic biological subsequences represented by the one or more fingerprint data strings; and (c) constructing, for each occurrence in step b, a processed biological sequence comprising fingerprint markers associated with the fingerprint data strings representing the resulting characteristic biological subsequences. Figure 4 shows a schematic representation of a sequence processing unit 310 that processes a biological sequence 200 using the repository of fingerprint data strings 100, thereby obtaining a processed biological sequence 210.

[0110] An advantage of embodiments of the present invention is that biological sequences can be processed relatively easily and efficiently. A further advantage of embodiments of the present invention is that biological sequences can be analyzed in a lexical or semantic manner.

[0111] An advantage of embodiments of the present invention is that a processed biological sequence can be constructed by replacing identified characteristic biological subsequences therein with markers associated with the corresponding fingerprint data strings.

[0112] An advantage of embodiments of the present invention is that portions of a biological sequence that do not correspond to one of the characteristic biological subsequences can be handled in a variety of ways. A further advantage of some embodiments is that the biological sequence can be processed in a fully reversible manner (i.e., no information is lost by processing). A further advantage of alternative embodiments of the present invention is that the biological sequence can be processed such that the more important information is distilled into a more compressed form.

[0113] An advantage of embodiments of the present invention is that processed biological sequences can be compressed to occupy less storage space than their unprocessed counterparts.

[0114] An advantage of embodiments of the present invention is that matching portions of biological sequences to characteristic biological subsequences is not limited to just primary structure, but secondary / tertiary / quaternary structure can also be taken into account.

[0115] An advantage of embodiments of the present invention is that the secondary / tertiary / quaternary structure of a biological subsequence can be at least partially elucidated based on the known secondary / tertiary / quaternary structures of the characteristic biological subsequences contained therein.A further advantage of embodiments of the present invention is that the design of biological sequences (e.g., protein design) can be aided or facilitated.

[0116] In an embodiment, the biological sequence to be processed may be a biological sequence of a biological polymer fragment, obtainable by the sequencing method according to the first aspect.

[0117] In some embodiments, the marker may be a reference string. Such a reference string may, for example, point to a corresponding fingerprint data string in a repository. In other embodiments, the marker may be such a fingerprint data string, or a portion thereof.

[0118] In an embodiment, the biological sequence includes (i) one or more first portions, each of which corresponds to one of the characteristic biological subsequences represented by the one or more fingerprint data strings, and (ii) one or more second portions, each of which does not correspond to any of the characteristic biological subsequences represented by the one or more fingerprint data strings. In an embodiment, constructing the processed biological sequence in step c may include replacing at least one first portion with a corresponding marker. In an embodiment, constructing the processed biological sequence in step c may further include adding position information for the first portion to the processed biological sequence (e.g., added to the marker). In an embodiment, constructing the processed biological sequence in step c may include leaving at least one second portion unchanged and / or replacing at least one second portion with an indicator of the length of the second portion and / or removing at least one second portion entirely. If the second portion is to remain unchanged, the biological sequence can be advantageously processed in a fully reversible manner.

[0119] In an embodiment, the processed biological sequence can be formulated in a compressed format. For example, by replacing characteristic biological subsequence (i.e., the first part) with a reference string, and / or by replacing the second part with its length indicator or by completely removing it, the processed biological sequence requires less storage space than the original (i.e., unprocessed) biological sequence. Additional data compression can be achieved by utilizing the path that can represent multiple fingerprints by interrelationship.

[0120] In embodiments, the one or more fingerprint data strings may be in a biological format different from the biological sequence (e.g., protein vs. DNA vs. RNA sequence information), and step b may further include translating or transcribing the characteristic biological subsequence prior to searching.

[0121] In an embodiment, the search in step b may include searching for partial or equivalent matches (e.g., equivalent codons, or different amino acids that result in the same secondary / tertiary / quaternary structure). In an embodiment, the search in step b may consider the secondary / tertiary / quaternary structure of the characteristic biological subsequence. Secondary, tertiary, and quaternary structures are typically more evolutionarily conserved (e.g., because the secondary / tertiary / quaternary structure of its active site is substantially conserved), and variations in the primary structure that do not change the function of the biopolymer often occur. Thus, the secondary / tertiary / quaternary structures may reveal relevant information about the biopolymer that would be lost if a strict search for a perfectly matching primary structure were performed.

[0122] In a preferred embodiment, the search for the occurrence of characteristic biological subsequences in step b may be performed in a specific order. In an embodiment, the order may be based on the length and number of combinations of the characteristic biological subsequences. In an embodiment, the search may be performed in order, starting from the longest characteristic biological subsequence with the smallest number of combinations and ending with the shortest characteristic biological subsequence with the largest number of combinations. In a preferred embodiment, the order may be the characteristic biological subsequences from longest to shortest, or in the case of characteristic biological subsequences of the same length, the number of combinations may be from smallest to largest. In other embodiments, the order may be the number of combinations from smallest to largest, or in the case of characteristic biological subsequences with the same number of combinations, the number of characteristic biological subsequences may be from longest to shortest. In an embodiment, the order may further take into account additional data such as contextual data (e.g., to determine the order within a set of characteristic biological subsequences of the same length and with the same number of combinations).

[0123] In an embodiment, the method may include a further step d, after step c, of at least partially inferring the secondary / tertiary / quaternary structure of the processed biological subsequence based on the structural data as described above. This at least partial elucidation of the secondary / tertiary / quaternary structure may help to support and / or facilitate the design of the biological sequence. In an embodiment where a single primary structure of a characteristic biological subsequence is associated with multiple secondary or tertiary or quaternary structures, the secondary / tertiary / quaternary structure may be disambiguated based on the context in which the characteristic biological activity is found (e.g. the characteristic biological subsequence it surrounds). The information required for such disambiguation may be found, for example, in an (annotated) repository of fingerprint data strings. This may take the form of, for example, data (e.g. relationship data) relating to the relationship in terms of secondary / tertiary / quaternary structure between the characteristic biological subsequence and one or more further characteristic biological subsequences, as described above. For example, a particular first HYFT™ fingerprint is known to adopt either a helix or a turn configuration as a secondary structure, while a particular second HYFT™ fingerprint is known to always adopt a helix configuration if it is within a certain distance from the first HYFT™. In such a case, the HYFT™ pattern of the HYFT™ fingerprint, if observed, can be used to disambiguate the secondary structure of the first HYFT™. Similarly, the information used can be any of the types of data as described above, or any other information that can, alone or in combination, facilitate disambiguation (e.g., medical data).

[0124] In embodiments where the fingerprint data string is inherently oriented and includes position information, step c may include constructing the processed biological sequence as a directed graph. In embodiments, the directed graph may be a directed acyclic graph. Note that when referring to an acyclic graph, this does not mean that loops cannot occur, but rather that the overall graph is not cyclic. The resulting graph representation of the reconstructed sequence obtained in embodiments of the present invention may be referred to as a HYFT™ graph. Such a HYFT™ graph may enable a universal graph representation of genomes.

[0125] In embodiments, constructing the processed biological sequence may include considering the spacing between different fingerprint data strings and / or may include considering the direction (e.g., unique direction) of the fingerprint data strings to construct a directed graph.

[0126] In an embodiment, constructing the processed biological sequence may include considering structural and / or spatial shape information embedded within the fingerprint data string to construct a directed graph, and / or may include considering syntactic information embedded within the fingerprint data string.

[0127] In an embodiment, the search in step b may take into account any of position information, spacing information between different elements of the characteristic biological sequences, secondary structure, and / or tertiary structure, and / or quaternary structure of the characteristic biological subsequences, and / or structural variations of the characteristic biological subsequences.

[0128] By way of illustration, but not limitation, embodiments of the present invention provide the following examples of how a particular sequence can be searched. The method includes, in a first step, identifying HYFT™ present in the sequence to be searched. The method then further includes querying a reference database by searching all sequences in the reference database that also contain that HYFT™. The different sequences found are then sorted, for example by length, and the location of HYFT™ in the sequence is identified. Further, alignment is performed. In some embodiments, alignment can be performed using Navarro-Levenshtein matching. A more detailed description of Navarro-Levenshtein matching can be found, for example, in Navarro, Theoretical Computer Science 237 (2000) 455-463. Alignment can be performed by a directed graph, for example a directed acyclic graph. The latter may be a universal genomic reference graph, but embodiments are not limited thereto. Alignment can include identification of variants to a particular sequence. To carry out the above steps, the sequence may be further processed, thereby removing, for example, dead ends and loops.

[0129] Also described is a processed biological sequence obtainable by a computer-implemented method for processing a biological sequence as described above. A processed biological sequence 210 is shown diagrammatically in FIG.

[0130] Also described is a computer implemented method for constructing and / or updating a repository of processed biological sequences, comprising inputting the processed biological sequences as described above into said repository. Figure 4 shows diagrammatically a repository construction unit 320 for storing processed biological sequences 210 in a repository of processed biological sequences 220.

[0131] An advantage of embodiments of the present invention is that a repository of processed biological sequences can be built and stored.

[0132] Also described is a repository of processed biological sequences obtainable by a computer-implemented method for constructing and / or updating a repository of processed biological sequences as described above. The repository 220 is shown diagrammatically in FIG.

[0133] An advantage is that the repository of processed biological sequences can be quickly searched and navigated, and a further advantage is that the storage size of the repository can be relatively small compared to known databases by populating it with compressed processed biological sequences.

[0134] In an embodiment, the repository of processed biological sequences may be combined with a repository of fingerprint data strings.

[0135] In an embodiment, the repository may be a repository of processed biological fragment sequences (ie, processed biological sequences of biopolymer fragments).

[0136] In embodiments, the repository may be a database. In some embodiments, the repository of processed biological sequences may be an indexed repository. The repository may be indexed, for example, based on fingerprint markers (corresponding to characteristic biological subsequences) present in each processed biological sequence. In other embodiments, the repository may be a graph repository.

[0137] Also described is a computer-implemented method for comparing a first biological sequence with a second biological sequence, the computer-implemented method comprising: (a) processing the first biological sequence by the computer-implemented method as described above to obtain a first processed biological sequence or obtaining the first processed biological sequence from a repository of processed biological sequences as described above; (b) processing the second biological sequence by the computer-implemented method as described above to obtain a second processed biological sequence or obtaining the second processed biological sequence from a repository of processed biological sequences as described above; and (c) comparing at least the fingerprint marker of the first processed biological sequence with the fingerprint marker of the second processed biological sequence. Figure 5 shows a schematic diagram of a comparison unit 330, which compares at least the first biological sequence 211 and the second biological sequence 212 with an output result 400.

[0138] An advantage of the embodiments of the present invention is that the comparison of biological sequences can be changed from an NP-complete or NP-hard problem to a polynomial-time problem.Another advantage of the embodiments of the invention is that the comparison can be performed in a significantly reduced time and scales well with increasing complexity (e.g., increasing length or number of biological sequences).Another advantage of the embodiments of the present invention is that the required computational power and storage space can be reduced.

[0139] An advantage of embodiments of the present invention is that the degree of similarity between biological sequences can be calculated. A further advantage of embodiments of the present invention is that multiple biological sequences can be ranked based on their degree of similarity.

[0140] An advantage of embodiments of the present invention is that sequence similarity searches can be performed quickly and easily (eg, in polynomial time).

[0141] An advantage of embodiments of the present invention is that compared biological sequences can be easily and quickly (eg, in polynomial time) aligned.

[0142] An advantage of the embodiments is that multiple sequences may also be easily and quickly compared and aligned. A further advantage of the embodiments is that there is no accumulation of errors during alignment, as is the case with currently known methods (e.g., based on progressive alignment).

[0143] An advantage of embodiments of the present invention is that sequences of biopolymer fragments can be easily and quickly aligned and fused to reconstruct the original biopolymer sequence.

[0144] By using characteristic biological subsequences according to embodiments of the present invention (through fingerprint markers in the processed biological sequences), the problem of comparing sequences is advantageously reformulated from an NP-complete or NP-hard problem to a polynomial-time problem.In fact, identifying fingerprints in sequences and then comparing sequences based on these fingerprints can be considered as a lexical approach, but is computationally much simpler than currently used algorithms (e.g., comparing full sequences based on sliding window approach).Therefore, this comparison can be performed significantly faster while requiring less computing power and storage space, and also scales well with increasing complexity (e.g., increasing length or number of biological sequences).

[0145] In embodiments, the second biological sequence may be a reference sequence.

[0146] In an embodiment, step c may include identifying whether one or more characteristic biological subsequences (represented by fingerprint markers) of the first processed biological sequence correspond (e.g., match) with one or more characteristic biological subsequences (represented by fingerprint markers) of the second processed biological sequence. In an embodiment, step c may include identifying whether the corresponding characteristic biological subsequences appear in the same order in the first processed biological sequence and in the second processed biological sequence. In an embodiment, step c may include identifying whether one or more pairs of characteristic biological subsequences in the first processed biological sequence and one or more corresponding pairs of characteristic biological subsequences in the second processed biological sequence have the same or similar (e.g., differ by less than 1000 sequence units, e.g., less than 100 sequence units, preferably less than 50 sequence units, even more preferably less than 20 sequence units, and most preferably less than 10 sequence units) intervals.

[0147] In an embodiment, step c may further include comparing one or more second portions of the first processed biological sequence with one or more second portions of the second processed biological sequence. In an embodiment, comparing one or more second portions may include comparing corresponding second portions (i.e., second portions that appear between adjacent pairs of characteristic biological subsequences in the first processed biological sequence and second portions that appear between corresponding adjacent pairs of characteristic biological subsequences in the first processed biological sequence).

[0148] In embodiments, step c may further include calculating a measure representing the degree of similarity (e.g., Levenshtein distance) between the first biological sequence and the second biological sequence. In embodiments, the degree of similarity may be calculated based on multiple variables, such as combining a measure of syntactic similarity with a measure of structural similarity.

[0149] In an embodiment, the method may be used in sequence similarity searches by comparing the query sequence with one or more other biological sequences (e.g. corresponding to a sequence database to be searched, e.g. in the form of a repository of processed biological sequences). In an embodiment, a degree of similarity may be calculated for each of the other biological sequences. In an embodiment, the method may include a further step of ranking the biological sequences (e.g. by decreasing degree of similarity). In an embodiment, the method may include filtering the biological sequences. Filtering may be performed before and / or after step c. For example, filtering may be performed by selecting for comparison from the database only those biological sequences that fit certain criteria, for example based on the organism or group of organisms from which they originate (e.g. plants, animals, humans, microorganisms, etc.), whether their secondary / tertiary / quaternary structure is known, their length, etc. Alternatively, filtering may be performed after the comparison has been performed (e.g. only sequences above a certain threshold of similarity may be selected), based on the same criteria or based on a calculated degree of similarity. In contrast to sequence similarity searches in the prior art (where an alignment step is typically required, and then a similarity measure is established from it), alignment is not strictly necessary for similarity searches according to the embodiments.In fact, similar sequences can already be found without alignment by searching for sequences with the same fingerprint (optionally taking into account their order and their spacing as well), thus making it possible to further speed up the search.Notwithstanding the above, alignment according to the embodiments (see below) is also computationally simplified, and one can choose to perform alignment anyway, even if it is not strictly necessary.

[0150] Thus, the method makes it possible to determine (and optionally measure) the similarity between a first and a second biological sequence, and such a comparison is the basis for other methods, such as methods for alignment and assembly (see below).

[0151] In an embodiment, the method may be for aligning a first biological sequence with a second biological sequence. In an embodiment, step c may further include aligning the fingerprint markers of the first processed biological sequence with the fingerprint markers of the second processed biological sequence. Figure 5 shows a schematic output result 400 from the comparison unit 330 (better called "alignment unit 330" in this case) where the biological sequence is aligned by the fingerprint markers.

[0152] Therefore, in the embodiment, alignment is also simplified, since good alignment can already be obtained by simply aligning fingerprints.Again, this significantly reduces the computational complexity of the problem.Furthermore, in the prior art methods, such as those based on progressive alignment, there is an accumulation of alignment errors, and misalignment with one of the previous sequences typically propagates, causing further misalignment in the subsequent sequences.On the contrary, there is no such propagation of errors, since there is always the same individual set of fingerprint markers that are aligned (or at least are attempted to be aligned) in one (multiple) alignments.

[0153] In an embodiment, the method may further comprise aligning the corresponding second parts afterwards. The alignment of the second parts may be carried out, for example, by using one of the alignment methods known in the prior art. In fact, the "skeleton" of the alignment is already provided by aligning the fingerprint markers, so only the alignment between these markers is fleshed out later. Since each of these second parts is typically relatively short compared to the full length of the biological sequence, known methods are typically capable of carrying out such alignment relatively quickly and efficiently.

[0154] In an embodiment, the method may be for performing multiple sequence alignments (i.e., the method may include aligning three or more biological sequences). In an embodiment, the method may include aligning fingerprint markers of a third (or fourth, etc.) processed biological sequence with fingerprint markers of the first and / or second processed biological sequence. This is shown diagrammatically in Figure 5, where the alignment unit 330 may also compare and align any number of further processed biological sequences 213-216.

[0155] In an embodiment, the method may be used in variant calling. In the case of sequence alignment between two biological sequences, variant calling may identify variants (e.g., mutations) between the query sequence and the reference sequence. In the case of multiple sequence alignment, variant calling may optionally identify possible variations in a set of related sequences with respect to the reference sequence, which may include determining their frequency of occurrence. Furthermore, identifying variants may be performed based on primary structure, but may also consider secondary / tertiary / quaternary structure. Thus, identifying variants may be performed based on primary structure, based on secondary / tertiary / quaternary structure, and based on any possible correlation of distances correlating to HYFT™ within the sequence, or based on distance information related to subsequent or previous HYFT™. Identifying variants may be based on codon table variations, and thus in the same variant analysis, first-hand information on DNA, RNA, and amino acid variations may be collected.

[0156] In an embodiment, the method may be for performing sequence assembly. In an embodiment, the method includes: (a) providing a first biological sequence, the first biological sequence being a biological sequence of a first biopolymer fragment; (b) providing a second biological sequence, the second biological sequence being either a biological sequence of a second biopolymer fragment or a reference biological sequence; (c) aligning the first biological sequence to the second biological sequence; and (d) fusing the first biological sequence with the second biological sequence to obtain a constructed biological sequence. Figure 6 shows a schematic diagram of a sequence construction unit 340 that outputs a constructed biological sequence 510 by first aligning (by their fingerprint markers) and then fusing any number of biological sequences 500 (including at least a first biological sequence 501 and a second biological sequence 502).

[0157] In embodiments, steps ad of the method may be repeated to align and fuse any number of biopolymer fragments.

[0158] To facilitate sequencing, longer biopolymers may be fragmented so that individual fragments are sequenced faster and more easily (e.g., they may be sequenced in parallel), as known in the art. Sequence assembly is then typically used to align and fuse fragment sequences to reconstruct the original sequence. This may also be referred to as "read mapping," in which "reads" from fragment sequences are "mapped" to a second biopolymer sequence. Depending on the type of sequence assembly being performed, e.g., de novo assembly versus mapping assembly, the second biopolymer sequence may be selected to be the second biopolymer fragment or the reference sequence, as appropriate. As used herein, de novo assembly is an assembly from scratch, without the use of a template (e.g., scaffold sequence). Conversely, mapping assembly is an assembly by mapping one or more biopolymer fragment sequences to an existing scaffold sequence (e.g., reference sequence), which is typically similar, but not necessarily identical, to the sequence to be reconstructed in the future. The reference sequence may, for example, be based on (part of) a complete genome or transcriptome, or may have been obtained from a previous de novo assembly.

[0159] In an embodiment, the method may further comprise a step e after step d, in which the constructed biological sequence is aligned to a second biological sequence as described above. This additional alignment can be used to perform variant calling of the constructed biological sequence with respect to the second biological sequence (e.g., reference sequence).

[0160] In an embodiment, the fingerprint data string may include location information.

[0161] In embodiments, the method may further include detecting variations such as, for example, but not limited to, indels, deletions, insertions, and / or repeats.

[0162] In an embodiment, providing the first biological sequence and / or the second biological sequence may be performed using the methods described above.

[0163] Also described are storage devices containing repositories of fingerprint data strings as described above and / or repositories of processed biological sequences as described above.

[0164] Further described is a processing system including such a storage device and comprising a processor adapted to obtain a fingerprint data string from the storage device and / or store a fingerprint data string in the storage device and / or search for a fingerprint data string in the storage device.

[0165] Also described is a data processing system adapted to (eg including means for) performing any of the computer-implemented methods as described above.

[0166] A system may typically take different forms depending on the method it is intended to perform. In an embodiment, the system may be or include a sequence processing unit, a variant calling unit, a repository building unit, a comparison unit, an alignment unit, or a sequence building unit. In an embodiment, a general-purpose data processing means (e.g., a personal computer or a smartphone) or a distributed computing environment (e.g., a cloud-based system) may be configured to perform one or more of these functions. A distributed computing environment may include, for example, a server device and a networked client device. Herein, the server device may perform most of one or more of the methods, including storing a repository of fingerprint data strings and a repository of processed biological sequences. On the other hand, the networked client device may communicate instructions (e.g., inputs such as query sequences and settings such as search preferences) with the server device and may receive the output of the method.

[0167] Also described is a computer program (product) that includes instructions that, when executed by a computer (system), cause the computer to perform any of the computer-implemented methods as described above.

[0168] Also described is a computer program product comprising instructions which, when executed by a computer system, cause the computer system to obtain, retrieve or store fingerprint data strings from, at or in, respectively, a repository of fingerprint data strings.

[0169] Also described is a computer readable medium containing instructions that, when executed by a computer (system), cause the computer to perform any of the computer-implemented methods as described above.

[0170] Also described is the use of a repository of fingerprint data strings as described above for one or more selected from sequencing a biopolymer or biopolymer fragment, performing sequence assembly, processing biological sequences, building a repository of processed biological sequences, comparing a first biological sequence to a second biological sequence, aligning a first biological sequence to a second biological sequence, performing multiple sequence alignments, performing sequence similarity searches, performing variant calling, and identifying targets or biomarkers.

[0171] Also described is the use of the processed biological sequences as described above or a repository of processed biological sequences as described above for one or more selected from comparing a first biological sequence to a second biological sequence, aligning the first biological sequence to a second biological sequence, performing a multiple sequence alignment, performing a sequence similarity search, performing variant calling, and identifying targets or biomarkers.

[0172] In embodiments, any feature of any embodiment of any of the above aspects may independently be as correspondingly described for any embodiment of any of the other aspects or any of the other described subject matter.

[0173] Hereinafter, aspects of some embodiments will be described by detailed description of some embodiments. Other embodiments of the present invention may be constructed according to the knowledge of those skilled in the art without departing from the true technical teachings of the present invention, and it is clear that the embodiments are limited only by the terms of the appended claims.

[0174] Example 1: Association of biological information according to the present invention Example 1a: Discovery of Biological Sequences with Equivalent Biological Functions A proof-of-concept information acquisition was carried out for agricultural applications. Within this proof-of-concept, the HYFT™ protein fingerprint "WIGLVFL" was identified as a relatively frequent fingerprint within its domain. All protein sequences containing "WIGLVFL" were then retrieved from the repository of biological sequences processed as described herein and the results were analyzed. Notably, when the biological functions of the retrieved sequences were examined using public databases, most of them were found to be related to photosynthesis, across different species. Therefore, HYFT™ "WIGLVFL" was found to be an anchor point associated with different, but functionally related, biological entities.

[0175] Example 1b: Discovering Associations Between Related Biological Sequences As another proof of concept, a simple text search was performed for protein sequences with the name "fibroblast growth factor receptor 2". The corresponding results were obtained from a repository of processed biological sequences as described herein. Analysis of the obtained results revealed that virtually all protein sequences have the most stringent HYFT™ (i.e., the longest HYFT™ among them with the fewest number of combinations) as either "WSLIMES" or "WIKHVEK". Based on this, the repository of processed biological sequences and / or the repository of fingerprint data strings can be annotated with this information, so that it can be used whenever information is sought about biological entities for which the HYFTS™ "WSLIMES" and / or "WIKHVEK" are considered to be representative.

[0176] It should be noted here that different entry and exit points related through HYTF™ are possible. For example, a text search as described above can be performed to obtain, for example, information on the species in which such sequences appear. In another example, a specific protein domain may be used, and then a representative HYFT™ may be determined, through which a list of proteins sharing similar domains may be generated. Similarly, a representative HYFT™ may be identified for a drug target, through which other potential targets of the drug may be revealed, allowing, for example, to predict and / or rationalize side effects.

[0177] Example 1c: Finding Associations Between Patients with Equivalent Medical Conditions In yet another proof of concept, publicly available data from a cancer research study on the BRCA1 gene in different subjects (WEIGELT, Britta, et al. Diverse BRCA1 and BRCA2 reversion mutations in circulating cell-free DNA of therapy-resistant breast or ovarian cancer. Clinical Cancer Research, 2017, 23.21:6708-6720.) was processed. Most typically, a certain pattern of four HYFTs was found to be present. However, surprisingly, subjects reported to be resistant to chemotherapy were found to lack the second HYFT in this pattern ("TKCDHIF" in the corresponding protein). This is shown diagrammatically in FIG. 7 for a select subset of subjects.

[0178] Therefore, the absence of this "TKCDHIF" in protein sequence or the absence of the corresponding DNA sequence that codes for this protein sequence in the BRCA1 gene of target is considered to indicate chemotherapy resistance.Therefore, this knowledge can be used to quickly identify the patients who may be less effective in chemotherapy in this context, and provide them with adjusted treatment.

[0179] (The reference sequence for the BRCA1 protein is publicly available in the UniProt database under accession number P38398, sequence version 2, entry versi.

[0180] Example 1d: Processing of Sequencing Reads By way of illustration, but not by way of limitation, an example of a possible sequencing implementation is shown in FIG. 8. This figure illustrates different possible method steps of a sequencing method according to an embodiment of the present invention. The method includes after obtaining at least a first read for a biopolymer or biopolymer fragment, and during further receiving reads to analyze the input reads, e.g., the received reads, having a fingerprint, typically referred to as HYFT™, to sequence the biopolymer or biopolymer fragment. After the analysis, alignment can be performed to obtain a graph representing the sequence of the biopolymer or biopolymer fragment. The alignment can be performed by alignment on a directed graph, e.g., a directed acyclic graph. The latter may be a universal genome reference graph, but the embodiment is not limited thereto. The alignment can include the identification of variants for a particular sequence. Nevertheless, other intermediate steps such as building an overview graph may also be performed, whereby the processed (e.g., analyzed) sequences are grouped around one or more fingerprints that are common or linked between the processed sequences, so as to collapse the data by sorting in the overview graph. Such folding may be performed character by character, and nodes may be split when the characters differ. The method may also include forming a sub-read graph, whereby in this step typically dead ends or bubbles are removed. It should be noted that, alternatively or additionally, removing dead ends and / or bubbles may be performed in other steps of the method. The method may further include forming a read graph, where the sub-read graphs are combined. Further, by way of example, but not by way of limitation, embodiments of the invention are illustrated in different steps in Figs. 9-12. Fig. 8 illustrates the step of parsing input reads with HYFT™. It should be noted that some of the sequences shown in the figures do not form part of the invention in themselves, but are introduced only to illustrate the processing of such data. Specific fingerprints of the repository, i.e. occurrences of HYFT™, are identified in the reads.FIG. 9 illustrates the construction of an overview graph, whereby the different processed sequences are grouped around the linked HYFT™ found. FIG. 10 illustrates the construction of the overview graph by collapsing it by sorting it. The latter can be performed character by character and by splitting nodes when the characters differ. Additionally, a track can be kept of the sequences that are above the nodes. Typically starting from the HYFT™ fingerprint, one can typically move in one direction (e.g., to the right). FIG. 12 illustrates a cleaning step in which loose ends are removed. Alternatively or additionally, bubbles or small internal loops can also be resolved.

[0181] Example 2: Processing of the Protein Data Bank Example 2a: Analysis of the Protein Data Bank - with reference to HYFT™ fingerprints discovered therein To illustrate the widespread presence of HYFT™ fingerprints in biological information sources, the Protein Data Bank (PDB) was taken as an example of a large-scale, publicly available biological sequence database and processed according to the present invention using a repository of fingerprint data strings obtained as described above. The results were analyzed with respect to various indicators, a selection of which is presented below.

[0182] Figures 13 and 14 show the HYFT™ coverage percentages of processed protein sequences up to 50 in length and over 5000 in length, respectively, where the coverage percentage is the portion of the total length of the sequence that the sequence unit is attributed to the HYFT™ fingerprint. In other words, the coverage percentage is the sum of the lengths of one or more first portions divided by the total length of the sequence.

[0183] The inverse statistic, i.e., the portion of the total length of the sequence that is not covered by the HYFT™ fingerprint (or the sum of the lengths of one or more second portions divided by the total length of the sequence), is shown in Figure 15 for lengths up to >5000.

[0184] In connection with the above, FIG. 16 shows an overview of the number of HYFT™ obtained per processed sequence in the form of a frequency distribution.

[0185] Remarkably, these charts show that at least one HYFT™ fingerprint was found in all processed biological sequences. In fact, there was not a single PDB sequence that was not covered by one or more HYFT™. Furthermore, longer sequences are broadly covered by HYFT™ patterns, with the spread of coverage generally thinning out as the sequence length increases. On average, coverage rates approaching 80% are achieved.

[0186] FIG. 17 shows typical intervals observed, along with the frequency distribution of the lengths of second portions appearing before and after the HYFT™ fingerprint.

[0187] Overall, the above results support that virtually any protein sequence (and extended DNA and / or RNA sequence) can be rewritten as a string of one or more HYFT™ (i.e., HYFT™ patterns) based on the repository of HYFT™ fingerprint data strings according to the present invention. Moreover, due to the generally good coverage rates achieved, the processed sequences still retain essential features of their unprocessed counterparts, in particular, not only the identified HYFT™ is retained, which is extended with additional data (see above), such as the intervals before, between, and after the identified HYFT™ (i.e., the length of the second part). High-performance indexing based on HYFT™ patterns can be achieved with near perfect retrieval rates.

[0188] Example 2b: Effect of the matching strategy used Since different strategies can be used when processing biological sequences according to the present invention, the difference between two different approaches was examined. In the first approach, biological sequences in the PDB database were searched for all occurrences of HYFT fingerprints, including HYFT fingerprint overlaps, in the order in which HYFT fingerprints become insignificant. In the second approach, biological sequences in the PDB database were searched using a more rigorous format. Searches were performed in the order of HYFT fingerprints from longest to shortest, and in the case of the same length, in the order of the number of combinations from smallest to largest, with no HYFT overlaps allowed (i.e., the parts found to correspond to HYFT are then excluded from further HYFT searches). The goal of the second approach is to identify the minimum number of HYFT™ that describe the processed biological sequence while ensuring good coverage by not allowing overlaps and prioritizing more stringent HYFT™ (i.e., smaller number of combinations and longer length) over less stringent HYFT™ (i.e., larger number of combinations and shorter length).

[0189] In Figure 18, the different match numbers found per biological sequence are plotted against each other.As can be seen, a generally linear relationship is found, and in fact, the number of matches of the second approach, which is more stringent, is generally about 5 times less than that of the first approach.These lower match numbers lead to an increase in processing time and storage space required, both for identifying HYFT fingerprints and for using the processed sequences in further methods thereafter, but still fully characterize the entire sequence.Therefore, the second approach seems to strike the best balance and is generally preferred.

[0190] However, it should be noted that, notwithstanding the above, the number and quality of matches found using the first approach are lower and better than the equivalent k-mer approach.Thus, the second approach may generally be preferred over the first approach, but the first approach nevertheless remains more advantageous than known technical methods.

[0191] Example 3: Comparison between sequence searches known in the prior art and those described herein Example 3a: Using Short Search Strings Two separate searches were performed based on the search string "AVFPSIVGRPRHQGVMVGMGQKDSY", which corresponds to a relatively short protein sequence having a length of 25 sequence units and may be, for example, a protein fragment in protein sequencing. Such a search may be used after sequencing of a fragment, for example, as part of identifying a suitable reference sequence to be used in sequence assembly with the fragment.

[0192] The initial search was performed using BLAST (Basic Local Alignment Search Tool), more specifically, "Protein BLAST" (available at the URL: https: / / blast.ncbi.nlm.nih.gov / Blast.cgi?PROGRAM=blastp&PAGE_TYPE=BlastSearch&LINK_LOC=blasthome). The following search parameters were used: database = Protein Data Bank (pdb), algorithm = blastp (protein-protein BLAST), max target sequences = 1000, short query = auto-adjust parameters for short input sequences, expectation threshold = 20000, word size = 2, matrix = PAM30, composition adjustment = no adjustment. BLAST took more than 30 seconds for this search, after which 604 search results were returned.

[0193] On the other hand, based on the principles of the present invention, it was determined that "IVGRPRHQGVM" is a characteristic biological subsequence (i.e., a "HYFT™ fingerprint") contained in the short protein sequence described above. Therefore, a second search was performed in a repository of processed biological sequences based on the search string "IVGRPRHQGVM". This repository is based on the same protein database used in BLAST (i.e., Protein Data Bank, PDB), which was previously processed using a repository of fingerprint data strings (see above). That is, characteristic biological subsequences represented by fingerprint data strings were identified and labeled in a set of publicly available biological sequences. This search returned 661 results. In contrast to BLAST, in this case the time frame required was only 196 milliseconds. It was therefore observed that even for such relatively short sequences, the method of the present invention can reduce the time required by more than 150 times compared to known methods.

[0194] Now, referring to Fig. 19, Fig. 20 and Fig. 21, the results of both of these searches are shown in terms of their total length (Fig. 19), their Levenshtein distance (Fig. 20) and their longest common substring (Fig. 21) (BLAST = dotted line, our method = solid line). For each graph, the search results are shown in order from low to high in terms of the plotted parameter (i.e. total length, Levenshtein distance or longest common substring). Furthermore, one of the search results, namely the protein sequence 5NW4_V (i.e. the first result listed by BLAST), was selected as a reference, for which the Levenshtein distance and the longest common substring were calculated. As can be seen from these figures, our method obtained smaller variations in total length (characterized by a relative plateau spanning a significant portion of the results), significantly shorter Levenshtein distances and significantly larger longest common substrings over the entire range of the search results compared to the BLAST results. These combinations suggest that the method of the present invention was able to identify results that were more relevant to the search performed.

[0195] Example 3b: Using long proteins as search strings Repeating the previous example, this time the complete protein sequence 3MN5_A (having a length of 359 sequence units) was searched.

[0196] An initial search using BLAST returned 88 results.

[0197] Meanwhile, based on the principles of the present invention, it was determined that six distinctive biological subsequences (i.e., "HYFT™ fingerprints") can be found in sequence 3MN5_A, which are represented as follows: +4641474444415052415646_1, +495647525052485147564d_1, +4949544e5744444d454b49_1, +494d464554464e5650414d_1, +494b454b4c435956414c44_1, and +49474d4553414749484554_1. Here, for example, "49474d4553414749484554" corresponds to the respective subsequence in hexadecimal format. A second search was therefore performed in the same repository of processed biological sequences as in the previous example to find those protein sequences that contained the same six characteristic biological subsequences in the same order. This search returned 661 results.

[0198] Now, referring to Figures 22, 23 and 24, the results of both of these searches are shown in terms of their total length (Figure 22), their Levenshtein distance (Figure 23) and their longest common substring (Figure 24) (BLAST = dotted line, our method = solid line). For each graph, the search results are shown in order from low to high in terms of the plotted parameter (i.e. total length, Levenshtein distance or longest common substring). In this case, the Levenshtein distance and the longest common substring were calculated for the original query sequence 3MN5_A. As can be seen from these figures, the characteristics of the search results for both methods are relatively comparable even at the extremes. However, our method produced plateau results in the intermediate range, with little variation in total length, short Levenshtein distance and rather long longest common substring. These combinations suggest that the method of the present invention was able to identify more relevant results.

[0199] Although preferred embodiments, specific structures and configurations, and materials have been discussed herein for the device according to the present invention, it should be understood that various changes or modifications in form and details may be made without departing from the scope and technical teachings of the present invention. For example, any formulas given above are merely representative of procedures that may be used. Functions may be added or deleted from the block diagrams, and operations may be interchanged between functional blocks. Steps may be added or deleted to the methods described within the scope of the present embodiments.

Claims

1. 1. A computer-implemented method for obtaining information about a biological entity based on at least one biological sequence, comprising: providing a repository of fingerprint data strings for a biological sequence database, each fingerprint data string representing a distinct biological subsequence composed of sequence units, each distinct biological subsequence having a number of combinations in said biological sequence database that is less than the total number of distinct sequence units available for combination, said number of combinations of a biological subsequence being defined as the number of distinct sequence units that appear as consecutive sequence units of said biological subsequence in said biological sequence database; b. determining one or more fingerprint data strings representative of said biological entity from said repository of fingerprint data strings; c. searching a repository containing information associated with one or more representative fingerprint data strings for information associated with said fingerprint data strings; d. A computer-implemented method comprising obtaining and / or using said information.

2. The one or more fingerprint data strings representing the biological entity include: - said fingerprint data string representing the longest distinct biological subsequence found in said at least one biological sequence; or - if two or more longest characteristic biological subsequences are found, the characteristic biological subsequence among the longest characteristic biological subsequences having the smallest number of combinations; The computer-implemented method of claim 1 , comprising:

3. and processing the sequencing reads for the biopolymer or biopolymer fragment in view of the information contained in said repository of fingerprint data strings. the information associated with the fingerprint data strings contained in the repository includes combined data representing the different sequence units that appear as consecutive sequence units of the corresponding characteristic biological subsequences in the biological sequence database; - step b includes searching the reads for occurrences of one or more of the characteristic biological subsequences represented by the fingerprint data strings; and - step d comprises, for each occurrence, validating or rejecting the read by determining whether the sequence units contiguous with the characteristic biological subsequence match the combined data of the repository; and / or - step b includes searching the beginning and / or the tail of the reads for an occurrence of one of the characteristic biological subsequences represented by the fingerprint data string; and The computer-implemented method of claim 1 or 2, wherein step d comprises predicting contiguous sequence units from the combined data of the repository.

4. The computer-implemented method of claim 3 , performed on a batch of reads.

5. 5. The computer-implemented method of claim 3 or 4, wherein the fingerprint data string is inherently oriented and includes positional information, and the method comprises the further step of aligning the processed reads with a directed graph using the characteristic biological subsequences identified in step b.

6. A computer implemented method for associating information with one or more fingerprint data strings as defined in any one of claims 1 to 5, comprising: a. providing a biological sequence of a biological entity, said biological entity sharing equivalent information; b. searching said biological sequence for equivalent characteristic biological subsequences; c) associating said equivalent information with said fingerprint data string representing said equivalent characteristic biological subsequence.

7. Before step a, 7. The computer-implemented method of claim 6, further comprising the step of: a'. searching a data pool for biological entities that share equivalent information.

8. The computer-implemented method of claim 7 , wherein the data pool comprises sequencing data or a biological sequence database.

9. A data processing system adapted to carry out the computer implemented method of any of claims 1 to 8.

10. A computer program comprising instructions which, when executed by a computer, cause the computer to carry out a computer-implemented method according to any one of claims 1 to 8.

11. A computer readable medium comprising instructions which, when executed by a computer, cause the computer to perform a computer-implemented method according to any one of claims 1 to 8.

Images