Peptide sequence assembly method and apparatus based on de bruijn graph

Abstract

The application provides a de Bruijn graph-based peptide sequence assembly method and device, the method comprises the following steps: obtaining light and heavy chain sequence data, and creating a sequence alignment database using the light and heavy chain sequence data; performing k-mer processing on peptide sequence test data to obtain a k-mer sequence set; aligning the k-mer sequence set with the sequence alignment database to obtain an alignment score; if the k-mer sequence set is mixed light and heavy chain data, the alignment score is divided into two categories of light chain and heavy chain, and a de Bruijn graph is constructed respectively, if the k-mer sequence set is light chain or heavy chain data, a de Bruijn graph is directly constructed; performing sequence assembly in the de Bruijn graph to obtain assembled peptide sequences. The application can perform peptide sequence assembly under the condition of distinguishing light and heavy chains, the assembly efficiency is greatly improved, and the assembly result is effective and reliable.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of bioinformatics, in particular to a peptide sequence assembly method based on deBruijn graph and a peptide sequence assembly device based on deBruijn graph. BACKGROUND

[0002] Peptide sequence assembly is an analysis method in bioinformatics. This method constructs short fragment peptide sequences into longer continuous sequences through sequence alignment and sequence merging calculation. The traditional sequence assembly method is limited to comparing the information between two fragments greater than a certain threshold, which ignores the comparison between multiple sequence fragments. At the same time, the traditional sequence assembly method can only be applied to some small species sequence fragments, and the data volume processed is greatly limited. In the past few decades, new generation sequencing technology has developed rapidly. Compared with the first generation of sequencing, the length of DNA base sequence obtained by new generation sequencing technology is relatively shorter, the data volume is larger, and the constructed graph will consume more memory occupancy. Therefore, the deBruijn graph-based sequence assembly method is proposed. Compared with the traditional assembly method, the deBruijn graph-based sequence assembly method can greatly improve the assembly accuracy, for example, it has certain improvement in filtering error information, repeated region discovery, etc., but it also has some shortcomings. The peptide sequences obtained from the sequencing stage are not distinguished between light and heavy chains, which causes problems in assembly. SUMMARY

[0003] The present application provides a peptide sequence assembly method and device based on deBruijn graph, which can perform peptide sequence assembly under the condition of distinguishing light and heavy chains, greatly improves the assembly efficiency, and the assembly result is effective and reliable.

[0004] The technical scheme adopted by the present application is as follows:

[0005] A peptide sequence assembly method based on deBruijn graph, comprising the following steps: obtaining light and heavy chain sequence data, and creating a sequence alignment database using the light and heavy chain sequence data; performing k-mer processing on peptide sequence test data to obtain a k-mer sequence set; aligning the k-mer sequence set with the sequence alignment database to obtain an alignment score; if the k-mer sequence set is mixed light and heavy chain data, the alignment score is divided into two categories of light chain and heavy chain, and a deBruijn graph is constructed respectively, if the k-mer sequence set is light chain or heavy chain data, a deBruijn graph is directly constructed; performing sequence assembly in the deBruijn graph to obtain assembled peptide sequences.

[0006] Obtaining light and heavy chain sequence data, and using the light and heavy chain sequence data to create a sequence alignment database, specifically comprising: obtaining peptide sequence data that has been distinguished by light and heavy chains from an IMGT database, and creating a sequence alignment database from the obtained data through Blast software.

[0007] Performing a sequence alignment operation on the k-mer sequence set and the sequence alignment database through Blast software to obtain the alignment score.

[0008] Constructing a deBruijn graph, specifically comprising: calculating a weight value according to the confidence of the peptide sequence and the alignment score, and constructing the deBruijn graph based on the weight value.

[0009] Performing sequence assembly in the deBruijn graph to obtain assembled peptide sequences, specifically comprising: finding directed edges from high to low according to the weight value in the deBruijn graph, traversing the path of the directed edges once and only once, and assembling short fragment peptide sequences into continuous sequence modules; and splicing the continuous sequence modules together to ultimately obtain assembled peptide sequences.

[0010] A peptide sequence assembly device based on a deBruijn graph, comprising: a data preprocessing module for obtaining light and heavy chain sequence data, and using the light and heavy chain sequence data to create a sequence alignment database, and performing k-mer processing on peptide sequence test data to obtain a k-mer sequence set; a sequence alignment module for aligning the k-mer sequence set with the sequence alignment database to obtain an alignment score; a construction module for, when the k-mer sequence set is light and heavy chain mixed data, dividing the alignment score into two categories of light chains and heavy chains, and respectively constructing a deBruijn graph, and when the k-mer sequence set is heavy chain or light chain data, directly constructing a deBruijn graph; and an assembly module for performing sequence assembly in the deBruijn graph to obtain assembled peptide sequences.

[0011] The data preprocessing module is specifically configured to obtain peptide sequence data that has been distinguished by light and heavy chains from an IMGT database, and create a sequence alignment database from the obtained data through Blast software.

[0012] The sequence alignment module is specifically configured to perform a sequence alignment operation on the k-mer sequence set and the sequence alignment database through Blast software to obtain the alignment score.

[0013] The construction module is specifically configured to calculate a weight value according to the confidence of the peptide sequence and the alignment score, and construct the deBruijn graph based on the weight value.

[0014] The assembling module is specifically used for finding the directed edges from high to low according to the weight values in the deBruijn graph, traversing the path of the directed edges once and only once, assembling the short fragment peptide sequences into continuous sequence modules, splicing the continuous sequence modules together, and finally obtaining the assembled peptide sequence.

[0015] The beneficial effects of the present application are:

[0016] The present application can assemble the peptide sequence fragments under the condition of distinguishing the light and heavy chains, and the assembling efficiency is greatly improved, and the assembling result is effective and reliable. BRIEF DESCRIPTION OF DRAWINGS

[0017] Figure 1 The flow chart of the deBruijn graph-based peptide sequence assembling method of the embodiment of the present application is shown in the figure.

[0018] Figure 2 The process diagram of data processing and comparison of one embodiment of the present application is shown in the figure.

[0019] Figure 3 The deBruijn graph construction and sequence assembling process diagram of the light and heavy chain mixed data of one embodiment of the present application is shown in the figure.

[0020] Figure 4 The deBruijn graph construction and sequence assembling process diagram of the light chain or heavy chain data of one embodiment of the present application is shown in the figure.

[0021] Figure 5 The flow chart of the deBruijn graph-based peptide sequence assembling method of one specific embodiment of the present application is shown in the figure.

[0022] Figure 6 The block diagram of the deBruijn graph-based peptide sequence assembling device of the embodiment of the present application is shown in the figure. DETAILED DESCRIPTION

[0023] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0024] As shown in the figure, the deBruijn graph-based peptide sequence assembling method of the embodiment of the present application includes the following steps: Figure 1

[0025] S1, obtain light and heavy chain sequence data, and create a sequence alignment database using the light and heavy chain sequence data.

[0026] ​S2, the peptide sequence test data is processed by k-mer to obtain a set of k-mer sequences.

[0027] S3. Align the k-mer sequence set with the sequence alignment database to obtain the alignment score.

[0028] S4. If the k-mer sequence set contains mixed light and heavy chain data, the alignment scores are divided into light chain and heavy chain categories, and deBruijn graphs are constructed for each category. If the k-mer sequence set contains either light chain or heavy chain data, deBruijn graphs are constructed directly.

[0029] S5. Sequence assembly is performed in the deBruijn diagram to obtain the assembled peptide sequence.

[0030] In one embodiment of the present invention, step S1 is used to create a database required for sequence alignment, and step S2 is used to obtain test data for sequence alignment. Figure 2 As shown, step S1 specifically includes obtaining peptide sequence data with distinguished light and heavy chains from currently available protein databases, such as the IMGT database, and creating a sequence alignment database using Blast software. Step S2 specifically includes processing the peptide sequences obtained from sequencing with k-mer to obtain a k-mer sequence set.

[0031] like Figure 2 As shown, step S3 specifically includes performing sequence alignment operations between the k-mer sequence set and the sequence alignment database using Blast software to obtain alignment scores.

[0032] like Figure 3 As shown, for k-mer sequence sets containing both light and heavy chains, the alignment scores of the light chain data and the heavy chain data can be selected separately, and deBruijn graphs for the light chain and heavy chain can be constructed respectively. Figure 4 As shown, for k-mer sequence sets containing only light or heavy chain data (i.e., data of either type), there is no need to differentiate based on alignment scores; a deBruijn graph can be directly constructed. Each edge in the graph has a weight value, which can be calculated based on the peptide sequence confidence and alignment score, and the deBruijn graph is constructed based on these weight values.

[0033] The specific process for constructing the deBruijn graph is as follows: Assume that the peptide sequences obtained during the sequencing stage are a set of reads (sequencing fragments) X = {X1, X2, L, X...} n Each read is divided into a set of k-mer sequences K = {k1, k2, ..., k} consisting of several consecutive bases. nwherein k-mer refers to a base sequence composed of k consecutive bases in a read, and each k-mer is a vertex in the graph, if there are two k-mers, one of which k1 has the same k-1 bases at the back as the other k2 has at the front, then there must be a directed edge from k1 to k2 between k1 and k2, and meanwhile, the directed edge is assigned a weight value (α, β), and the calculation formula of the weight value (α, β) is as follows:

[0034]

[0035] wherein the calculation formula of s and t is as follows:

[0036]

[0037]

[0038] s.t.b[k]={k-1,1,L,1,k-1}

[0039] wherein l is the length of a sequencing peptide sequence, λ is the percentage of the corresponding alignment score, k is the length of a k-mer sequence, and array a[ i ] is a confidence set of a sequencing peptide sequence, and so on.

[0040] Then, the directed edges in the deBruijn graph can be searched according to the weight value from high to low, and the path of traversing the directed edges once and only once is searched, so as to assemble the peptide sequence of the short fragment into a continuous sequence module. Finally, the continuous sequence modules are spliced together to obtain the assembled peptide sequence.

[0041] The overall flow of the peptide sequence assembly method based on the deBruijn graph of one specific embodiment of the present application is shown in Figure 5 .

[0042] According to the peptide sequence assembly method based on the deBruijn graph of the embodiment of the present application, the peptide sequence fragments can be assembled under the condition of distinguishing the light and heavy chains, the assembly efficiency is greatly improved, and the assembly result is effective and reliable.

[0043] Corresponding to the peptide sequence assembly method based on the deBruijn graph of the above-mentioned embodiment, the present application further provides a peptide sequence assembly device based on the deBruijn graph.

[0044] As Figure 6As shown, the de Bruijn graph-based peptide sequence assembling device of the embodiment of the present application comprises a data preprocessing module 10, a sequence alignment module 20, a construction module 30 and an assembling module 40. The data preprocessing module 10 is configured to acquire light chain and heavy chain sequence data, create a sequence alignment database using the light chain and heavy chain sequence data, and perform k-mer processing on peptide sequence test data to obtain a k-mer sequence set. The sequence alignment module 20 is configured to align the k-mer sequence set with the sequence alignment database to obtain an alignment score. The construction module 30 is configured to, when the k-mer sequence set is mixed light chain and heavy chain data, divide the alignment score into two categories of light chain and heavy chain, and construct a de Bruijn graph for each category, and when the k-mer sequence set is heavy chain or light chain data, directly construct a de Bruijn graph. The assembling module 40 is configured to perform sequence assembly in the de Bruijn graph to obtain an assembled peptide sequence.

[0045] In an embodiment of the present application, the data preprocessing module 10 is specifically configured to acquire peptide sequence data that has been distinguished into light chain and heavy chain from an IMGT database, and create a sequence alignment database using the acquired data through Blast software. The sequence alignment module 20 is specifically configured to perform sequence alignment operation on the k-mer sequence set and the sequence alignment database through Blast software to obtain an alignment score. The construction module 30 is specifically configured to calculate a weight value according to the confidence of the peptide sequence and the alignment score, and construct a de Bruijn graph based on the weight value. The assembling module 40 is specifically configured to find directed edges in the de Bruijn graph from high to low according to the weight value, traverse the path of the directed edges once and only once, assemble short fragment peptide sequences into a continuous sequence module, splice the continuous sequence modules together, and finally obtain an assembled peptide sequence.

[0046] More specific embodiments can refer to the above-described embodiments of the de Bruijn graph-based peptide sequence assembling method, and will not be described here.

[0047] The de Bruijn graph-based peptide sequence assembling device according to the embodiment of the present application can perform peptide sequence assembly on peptide sequence fragments under the condition of distinguishing light chain and heavy chain, greatly improves the assembly efficiency, and effectively and reliably obtains the assembly result.

[0048] In the description of the present application, the meaning of "a plurality of" is two or more, unless otherwise explicitly and specifically limited.

[0049] In the present application, unless specifically defined otherwise, the terms "mount", "connected", "connecting", "fixed", "unfixed", and the like should be construed broadly and can include fixed connections, detachable connections, or integral connections; mechanical connections, electrical connections, or direct connections, or indirect connections via an intermediate medium, or internal communication between two elements, or interaction between two elements. Those skilled in the art can understand the specific meanings of the above terms in the present application according to the specific circumstances.

[0050] In the present application, unless specifically defined otherwise, the first feature "on" or "under" the second feature can be direct contact between the first and second features, or indirect contact between the first and second features through an intermediate medium. Moreover, the first feature "above", "over", and "on" the second feature can be directly above or obliquely above the second feature, or simply indicate that the first feature is higher in horizontal height than the second feature. The first feature "below", "under", and "under" the second feature can be directly below or obliquely below the second feature, or simply indicate that the first feature is lower in horizontal height than the second feature.

[0051] In the description of the present application, the description of the terms "one embodiment", "some embodiments", "an example", "a specific example", or "some examples" and the like means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In the present application, the illustrative description of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any suitable manner in any one or more embodiments or examples. In addition, those skilled in the art can combine and combine different embodiments or examples described in the present application and the features of different embodiments or examples, without contradiction.

[0052] Any process or method descriptions in flow charts or described elsewhere herein can be understood as representing code modules, segments, or portions of code that include one or more executable instructions for performing specific logic functions or steps in the process. The scope of preferred embodiments of the present application encompasses the additional implementation in which the functions can be performed in different order, including substantially concurrently or in reverse order, and the functions can be performed in accordance with the involved functions, and those skilled in the art should understand.

[0053] The logic and / or steps represented in flow diagrams or otherwise described herein, for example, can be considered as a sequence of instructions to implement logic functions, and can be embodied in any computer-readable medium for use by an instruction execution system, apparatus, or device, such as a computer-based system, processor- containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. In the context of this specification, a "computer-readable medium" can be any means that can contain, store, communicate, propagate or transport the program for use by or in connection with the instruction execution system, apparatus, or device. The computer-readable medium can be a machine-readable storage device (e.g., magnetic, optical or other) a machine-readable storage diskette (e.g., floppy, flexible or other), a machine-readable storage card (e.g., RAM, ROM or other), a machine-readable storage tape (e.g., magnetic, optical or other), a machine-readable storage medium (e.g., a portable memory chip), a machine-readable signal, a machine-readable propagated signal, a machine-readable compressed signal, and the like. More specific examples (a non-exhaustive list) of the computer-readable medium include the following: an electrical connection having one or more wires (e.g., a bus that has thin film resistors for

[0054] It should be understood that aspects of the application can be implemented in hardware, software, firmware or combinations thereof. In the embodiments described above, various steps or methods can be implemented, in part, or in whole, in software, firmware, hardware, or combinations thereof. For example, if implemented in software, an example of which is illustrated in FIG. 8, the software implementation can be operated on a processor, or by a processor, such as the processor 802. The software that embodies the methods described above can be stored on a computer readable medium, such as the storage 804, before it is uploaded to the processor 802. The processor 802 then executes the sequences of instructions (e.g., the software) that embody the various methods to generate a graphical user interface as described herein. The software is provided on the computer readable medium to the processor 802 in the form of a system program, a program product, or other serially formatted data. The software as an example is provided on a program carrier, such as a diskette, CD-ROM or hard disk, or is provided as signals over a network 806. The software is delivered to a user or a system on a computer readable medium, such as a floppy disk, CD-ROM, hard disk, or memory stick, or over a network 806. The software described herein can be applied to a programmable control unit, a programmable data processing unit, a programmable controller, a programmable electronic device, or other programmable machine to produce a machine, such as a computer, that can implement or execute the various methods described herein. The software can be implemented in a standardized or proprietary language, including machine code, assembly language, object code, a high-level language, or a combination thereof. The software can be loaded into a memory of a machine such as a computer, a programmable control unit, a programmable data processing unit, a programmable controller, a programmable electronic device, or other programmable machine to produce a machine, such as a computer, that can implement or execute the various methods described herein.

[0055] Those skilled in the art can understand that all or part of the steps involved in the above-mentioned embodiment method can be completed by programs instructing related hardware, and the programs can be stored in a computer readable storage medium. When the programs are executed, one or a combination of the steps of the method embodiment is included.

[0056] In addition, each function unit in each embodiment of the present application can be integrated in one processing module, or each unit can exist physically separately, or two or more units can be integrated in one module. The integrated module can be realized in the form of hardware, or in the form of software function module. When the integrated module is realized in the form of software function module and sold or used as an independent product, it can also be stored in a computer readable storage medium.

[0057] Although the embodiments of the present application have been shown and described above, it should be understood by those ordinary skilled in the art that the above embodiments are exemplary and cannot be understood as limiting the present application, and those ordinary skilled in the art can make changes, modifications, replacements and variations to the above embodiments within the scope of the present application.

Claims

1. A method for assembling a peptide sequence based on a de Bruijn graph, characterized in that, The method comprises the following steps: Obtaining light and heavy chain sequence data, and creating a sequence alignment database using the light and heavy chain sequence data; Performing k-mer processing on the peptide sequence test data to obtain a k-mer sequence set; Aligning the k-mer sequence set with the sequence alignment database to obtain an alignment score; If the k-mer sequence set is mixed light and heavy chain data, the alignment score is divided into two categories of light chain and heavy chain, and a deBruijn graph is constructed respectively, and if the k-mer sequence set is light chain or heavy chain data, a deBruijn graph is directly constructed; Performing sequence assembly in the deBruijn graph to obtain assembled peptide sequences, Constructing a deBruijn graph, specifically comprising: calculating a weight value according to the confidence of the peptide sequence and the alignment score, and constructing the deBruijn graph based on the weight value, The specific procedure of constructing the de Bruijn graph is as follows: assuming that the peptide sequence obtained in the sequencing stage is a read (sequencing fragment) set , each read in the set is divided into a k-mer sequence set composed of a plurality of consecutive bases , wherein the k-mer refers to a base sequence composed of consecutive bases in a read, each k-mer is a vertex in the graph, and if there are two k-mers, one of which k has the same last k -1 base as the other k has the same first k -1 base as the other k , then there must be a directed edge between k 1 and k 2, which is directed from k 1 to k 2, and at the same time, a weight value is assigned to the directed edge, and the calculation formula of the weight value is as follows: wherein s and t The calculation formula is as follows: wherein, l is the length of a sequenced peptide sequence, λ is the percentage of the corresponding alignment score, k is the length of a k-mer sequence, the array is the set of confidences of a sequenced peptide sequence, and so on.

2. The de Bruijn graph based peptide sequence assembly method according to claim 1, wherein, Obtaining light and heavy chain sequence data, and creating a sequence alignment database using the light and heavy chain sequence data, specifically comprising: Obtaining peptide sequence data that has been distinguished between light and heavy chains from an IMGT database, and creating a sequence alignment database using the obtained data through Blast software.

3. The de Bruijn graph-based peptide sequence assembly method according to claim 1 or 2, characterized in that, Performing sequence alignment operation on the k-mer sequence set and the sequence alignment database through Blast software to obtain the alignment score.

4. The de Bruijn graph based peptide sequence assembly method of claim 3, wherein, Performing sequence assembly in the deBruijn graph to obtain assembled peptide sequences, specifically comprising: Finding directed edges in the deBruijn graph from high to low according to the weight value, traversing the path of the directed edges once and only once, and assembling short fragment peptide sequences into continuous sequence modules; Splicing the continuous sequence modules together to finally obtain assembled peptide sequences.

5. A de Bruijn graph based peptide sequence assembly apparatus implementing the method of any one of claims 1-4, characterized by, Comprise: A data preprocessing module for obtaining light and heavy chain sequence data, and creating a sequence alignment database using the light and heavy chain sequence data, and performing k-mer processing on the peptide sequence test data to obtain a k-mer sequence set; A sequence alignment module for aligning the k-mer sequence set with the sequence alignment database to obtain an alignment score; A construction module for dividing the alignment score into two categories of light chain and heavy chain when the k-mer sequence set is mixed light and heavy chain data, and constructing a deBruijn graph respectively, and directly constructing a deBruijn graph when the k-mer sequence set is heavy chain or light chain data; An assembly module for performing sequence assembly in the deBruijn graph to obtain assembled peptide sequences.

6. The de Bruijn graph based peptide sequence assembly apparatus of claim 5, wherein, The data preprocessing module is specifically configured to obtain peptide sequence data that has been distinguished between light and heavy chains from an IMGT database, and create a sequence alignment database using the obtained data through Blast software.

7. The de Bruijn graph based peptide sequence assembly apparatus according to claim 5 or 6, c h a r a c t e r i z e d by The sequence alignment module is specifically configured to perform sequence alignment operation on the k-mer sequence set and the sequence alignment database through Blast software to obtain the alignment score.

8. The de Bruijn graph based peptide sequence assembly apparatus of claim 7, wherein, The assembling module is specifically used for finding the directed edges from high to low according to the weight values in the de Bruijn graph, traversing the path of the directed edges once and only once, assembling the short fragment peptide sequences into continuous sequence modules, splicing the continuous sequence modules together, and finally obtaining the assembled peptide sequence.

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