Gene data compression method, device and related equipment in the context of high-throughput sequencing

Abstract

The present application discloses a method, apparatus, and related equipment for compressing genetic data in the context of high-throughput sequencing. The method comprises: obtaining genetic data to be compressed, and obtaining metadata, base data, and quality data of each short sequence from the genetic data; compressing the metadata of each short sequence using incremental coding technology or run-length coding technology; comparing the base data of each short sequence using a preset reference genome, and compressing the base data of each short sequence based on the comparison results; and compressing the quality data of each short sequence using a combination of run-length coding technology, contextual statistical model, ANS+FSE coding technology, arithmetic coding technology, and / or Huffman coding technology. The present application improves the compression rate by using compression methods adapted to different parts of the data, thereby saving storage space for massive genetic data.

Description

Technical Field

[0001] The present application relates to the field of gene sequencing technology, and more specifically, to a gene data compression method, apparatus, and related equipment in the context of high-throughput sequencing. Background Art

[0002] As an important means of exploring the mysteries of life, gene sequencing technology has become an important branch of bioinformatics research. It has been widely used in species identification, genetic testing, disease diagnosis, etc. The rapid development of gene sequencing technology has laid a solid foundation for precision medicine.

[0003] With the advancement of gene sequencing technology, sequencing costs have become increasingly lower, leading to a growing scale of sequencing operations and an explosive growth in the amount of gene sequencing data. Although the cost of disk storage has steadily decreased over time, it has not kept pace with the dramatic changes in sequencing costs and volume. Current genetic analysis processes mostly use compression software designed for general data processing, failing to leverage the unique characteristics of genetic data. Furthermore, compression is performed as a separate step in the process, resulting in insufficient speed and compression ratio. This also limits the effectiveness of data compression in relieving the massive amount of genetic data.

[0004] How to efficiently compress sequencing data with existing computing and storage resources is a technical issue worth exploring. Summary of the Invention

[0005] In view of this, the present application provides a gene data compression method, apparatus and related equipment in the context of high-throughput sequencing to achieve efficient compression of gene data.

[0006] To achieve the above objectives, the present application provides, in a first aspect, a method for compressing genetic data in the context of high-throughput sequencing, comprising:

[0007] Obtaining genetic data to be compressed, and obtaining metadata, base data, and quality data of each short sequence from the genetic data;

[0008] Compressing the metadata of each short sequence using an incremental encoding technology or a run-length encoding technology to obtain a first compression result;

[0009] Comparing the base data of each short sequence using a preset reference genome, and compressing the base data of each short sequence according to the comparison result to obtain a second compression result;

[0010] The quality data of each short sequence is compressed by combining the run-length coding technology, the context statistical model, the ANS+FSE coding technology, the arithmetic coding technology and / or the Huffman coding technology to obtain a third compression result.

[0011] Preferably, the process of compressing the quality data of each short sequence by combining the run-length coding technology, the context statistical model, the ANS+FSE coding technology, the arithmetic coding technology and / or the Huffman coding technology to obtain the third compression result includes:

[0012] Determining the complexity of the quality data of each short sequence through a preset adaptive model, and determining a context statistical model of a first target order based on the complexity;

[0013] compressing the quality data using the context statistical model of the first target order to obtain a first intermediate compression result;

[0014] The first intermediate compression result is compressed using a run-length coding technology, an ANS+FSE coding technology, an arithmetic coding technology or a Huffman coding technology to obtain a third compression result.

[0015] Preferably, the process of comparing the base data of each short sequence using a preset reference genome and compressing the base data according to the comparison results includes:

[0016] Divide the base data in each short sequence into multiple subsequences;

[0017] Using a hash comparison method to compare each subsequence with a preset reference genome to obtain matching information for each subsequence, the matching information including mismatch values;

[0018] For a subsequence whose mismatch value is less than or equal to a preset threshold, compressing the subsequence based on the matching information of the subsequence;

[0019] For a subsequence whose mismatch value is greater than a preset threshold, the subsequence is compressed by combining a run-length coding technique, a context statistical model, an ANS+FSE coding technique, an arithmetic coding technique and / or a Huffman coding technique.

[0020] Preferably, the process of using a hash comparison method to compare each subsequence with a preset reference genome to obtain matching information of each subsequence includes:

[0021] Using the hash value of each subsequence as a query condition, a query is performed in the preset hash table to obtain the matching information of each subsequence;

[0022] The preset hash table records the hash value of each reference subsequence in the reference genome and the position information of each reference subsequence in the reference genome, and each reference subsequence is obtained by dividing the reference genome.

[0023] Preferably, the matching information includes position information of the subsequence corresponding to the reference genome; and the process of compressing the subsequence based on the matching information of the subsequence for a subsequence whose mismatch value is less than or equal to a preset threshold comprises:

[0024] sorting the subsequences based on the position information and the mismatch value to obtain sorted subsequences;

[0025] Based on the position information of the subsequences, incremental coding compression is performed on the sorted subsequences.

[0026] Preferably, the process of compressing the subsequence whose mismatch value is greater than a preset threshold by combining a run-length coding technique, a context statistical model, an ANS+FSE coding technique, an arithmetic coding technique and / or a Huffman coding technique comprises:

[0027] Preprocessing the subsequence using run-length coding to obtain a subsequence after redundancy elimination;

[0028] Determining a context statistical model of a second target order based on the complexity of the subsequence after redundancy elimination;

[0029] compressing the subsequence after redundancy elimination using the context statistical model of the second target order to obtain a second intermediate compression result;

[0030] The second intermediate compression result is compressed using a run-length coding technology, an ANS+FSE coding technology, an arithmetic coding technology or a Huffman coding technology.

[0031] Preferably, the process of obtaining the gene data to be compressed and obtaining metadata, base data and quality data of each short sequence from the gene data includes:

[0032] When the available space in the first reserved area of ​​the first memory reaches a preset capacity, obtaining a gene data block of a preset size, wherein the gene data block is a set of short sequences transmitted in real time from the sequencing platform, and the gene data to be compressed includes a plurality of the gene data blocks;

[0033] Inputting the gene data block into a first reserved area of ​​the first memory, wherein the first reserved area has a capacity of accommodating N gene data blocks, and the preset size is not greater than the preset capacity;

[0034] Obtaining metadata, base data, and quality data of each short sequence from the gene data block;

[0035] The process of compressing the metadata and quality data of each short sequence to obtain a first compression result, compressing the base data to obtain a second compression result, and compressing the quality data of each short sequence to obtain a third compression result includes:

[0036] compressing metadata of each short sequence in the gene data block to obtain a first compression result of the gene data block;

[0037] compressing the base data in each short sequence in the gene data block to obtain a second compression result of the gene data block;

[0038] compressing the quality data of each short sequence in the gene data block to obtain a third compression result of the gene data block;

[0039] After obtaining the first compression result of the gene data block, obtaining the second compression result of the gene data block, and obtaining the third compression result of the gene data block, the method further includes:

[0040] generating a gene data compression block of the gene data block based on the first compression result, the second compression result, and the third compression result of the gene data block;

[0041] retaining the gene data compressed block in a second reserved area of ​​the first memory and releasing the gene data block from the first memory, wherein the second reserved area has a capacity of accommodating M gene data compressed blocks;

[0042] When the number of gene data compressed blocks in the first memory reaches J, outputting the J gene data compressed blocks to the second memory and releasing the J gene data compressed blocks from the first memory;

[0043] Among them, N, M, and J are all preset natural numbers, and J is not greater than M.

[0044] Preferably, it also includes:

[0045] Obtaining a processing speed of a first operation, the first operation comprising: obtaining a gene data block of a preset size, and inputting the gene data block into a first reserved area of ​​the first memory;

[0046] Obtaining a processing speed for a second operation, the second operation comprising: compressing metadata and quality data of each short sequence in the gene data block to obtain a first compression result of the gene data block, compressing base data in each short sequence in the gene data block to obtain a second compression result of the gene data block, and compressing quality data of each short sequence in the gene data block to obtain a third compression result of the gene data block;

[0047] Computing resources allocated to the first operation and the second operation are determined based on a processing speed of the first operation and a processing speed of the second operation.

[0048] A second aspect of the present application provides a gene data compression device in the context of high-throughput sequencing, comprising:

[0049] A data acquisition unit, configured to acquire metadata, base data, and quality data of each short sequence from the genetic data to be compressed, wherein the genetic data is a set of short sequences transmitted in real time from the sequencing platform;

[0050] a universal compression unit, configured to compress the metadata of each short sequence using an incremental encoding technique or a run-length encoding technique to obtain a first compression result;

[0051] a bioinformatics compression unit, configured to compare the base data of each short sequence using a preset reference genome, and compress the base data according to the comparison result to obtain a second compression result;

[0052] The quality compression unit is used to compress the quality data of each short sequence by combining the run-length coding technology, the context statistical model, the ANS+FSE coding technology, the arithmetic coding technology and / or the Huffman coding technology to obtain a third compression result.

[0053] A third aspect of the present application provides a gene data compression device in the context of high-throughput sequencing, comprising: a memory and a processor;

[0054] The memory is used to store programs;

[0055] The processor is used to execute the program to implement each step of the above-mentioned gene data compression method in the context of high-throughput sequencing.

[0056] In a fourth aspect, the present application provides a storage medium having a computer program stored thereon. When the computer program is executed by a processor, the computer program implements the various steps of the gene data compression method in the context of high-throughput sequencing as described above.

[0057] As can be seen from the above technical solution, the present application first obtains the genetic data to be compressed, and then obtains the metadata, base data and quality data of each short sequence from the genetic data. It is understandable that the metadata is a general description, the base data is rich in biological characteristics, and the quality data has a relatively high internal correlation. Due to the data characteristics of the metadata, the metadata of each short sequence is compressed using incremental encoding technology or run-length encoding technology to obtain a first compression result. Due to the biological characteristics of the base data, the base data of each short sequence is compared using a preset reference genome, and the base data of each short sequence is compressed based on the comparison results to obtain a second compression result. Due to the data characteristics of the quality data, the quality data of each short sequence is compressed in combination with run-length encoding technology, context statistical model, ANS+FSE encoding technology, arithmetic coding technology and / or Huffman coding technology to obtain a third compression result. The present application can improve the compression rate by compressing based on different parts of the data using compression methods that are compatible with them, thereby saving storage space for massive genetic data. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without any creative work.

[0059] Figure 1 Schematic diagram of a gene data compression method in the context of high-throughput sequencing disclosed in an embodiment of the present application;

[0060] Figure 2 Schematic diagram of a gene data compression method in the context of high-throughput sequencing disclosed in an embodiment of the present application;

[0061] Figure 3 A schematic diagram illustrating the stream parallel processing disclosed in the embodiments of the present application is provided;

[0062] Figure 4 A schematic diagram of a simulation task result comparison device disclosed in an embodiment of the present application;

[0063] Figure 5 A schematic diagram of a simulation task result comparison device disclosed in an embodiment of the present application. DETAILED DESCRIPTION

[0064] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application. The following are the relevant glossaries:

[0065] High-throughput sequencing, also known as "next-generation" sequencing technology, is characterized by its ability to sequence hundreds of thousands to millions of DNA molecules in parallel and generally has shorter read lengths. Sequencing involves analyzing the base sequence of a specific DNA fragment, specifically the arrangement of adenine (A), thymine (T), cytosine (C), and guanine (G). The emergence of rapid DNA sequencing methods has greatly advanced research and development in biology and medicine.

[0066] Base Calling: Identify base types (DNA sequences) from raw images (row images) using computer vision, write the results to a cal file, and finally generate a sequencing report and FastQ data.

[0067] Sequence alignment: The process of aligning two or more sequences and noting their similarities. Sequences can be separated by gaps (usually indicated by a dash "-"). Corresponding identical or similar symbols (A, T (or U), C, and G in nucleic acids, and single-letter amino acid residues in proteins) are arranged in the same column. This is commonly used to study sequences that have evolved from a common ancestor, particularly biological sequences such as protein or DNA sequences. In an alignment, mismatches correspond to mutations, while gaps correspond to insertions or deletions.

[0068] Reference genome: refers to the genome sequence of the species, which is a complete genome sequence that has been assembled and is often used as a standard reference for the species, such as the human genome reference sequence (fasta format).

[0069] K-mer: A subsequence of length K in a short sequence.

[0070] Networking technology: Networking technology refers to the technology used to build networks, and is categorized into Ethernet and ATM LAN technologies. Ethernet networking is extremely flexible and simple, utilizing a variety of physical media and topologies. It is the most widely used network type both domestically and internationally, and has become a mainstream networking technology. Ethernet transmission rates are categorized into 10Mb / s, 100Mb / s, and 1000Mb / s.

[0071] The following describes the gene data compression method in the context of high-throughput sequencing provided by the embodiments of this application. Figure 1 The gene data compression method in the context of high-throughput sequencing provided in the embodiments of the present application may include the following steps:

[0072] Step S101 : Obtain gene data to be compressed, and obtain metadata, base data, and quality data of each short sequence from the gene data.

[0073] Genetic data usually consists of tens of thousands of short sequences, each of which can contain the following four lines:

[0074] The first line is metadata, starting with @, followed by a sequence identifier and an optional description.

[0075] The second line is the base data, which is essentially a letter sequence consisting of five letters: A, C, G, T and N. This is the DNA sequence we really care about. N represents the bases that cannot be recognized during sequencing.

[0076] The third line begins with a + character and is sometimes followed by the same sequence identifier as line 1.

[0077] The fourth line is the quality data, which represents the quality score and describes the reliability of each base. It is a string of ASCII characters.

[0078] It should be noted that the lengths of the second and fourth rows must be equal, and each element in them must correspond one-to-one. As can be seen from the composition of the short sequence, the metadata in the first row is a general description and can be compressed using common compression methods; the base data in the second row is rich in biological characteristics and can be compressed using reference genome alignment. The quality data in the fourth row has relatively high internal correlation and can be compressed using methods such as run-length encoding.

[0079] Step S102 : compressing metadata of each short sequence using an incremental coding technique or a run-length coding technique context statistical model to obtain a first compression result.

[0080] Metadata in genomic data from different sequencing platforms may have different formats. Typically, metadata begins with the symbol "@" followed by a sequence identifier and other optional information, such as the instrument name, flow cell channel, block number within the flow cell channel, "x" coordinate of the cluster within the block, "y" coordinate of the cluster within the block, sample number in mixed multi-samples, paired-end identifier, and sequence length.

[0081] For metadata in genetic data, we can first use delimiters (punctuation marks) to parse them into different data segments. Delimiters generally include dots, spaces, underscores, hyphens, slashes, equal signs, and colons. Then, we use recognition algorithms for different data segments to classify and process them. Fixed information such as instrument name, sequence length, and double-end identifiers is not encoded or compressed. Other data segments use different compression encoding methods, including incremental encoding, run-length encoding, and contextual statistical models.

[0082] For data segments that only contain consecutive integers, incremental encoding is used to record the difference between the previous and next data segments; for data segments that contain letters and numbers, improved run-length encoding is used to record the repeated characters in the previous and next data segments, and incremental encoding is used to record the difference between the previous and next data segments.

[0083] Step S103 : aligning the base data of each short sequence using a preset reference genome, and compressing the base data of each short sequence according to the alignment result to obtain a second compression result.

[0084] It should be noted that the reference genome here refers to the genome sequence of the sequenced species, that is, the complete genome sequence that has been assembled, and the complete genome sequence is often used as the standard reference for the species.

[0085] For example, when comparing base data in genetic data, the present application can compare the bases in the base data in the genetic data with the reference genome, find the position of each base data on the reference gene, and thus obtain the comparison result.

[0086] For the matched base data, it can be represented by its position in the reference genome, that is, it can be recorded by the position without retaining the original base data. The recorded matching information is sorted and then incrementally encoded and compressed to achieve the effect of data compression; for the unmatched base data, a general compression method can be used for compression.

[0087] Step S104 , compressing the quality data of each short sequence by combining the run-length coding technology, the context statistical model, the ANS+FSE coding technology, the arithmetic coding technology and / or the Huffman coding technology to obtain a third compression result.

[0088] Specifically, it may include:

[0089] S1, determining the complexity of the quality data in each short sequence through a preset adaptive model, and determining a context statistical model of a first target order based on the complexity.

[0090] The complexity of the quality score may be 4 quality values, 8 quality values, or 40 quality values. Based on different quality values, the first target order may be 1st order, 2nd order, 3rd order, 4th order, 5th order, or 6th order. After the complexity of the quality data is statistically analyzed using a preset adaptive model, the order of the context statistical model suitable for the quality data is determined, thereby achieving a better compression effect.

[0091] S2: compress the quality data using a context statistical model of a first target order to obtain a first intermediate compression result.

[0092] The first intermediate compression result is only an intermediate output and will not be output as a file to a storage device such as a disk.

[0093] S3, compressing the first intermediate compression result using a run-length coding technology, an ANS+FSE coding technology, an arithmetic coding technology, or a Huffman coding technology to obtain a third compression result.

[0094] Among them, ANS (Asymmetric Numeral System) + FSE (Finite State Entropy) is a lossless compression technology. By compressing the first intermediate compression result using any of these lossless compression technologies, the compression rate can be further improved.

[0095] In addition, before determining the complexity of the quality data in each short sequence through the preset adaptive model in the above S1, run-length coding can be used for preprocessing to eliminate redundancy.

[0096] The present application first obtains the genetic data to be compressed, and obtains the metadata, base data and quality data of each short sequence from the genetic data. It is understandable that the metadata is a general description, the base data is rich in biological characteristics, and the quality data has a relatively high correlation. Due to the data characteristics of the metadata, the metadata of each short sequence is compressed using incremental coding technology or run-length coding technology to obtain a first compression result. Due to the biological characteristics of the base data, the base data of each short sequence is compared using a preset reference genome, and the base data of each short sequence is compressed based on the comparison results to obtain a second compression result. Due to the data characteristics of the quality data, the quality data of each short sequence is compressed in combination with run-length coding technology, context statistical model, ANS+FSE coding technology, arithmetic coding technology and / or Huffman coding technology to obtain a third compression result. The present application can improve the compression rate by compressing based on different parts of the data using compression methods that are compatible with them, thereby saving storage space for massive genetic data.

[0097] In some embodiments of the present application, the process of comparing the base data of each short sequence using a preset reference genome and compressing the base data according to the comparison results in step S103 may include:

[0098] S1, divide the base data in each short sequence into multiple subsequences.

[0099] For example, the base data may be divided into multiple subsequences with a step length of 20.

[0100] S2, using the hash comparison method to compare each subsequence with the preset reference genome to obtain the matching information of each subsequence.

[0101] The matching information includes a mismatch value (Mismatch).

[0102] S3. For subsequences whose mismatch values ​​are less than or equal to a preset threshold, compress these subsequences based on the matching information of the subsequences.

[0103] For example, the position on the reference genome in the sequence alignment, mismatch and other information are recorded without retaining the original subsequence. The recorded matching information is sorted and then incrementally encoded and compressed.

[0104] S4, for a subsequence whose mismatch value is greater than a preset threshold, compress the subsequence by combining a run-length coding technique, a context statistical model, an ANS+FSE coding technique, an arithmetic coding technique and / or a Huffman coding technique.

[0105] For example, run-length coding is used for preprocessing to eliminate redundancy, and then an adaptive statistical model is used to understand the complexity of base data and quality data in short sequences. Dynamic context mixing algorithms of different orders are then used for data modeling according to different complexities, and finally a combination of arithmetic coding and Huffman coding is used for compression.

[0106] In some embodiments of the present application, the process of using a hash comparison method in S2 to compare each subsequence with a preset reference genome to obtain matching information for each subsequence may include:

[0107] The hash value of each subsequence is used as a query condition to query the preset hash table to obtain the matching information of each subsequence.

[0108] The preset hash table records the hash value of each reference subsequence in the reference genome and the position information of each reference subsequence in the reference genome, and each reference subsequence is obtained by dividing the reference genome.

[0109] In some embodiments of the present application, the process of obtaining reference subsequences from the reference genome may include:

[0110] A plurality of overlapping reference subsequences of length K are divided from the reference genome at a preset step size, where K is a preset length value. For example, K can be 20.

[0111] Specifically, the sequences in the reference genome R are first segmented into overlapping k-mer-long subsequences. Then, a hash table HR is created, and a hash function is used to map the k-mer-long subsequences to their source locations in the reference genome. Next, the short sequences in the gene data blocks are also segmented into k-mer-long subsequences. Finally, data matching is performed by querying the hash table composed of k-mer-long subsequences of the genome, and the mismatch value Mismatch is calculated.

[0112] If the Mismatch in the short sequence is less than or equal to the predefined threshold P, it means that all subsequences of the short sequence are adjacent to each other in the correct order and completely match the subsequences of the reference genome. The short sequence can be located on the reference genome and the matching information is recorded.

[0113] The matching information may include the matching position MATCHpos, the matching length MATCHlen, the matching type MATCHtype, the mismatch position MISpos, and the mismatch base MISalt.

[0114] If the mismatch in the short sequence is less than or equal to a predefined threshold P, the match information includes the match position, match length, and the match type "P (Perfect Match)". Otherwise, the match information includes the match position, match length, and match type (I (Insertion), D (Deletion)), the mismatch position, and the mismatch base. For more details, refer to the CIGAR information in the sequence alignment results. The matching information is then sorted by matching position. This sorted matching position facilitates further compression of the matching information. Finally, an incremental encoding scheme is used to record the differences between the two matching information.

[0115] It should be noted that when a short sequence contains SNP / INDEL, the mismatch must be located in one of the subsequences of the short sequence, while the other subsequences can be completely matched with the reference genome subsequence.

[0116] In this way, the fully matched seed sequence site can be used as the anchor point, and the area adjacent to the anchor point can be accurately aligned with the remaining incompletely matched subsequences (possible mismatched subsequences) through the classic alignment algorithm to find the final matching site of the sequence and record the matching information.

[0117] In some embodiments of the present application, the process of compressing the subsequences in S3 based on the matching information of the subsequences for subsequences whose mismatch values ​​are less than or equal to a preset threshold may include:

[0118] S31 , sorting the subsequences based on the position information and the mismatch value to obtain sorted subsequences.

[0119] S32: Perform incremental coding compression on the sorted subsequences based on the position information of these subsequences.

[0120] In some embodiments of the present application, the process of compressing a subsequence whose mismatch value is greater than a preset threshold in S4 by combining a run-length coding technique, a context statistical model, an ANS+FSE coding technique, an arithmetic coding technique, and / or a Huffman coding technique may include:

[0121] S41, pre-processing the subsequence using run-length coding to obtain a subsequence after redundancy is eliminated.

[0122] S42: Determine a context statistical model of a second target order based on the complexity of the subsequence after redundancy elimination.

[0123] S43 , compressing the subsequence after eliminating redundancy using a context statistical model of a second target order to obtain a second intermediate compression result.

[0124] The second target order can be order 0, order 1 or order 2. After the complexity of the subsequence is statistically analyzed using a preset adaptive model, the order of the context statistical model suitable for the subsequence is determined, thereby achieving a better compression effect.

[0125] Specifically, the character types of the subsequence (base sequence) include A / T / C / G / N. If the GC content is greater than 80% or less than 20%, the 0th-order context statistical model is selected for compression through prediction; if the GC content is between 60% and 80%, or the GC content is between 20% and 40%, the 1st-order context statistical model is selected for compression through prediction; if the GC content is between 40% and 60%, the 2nd-order context statistical model is selected for compression through prediction.

[0126] S44, compressing the second intermediate compression result using a run-length coding technology, an ANS+FSE coding technology, an arithmetic coding technology, or a Huffman coding technology.

[0127] In some embodiments of this application, please refer to Figure 2 The process of obtaining the gene data to be compressed in step S101 and obtaining metadata, base data, and quality data of each short sequence from the gene data may include:

[0128] S1, when the available space in the first reserved area of ​​the first memory reaches a preset capacity, obtaining a gene data block of a preset size.

[0129] The gene data block is a set of short sequences transmitted in real time from a sequencing platform, and the gene data to be compressed includes multiple gene data blocks.

[0130] S2: Input the gene data block into a first reserved area of ​​a first memory.

[0131] Among them, the first reserved area has the ability to accommodate N gene data blocks, and the preset size is not greater than the preset capacity. N is a preset natural number. For example, the first memory can be a memory, especially a DDR memory. Since the gene data is large and the memory of the computing device is limited, it can usually be expanded to DDR memory. DDR memory transmits data once on the rising edge and falling edge of the clock signal, which makes the data transmission speed of DDR memory twice that of traditional SDRAM. Moreover, since only the falling edge signal is used, it does not cause an increase in energy consumption. As for the addressing and control signals, they are the same as traditional SDRAM and are only transmitted on the rising edge of the clock.

[0132] The short sequence set includes multiple short sequences (reads), wherein the size of the gene data block can be defined by the number of short sequences. For example, the preset size of the gene data block can be set to 40,000 to 500,000 short sequences based on the memory capacity of the computing device.

[0133] S3, obtaining metadata, base data and quality data of each short sequence from the gene data block.

[0134] Based on this, the process of compressing the metadata of each short sequence in step S102 to obtain a first compression result, compressing the base data of each short sequence in step S103 to obtain a second compression result, and compressing the quality data of each short sequence in step S104 to obtain a third compression result may include:

[0135] S4, compressing the metadata and quality data of each short sequence in the gene data block to obtain a first compression result of the gene data block.

[0136] S5, compressing the base data in each short sequence in the gene data block to obtain a second compression result of the gene data block.

[0137] S6, compressing the quality data of each short sequence in the gene data block to obtain a third compression result of the gene data block.

[0138] Based on this, after obtaining the first compression result, the second compression result, and the third compression result of the gene data block in the above S4, S5, and S6, the following steps may also be performed:

[0139] S7: Generate a gene data compression block of the gene data block based on the first compression result, the second compression result, and the third compression result of the gene data block.

[0140] S8: retain the compressed gene data block in a second reserved area of ​​the first memory, and release the gene data block from the first memory.

[0141] The second reserved area has the capacity to accommodate M genetic data compressed blocks, where M is a preset natural number. It should be noted that, typically, the space size of a genetic data compressed block is smaller than the space size of the corresponding genetic data block. However, considering that the speed of pulling genetic data blocks may be inconsistent with the speed calculated based on the data compressed blocks, the size relationship between the capacity N of the first reserved area and the second reserved area M must be further set based on the aforementioned two speeds to avoid wasting space.

[0142] S9 , when the number of gene data compression blocks in the first memory reaches J, output the J gene data compression blocks to the second memory, and release the J gene data compression blocks from the first memory.

[0143] Wherein, J is a preset natural number, and J is not greater than M. Exemplarily, the second memory may be a hard disk in a computing device, that is, each J data compression blocks are output as a file slice, and finally a complete sequencing file is restored from each file slice.

[0144] Understandably, see Figure 3, the above S1 to S7 are executed in a loop, where S1 and S2 are equivalent to the gene data block unloading process, which realizes the continuous delivery of gene data blocks from the sequencing platform to the first reserved area of ​​the first memory until the first reserved area can no longer accommodate more gene data blocks; S3 to S7 are equivalent to the gene data block compression process. After S3 to S6 compress the various types of data in the gene data block and generate gene data compression blocks, S7 retains the obtained gene data compression blocks in the second reserved area of ​​the first memory, and the space in the first reserved area released by S7 is used to receive new gene data blocks and gene data compression blocks. In this way, for each gene data block, S1 to S7 can perform sequencing data compression in a streaming manner; at the same time, S1 to S7 of each gene data block can be executed in high parallelism. By processing each gene data block output by the sequencing platform in a streaming and parallel manner, there is no need to wait for all gene data to be collected before processing. While saving waiting time, it improves the utilization efficiency of computing resources and improves the overall speed from sequencing platform data unloading to bioinformatics analysis, which helps to improve the delivery efficiency of bioinformatics sequencing.

[0145] In some embodiments of the present application, the gene data compression method in the context of high-throughput sequencing may further include:

[0146] S10: Obtain a processing speed of the first operation.

[0147] The second operation corresponds to the above-mentioned S1 and S2, and includes: obtaining a gene data block of a preset size, and inputting the gene data block into the first reserved area of ​​the first memory.

[0148] S11, obtaining a processing speed of the second operation.

[0149] Among them, the second operation corresponds to the above-mentioned S4, S5 and S6, including: compressing the metadata of each short sequence in the gene data block to obtain a first compression result of the gene data block, compressing the base data in each short sequence in the gene data block to obtain a second compression result of the gene data block, and compressing the quality data of each short sequence in the gene data block to obtain a third compression result of the gene data block.

[0150] S12: Determine computing resources allocated to the first operation and the second operation based on the processing speed of the first operation and the processing speed of the second operation.

[0151] Specifically, if the processing speed of the first operation is lower than that of the second operation, there are fewer gene data blocks in the first reserved area and the computing resources are relatively sufficient. One gene data block can simultaneously start the three processes of base data comparison compression, metadata compression and quality data compression, and allocate appropriate computing resources to the corresponding processes.

[0152] When the metadata or quality data compression of each gene data block is completed, the result will be stored in the second reserved area and the computing resources will be released; when the base data comparison task is completed, the comparison result will be stored in the second reserved area, and at the same time, subsequent compression processing will be performed based on the comparison result of the base data. After completion, it will be stored in the second reserved area and the computing resources will be released.

[0153] If the processing speed of the first operation is greater than that of the second operation, there will be multiple retained gene data blocks in the first reserved area. At this time, fewer computing threads can be allocated to a gene data block, and the data comparison of the gene data block will be prioritized. During the comparison process, if there are resources that can be used for the compression of metadata and quality data, the corresponding resources will be allocated. Otherwise, the data compression and storage will be performed again after the gene data block comparison is completed.

[0154] The adaptive dynamic resource allocation algorithm described above dynamically provides computing resources based on the number of genetic data blocks in the first reserved area, ensuring first-in, first-out (FIFO) as much as possible while also enabling task management and scheduling that automatically adjusts computing resources. Elastic scaling ensures sufficient resources should resource demand increase within the next timeframe, preventing resource shortages and supply delays. When resource demand surges, the elastically released resources effectively act as a buffer.

[0155] The following describes a gene data compression device in the context of high-throughput sequencing provided in an embodiment of the present application. The gene data compression device in the context of high-throughput sequencing described below and the gene data compression method in the context of high-throughput sequencing described above can refer to each other.

[0156] See Figure 4 The gene data compression device in the context of high-throughput sequencing provided in the embodiments of the present application may include:

[0157] A data acquisition unit 21 is used to acquire metadata, base data, and quality data of each short sequence from the genetic data to be compressed, where the genetic data is a set of short sequences transmitted in real time from the sequencing platform;

[0158] The general compression unit 22 is configured to compress the metadata of each short sequence using an incremental coding technique or a run-length coding technique and a context statistical model to obtain a first compression result;

[0159] The bioinformatics compression unit 23 is configured to compare the base data of each short sequence using a preset reference genome, and compress the base data according to the comparison result to obtain a second compression result.

[0160] The quality compression unit 24 is used to compress the quality data of each short sequence by combining the run-length coding technology, the context statistical model, the ANS+FSE coding technology, the arithmetic coding technology and / or the Huffman coding technology to obtain a third compression result.

[0161] In some embodiments of the present application, the process of the bioinformatics compression unit 23 comparing the base data of each short sequence using a preset reference genome and compressing the base data according to the comparison results may include:

[0162] Divide the base data in each short sequence into multiple subsequences;

[0163] Using a hash comparison method to compare each subsequence with a preset reference genome to obtain matching information for each subsequence, the matching information including mismatch values;

[0164] For a subsequence whose mismatch value is less than or equal to a preset threshold, compressing the subsequence based on the matching information of the subsequence;

[0165] For a subsequence whose mismatch value is greater than a preset threshold, the subsequence is compressed by combining a run-length coding technique, a context statistical model, an ANS+FSE coding technique, an arithmetic coding technique and / or a Huffman coding technique.

[0166] In some embodiments of the present application, the process in which the bioinformatics compression unit 23 compares each subsequence with a preset reference genome using a hash comparison method to obtain matching information for each subsequence may include:

[0167] Using the hash value of each subsequence as a query condition, a query is performed in the preset hash table to obtain the matching information of each subsequence;

[0168] The preset hash table records the hash value of each reference subsequence in the reference genome and the position information of each reference subsequence in the reference genome, and each reference subsequence is obtained by dividing the reference genome.

[0169] In some embodiments of the present application, the matching information includes position information of the subsequence corresponding to the reference genome; the process of the bioinformatics compression unit 23 compressing the subsequence based on the matching information of the subsequence for a subsequence whose mismatch value is less than or equal to a preset threshold may include:

[0170] sorting the subsequences based on the position information and the mismatch value to obtain sorted subsequences;

[0171] Based on the position information of the subsequences, incremental coding compression is performed on the sorted subsequences.

[0172] In some embodiments of the present application, the process of compressing the subsequence whose mismatch value is greater than a preset threshold by the bioinformatics compression unit 23 in combination with the run-length coding technology, the context statistical model, the ANS+FSE coding technology, the arithmetic coding technology and / or the Huffman coding technology may include:

[0173] Preprocessing the subsequence using run-length coding to obtain a subsequence after redundancy elimination;

[0174] Determining a context statistical model of a second target order based on the complexity of the subsequence after redundancy elimination;

[0175] compressing the subsequence after redundancy elimination using the context statistical model of the second target order to obtain a second intermediate compression result;

[0176] The second intermediate compression result is compressed using a run-length coding technology, an ANS+FSE coding technology, an arithmetic coding technology or a Huffman coding technology.

[0177] In some embodiments of the present application, the process of compressing the quality data of each short sequence by the quality compression unit 24 by combining the run-length coding technology, the context statistical model, the ANS+FSE coding technology, the arithmetic coding technology and / or the Huffman coding technology may include:

[0178] Determining the complexity of the quality data of each short sequence through a preset adaptive model, and determining a context statistical model of a first target order based on the complexity;

[0179] compressing the quality data using the context statistical model of the first target order to obtain a first intermediate compression result;

[0180] The first intermediate compression result is compressed using a run-length coding technology, an ANS+FSE coding technology, an arithmetic coding technology or a Huffman coding technology to obtain a third compression result.

[0181] In some embodiments of the present application, the process in which the data acquisition unit 21 acquires the gene data to be compressed and obtains metadata, base data, and quality data of each short sequence from the gene data may include:

[0182] When the available space in the first reserved area of ​​the first memory reaches a preset capacity, obtaining a gene data block of a preset size, wherein the gene data block is a set of short sequences transmitted in real time from the sequencing platform, and the gene data to be compressed includes a plurality of the gene data blocks;

[0183] Inputting the gene data block into a first reserved area of ​​the first memory, wherein the first reserved area has a capacity of accommodating N gene data blocks, and the preset size is not greater than the preset capacity;

[0184] Obtaining metadata, base data, and quality data of each short sequence from the gene data block;

[0185] The process of the general compression unit 22 compressing the metadata of each short sequence to obtain a first compression result, compressing the base data of each short sequence to obtain a second compression result, and compressing the quality data of each short sequence to obtain a third compression result may include:

[0186] compressing metadata of each short sequence in the gene data block to obtain a first compression result of the gene data block;

[0187] compressing base data of each short sequence in the gene data block to obtain a second compression result of the gene data block;

[0188] The quality data of each short sequence in the gene data block is compressed to obtain a third compression result of the gene data block.

[0189] Based on this, the gene data compression device in the context of high-throughput sequencing may further include a compression block processing unit for:

[0190] After obtaining the first compression result of the gene data block, obtaining the second compression result of the gene data block, and obtaining the third compression result of the gene data block,

[0191] generating a gene data compression block of the gene data block based on the first compression result, the second compression result, and the third compression result of the gene data block;

[0192] retaining the gene data compressed block in a second reserved area of ​​the first memory and releasing the gene data block from the first memory, wherein the second reserved area has a capacity of accommodating M gene data compressed blocks;

[0193] When the number of gene data compressed blocks in the first memory reaches J, outputting the J gene data compressed blocks to the second memory, and releasing the J gene data compressed blocks from the first memory;

[0194] Among them, N, M, and J are all preset natural numbers, and J is not greater than M.

[0195] In some embodiments of the present application, the gene data compression device in the context of high-throughput sequencing may further include a resource allocation unit for:

[0196] Obtaining a processing speed of a first operation, the first operation comprising: obtaining a gene data block of a preset size, and inputting the gene data block into a first reserved area of ​​the first memory;

[0197] Obtaining a processing speed for a second operation, the second operation comprising: compressing metadata of each short sequence in the gene data block to obtain a first compression result of the gene data block, compressing base data in each short sequence in the gene data block to obtain a second compression result of the gene data block, and compressing quality data of each short sequence in the gene data block to obtain a third compression result of the gene data block;

[0198] Computing resources allocated to the first operation and the second operation are determined based on a processing speed of the first operation and a processing speed of the second operation.

[0199] The gene data compression device in the context of high-throughput sequencing provided in the embodiment of the present application can be applied to gene data compression equipment in the context of high-throughput sequencing, such as computers. Optionally, Figure 5 The hardware structure diagram of the gene data compression device in the context of high-throughput sequencing is shown. Figure 5 The hardware structure of the gene data compression device in the context of high-throughput sequencing may include: at least one processor 31, at least one communication interface 32, at least one memory 33 and at least one communication bus 34.

[0200] In the embodiment of the present application, the number of the processor 31, the communication interface 32, the memory 33, and the communication bus 34 is at least one, and the processor 31, the communication interface 32, and the memory 33 communicate with each other through the communication bus 34;

[0201] The processor 31 may be a central processing unit (CPU), or an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application;

[0202] The memory 32 may include a high-speed RAM memory, and may also include a non-volatile memory, such as at least one disk memory;

[0203] The memory 33 stores a program, and the processor 31 can call the program stored in the memory 33, wherein the program is used to:

[0204] Obtaining genetic data to be compressed, and obtaining metadata, base data, and quality data of each short sequence from the genetic data;

[0205] Compressing the metadata of each short sequence using an incremental coding technique or a run-length coding technique context statistical model to obtain a first compression result;

[0206] Comparing the base data of each short sequence using a preset reference genome, and compressing the base data of each short sequence according to the comparison result to obtain a second compression result;

[0207] Determining the complexity of the quality data of each short sequence through a preset adaptive model, and determining a context statistical model of a first target order based on the complexity;

[0208] compressing the quality data using the context statistical model of the first target order to obtain a first intermediate compression result;

[0209] The first intermediate compression result is compressed using a run-length coding technology, an ANS+FSE coding technology, an arithmetic coding technology or a Huffman coding technology to obtain a third compression result.

[0210] Optionally, the detailed functions and extended functions of the program may refer to the above description.

[0211] An embodiment of the present application further provides a storage medium, which may store a program suitable for execution by a processor, wherein the program is used to:

[0212] Obtaining genetic data to be compressed, and obtaining metadata, base data, and quality data of each short sequence from the genetic data;

[0213] Compressing the metadata of each short sequence using an incremental coding technique or a run-length coding technique context statistical model to obtain a first compression result;

[0214] Comparing the base data of each short sequence using a preset reference genome, and compressing the base data of each short sequence according to the comparison result to obtain a second compression result;

[0215] Determining the complexity of the quality data of each short sequence through a preset adaptive model, and determining a context statistical model of a first target order based on the complexity;

[0216] compressing the quality data using the context statistical model of the first target order to obtain a first intermediate compression result;

[0217] The first intermediate compression result is compressed using a run-length coding technology, an ANS+FSE coding technology, an arithmetic coding technology or a Huffman coding technology to obtain a third compression result.

[0218] Optionally, the detailed functions and extended functions of the program may refer to the above description.

[0219] In summary:

[0220] The present application first obtains the genetic data to be compressed, and obtains the metadata, base data and quality data of each short sequence from the genetic data. It is understandable that the metadata is a general description, the base data is rich in biological characteristics, and the quality data has a relatively high correlation. Due to the data characteristics of the metadata, the metadata of each short sequence is compressed using incremental coding technology or run-length coding technology to obtain a first compression result. Due to the biological characteristics of the base data, the base data of each short sequence is compared using a preset reference genome, and the base data of each short sequence is compressed based on the comparison results to obtain a second compression result. Due to the data characteristics of the quality data, the quality data of each short sequence is compressed in combination with run-length coding technology, context statistical model, ANS+FSE coding technology, arithmetic coding technology and / or Huffman coding technology to obtain a third compression result. The present application can improve the compression rate by compressing based on different parts of the data using compression methods that are compatible with them, thereby saving storage space for massive genetic data.

[0221] Finally, it should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article, or device comprising the element.

[0222] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The various embodiments can be combined as needed, and the same or similar parts can be referenced to each other.

[0223] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present application. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application is not limited to the embodiments shown herein, but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A gene data compression method in the context of high-throughput sequencing, characterized in that: include: Obtaining genetic data to be compressed, and obtaining metadata, base data, and quality data of each short sequence from the genetic data; Compressing the metadata of each short sequence using an incremental encoding technology or a run-length encoding technology to obtain a first compression result; Comparing the base data of each short sequence using a preset reference genome, and compressing the base data of each short sequence according to the comparison result to obtain a second compression result; Combining a run-length coding technique, a context statistical model, an ANS+FSE coding technique, an arithmetic coding technique, and / or a Huffman coding technique to compress the quality data of each short sequence to obtain a third compression result; The process of comparing the base data of each short sequence using a preset reference genome and compressing the base data according to the comparison results includes: Divide the base data in each short sequence into multiple subsequences; Using a hash comparison method to compare each subsequence with a preset reference genome to obtain matching information for each subsequence, the matching information including mismatch values; For a subsequence whose mismatch value is less than or equal to a preset threshold, compressing the subsequence based on the matching information of the subsequence; For subsequences whose mismatch value is greater than a preset threshold, compressing the subsequence by combining run-length coding technology, context statistical model, ANS+FSE coding technology, arithmetic coding technology and / or Huffman coding technology; The matching information includes position information of the subsequence corresponding to the reference genome; and the process of compressing the subsequence based on the matching information of the subsequence for a subsequence whose mismatch value is less than or equal to a preset threshold comprises: sorting the subsequences based on the position information and the mismatch value to obtain sorted subsequences; Based on the position information of the subsequences, incremental coding compression is performed on the sorted subsequences.

2. The method according to claim 1, characterized in that The process of compressing the quality data of each short sequence by combining the run-length coding technology, the context statistical model, the ANS+FSE coding technology, the arithmetic coding technology and / or the Huffman coding technology to obtain the third compression result includes: Determining the complexity of the quality data of each short sequence through a preset adaptive model, and determining a context statistical model of a first target order based on the complexity; compressing the quality data using the context statistical model of the first target order to obtain a first intermediate compression result; The first intermediate compression result is compressed using a run-length coding technology, an ANS+FSE coding technology, an arithmetic coding technology or a Huffman coding technology to obtain a third compression result.

3. The method according to claim 1, characterized in that The process of using the hash comparison method to compare each subsequence with a preset reference genome to obtain matching information of each subsequence includes: Using the hash value of each subsequence as a query condition, a query is performed in the preset hash table to obtain the matching information of each subsequence; The preset hash table records the hash value of each reference subsequence in the reference genome and the position information of each reference subsequence in the reference genome, and each reference subsequence is obtained by dividing the reference genome.

4. The method according to claim 1, wherein The process of compressing the subsequence whose mismatch value is greater than a preset threshold by combining the run-length coding technology, the context statistical model, the ANS+FSE coding technology, the arithmetic coding technology and / or the Huffman coding technology includes: Preprocessing the subsequence using run-length coding to obtain a subsequence after redundancy elimination; Determining a context statistical model of a second target order based on the complexity of the subsequence after redundancy elimination; compressing the subsequence after redundancy elimination using the context statistical model of the second target order to obtain a second intermediate compression result; The second intermediate compression result is compressed using a run-length coding technology, an ANS+FSE coding technology, an arithmetic coding technology or a Huffman coding technology.

5. The method according to any one of claims 1 to 4, characterized in that The process of obtaining the gene data to be compressed and obtaining metadata, base data, and quality data of each short sequence from the gene data includes: When the available space in the first reserved area of ​​the first memory reaches a preset capacity, obtaining a gene data block of a preset size, wherein the gene data block is a set of short sequences transmitted in real time from the sequencing platform, and the gene data to be compressed includes a plurality of the gene data blocks; Inputting the gene data block into a first reserved area of ​​the first memory, wherein the first reserved area has a capacity of accommodating N gene data blocks, and the preset size is not greater than the preset capacity; Obtaining metadata, base data, and quality data of each short sequence from the gene data block; The process of compressing the metadata of each short sequence to obtain a first compression result, compressing the base data of each short sequence to obtain a second compression result, and compressing the quality data of each short sequence to obtain a third compression result includes: compressing metadata of each short sequence in the gene data block to obtain a first compression result of the gene data block; compressing base data of each short sequence in the gene data block to obtain a second compression result of the gene data block; compressing the quality data of each short sequence in the gene data block to obtain a third compression result of the gene data block; After obtaining the first compression result of the gene data block, obtaining the second compression result of the gene data block, and obtaining the third compression result of the gene data block, the method further includes: generating a gene data compression block of the gene data block based on the first compression result, the second compression result, and the third compression result of the gene data block; retaining the gene data compressed block in a second reserved area of ​​the first memory and releasing the gene data block from the first memory, wherein the second reserved area has a capacity of accommodating M gene data compressed blocks; When the number of gene data compressed blocks in the first memory reaches J, outputting the J gene data compressed blocks to the second memory, and releasing the J gene data compressed blocks from the first memory; Among them, N, M, and J are all preset natural numbers, and J is not greater than M.

6. The method according to claim 5, characterized in that Also includes: Obtaining a processing speed of a first operation, the first operation comprising: obtaining a gene data block of a preset size, and inputting the gene data block into a first reserved area of ​​the first memory; Obtaining a processing speed for a second operation, the second operation comprising: compressing metadata of each short sequence in the gene data block to obtain a first compression result of the gene data block, compressing base data in each short sequence in the gene data block to obtain a second compression result of the gene data block, and compressing quality data of each short sequence in the gene data block to obtain a third compression result of the gene data block; Computing resources allocated to the first operation and the second operation are determined based on a processing speed of the first operation and a processing speed of the second operation.

7. A gene data compression device in the context of high-throughput sequencing, characterized in that: include: A data acquisition unit, configured to acquire metadata, base data, and quality data of each short sequence from the genetic data to be compressed, wherein the genetic data is a set of short sequences transmitted in real time from the sequencing platform; a universal compression unit, configured to compress the metadata of each short sequence using an incremental encoding technique or a run-length encoding technique to obtain a first compression result; a bioinformatics compression unit, configured to compare the base data of each short sequence using a preset reference genome, and compress the base data according to the comparison result to obtain a second compression result; a quality compression unit, configured to compress the quality data of each short sequence by combining a run-length coding technique, a context statistical model, an ANS+FSE coding technique, an arithmetic coding technique, and / or a Huffman coding technique to obtain a third compression result; The bioinformatics compression unit compares the base data of each short sequence with a preset reference genome and compresses the base data according to the comparison results, including: Divide the base data in each short sequence into multiple subsequences; Using a hash comparison method to compare each subsequence with a preset reference genome to obtain matching information for each subsequence, the matching information including mismatch values; For a subsequence whose mismatch value is less than or equal to a preset threshold, compressing the subsequence based on the matching information of the subsequence; For subsequences whose mismatch value is greater than a preset threshold, compressing the subsequence by combining run-length coding technology, context statistical model, ANS+FSE coding technology, arithmetic coding technology and / or Huffman coding technology; The matching information includes position information of the subsequence corresponding to the reference genome; the bioinformatics compression unit compresses the subsequence based on the matching information of the subsequence for a subsequence whose mismatch value is less than or equal to a preset threshold, including: sorting the subsequences based on the position information and the mismatch value to obtain sorted subsequences; Based on the position information of the subsequences, incremental coding compression is performed on the sorted subsequences.

8. A gene data compression device in the context of high-throughput sequencing, characterized in that: include: memory and processor; The memory is used to store programs; The processor is configured to execute the program to implement the various steps of the gene data compression method in the context of high-throughput sequencing as claimed in any one of claims 1 to 6.

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