CN115114238A - An error-correction-based genome sequencing data lossless compression method and related equipment - Google Patents

Abstract

The invention discloses a genome sequencing data lossless compression method based on error correction and related equipment, wherein the method comprises the following steps: identifying and correcting sequencing base errors in the original sequencing short fragment, and recording base error information, wherein the base error information comprises base positions of sequencing errors and original bases; classifying the original sequencing short fragment into the corrected index area file, and adding sequencing error correction information into the index area file; sequencing and compressing the base sequences in the original sequencing short segments in different index region files to obtain a compression result file of the genome sequencing data. The invention realizes the efficient correction of sequencing errors of the sequencing short fragments, enables more similar sequencing short fragments to be distributed into the same barrel by correcting the sequencing errors in the basic groups, further improves the compression efficiency of the sequencing short fragments in the subsequent barrel, and realizes the lossless compression of genome sequencing data by recording and correcting the barrel index sequences in the sequencing short fragments.

Description

An error-correction-based genome sequencing data lossless compression method and related equipment

technical field

The present invention relates to the technical field of data compression, in particular to a method, system, terminal and computer-readable storage medium for lossless compression of genome sequencing data based on error correction.

Background technique

The emergence of next-generation high-throughput genome sequencing technology (a technology that can perform parallel sequencing of a large number of nucleic acid molecules at a time, usually a sequencing reaction can produce no less than 100Mb of sequencing data) has greatly reduced the cost of genome sequencing, At the same time, the sequencing speed has also been greatly improved. For example, the Human Genome Project cost $3 billion and took 13 years to complete using first-generation Sanger (dideoxy) sequencing technology, while using modern high-throughput genome sequencing technology costs less than $1,000 and can be completed in a few days. can be completed.

However, in terms of the measured genome sequence length, the base sequence generated by the second-generation sequencing technology is much shorter than that of the first-generation sequencing technology. The length of the first-generation sequencing technology can reach hundreds of bases or even tens of thousands of bases. The short genome sequencing fragments produced by quantitative genome sequencing are only a few tens of bases. Due to the needs of subsequent genomic data analysis, in order to be able to splicing the sequenced short fragments into a long genomic sequence, it is necessary to require sufficient overlap between the sequenced short fragments. This requires sufficient sequencing depth during genome sequencing, that is, the same genome sequence data needs to be measured tens or even hundreds of times at the same time, thus generating a large amount of sequencing short fragment data. Since each base locus is sequenced an average of tens to hundreds of times, there is a large amount of redundant data in the sequenced short fragments, which provides the basis for the compression of genome sequencing data.

However, due to the existence of sequencing errors in the second-generation sequencing technology, some sequencing short fragments are assigned to the error bucket. This misclassification not only affects the compression of the sequenced short segment sequence itself, but also affects the compression of other sequenced short segment sequences in the bucket, thereby affecting the compression effect of the overall sequencing data.

Therefore, the existing technology still needs to be improved and developed.

SUMMARY OF THE INVENTION

The main purpose of the present invention is to provide a method, system, terminal and computer-readable storage medium for lossless compression of genome sequencing data based on error correction, aiming to solve the problem of wrong allocation of short sequence sequences due to sequencing in the prior art during genome sequencing. A problem that affects the compression effect of the overall sequencing data.

In order to achieve the above object, the present invention provides a method for lossless compression of genome sequencing data based on error correction. The method for lossless compression of genome sequencing data based on error correction includes the following steps:

Identify and correct sequencing base errors in the original sequencing short fragments, and record the base error information, the base error information includes the base position of the sequencing error and the original base;

Sort the original sequencing short fragments into the corrected index region file, and add sequencing error correction information to the index region file;

The base sequences in the original sequencing short fragments in different index region files are sorted and compressed to obtain a compressed result file of the genome sequencing data.

The error-correction-based genome sequencing data lossless compression method, wherein the identifying and correcting sequencing base errors in the original sequencing short fragments specifically includes:

Count the number of k-mers, where k-mers are all base subsequences of length k in the original sequencing short fragment;

Obtain the high-frequency k-mer, insert the high-frequency k-mer into the Bloom filter, and the number of occurrences of the k-mer greater than the preset threshold is the high-frequency k-mer;

Correct the sequenced base errors in the index region or the entire region in the original sequenced short fragments based on the Bloom filter.

The error-correction-based genome sequencing data lossless compression method, wherein the high-frequency k-mers are generated by correctly sequenced bases.

In the method for lossless compression of genome sequencing data based on error correction, wherein, the index region is the minimum value of all k-mers of forward and reverse complementary sequences in the original sequencing short fragment.

In the method for lossless compression of genome sequencing data based on error correction, the bases in the index region are arranged in the order of letters A, C, G, and T.

The error-correction-based genome sequencing data lossless compression method, wherein the base sequences in the original sequencing short fragments in different index region files are sorted and compressed to obtain a compression result file of the genome sequencing data, specifically include:

After the clustering of all sequencing short fragments is completed, split and sort all original sequencing short fragments in the same index;

After all the original sequencing short fragments in the same index are split and sorted, compression is performed to obtain a compressed result file of the genome sequencing data.

The error-correction-based genome sequencing data lossless compression method, wherein after the clustering of all short sequenced fragments is completed, splitting and sorting all the original short fragments of sequencing in the same index specifically includes:

Use the transformation tran(r) to split and transform the sequencing short fragment r;

The split conversion divides the sequencing short fragment r into three regions, namely Pre(r), Index(r) and Su f(r), such that r=Pre(r) o Index(r) o Su f(r), where ο represents string concatenation.

The error-correction-based genome sequencing data lossless compression method, wherein the base sequences in the original sequencing short fragments in different index region files are sorted and compressed to obtain a compression result file of the genome sequencing data, and then Also includes:

Calculate the compression ratio of the genome sequencing data, where the compression ratio represents the number of bits occupied by each base after compression, and the lower the compression ratio, the smaller the occupied space, and the better the compression effect.

In addition, in order to achieve the above object, the present invention also provides an error-correction-based genome sequencing data lossless compression system, wherein the error-correction-based genome sequencing data lossless compression system includes:

An identification and correction module is used to identify and correct the sequencing base errors in the original short sequenced fragments, and record the base error information, the base error information includes the base position of the sequencing error and the original base;

The classification record module is used to classify the original sequencing short fragments into the corrected index region file, and add sequencing error correction information to the index region file;

The sorting and compression module is used to sort and compress the base sequences in the original sequencing short fragments in different index region files to obtain a compressed result file of the genome sequencing data.

In the error-correction-based genome sequencing data lossless compression system, the identification and correction module specifically includes:

Statistical unit, used to count the number of k-mers, k-mers are all base subsequences of length k in the original sequencing short fragment;

The insertion unit is used to obtain high-frequency k-mers, and insert high-frequency k-mers into the Bloom filter. The high-frequency k-mers whose occurrences are greater than the preset threshold are high-frequency k-mers;

The correction unit is used to correct the sequenced base errors in the index region or the entire region in the original sequenced short fragment based on the Bloom filter.

The error-correction-based genome sequencing data lossless compression system, wherein the sequencing and compression module specifically includes:

The splitting and sorting unit is used to split and sort all the original short sequenced fragments in the same index after the sequence clustering of all the short sequenced fragments is completed;

The data compression unit is used to compress to obtain a compressed result file of the genome sequencing data after the splitting and sorting of all the original sequencing short fragments in the same index are completed.

In addition, in order to achieve the above object, the present invention also provides a terminal, wherein the terminal includes: a memory, a processor, and error correction-based genome sequencing data stored in the memory and executable on the processor A lossless compression program, when the error-correction-based genome sequencing data lossless compression program is executed by the processor, implements the steps of the error-correction-based genome sequencing data lossless compression method as described above.

In addition, in order to achieve the above object, the present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores an error-correction-based genome sequencing data lossless compression program, and the error-correction-based genome sequencing data When the lossless compression program is executed by the processor, the steps of the above-described method for lossless compression of genome sequencing data based on error correction are realized.

In the present invention, the sequencing base errors in the original sequencing short fragments are identified and corrected, and the base error information is recorded, and the base error information includes the base positions of the sequencing errors and the original bases; the original sequencing short fragments are classified into into the corrected index region file, and add sequencing error correction information to the index region file; sort and compress the base sequences in the original sequencing short fragments in different index region files to obtain the compression of the genome sequencing data result file. The present invention realizes efficient correction of sequencing errors in short sequenced segments. By correcting the sequencing errors in the bases, more similar sequenced short segments are allocated to the same bucket, thereby increasing the data redundancy density in the bucket, thereby improving the The compression efficiency of subsequent in-bucket sequencing short fragments is recorded. By recording and correcting the bucket index sequence in the sequencing short fragments, and recording the corrected position and original base information, the corrected base information can be recovered without loss during decompression. Achieve lossless compression of genome sequencing data.

Description of drawings

Fig. 1 is the flow chart of the preferred embodiment of the genome sequencing data lossless compression method based on error correction of the present invention;

2 is a schematic overall flow diagram of a preferred embodiment of the method for lossless compression of genome sequencing data based on error correction of the present invention;

3 is a schematic diagram of bucketing errors caused by sequencing errors in a preferred embodiment of the error-correction-based genome sequencing data lossless compression method of the present invention;

Fig. 4 is the Minimizer schematic diagram of the sequence in the preferred embodiment of the genome sequencing data lossless compression method based on error correction of the present invention;

5 is a schematic diagram of the rearrangement of sequences in a bucket in a preferred embodiment of the error-correction-based genome sequencing data lossless compression method of the present invention;

6 is a schematic diagram of the comparison of the compression ratio results of the compression method of the present invention and the compression method of genome sequencing data in other three industries in a preferred embodiment of the error-correction-based genome sequencing data lossless compression method of the present invention;

7 is a schematic diagram of the principle in a preferred embodiment of the error-correction-based genome sequencing data lossless compression system of the present invention;

FIG. 8 is a schematic diagram of an operating environment of a preferred embodiment of the terminal of the present invention.

Detailed ways

In order to make the objectives, technical solutions and advantages of the present invention clearer and clearer, the present invention will be further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are only used to explain the present invention, but not to limit the present invention.

The method for lossless compression of genome sequencing data based on error correction according to a preferred embodiment of the present invention, as shown in FIG. 1 and FIG. 2 , the method for lossless compression of genome sequencing data based on error correction includes the following steps:

Step S10 , identifying and correcting the sequencing base errors in the original sequencing short segment, and recording the base error information, where the base error information includes the base position of the sequencing error and the original base.

Specifically, the step S10 specifically includes: counting the number of k-mers, where k-mers are all base subsequences with a length of k in the original sequencing short fragment; obtaining high-frequency k-mers, and inserting high-frequency k-mers into In the Bloom filter, the number of occurrences of k-mers greater than the preset threshold is a high-frequency k-mer; based on the Bloom filter, the sequenced base errors in the index region or the entire region in the original sequencing short fragment are corrected, wherein the said The base error information includes the base position of the sequencing error and the original base.

As shown in Figure 2, the original sequencing short fragments are represented by reads, and the sequencing errors in the original sequencing short fragments (reads) are identified and corrected, which can be divided into two ways. One is to identify and correct the bases in the index region of the reads. Identify and correct all bases in reads, and record the base error information (including the base position of the sequencing error and the original base, so that the original sequence can be restored without loss during decompression, so as to achieve the purpose of lossless compression).

Among them, k-mer is the base subsequence of length k in the reads, and the reads sequence and its reverse complementary sequence are counted separately. In the reverse complementary sequence pool, A→T, C→G; then the high frequency k -mer (the number of occurrences of k-mers above a certain threshold is called high-frequency k-mers, because the base sequencing error rate is relatively low, high-frequency k-mers are generally generated by correctly sequenced bases) Insert into Bloom Filter (Bloom filter), Bloom filter is a long binary vector and a series of random mapping functions, Bloom filter can be used to retrieve whether an element is in a set, its advantage is to query a certain element at time Whether the efficiency in the collection is O(1); finally correct the base errors in the index region of reads (Minimizer, as shown in Figure 3) or the entire region; the index region of reads (also known as Minimizer) is the forward sum in the sequence. The minimum value of all k-mers of the reverse complement sequence, in which the bases are arranged in the order of the letters A, C, G, T (as shown in Figure 4).

Step S20, classifying the original sequencing short fragments into the corrected index region file, and adding sequencing error correction information to the index region file.

Specifically, the reads are classified into the corrected Minimizer file, and the sequencing error correction information is added to the index region file together, so that the original sequencing short fragment sequence can be restored without loss during decompression; when the sequencing base errors are corrected After that, the Minimizer value of the reads needs to be recalculated.

Step S30, sorting and compressing the base sequences in the original sequencing short fragments in different index region files to obtain a compressed result file of the genome sequencing data.

Specifically, the step S30 specifically includes: after the sequence clustering of all the short sequenced fragments is completed, splitting and sorting all the original short sequenced fragments in the same index; when the splitting and sorting of all the original short sequenced fragments in the same index is completed Then, perform compression to obtain a compressed result file of the genome sequencing data.

When the clustering of all sequenced short fragments is completed, split and sort all the reads in the same index. For example, for sequence r, use the transformation tran(r) to split and transform it, as shown in Figure 5, the transformation will The sequenced short fragment r is divided into three regions, namely Pre(r), Index(r) and Su f(r). For example, Pre(r) is GCGTTGGCCCT, Index(r) is AAGAGGCC, Su f(r) is GCGATGCTCCGTTATCTCTCGTTG, Let r=Pre(r)οIndex(r)οSu f(r), where ο represents string concatenation, and Index(r) is the bucket index where the sequenced short segment is placed. Set the conversion to tran(r)=Su f(r)οPre(r)οIndexPos, where IndexPos is the position offset of the bucket index Index(r) from the Minimizer of the sequencing short fragment, for example, Su f(r) at this time is GCGATGCTCCGTTATCTCTCGTTG, Pre(r) is GCGTTGGCCCT, and IndexPos is 10. Tran is called the Sequencing Short Split Transform, which splits the Sequencing Short into a specific offset and swaps the second and first substrings produced by this split. The benefits brought by this transformation are: first, it removes the explicit redundancy in the sequenced short fragments, that is, the bucket index sequence string Index(r), because all the sequenced short fragments in the bucket share the bucket index, Second, by moving the bucket-indexed subsequences to the front of each sequenced short segment, these adjusted sequence prefixes are more likely to share similar sequences, which can improve the redundancy of downstream compression tools to discover and exploit these shared substrings.

Further, in order to verify the effectiveness of the two methods of the method of the present invention: BICmin and BICall in the compression of genome sequencing data, BICmin (correcting only the Minimizer region) and BICall (correcting the entire sequence) methods are used for real genomes of five different species. The sequencing data is compressed and compared with the compression results of the genome sequencing data compression methods in other three industries: SCALCE, ORCOM and MINCE. As shown in Figure 6, the dataset IDs of five different species are SRR554369_1 and MH0001, respectively. 081026_1, SRR327342_1, SRR870667_1, ERR174310_1, five different methods correspond to their respective compression ratios (Bits Per Base). For example, when using BICmin, SRR554369_1, MH0001.081026_1, SRR327342_1, SRR870667_1, ERR172610_1 correspond to compression ratios of 0.7, 0.0.7, and ERR172610_1 respectively. , 0.88, 0.67; for example, when using BICall, the compression ratios corresponding to SRR554369_1, MH0001.081026_1, SRR327342_1, SRR870667_1, and ERR174310_1 are 0.41, 0.68, 0.24, 0.88, and 0.66, respectively.

The compression ratio here represents the number of bits occupied by each base after compression. The lower the compression rate, the smaller the space occupied and the better the compression effect. It can be seen from Figure 6 that the BICall method has a slight improvement in the compression effect compared with BICmin, but the improvement effect is not very obvious, because in the BICall method, after removing the compression effect caused by the different buckets of sequencing short fragments, Although the improvement of sequencing error correction improves the consistency of the sequences in the bucket, it still stores the position of the corrected base and the original base information in the sequence record, which still occupies part of the storage space. Therefore, the BIC method provides two compression methods, one is It is a method of correcting only the base errors that can change the index value of the sequenced short fragments: BICmin, and the other is a method of correcting all the base errors in the sequenced short fragments: BICall.

For high-throughput genomic data, due to the sequencing technology, there are a large number of base sequencing errors, and such sequencing errors will reduce the sequence consistency between short sequences (reads) and affect the compression effect of genomic sequencing data. . The invention utilizes the high-throughput information of the genome sequencing data to correct the bases of the sequencing error sites, and establishes a character representation method for error correction of different bases at the same time. Lossless compression is achieved, and this method has achieved good compression results in the lossless compression experiments of existing public genome sequencing datasets.

Beneficial effects:

(1) The method of the present invention designs an efficient sequence error correction method. By counting the number of k-mers, inserting high-frequency k-mers into the Bloom Filter filter corrects the erroneous bases in the bucket index region and realizes sequencing. Correction of short fragment sequencing errors.

(2) The method of the present invention corrects the sequencing errors in the bases, so that more similar sequencing short fragments are allocated to the same bucket, which increases the data redundancy density in the bucket, thereby improving the subsequent sequencing short clips in the bucket. segment compression efficiency.

(3) The method of the present invention can restore the corrected base information without loss during decompression by recording the bucket index sequence in the corrected sequencing short segment, and at the same time recording the corrected position and original base information, so as to realize the recovery of the genome sequencing data. lossless compression.

(4) The method of the present invention performs compression experiments on real genome sequencing data of five different species, and compares the results with the compression results of three compression methods (SCALCE, ORCOM method, and MINCE method) in the industry. The experimental results show that , the method of the present invention can effectively compress the genome sequencing data.

Further, as shown in FIG. 7 , based on the above-mentioned error-correction-based genome sequencing data lossless compression method, the present invention also provides an error-correction-based genome sequencing data lossless compression system, wherein the error-correction-based genome sequencing data lossless compression system is further provided. The lossless compression system for sequencing data includes:

The identification and correction module 51 is used to identify and correct the sequencing base errors in the original sequencing short fragments, and record the base error information, and the base error information includes the base position of the sequencing error and the original base;

The classification recording module 52 is used to classify the original sequencing short fragments into the corrected index region file, and add sequencing error correction information to the index region file;

The sorting and compressing module 53 is used for sorting and compressing the base sequences in the original sequencing short fragments in different index region files to obtain a compressed result file of the genome sequencing data.

Wherein, the identifying and correcting module 51 specifically includes:

Statistical unit, used to count the number of k-mers, k-mers are all base subsequences of length k in the original sequencing short fragment;

The insertion unit is used to obtain high-frequency k-mers, and insert high-frequency k-mers into the Bloom filter. The high-frequency k-mers whose occurrences are greater than the preset threshold are high-frequency k-mers;

The correction unit is used to correct the sequenced base errors in the index region or the entire region in the original sequenced short fragment based on the Bloom filter.

Wherein, the sorting and compression module 53 specifically includes:

The splitting and sorting unit is used to split and sort all the original short sequenced fragments in the same index after the sequence clustering of all the short sequenced fragments is completed;

The data compression unit is used to compress to obtain a compressed result file of the genome sequencing data after the splitting and sorting of all the original sequencing short fragments in the same index are completed.

Further, as shown in FIG. 8 , based on the above-mentioned method and system for lossless compression of genome sequencing data based on error correction, the present invention also provides a terminal correspondingly, and the terminal includes a processor 10 , a memory 20 and a display 30 . FIG. 8 only shows some components of the terminal, but it should be understood that it is not required to implement all the shown components, and more or less components may be implemented instead.

In some embodiments, the memory 20 may be an internal storage unit of the terminal, such as a hard disk or a memory of the terminal. In other embodiments, the memory 20 may also be an external storage device of the terminal, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD card) equipped on the terminal. ) card, flash card (Flash Card) and so on. Further, the memory 20 may also include both an internal storage unit of the terminal and an external storage device. The memory 20 is used to store application software and various types of data installed in the terminal, such as program codes of the installation terminal. The memory 20 can also be used to temporarily store data that has been output or is to be output. In one embodiment, an error-correction-based genome sequencing data lossless compression program 40 is stored on the memory 20, and the error-correction-based genome sequencing data lossless compression program 40 can be executed by the processor 10, so as to realize the error-correction-based genome sequencing data lossless compression program 40 in the present application. The wrong method for lossless compression of genome sequencing data.

In some embodiments, the processor 10 may be a central processing unit (Central Processing Unit, CPU), a microprocessor or other data processing chips, which are used to execute program codes or process data stored in the memory 20, such as The error-correction-based genome sequencing data lossless compression method and the like are performed.

In some embodiments, the display 30 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode, organic light-emitting diode) touch device, and the like. The display 30 is used for displaying information on the terminal and for displaying a visual user interface. The components 10-30 of the terminal communicate with each other via the system bus.

In one embodiment, when the processor 10 executes the error correction-based genome sequencing data lossless compression program 40 in the memory 20, the following steps are implemented:

Identify and correct sequencing base errors in the original sequencing short fragments, and record the base error information, the base error information includes the base position of the sequencing error and the original base;

Sort the original sequencing short fragments into the corrected index region file, and add sequencing error correction information to the index region file;

The base sequences in the original sequencing short fragments in different index region files are sorted and compressed to obtain a compressed result file of the genome sequencing data.

Wherein, identifying and correcting sequencing base errors in the original sequencing short fragments specifically includes:

Count the number of k-mers, where k-mers are all base subsequences of length k in the original sequencing short fragment;

Obtain the high-frequency k-mer, insert the high-frequency k-mer into the Bloom filter, and the number of occurrences of the k-mer greater than the preset threshold is the high-frequency k-mer;

Correct the sequenced base errors in the index region or the entire region in the original sequenced short fragments based on the Bloom filter.

The error-correction-based genome sequencing data lossless compression method, wherein the high-frequency k-mers are generated by correctly sequenced bases.

Wherein, the index region is the minimum value of all k-mers of forward and reverse complementary sequences in the original sequencing short fragment.

Wherein, the bases in the index region are arranged in the order of letters A, C, G, and T.

Wherein, sorting and compressing the base sequences in the original sequencing short fragments in different index region files to obtain a compressed result file of the genome sequencing data, specifically including:

After the clustering of all sequencing short fragments is completed, split and sort all original sequencing short fragments in the same index;

After all the original sequencing short fragments in the same index are split and sorted, compression is performed to obtain a compressed result file of the genome sequencing data.

Wherein, after the clustering of all sequencing short fragments is completed, all original sequencing short fragments in the same index are split and sorted, specifically including:

Use the transformation tran(r) to split and transform the sequencing short fragment r;

The split conversion divides the sequencing short fragment r into three regions, namely Pre(r), Index(r) and Su f(r), such that r=Pre(r) o Index(r) o Su f(r), where ο represents string concatenation.

Wherein, sorting and compressing the base sequences in the original sequencing short fragments in different index region files to obtain a compressed result file of the genome sequencing data, and then further including:

Calculate the compression ratio of the genome sequencing data, where the compression ratio represents the number of bits occupied by each base after compression, and the lower the compression ratio, the smaller the occupied space, and the better the compression effect.

In summary, the present invention provides a method, system, terminal and computer-readable storage medium for lossless compression of genome sequencing data based on error correction, the method includes: identifying and correcting sequencing base errors in the original sequencing short segment, And record the base error information, the base error information includes the base position of the sequencing error and the original base; classify the original sequencing short fragments into the corrected index area file, and add the sequencing error correction information to the index In the region file; sort and compress the base sequences in the original sequencing short fragments in different index region files to obtain the compressed result file of the genome sequencing data. The present invention realizes efficient correction of sequencing errors in short sequenced segments. By correcting the sequencing errors in the bases, more similar sequenced short segments are allocated to the same bucket, thereby increasing the data redundancy density in the bucket, thereby improving the The compression efficiency of subsequent in-bucket sequencing short fragments is recorded. By recording and correcting the bucket index sequence in the sequencing short fragments, and recording the corrected position and original base information, the corrected base information can be recovered without loss during decompression. Achieve lossless compression of genome sequencing data.

It should be noted that, herein, the terms "comprising", "comprising" or any other variation thereof are intended to encompass non-exclusive inclusion, such that a process, method, article or terminal comprising a series of elements includes not only those elements, It also includes other elements not expressly listed or inherent to such a process, method, article or terminal. Without further limitation, an element defined by the phrase "comprising a..." does not preclude the presence of additional identical elements in the process, method, article or terminal that includes the element.

It should be understood that the application of the present invention is not limited to the above examples. For those of ordinary skill in the art, improvements or transformations can be made according to the above descriptions, and all these improvements and transformations should belong to the protection scope of the appended claims of the present invention.

Claims (13)

1. The error correction-based genome sequencing data lossless compression method is characterized by comprising the following steps of:

identifying and correcting sequencing base errors in the original sequencing short fragment, and recording base error information, wherein the base error information comprises base positions and original bases of the sequencing errors;

classifying the original sequencing short fragment into the corrected index area file, and adding sequencing error correction information into the index area file;

sequencing and compressing the base sequences in the original sequencing short segments in different index region files to obtain a compression result file of the genome sequencing data.

2. The method for lossless compression of genome sequencing data based on error correction according to claim 1, wherein the identifying and correcting sequencing base errors in the original sequencing short fragment specifically comprises:

counting the number of k-mers, wherein the k-mers are all base subsequences with the length of k in the original sequencing short fragment;

obtaining a high-frequency k-mer, inserting the high-frequency k-mer into a bloom filter, wherein the high-frequency k-mer is selected when the occurrence frequency of the k-mer is greater than a preset threshold;

and correcting sequencing base errors in the index region or the whole region in the original sequencing short fragment based on the bloom filter.

3. The method of claim 2, wherein the high frequency k-mers are generated from correctly sequenced bases.

4. The method of claim 2, wherein the index region is the minimum of all k-mers of the forward and reverse complement sequences in the original sequenced short segment.

5. The method of claim 4, wherein the bases in the index region are arranged in the order of A, C, G, T letters.

6. The method for lossless compression of genome sequencing data based on error correction according to claim 1, wherein the sequencing and compression of the base sequences in the original sequencing short segments in different index region files to obtain the compressed result file of genome sequencing data specifically comprises:

after all sequencing short fragment sequences are clustered, splitting and sequencing all original sequencing short fragments in the same index;

and after splitting and sequencing all the original sequencing short fragments in the same index, compressing to obtain a compression result file of the genome sequencing data.

7. The error-correction-based genome sequencing data lossless compression method according to claim 6, wherein after all sequencing short fragment sequences are clustered, splitting and sequencing all original sequencing short fragments in the same index, specifically comprising:

using a transform tran (r) to perform resolution transformation on the sequencing short fragment r;

the resolution transformation divides the sequenced short fragment r into three regions, pre (r), index (r) and Suf (r), respectively, such that

Wherein

Representing string concatenation.

8. The method for lossless compression of genome sequencing data based on error correction according to claim 1, wherein the base sequences in the original sequencing short segments in different index region files are sequenced and compressed to obtain a compressed result file of genome sequencing data, and then further comprising:

and calculating the compression ratio of the genome sequencing data, wherein the compression ratio represents the number of bits occupied by each base after compression, and the lower the compression ratio, the smaller the occupied space and the better the compression effect.

9. An error correction based genome sequencing data lossless compression system, wherein the error correction based genome sequencing data lossless compression system comprises:

the identification and correction module is used for identifying and correcting sequencing base errors in the original sequencing short fragment and recording base error information, wherein the base error information comprises the base position of the sequencing error and an original base;

the classification recording module is used for classifying the original sequencing short fragments into the corrected index area file and adding sequencing error correction information into the index area file;

and the sequencing compression module is used for sequencing and compressing the base sequences in the original sequencing short segments in different index region files to obtain a compression result file of the genome sequencing data.

10. The system for lossless compression of genome sequencing data based on error correction according to claim 9, wherein the identification and correction module specifically includes:

the statistical unit is used for counting the number of k-mers, wherein the k-mers are all base subsequences with the length of k in the original sequencing short fragment;

the inserting unit is used for obtaining a high-frequency k-mer and inserting the high-frequency k-mer into the bloom filter, wherein the high-frequency k-mer is selected when the occurrence frequency of the k-mer is greater than a preset threshold;

and the correcting unit is used for correcting sequencing base errors in the index region or the whole region in the original sequencing short fragment based on the bloom filter.

11. The error-correction-based genome sequencing data lossless compression system according to claim 9, wherein the sequencing compression module specifically includes:

the splitting and sequencing unit is used for splitting and sequencing all original sequencing short fragments in the same index after all sequencing short fragment sequences are clustered;

and the data compression unit is used for compressing to obtain a compression result file of the genome sequencing data after the splitting and sequencing of all the original sequencing short fragments in the same index are completed.

12. A terminal, characterized in that the terminal comprises: a memory, a processor, and an error correction based genome sequencing data lossless compression program stored on the memory and executable on the processor, the error correction based genome sequencing data lossless compression program when executed by the processor implementing the steps of the error correction based genome sequencing data lossless compression method of any one of claims 1-8.

13. A computer-readable storage medium, characterized in that the computer-readable storage medium stores an error correction based genome sequencing data lossless compression program, which when executed by a processor implements the steps of the error correction based genome sequencing data lossless compression method according to any one of claims 1 to 8.

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