A method for processing raw data of nucleic acid third-generation sequencing and its application

Abstract

The present application discloses a method for processing raw data of nucleic acid third-generation sequencing and its application. The method for processing the raw data of the third-generation nucleic acid sequencing of the present application includes comparing the second-generation short-sequence data to the third-generation self-error-correcting data, and counting the single bases of the third-generation self-error-correcting data in the comparison results Coverage depth, mask the area with a single base coverage depth lower than the threshold as N, and use the second-generation sequencing hole-filling software to fill holes in the N-shielded area to obtain nucleic acid third-generation sequencing with a low single-base error rate data. The method for processing the raw data of the third-generation nucleic acid sequencing of the present application uses the second-generation short-sequence data to compare with the third-generation long-sequence data, and uses the hole-filling software of the second-generation sequencing to compare the single base coverage in the comparison results The low-depth N-shielding region is complemented, which effectively reduces the single-base error rate in the third-generation sequencing data and improves the sequencing quality.

Description

technical field [0001] The present application relates to the field of nucleic acid sequencing data processing, in particular to a processing method and application of nucleic acid third-generation sequencing raw data. Background technique [0002] With the maturity and popularization of the next-generation sequencing technology (Next-generation sequencing, NGS), the cost of sequencing has been greatly reduced. Among them, the next-generation sequencer Hiseq2500 can produce 600Gb of data in one run, which is equivalent to 200 times the human genome. Although the next-generation sequencing technology can quickly produce a large amount of data, it has a fatal shortcoming that the sequencing read length is too short. As we all know, the most important indicators for genome assembly are N50 and the integrity of the genome. The read length is too short, and most of its assembly algorithms are based on the idea of ​​de Bruijngraph (de Bruijngraph), so that the biggest challenge en...

AI Technical Summary

Problems solved by technology

Although the next-generation sequencing technology can quickly produce a large amount of data, it has a fatal shortcoming that the sequencing read length is too short. As we all know, the most important indicators for genome assembly are N50 and the integrity of the genome. The read length is too short, and most of its assembly algorithms are based on the idea of ​​de Bruijngraph (de Bruijngraph), so that the biggest challenge encountered in assembly is to solve highly repetitive and highly heterozygous genomes

[0007] However, the biggest problem with the third-generation data lies in its extremely high single-base error rate, as high as 15%.

The error types are mainly indels, which are randomly distributed and can be corrected to a certain extent by means of biological information. Currently, the three-generation assembly processes released, such as SMRT, Falcon, Pbcr, and Canu, all have the third-generation data self-correction The function can reduce the error rate of the third-generation data by more than 20 times from 15% to about 3%, but the 3% error rate still interferes a lot with the assembly software based on the OLC (Overlap-Layout-Consensus) algorithm

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