Processing method for nucleic acid third generation sequencing raw data and application thereof

Abstract

The invention discloses a processing method for nucleic acid third generation sequencing raw data and an application thereof. The processing method for the nucleic acid third generation sequencing rawdata includes comparing second generation short sequence data to third generation self-correcting data, obtaining the single-base covering depth of the third generation self-correcting data in a comparison result, shielding an area where the single-base covering depth is lower than the threshold as N, and adopting second generation sequencing hole filling software to fill the shielded area N to obtain the nucleic acid third generation sequencing data with low single-base error rate. According to the processing method for the nucleic acid third generation sequencing raw data, the second generation short sequence data is compared with third generation long sequence data, the second generation sequencing hole filling software is utilized to make up the shielded area N with the low single-base covering depth in the comparison result, the single-base error rate in the third generation sequencing data is effectively reduced, the sequencing quality is improved.

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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