Method and system for filtering sequence segments in short-sequence assembly

Abstract

The invention discloses a method for filtering sequence segments in a short-sequence assembly. The method comprises the following steps: receiving measured sequences, respectively performing base slide cutting to received measured sequences one by one to obtain short strings with fixed base length; storing sequence values and occurrence frequency of the short strings as a node; calculating a short string frequency threshold value; and filtering the short strings with frequency smaller than the threshold value. The invention further provides a system for filtering sequence segments in the short-sequence assembly. The method and system for filtering sequence segments in the short-sequence assembly has the advantages of filtering wrong short strings, decreasing assembled and spliced short string sets, reducing internal memory required by assembling and splicing programs, improving the performance of the assembling and splicing programs, performing statistics to the frequency of the short strings while storing short string modes, and being simple in operation and small in error.

Description

technical field [0001] The invention relates to the technical field of genetic engineering, in particular to a method and system for filtering sequence fragments in short sequence assembly. Background technique [0002] The short sequences generated by the new sequencing technology have the following two characteristics: first, the sequence length is short; second, the data volume is large. Commonly used software such as phrap for long sequence assembly is based on the overlap between sequences for splicing and assembly. This method will have a problem of too much calculation when applied to short sequences, and has no practical application value. Emerging short-sequence assemblies are limited by memory, time, etc., and have only been successfully applied to smaller prokaryotic genomes so far. Next-generation sequencing analysis has the following difficulties: First, massive sequence fragments, the length of the genome source sequence ranges from 100,000 bases (such as porc...

AI Technical Summary

Problems solved by technology

For the second problem, high-throughput data itself can generate large-scale k-mer nodes, which will be constructed into graphs for analysis, and the number of k-mer nodes will increase due to the introduction of sequencing errors. 5 times larger, for example, the human genome sequencing data will generate about 15G k-mer; if the k-mer generated by the sequencing error enters the computer for direct processing, it will consume huge memory, for example, if the human genome sequencing data is not processed If the sequence is filtered and cleaned, it will consume about 2T of memory to store the graph constructed by these k-mers; the wrong sequence in the sequencing data will also form wrong links in the constructed graph, Tip type error, bubble type error, these errors It is mixed with the repetitive sequence and gene mutation point of the source genome sequence itself, which will make subsequent gene sequence analysis impossible

Images