A scaffolding method based on the statistical characteristics of the double-ended read insert size

Abstract

The invention discloses a scaffolding method based on statistical characteristic of double-end insert size, comprising the steps of first of all, pre-processing contig noise compared by double-end reading followed by constructing scaffold diagram in which each node represents a contig; estimating expected value compared with double-end reading between two nodes based on insert size distribution; and based on actual numbers compared with double-end reading between two nodes and expected value, determining whether there are edge and edge weight between two nodes. Then, methods of iteration and linear planning are applied to solve possible conflicts in scaffold diagram. Lastly, breadth-first traverse algorithm is applied to determine scaffold in the scaffold diagram. The scaffolding method based on statistical characteristic of double-end insert size is simple and easy to use, showing good scaffolding results concerning different true data and higher accuracy compared with other scaffolding methods.

Description

technical field [0001] The invention relates to the field of bioinformatics, in particular to a scaffolding method based on the statistical characteristics of double-end read insert size. Background technique [0002] Genome generally refers to all coding and non-coding deoxyribonucleic acid (DNA) sequences, which are composed of four bases: adenine (A), thymine (T), cytosine (C) and guanine (G) The sequence, that is, the genome sequence is a string, which only contains four characters A, T, G, and C. Another character N is also included in the actual genome sequence, representing that the base at this position cannot be determined. Through genome sequencing, short-segment base sequences (reads or reads) on a large number of genome sequences can be obtained. A collection of reads obtained from genome sequencing, generally with relatively short read lengths. Sequence assembly methods use these short reads to restore the complete original genome sequence. With the rapid de...

AI Technical Summary

Problems solved by technology

This strategy considers high-credibility and low-credibility edges together, and ignores the restrictive effect of high-confidence edges on other edges, and it is easy to remove some high-confidence edges.

[0009] (3) When traversing the scaffold graph, extracting paths and generating scaffolds, the optimization goal is often to maximize the number of matching double-ended reads on the entire path, while ignoring some characteristics of long nodes and short nodes

[0010] The existence of these problems limits the existing scaffolding methods to achieve more satisfactory results

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