High-throughput sequencing data-based genome de novo assembly method

Abstract

The invention provides a high-throughput sequencing data-based genome de novo assembly method, which comprises the following steps: (1) establishing a de Bruijn graph according to high-throughput sequencing data, and carrying out sequencing data error correction and super read assembly on the basis of the corrected de Bruijn graph; (2) utilizing super read to carry out primary contigs assembly; (3) taking specifically local primary contigs and reads, locally assembling, and combining all local assembly results; (4) sequencing contigs by a sub-graph segmentation algorithm and a simulated annealing algorithm to obtain final scaffolds. The errors brought by high-throughput sequencing are eliminated by de Bruijn graph correction, so that the data accuracy is improved; the sequencing read length is improved by establishing a super read method, and the contigs length is obviously enhanced; the processing capacity of repeated sequences is greatly enhanced by local assembly.

Description

technical field [0001] The invention relates to a genome assembly method, in particular to a genome de novo assembly method based on short sequence sequencing fragments. Background technique [0002] With the rapid development of second-generation sequencing technology and the rapid decline in sequencing costs, de novo genome sequencing is increasingly favored by researchers. However, using a large amount of short read data to restore the original appearance of the genome is also facing a huge challenge, and the most critical step is contigs assembly. De Bruijn graph construction is the core of the graph theory assembly algorithm, it is the core of the current mainstream de novo assembly algorithm, it is based on the overlapping information of kmer to construct the Euler graph, it is the cornerstone of contigs construction, so the development of the present invention will also be based on De Bruijn diagram. [0003] The current contigs assembly algorithms only construct th...

AI Technical Summary

Problems solved by technology

For the short sequences used in assembly, the general assembly software only performs simple filtering and error correction processing, and does not perform secondary processing on these most original short sequences, which limits the De Bruijn diagram to a large extent. Upper limit on kmer size in builds

Therefore, for genome assembly methods that do not process short sequences, the kmer size is relatively small, and more branches will be generated in the construction of the De Bruijn graph, which greatly increases the complexity of the De Bruijn graph, thereby reducing the assembly effect

[0004] In addition, a major feature of animal and plant genomes is the high proportion of repetitive sequences, and repetitive sequences will generate a large number of optional sites and branches during genome assembly, thereby increasing the difficulty of assembly

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