Genome annotation method utilizing second-generation and third-generation transcriptome sequencing data

Abstract

The invention relates to the technical field of genome annotation, and provides a genome annotation method utilizing second-generation and third-generation transcriptome sequencing data. The method comprises the following steps: 1, comparing a third-generation full-length transcriptome sequencing sequence to a target genome to obtain initial structure information of each encoding gene; 2, comparing the second-generation transcriptome sequencing short sequence to a target genome, and extracting intron cleavage site information from a comparison file; and 3, obtaining final structure informationof each coding gene by combining the initial structure information of each coding gene and the intron cleavage site information. According to the method, a full-length transcript sequence can be obtained without splicing a third-generation transcriptome and second-generation transcriptome sequencing data can provide a large amount of intron cleavage site evidences can also be fully considered, and the accuracy and efficiency of genome annotation are greatly improved.

Description

technical field [0001] The invention relates to the technical field of genome annotation, and relates to a method for annotating the coding gene structure of the whole genome of eukaryotic organisms, in particular to a method for genome annotation using second-generation and third-generation transcriptome sequencing data. Background technique [0002] Generally, after the whole genome sequence is assembled, the structure of the protein-coding gene needs to be predicted. Prediction usually uses a combination of three strategies: de novo prediction, prediction based on sequence homology of closely related species, and prediction based on transcriptome data. Since the transcriptome is the most direct evidence of the genes expressed by a species, the prediction results based on the transcriptome data are considered to be the most credible, and the weight given by the strategy when integrating the prediction results of all strategies to obtain the final gene set Generally the hi...

AI Technical Summary

Problems solved by technology

The advantage of these two strategies is that they can make full use of the information provided by the second-generation sequencing data, but they cannot directly use the third-generation sequencing data

The disadvantage of PASA is that it cannot make full use of the large amount of intron position information that NGS data can p...

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