Method for rapidly and accurately identifying high-throughput genome data pollution sources

Abstract

The invention discloses a method for rapidly and accurately identifying high-throughput genome data pollution sources. The method comprises the steps that original genome sequencing data for denovo sequencing are firstly assembled to obtain assembly results, gene prediction is conducted on the assembly results, amino acid sequences of proteins corresponding to genes are obtained through translation, and blast comparison is conducted on assembled genomic sequences and the amino acid sequences respectively with an NT database and an NR database of the NCBI to obtain homologous sequences serving as original comparison databases; species information corresponding to the sequences is extracted from the original comparison databases and is sequenced, the species corresponding to the sequences are sequenced from most to least, and whether exogenous pollution exists or not is comprehensively judged by combining with gene data results and amino acid data results. The method can reduce high-throughput genome sequencing data pollution and subsequent bioinformatics analysis influence of exogenous pollution sources in a genome denovo project to the most degree and improve pollution source identifying speed and efficiency.

Description

technical field [0001] The invention belongs to the technical field of molecular biology and relates to a method for quickly and accurately identifying pollution sources of high-throughput genome data. Background technique [0002] High-throughput sequencing technology (High-throughputsequencing), also known as "next generation" sequencing technology, can sequence hundreds of thousands to millions of DNA molecules at a time. [0003] In recent years, as the sequencing throughput of high-throughput sequencing technology continues to increase, the running time continues to shorten, the sequencing fragments continue to increase, and the cost continues to decrease, the application range of high-throughput sequencing technology is becoming wider and wider. More and more teams Choose to carry out scientific research and assisted breeding through high-throughput sequencing methods. With the sequencing of massive genetic data, more and more species have published whole-genome data, ...

AI Technical Summary

Problems solved by technology

[0004] However, the problem of exogenous contamination of sequencing samples has always been a problem that cannot be ignored, causing great impact and obstacles to subsequent data analysis

This method can better reduce the time cost of pollution identification work, but there are also obvious problems

It is because the sampling is random that it is difficult to accurately reflect the overall contamination of the sequencing data in the pollution identification analysis based on the sampling data.

Especially for projects with very deep sequencing depth and a very large amount of sequencing data, the proportion of sampling data in the total data is very limited, and it is almost inevitable that there will be deviations between the conclusions of pollution identification and the actual pollution, or even completely wrong conclusions. , for example, there is actually pollution caused by a certain pollution source species, but because the sequencing data itself is relatively large, the sampling data does not cover the pollution data, resulting in the inability to correctly identify the pollution caused by the pollution source species

[0010] Whether it is sampling or not, the common problem with both methods is that the reads obtained by next-generation sequencing are very short, generally between 100-250bp. For the accuracy of contamination identification, the threshold set in the comparison parameters Generally, they are relatively high (mainly including the two parameters of identity and evalue, and the thresholds are set to 90% and 1e-05 respectively), and the sequences lower than this threshold in the comparison result will be considered not to be the pollution source

For sites with frequent mutations, the genetic diversity itself is relatively high, which will lead to underestimation of the pollution situation in many cases

[0011] In short, there is currently insufficient understanding of the impact of pollution problems on analysis work; and in the currently commonly used methods of contamination identification and elimination, the comparison of all sequencing data with the NT database has the disadvantages of occupying a large amount of CPU resources and taking a long time ; The sampling method has the risk of inaccurate or even wrong assessment of the actual situation of pollution due to sampling; both methods have the risk of underestimated pollution caused by excessive thresholds, which in turn affects subsequent pollution removal and subsequent biological Informatics analysis work

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