Microbial data processing method for high-throughput sequencing

Abstract

The invention discloses a microbial data processing method for high-throughput sequencing. According to the method, contig assembling and binning are performed on microbial 16s RNA read segments of high-throughput sequencing; microbial contigs are marked with q-PCR, so that the microbial contigs comprise marker genes; biological contigs containing the marker gene are removed, so that high-qualitymicrobial metagenomic sequencing data are obtained. Sequence clustering and other methods are adopted to identify and remove sequences from pollutants, so that the microbial metagenomic sequencing data with higher purity can be obtained, and therefore, gene expression results based on the microbial metatranscriptomic sequencing data are more accurate. The method of the invention, with the microbial metagenomic sequencing data as a research object, can improve the quality of the microbial metagenomic sequencing data based on bioinformatics ideas.

Description

Technical field [0001] The invention relates to a microbial data processing method for high-throughput sequencing, which belongs to the field of high-throughput sequencing quality control. Background technique [0002] Next-generation sequencing technology (NGS), also known as high-throughput sequencing, is characterized by high output and high resolution. It can read hundreds of thousands to millions of DNA molecules in parallel at a time, providing abundant genetic resources. While learning information, it can also greatly reduce sequencing costs and shorten sequencing time. Due to the large amount of data processed by high-throughput sequencing technology, the processing content is complicated, so the control of sequencing quality and the determination and elimination of pollution sources have become an important research topic. There are many factors affecting the quality of sequencing. The most common influencing factors are errors in operation. The main source of batch eff...

AI Technical Summary

Problems solved by technology

However, the false positive rate of read assignments remains high and does not take into account potentially valuable information such as abundance correlations of certain target species across multiple samples (with similar contaminants)

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