Research On Microbial Genomic Data Identification Methods

With the development of DNA sequencing technology,there has been an explosion of bioinformatics gene sequencing data.With the gradual deepening of researchers' research on bio-genetic information,a large amount of genomic sequencing data has emerged subsequently.The large amount of genome sequencing data provides a data base for bioinformatics research,but the subsequent analysis and processing of the data still exists Challenge.For inexperienced users without sufficient bioinformatics skills to make sequencing data rationally available to microorganisms through read analysis Identification,especially of bacteria,remains a challenge.And in the past few decades,diseases caused by fungi have attracted widespread attention.With the development of sequencing technology,the analysis of fungal sequencing has become a new direction in fungal research,but with the gradual development of fungal data increased and now lacks a sufficiently functional analytical pipeline.In particular,the identification and annotation of fungal-based chromosomal genomes.As is well known,many viruses throughout history have resulted in a large number of infections in the general population and the death of many of those infected.In addition to these viruses that can cause great harm to humans,many viruses with mild symptoms also require human attention.And over time,viruses are often mutating.The identification and analysis of viruses remains a challenge at a time when human studies of viruses are far from adequate and even complete genome sequencing data of viruses are still scarce.In order to improve the situation where the bioinformatics challenge of applying microbial genomic information is hampered by the results of valid analysis,this paper develops a Automated bioinformatics pipeline called PBGI,using Illumina,Pac Bio and Oxford Customized bioinformatics analysis of short or long sequencing data generated by multiple platforms,including the Nanopore platform.PBGI Bacterial identification by short or long sequence analysis is a user-friendly way to provide accurate analytical results.To improve the development of bioinformatics in fungi.This paper describes an automated bioinformatics pipeline called M4A1 for the identification and annotation of the fungal bioinformatics components developed by Illumina,the Ion torrent,Pacbio,and other multiple platform sequencing of short and long sequences.m4A1 can be sequenced by the One simple command line execution and fast execution in a proper runtime environment.This paper also proposes a virus species identification method based on a combination of deep learning and traditional methods,and builds on it to construct a virus analysis workflow.Several real and simulated virus sequencing data were tested using the virus species identification method proposed in this paper.The experimental results show that,compared to traditional identification methods,the method proposed in this paper in combination with deep learning combines the two methods in speed and accuracy on the advantage.