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TGStools: A Bioinformatics Suit to Facilitate Transcriptome Analysis of Long Reads from Third Generation Sequencing Platform.

ABSTRACT: Recent analyses show that transcriptome sequencing can be utilized as a diagnostic tool for rare Mendelian diseases. The third generation sequencing de novo detects long reads of thousands of base pairs, thus greatly expanding the isoform discovery and identification of novel long noncoding RNAs. In this study, we developed TGStools, a bioinformatics suite to facilitate routine tasks such as characterizing full-length transcripts, detecting shifted types of alternative splicing, and long noncoding RNAs (lncRNAs) identification in transcriptome analysis. It also prioritizes the transcripts with a visualization framework that automatically integrates rich annotation with known genomic features. TGStools is a Python package freely available at Github.

SUBMITTER: Chen D

PROVIDER: S-EPMC6678717 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature

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TGStools: A Bioinformatics Suit to Facilitate Transcriptome Analysis of Long Reads from Third Generation Sequencing Platform.

Chen Danze D Zhao Qianqian Q Jiang Leiming L Liao Shuaiyuan S Meng Zhigang Z Xu Jianzhen J

Genes 20190710 7

Recent analyses show that transcriptome sequencing can be utilized as a diagnostic tool for rare Mendelian diseases. The third generation sequencing de novo detects long reads of thousands of base pairs, thus greatly expanding the isoform discovery and identification of novel long noncoding RNAs. In this study, we developed TGStools, a bioinformatics suite to facilitate routine tasks such as characterizing full-length transcripts, detecting shifted types of alternative splicing, and long noncodi ...[more]

PMID: 31295871

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Project description:Since the first description of a commensal seminal microbiome using sequencing, less than a decade ago, interest in the composition of this microbiome and its relationship with fertility has been growing. Articles using next-generation sequencing techniques agree on the identification of the most abundant bacterial phyla. However, at the genus level, there is still no consensus on which bacteria are most abundant in human seminal plasma. This discrepancy may be due to methodological variability such as sample collection, bacterial DNA extraction methodology, which hypervariable regions of 16S rRNA gene have been amplified, or bioinformatic analysis. In the present work, seminal microbiota of 14 control samples and 42 samples of idiopathic infertile patients were characterized based on full-length sequencing of the 16S rRNA gene using MinION platform from Oxford Nanopore. These same samples had been analyzed previously using Illumina's MiSeq sequencing platform. Comparison between the results obtained with the two platforms has been used to analyze the impact of sequencing method on the study of the seminal microbiome's composition. Seminal microbiota observed with MinION were mainly composed of the phyla Firmicutes, Proteobacteria, Bacteroidetes and Actinobacteria, with the most abundant genera being Peptoniphilus, Finegoldia, Staphylococcus, Anaerococcus, Campylobacter, Prevotella, Streptococcus, Lactobacillus, Ezakiella and Enterococcus. This composition was similar to that found by the Illumina platform, since these 10 most abundant genera were also among the most abundant genera detected by the Nanopore platform. In both cases, the top 10 genera represented more than 70% of the classified reads. However, relative abundance of each bacterium did not correlate between these two platforms, with intraindividual variations of up to 50 percentage points in some cases. Results suggest that the effect of the sequencing platform on the characterization of seminal microbiota is not very large at the phylum level, with slightly variances in Firmicutes and Actinobacteria, but presents differences at the genus level. These differences could alter the composition and diversity of bacterial profiles or posterior analyses. This indicates the importance of conducting multi-platform studies to better characterize seminal microbioma.

Dataset Information

TGStools: A Bioinformatics Suit to Facilitate Transcriptome Analysis of Long Reads from Third Generation Sequencing Platform.

Publications

TGStools: A Bioinformatics Suit to Facilitate Transcriptome Analysis of Long Reads from Third Generation Sequencing Platform.

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