Dataset Information

BISR-RNAseq: an efficient and scalable RNAseq analysis workflow with interactive report generation.

ABSTRACT:

Background

RNA sequencing has become an increasingly affordable way to profile gene expression patterns. Here we introduce a workflow implementing several open-source softwares that can be run on a high performance computing environment.

Results

Developed as a tool by the Bioinformatics Shared Resource Group (BISR) at the Ohio State University, we have applied the pipeline to a few publicly available RNAseq datasets downloaded from GEO in order to demonstrate the feasibility of this workflow. Source code is available here: workflow: https://code.bmi.osumc.edu/gadepalli.3/BISR-RNAseq-ICIBM2019 and shiny: https://code.bmi.osumc.edu/gadepalli.3/BISR_RNASeq_ICIBM19. Example dataset is demonstrated here: https://dataportal.bmi.osumc.edu/RNA_Seq/.

Conclusion

The workflow allows for the analysis (alignment, QC, gene-wise counts generation) of raw RNAseq data and seamless integration of quality analysis and differential expression results into a configurable R shiny web application.

SUBMITTER: Gadepalli VS

PROVIDER: S-EPMC6923898 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

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Publications

BISR-RNAseq: an efficient and scalable RNAseq analysis workflow with interactive report generation.

Gadepalli Venkat Sundar VS Ozer Hatice Gulcin HG Yilmaz Ayse Selen AS Pietrzak Maciej M Webb Amy A

BMC bioinformatics 20191220 Suppl 24

<h4>Background</h4>RNA sequencing has become an increasingly affordable way to profile gene expression patterns. Here we introduce a workflow implementing several open-source softwares that can be run on a high performance computing environment.<h4>Results</h4>Developed as a tool by the Bioinformatics Shared Resource Group (BISR) at the Ohio State University, we have applied the pipeline to a few publicly available RNAseq datasets downloaded from GEO in order to demonstrate the feasibility of th ...[more]

PMID: 31861980

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Project description:Background: Over the last decade, we have observed in microbial ecology a transition from gene-centric to genome-centric analyses. Indeed, the advent of metagenomics combined with binning methods, single-cell genome sequencing as well as high-throughput cultivation methods have contributed to the continuing and exponential increase of available prokaryotic genomes, which in turn has favored the exploration of microbial metabolisms. In the case of metagenomics, data processing, from raw reads to genome reconstruction, involves various steps and software which can represent a major technical obstacle. Methods: To overcome this challenge, we developed SnakeMAGs, a simple workflow that can process Illumina data, from raw reads to metagenome-assembled genomes (MAGs) classification and relative abundance estimate. It integrates state-of-the-art bioinformatic tools to sequentially perform: quality control of the reads (illumina-utils, Trimmomatic), host sequence removal (optional step, using Bowtie2), assembly (MEGAHIT), binning (MetaBAT2), quality filtering of the bins (CheckM, GUNC), classification of the MAGs (GTDB-Tk) and estimate of their relative abundance (CoverM). Developed with the popular Snakemake workflow management system, it can be deployed on various architectures, from single to multicore and from workstation to computer clusters and grids. It is also flexible since users can easily change parameters and/or add new rules. Results: Using termite gut metagenomic datasets, we showed that SnakeMAGs is slower but allowed the recovery of more MAGs encompassing more diverse phyla compared to another similar workflow named ATLAS. Importantly, these additional MAGs showed no significant difference compared to the other ones in terms of completeness, contamination, genome size nor relative abundance. Conclusions: Overall, it should make the reconstruction of MAGs more accessible to microbiologists. SnakeMAGs as well as test files and an extended tutorial are available at https://github.com/Nachida08/SnakeMAGs.

Dataset Information

BISR-RNAseq: an efficient and scalable RNAseq analysis workflow with interactive report generation.

Background

Results

Conclusion

Publications

BISR-RNAseq: an efficient and scalable RNAseq analysis workflow with interactive report generation.

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