Dataset Information

NASA GeneLab RNA-seq consensus pipeline: standardized processing of short-read RNA-seq data.

ABSTRACT: With the development of transcriptomic technologies, we are able to quantify precise changes in gene expression profiles from astronauts and other organisms exposed to spaceflight. Members of NASA GeneLab and GeneLab-associated analysis working groups (AWGs) have developed a consensus pipeline for analyzing short-read RNA-sequencing data from spaceflight-associated experiments. The pipeline includes quality control, read trimming, mapping, and gene quantification steps, culminating in the detection of differentially expressed genes. This data analysis pipeline and the results of its execution using data submitted to GeneLab are now all publicly available through the GeneLab database. We present here the full details and rationale for the construction of this pipeline in order to promote transparency, reproducibility, and reusability of pipeline data; to provide a template for data processing of future spaceflight-relevant datasets; and to encourage cross-analysis of data from other databases with the data available in GeneLab.

SUBMITTER: Overbey EG

PROVIDER: S-EPMC8044432 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature

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NASA GeneLab RNA-seq consensus pipeline: standardized processing of short-read RNA-seq data.

Overbey Eliah G EG Saravia-Butler Amanda M AM Zhang Zhe Z Rathi Komal S KS Fogle Homer H da Silveira Willian A WA Barker Richard J RJ Bass Joseph J JJ Beheshti Afshin A Berrios Daniel C DC Blaber Elizabeth A EA Cekanaviciute Egle E Costa Helio A HA Davin Laurence B LB Fisch Kathleen M KM Gebre Samrawit G SG Geniza Matthew M Gilbert Rachel R Gilroy Simon S Hardiman Gary G Herranz Raúl R Kidane Yared H YH Kruse Colin P S CPS Lee Michael D MD Liefeld Ted T Lewis Norman G NG McDonald J Tyson JT Meller Robert R Mishra Tejaswini T Perera Imara Y IY Ray Shayoni S Reinsch Sigrid S SS Rosenthal Sara Brin SB Strong Michael M Szewczyk Nathaniel J NJ Tahimic Candice G T CGT Taylor Deanne M DM Vandenbrink Joshua P JP Villacampa Alicia A Weging Silvio S Wolverton Chris C Wyatt Sarah E SE Zea Luis L Costes Sylvain V SV Galazka Jonathan M JM

iScience 20210326 4

With the development of transcriptomic technologies, we are able to quantify precise changes in gene expression profiles from astronauts and other organisms exposed to spaceflight. Members of NASA GeneLab and GeneLab-associated analysis working groups (AWGs) have developed a consensus pipeline for analyzing short-read RNA-sequencing data from spaceflight-associated experiments. The pipeline includes quality control, read trimming, mapping, and gene quantification steps, culminating in the detect ...[more]

PMID: 33870146

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Project description:BackgroundRNA-seq has shown huge potential for phylogenomic inferences in non-model organisms. However, error, incompleteness, and redundant assembled transcripts for each gene in de novo assembly of short reads cause noise in analyses and a large amount of missing data in the aligned matrix. To address these problems, we compare de novo assemblies of paired end 90 bp RNA-seq reads using Oases, Trinity, Trans-ABySS and SOAPdenovo-Trans to transcripts from genome annotation of the model plant Ricinus communis. By doing so we evaluate strategies for optimizing total gene coverage and minimizing assembly chimeras and redundancy.ResultsWe found that the frequency and structure of chimeras vary dramatically among different software packages. The differences were largely due to the number of trans-self chimeras that contain repeats in the opposite direction. More than half of the total chimeras in Oases and Trinity were trans-self chimeras. Within each package, we found a trade-off between maximizing reference coverage and minimizing redundancy and chimera rate. In order to reduce redundancy, we investigated three methods: 1) using cap3 and CD-HIT-EST to combine highly similar transcripts, 2) only retaining the transcript with the highest read coverage, or removing the transcript with the lowest read coverage for each subcomponent in Trinity, and 3) filtering Oases single k-mer assemblies by number of transcripts per locus and relative transcript length, and then finding the transcript with the highest read coverage. We then utilized results from blastx against model protein sequences to effectively remove trans chimeras. After optimization, seven assembly strategies among all four packages successfully assembled 42.9-47.1% of reference genes to more than 200 bp, with a chimera rate of 0.92-2.21%, and on average 1.8-3.1 transcripts per reference gene assembled.ConclusionsWith rapidly improving sequencing and assembly tools, our study provides a framework to benchmark and optimize performance before choosing tools or parameter combinations for analyzing short-read RNA-seq data. Our study demonstrates that choice of assembly package, k-mer sizes, post-assembly redundancy-reduction and chimera cleanup, and strand-specific RNA-seq library preparation and assembly dramatically improves gene coverage by non-redundant and non-chimeric transcripts that are optimized for downstream phylogenomic analyses.

Dataset Information

NASA GeneLab RNA-seq consensus pipeline: standardized processing of short-read RNA-seq data.

Publications

NASA GeneLab RNA-seq consensus pipeline: standardized processing of short-read RNA-seq data.

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