FADU: a Quantification Tool for Prokaryotic Transcriptomic Analyses

ABSTRACT Quantification tools for RNA sequencing (RNA-Seq) analyses are often designed and tested using human transcriptomics data sets, in which full-length transcript sequences are well annotated. For prokaryotic transcriptomics experiments, full-length transcript sequences are seldom known, and c...

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Autores principales:	Matthew Chung, Ricky S. Adkins, John S. A. Mattick, Katie R. Bradwell, Amol C. Shetty, Lisa Sadzewicz, Luke J. Tallon, Claire M. Fraser, David A. Rasko, Anup Mahurkar, Julie C. Dunning Hotopp
Formato:	article
Lenguaje:	EN
Publicado:	American Society for Microbiology 2021
Materias:	bacteria differential expression operon polycistronic transcripts read count software Microbiology QR1-502
Acceso en línea:	https://doaj.org/article/b4053ee518834536913a290b2a6553e9
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spelling	oai:doaj.org-article:b4053ee518834536913a290b2a6553e92021-12-02T18:20:19ZFADU: a Quantification Tool for Prokaryotic Transcriptomic Analyses10.1128/mSystems.00917-202379-5077https://doaj.org/article/b4053ee518834536913a290b2a6553e92021-02-01T00:00:00Zhttps://journals.asm.org/doi/10.1128/mSystems.00917-20https://doaj.org/toc/2379-5077ABSTRACT Quantification tools for RNA sequencing (RNA-Seq) analyses are often designed and tested using human transcriptomics data sets, in which full-length transcript sequences are well annotated. For prokaryotic transcriptomics experiments, full-length transcript sequences are seldom known, and coding sequences must instead be used for quantification steps in RNA-Seq analyses. However, operons confound accurate quantification of coding sequences since a single transcript does not necessarily equate to a single gene. Here, we introduce FADU (Feature Aggregate Depth Utility), a quantification tool designed specifically for prokaryotic RNA-Seq analyses. FADU assigns partial count values proportional to the length of the fragment overlapping the target feature. To assess the ability of FADU to quantify genes in prokaryotic transcriptomics analyses, we compared its performance to those of eXpress, featureCounts, HTSeq, kallisto, and Salmon across three paired-end read data sets of (i) Ehrlichia chaffeensis, (ii) Escherichia coli, and (iii) the Wolbachia endosymbiont wBm. Across each of the three data sets, we find that FADU can more accurately quantify operonic genes by deriving proportional counts for multigene fragments within operons. FADU is available at https://github.com/IGS/FADU. IMPORTANCE Most currently available quantification tools for transcriptomics analyses have been designed for human data sets, in which full-length transcript sequences, including the untranslated regions, are well annotated. In most prokaryotic systems, full-length transcript sequences have yet to be characterized, leading to prokaryotic transcriptomics analyses being performed based on only the coding sequences. In contrast to eukaryotes, prokaryotes contain polycistronic transcripts, and when genes are quantified based on coding sequences instead of transcript sequences, this leads to an increased abundance of improperly assigned ambiguous multigene fragments, specifically those mapping to multiple genes in operons. Here, we describe FADU, a quantification tool for prokaryotic RNA-Seq analyses designed to assign proportional counts with the purpose of better quantifying operonic genes while minimizing the pitfalls associated with improperly assigning fragment counts from ambiguous transcripts.Matthew ChungRicky S. AdkinsJohn S. A. MattickKatie R. BradwellAmol C. ShettyLisa SadzewiczLuke J. TallonClaire M. FraserDavid A. RaskoAnup MahurkarJulie C. Dunning HotoppAmerican Society for Microbiologyarticlebacteriadifferential expressionoperonpolycistronic transcriptsread countsoftwareMicrobiologyQR1-502ENmSystems, Vol 6, Iss 1 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	bacteria differential expression operon polycistronic transcripts read count software Microbiology QR1-502
spellingShingle	bacteria differential expression operon polycistronic transcripts read count software Microbiology QR1-502 Matthew Chung Ricky S. Adkins John S. A. Mattick Katie R. Bradwell Amol C. Shetty Lisa Sadzewicz Luke J. Tallon Claire M. Fraser David A. Rasko Anup Mahurkar Julie C. Dunning Hotopp FADU: a Quantification Tool for Prokaryotic Transcriptomic Analyses
description	ABSTRACT Quantification tools for RNA sequencing (RNA-Seq) analyses are often designed and tested using human transcriptomics data sets, in which full-length transcript sequences are well annotated. For prokaryotic transcriptomics experiments, full-length transcript sequences are seldom known, and coding sequences must instead be used for quantification steps in RNA-Seq analyses. However, operons confound accurate quantification of coding sequences since a single transcript does not necessarily equate to a single gene. Here, we introduce FADU (Feature Aggregate Depth Utility), a quantification tool designed specifically for prokaryotic RNA-Seq analyses. FADU assigns partial count values proportional to the length of the fragment overlapping the target feature. To assess the ability of FADU to quantify genes in prokaryotic transcriptomics analyses, we compared its performance to those of eXpress, featureCounts, HTSeq, kallisto, and Salmon across three paired-end read data sets of (i) Ehrlichia chaffeensis, (ii) Escherichia coli, and (iii) the Wolbachia endosymbiont wBm. Across each of the three data sets, we find that FADU can more accurately quantify operonic genes by deriving proportional counts for multigene fragments within operons. FADU is available at https://github.com/IGS/FADU. IMPORTANCE Most currently available quantification tools for transcriptomics analyses have been designed for human data sets, in which full-length transcript sequences, including the untranslated regions, are well annotated. In most prokaryotic systems, full-length transcript sequences have yet to be characterized, leading to prokaryotic transcriptomics analyses being performed based on only the coding sequences. In contrast to eukaryotes, prokaryotes contain polycistronic transcripts, and when genes are quantified based on coding sequences instead of transcript sequences, this leads to an increased abundance of improperly assigned ambiguous multigene fragments, specifically those mapping to multiple genes in operons. Here, we describe FADU, a quantification tool for prokaryotic RNA-Seq analyses designed to assign proportional counts with the purpose of better quantifying operonic genes while minimizing the pitfalls associated with improperly assigning fragment counts from ambiguous transcripts.
format	article
author	Matthew Chung Ricky S. Adkins John S. A. Mattick Katie R. Bradwell Amol C. Shetty Lisa Sadzewicz Luke J. Tallon Claire M. Fraser David A. Rasko Anup Mahurkar Julie C. Dunning Hotopp
author_facet	Matthew Chung Ricky S. Adkins John S. A. Mattick Katie R. Bradwell Amol C. Shetty Lisa Sadzewicz Luke J. Tallon Claire M. Fraser David A. Rasko Anup Mahurkar Julie C. Dunning Hotopp
author_sort	Matthew Chung
title	FADU: a Quantification Tool for Prokaryotic Transcriptomic Analyses
title_short	FADU: a Quantification Tool for Prokaryotic Transcriptomic Analyses
title_full	FADU: a Quantification Tool for Prokaryotic Transcriptomic Analyses
title_fullStr	FADU: a Quantification Tool for Prokaryotic Transcriptomic Analyses
title_full_unstemmed	FADU: a Quantification Tool for Prokaryotic Transcriptomic Analyses
title_sort	fadu: a quantification tool for prokaryotic transcriptomic analyses
publisher	American Society for Microbiology
publishDate	2021
url	https://doaj.org/article/b4053ee518834536913a290b2a6553e9
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FADU: a Quantification Tool for Prokaryotic Transcriptomic Analyses

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