Selecting Superior De Novo Transcriptome Assemblies: Lessons Learned by Leveraging the Best Plant Genome

Whereas de novo assemblies of RNA-Seq data are being published for a growing number of species across the tree of life, there are currently no broadly accepted methods for evaluating such assemblies. Here we present a detailed comparison of 99 transcriptome assemblies, generated with 6 de novo assem...

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Main Authors:	Honaas, Loren A., Wafula, Eric K., Wickett, Norman J., Der, Joshua P., Zhang, Yeting, Edger, Patrick P., Altman, Naomi S., Pires, J. Chris, Leebens-Mack, James H., dePamphilis, Claude W.
Format:	Online
Language:	English
Published:	Public Library of Science 2016
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4701411/

id	pubmed-4701411
recordtype	oai_dc
spelling	pubmed-47014112016-01-15 Selecting Superior De Novo Transcriptome Assemblies: Lessons Learned by Leveraging the Best Plant Genome Honaas, Loren A. Wafula, Eric K. Wickett, Norman J. Der, Joshua P. Zhang, Yeting Edger, Patrick P. Altman, Naomi S. Pires, J. Chris Leebens-Mack, James H. dePamphilis, Claude W. Research Article Whereas de novo assemblies of RNA-Seq data are being published for a growing number of species across the tree of life, there are currently no broadly accepted methods for evaluating such assemblies. Here we present a detailed comparison of 99 transcriptome assemblies, generated with 6 de novo assemblers including CLC, Trinity, SOAP, Oases, ABySS and NextGENe. Controlled analyses of de novo assemblies for Arabidopsis thaliana and Oryza sativa transcriptomes provide new insights into the strengths and limitations of transcriptome assembly strategies. We find that the leading assemblers generate reassuringly accurate assemblies for the majority of transcripts. At the same time, we find a propensity for assemblers to fail to fully assemble highly expressed genes. Surprisingly, the instance of true chimeric assemblies is very low for all assemblers. Normalized libraries are reduced in highly abundant transcripts, but they also lack 1000s of low abundance transcripts. We conclude that the quality of de novo transcriptome assemblies is best assessed through consideration of a combination of metrics: 1) proportion of reads mapping to an assembly 2) recovery of conserved, widely expressed genes, 3) N50 length statistics, and 4) the total number of unigenes. We provide benchmark Illumina transcriptome data and introduce SCERNA, a broadly applicable modular protocol for de novo assembly improvement. Finally, our de novo assembly of the Arabidopsis leaf transcriptome revealed ~20 putative Arabidopsis genes lacking in the current annotation. Public Library of Science 2016-01-05 /pmc/articles/PMC4701411/ /pubmed/26731733 http://dx.doi.org/10.1371/journal.pone.0146062 Text en © 2016 Honaas et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Honaas, Loren A. Wafula, Eric K. Wickett, Norman J. Der, Joshua P. Zhang, Yeting Edger, Patrick P. Altman, Naomi S. Pires, J. Chris Leebens-Mack, James H. dePamphilis, Claude W.
spellingShingle	Honaas, Loren A. Wafula, Eric K. Wickett, Norman J. Der, Joshua P. Zhang, Yeting Edger, Patrick P. Altman, Naomi S. Pires, J. Chris Leebens-Mack, James H. dePamphilis, Claude W. Selecting Superior De Novo Transcriptome Assemblies: Lessons Learned by Leveraging the Best Plant Genome
author_facet	Honaas, Loren A. Wafula, Eric K. Wickett, Norman J. Der, Joshua P. Zhang, Yeting Edger, Patrick P. Altman, Naomi S. Pires, J. Chris Leebens-Mack, James H. dePamphilis, Claude W.
author_sort	Honaas, Loren A.
title	Selecting Superior De Novo Transcriptome Assemblies: Lessons Learned by Leveraging the Best Plant Genome
title_short	Selecting Superior De Novo Transcriptome Assemblies: Lessons Learned by Leveraging the Best Plant Genome
title_full	Selecting Superior De Novo Transcriptome Assemblies: Lessons Learned by Leveraging the Best Plant Genome
title_fullStr	Selecting Superior De Novo Transcriptome Assemblies: Lessons Learned by Leveraging the Best Plant Genome
title_full_unstemmed	Selecting Superior De Novo Transcriptome Assemblies: Lessons Learned by Leveraging the Best Plant Genome
title_sort	selecting superior de novo transcriptome assemblies: lessons learned by leveraging the best plant genome
description	Whereas de novo assemblies of RNA-Seq data are being published for a growing number of species across the tree of life, there are currently no broadly accepted methods for evaluating such assemblies. Here we present a detailed comparison of 99 transcriptome assemblies, generated with 6 de novo assemblers including CLC, Trinity, SOAP, Oases, ABySS and NextGENe. Controlled analyses of de novo assemblies for Arabidopsis thaliana and Oryza sativa transcriptomes provide new insights into the strengths and limitations of transcriptome assembly strategies. We find that the leading assemblers generate reassuringly accurate assemblies for the majority of transcripts. At the same time, we find a propensity for assemblers to fail to fully assemble highly expressed genes. Surprisingly, the instance of true chimeric assemblies is very low for all assemblers. Normalized libraries are reduced in highly abundant transcripts, but they also lack 1000s of low abundance transcripts. We conclude that the quality of de novo transcriptome assemblies is best assessed through consideration of a combination of metrics: 1) proportion of reads mapping to an assembly 2) recovery of conserved, widely expressed genes, 3) N50 length statistics, and 4) the total number of unigenes. We provide benchmark Illumina transcriptome data and introduce SCERNA, a broadly applicable modular protocol for de novo assembly improvement. Finally, our de novo assembly of the Arabidopsis leaf transcriptome revealed ~20 putative Arabidopsis genes lacking in the current annotation.
publisher	Public Library of Science
publishDate	2016
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4701411/
_version_	1613520054529294336

Selecting Superior De Novo Transcriptome Assemblies: Lessons Learned by Leveraging the Best Plant Genome

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