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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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Autores principales: 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.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4701411/
https://www.ncbi.nlm.nih.gov/pubmed/26731733
http://dx.doi.org/10.1371/journal.pone.0146062
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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.
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.
collection PubMed
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) N(50) 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.
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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. PLoS One 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) N(50) 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
spellingShingle Research Article
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
title 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_short 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
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4701411/
https://www.ncbi.nlm.nih.gov/pubmed/26731733
http://dx.doi.org/10.1371/journal.pone.0146062
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