Evaluation of de novo transcriptome assemblies from RNA-Seq data

Evaluation of de novo transcriptome assemblies from RNA-Seq data

De novo RNA-Seq assembly facilitates the study of transcriptomes for species without sequenced genomes, but it is challenging to select the most accurate assembly in this context. To address this challenge, we developed a model-based score, RSEM-EVAL, for evaluating assemblies when the ground truth...

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Main Authors: Li, Bo, Fillmore, Nathanael, Bai, Yongsheng, Collins, Mike, Thomson, James A, Stewart, Ron, Dewey, Colin N
Format: Online
Language:English
Published: BioMed Central 2014
Online Access:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4298084/
id pubmed-4298084
recordtype oai_dc
spelling pubmed-42980842015-02-03 Evaluation of de novo transcriptome assemblies from RNA-Seq data Li, Bo Fillmore, Nathanael Bai, Yongsheng Collins, Mike Thomson, James A Stewart, Ron Dewey, Colin N Method De novo RNA-Seq assembly facilitates the study of transcriptomes for species without sequenced genomes, but it is challenging to select the most accurate assembly in this context. To address this challenge, we developed a model-based score, RSEM-EVAL, for evaluating assemblies when the ground truth is unknown. We show that RSEM-EVAL correctly reflects assembly accuracy, as measured by REF-EVAL, a refined set of ground-truth-based scores that we also developed. Guided by RSEM-EVAL, we assembled the transcriptome of the regenerating axolotl limb; this assembly compares favorably to a previous assembly. A software package implementing our methods, DETONATE, is freely available at http://deweylab.biostat.wisc.edu/detonate. BioMed Central 2014-12-21 2014 /pmc/articles/PMC4298084/ /pubmed/25608678 http://dx.doi.org/10.1186/s13059-014-0553-5 Text en © Li et al.; licensee BioMed Central. 2014 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 work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
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 Li, Bo
Fillmore, Nathanael
Bai, Yongsheng
Collins, Mike
Thomson, James A
Stewart, Ron
Dewey, Colin N
spellingShingle Li, Bo
Fillmore, Nathanael
Bai, Yongsheng
Collins, Mike
Thomson, James A
Stewart, Ron
Dewey, Colin N
Evaluation of de novo transcriptome assemblies from RNA-Seq data
author_facet Li, Bo
Fillmore, Nathanael
Bai, Yongsheng
Collins, Mike
Thomson, James A
Stewart, Ron
Dewey, Colin N
author_sort Li, Bo
title Evaluation of de novo transcriptome assemblies from RNA-Seq data
title_short Evaluation of de novo transcriptome assemblies from RNA-Seq data
title_full Evaluation of de novo transcriptome assemblies from RNA-Seq data
title_fullStr Evaluation of de novo transcriptome assemblies from RNA-Seq data
title_full_unstemmed Evaluation of de novo transcriptome assemblies from RNA-Seq data
title_sort evaluation of de novo transcriptome assemblies from rna-seq data
description De novo RNA-Seq assembly facilitates the study of transcriptomes for species without sequenced genomes, but it is challenging to select the most accurate assembly in this context. To address this challenge, we developed a model-based score, RSEM-EVAL, for evaluating assemblies when the ground truth is unknown. We show that RSEM-EVAL correctly reflects assembly accuracy, as measured by REF-EVAL, a refined set of ground-truth-based scores that we also developed. Guided by RSEM-EVAL, we assembled the transcriptome of the regenerating axolotl limb; this assembly compares favorably to a previous assembly. A software package implementing our methods, DETONATE, is freely available at http://deweylab.biostat.wisc.edu/detonate.
publisher BioMed Central
publishDate 2014
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4298084/
_version_ 1613177911423008768

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