Evaluation of de novo Transcriptome Assemblies from RNA-Seq Data
Dec 21, 2014·
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Dr. Bo Li
N. fillmore
Y. bai
M. collins
J. a. thomson
R. stewart
C. n. dewey
Abstract
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 .
Type
Publication
Genome Biology
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Principal Scientist II
Dr. Bo Li is a Principal Scientist at Genentech, Inc. His research focuses on large-scale single-cell genomics data analysis.
Before joining in Genentech, he was an Assistant Professor of Medicine at Harvard Medical School and the director of Bioinformatics and Computational Biology at Center for Immunology and Inflammatory Diseases, Massachusetts General Hospital.
He received his Ph.D. in computer science from UW-Madison and completed two postdoctoral trainings with Dr. Lior Pachter at UC Berkeley and Dr. Aviv Regev at Broad Institute.
He is best known for developing RSEM, an impactful RNA-seq transcript quantification software. RSEM is cited 22,602 times (Google Scholar) and adopted by several big consortia such as TCGA, ENCODE, GTEx and TOPMed.
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