Title |
Evaluation of de novo transcriptome assemblies from RNA-Seq data
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Published in |
Genome Biology, December 2014
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DOI | 10.1186/s13059-014-0553-5 |
Pubmed ID | |
Authors |
Bo Li, Nathanael Fillmore, Yongsheng Bai, Mike Collins, James A Thomson, Ron Stewart, Colin 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. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
India | 3 | 38% |
United States | 2 | 25% |
United Kingdom | 1 | 13% |
France | 1 | 13% |
Unknown | 1 | 13% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Scientists | 5 | 63% |
Members of the public | 3 | 38% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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United States | 13 | 2% |
Germany | 9 | 1% |
Brazil | 5 | <1% |
Spain | 5 | <1% |
Italy | 3 | <1% |
Norway | 3 | <1% |
United Kingdom | 3 | <1% |
Japan | 2 | <1% |
Mexico | 2 | <1% |
Other | 17 | 2% |
Unknown | 629 | 91% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 167 | 24% |
Researcher | 157 | 23% |
Student > Master | 91 | 13% |
Student > Bachelor | 58 | 8% |
Student > Doctoral Student | 33 | 5% |
Other | 114 | 16% |
Unknown | 71 | 10% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 345 | 50% |
Biochemistry, Genetics and Molecular Biology | 149 | 22% |
Computer Science | 45 | 7% |
Environmental Science | 15 | 2% |
Engineering | 11 | 2% |
Other | 35 | 5% |
Unknown | 91 | 13% |