Abstract | ||
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Motivation: Transcriptome sequencing has long been the favored method for quickly and inexpensively obtaining a large number of gene sequences from an organism with no reference genome. Owing to the rapid increase in throughputs and decrease in costs of next- generation sequencing, RNA-Seq in particular has become the method of choice. However, the very short reads (e.g. 2 x 90 bp paired ends) from next generation sequencing makes de novo assembly to recover complete or full-length transcript sequences an algorithmic challenge. Results: Here, we present SOAPdenovo-Trans, a de novo transcriptome assembler designed specifically for RNA-Seq. We evaluated its performance on transcriptome datasets from rice and mouse. Using as our benchmarks the known transcripts from these well-annotated genomes (sequenced a decade ago), we assessed how SOAPdenovo-Trans and two other popular transcriptome assemblers handled such practical issues as alternative splicing and variable expression levels. Our conclusion is that SOAPdenovo-Trans provides higher contiguity, lower redundancy and faster execution. |
Year | DOI | Venue |
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2014 | 10.1093/bioinformatics/btu077 | BIOINFORMATICS |
Field | DocType | Volume |
Genome,De novo transcriptome assembly,RNA-Seq,Computer science,Transcriptome,Alternative splicing,DNA sequencing,Bioinformatics,Sequence assembly,Reference genome | Journal | 30 |
Issue | ISSN | Citations |
12 | 1367-4803 | 11 |
PageRank | References | Authors |
0.63 | 1 | 16 |
Name | Order | Citations | PageRank |
---|---|---|---|
Yinlong Xie | 1 | 23 | 1.67 |
Gengxiong Wu | 2 | 11 | 0.63 |
Jingbo Tang | 3 | 11 | 0.63 |
Ruibang Luo | 4 | 113 | 9.92 |
Jordan Patterson | 5 | 11 | 0.63 |
Shanlin Liu | 6 | 11 | 0.63 |
Weihua Huang | 7 | 11 | 0.63 |
Guangzhu He | 8 | 11 | 0.63 |
Shengchang Gu | 9 | 11 | 0.63 |
Shengkang Li | 10 | 11 | 0.97 |
Xin Zhou | 11 | 14 | 1.42 |
Tak-Wah Lam | 12 | 1860 | 164.96 |
Yingrui Li | 13 | 554 | 72.28 |
Xun Xu | 14 | 15 | 2.36 |
Gane Ka-Shu Wong | 15 | 162 | 17.81 |
Jun Wang | 16 | 9228 | 736.82 |