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AugmentingAssembly

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NIAID Data Ecosystem2026-03-09 收录
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Researchers interested in studying and constructing transcriptomes, especially for non-model species, face the conundrum of choosing from a number of available de novo and genome-guided assemblers. None of the popular assembly tools in use today achieve requisite sensitivity, specificity or recovery of full-length transcripts on their own. Here, we present a comprehensive comparative study of the performance of various assemblers. Additionally, we present an approach to combinatorially augment transciptome assembly by using both de novo and genome-guided tools. In our study, we obtained the best recovery and most full-length transcripts with Trinity and TopHat1-Cufflinks, respectively. The sensitivity of the assembly and isoform recovery was superior, without compromising much on the specificity, when transcripts from Trinity were augmented with those from TopHat1-Cufflinks.

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2015-12-14
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