Abstract
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.
Highlights
High-throughput technology has changed our understanding of many facets of biology, like diseases (Shendure & Lieberman Aiden, 2012), plant genetics (Egan, Schlueter & Spooner, 2012; Simon et al, 2009), and synthetic biology (Mitchell, 2011)
The transcript regions contiguously covered by reads were termed as Model Assembly (MA) fragments and were used as a valid reference for mapping the assemblies
RNA-seq using next-generation sequencing is a powerful technology to understand the transcriptome of an organism
Summary
High-throughput technology has changed our understanding of many facets of biology, like diseases (Shendure & Lieberman Aiden, 2012), plant genetics (Egan, Schlueter & Spooner, 2012; Simon et al, 2009), and synthetic biology (Mitchell, 2011). DNA microarray, a powerful technique, is dependent on gene annotation, and, the genome sequence information. This is circumvented by RNA sequencing (RNA-seq), which uses next-generation sequencing instruments, and can be leveraged even in the absence of a genome to study the transcripts and their expression. This shift from the semi-quantitative, hybridization-based approaches, as in DNA microarrays, to the quantitative, sequencing-based approaches has tremendously facilitated gene expression analysis.
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