Optimizing de novo transcriptome assembly from short-read RNA-Seq data: a comparative study.

ABSTRACT:

Background

With the fast advances in nextgen sequencing technology, high-throughput RNA sequencing has emerged as a powerful and cost-effective way for transcriptome study. De novo assembly of transcripts provides an important solution to transcriptome analysis for organisms with no reference genome. However, there lacked understanding on how the different variables affected assembly outcomes, and there was no consensus on how to approach an optimal solution by selecting software tool and suitable strategy based on the properties of RNA-Seq data.

Results

To reveal the performance of different programs for transcriptome assembly, this work analyzed some important factors, including k-mer values, genome complexity, coverage depth, directional reads, etc. Seven program conditio

SUBMITTER: Zhao QY 

PROVIDER: S-EPMC3287467 | biostudies-literature | 2011 Dec

REPOSITORIES: biostudies-literature