Corset: enabling differential gene expression analysis for de novo assembled transcriptomes

By N. Davidson, Alicia Oshlack. Genome Biology, 2014

668 citations recorded by the source corpus.

Next generation sequencing has made it possible to perform differential gene expression studies in non-model organisms. For these studies, the need for a reference genome is circumvented by performing de novo assembly on the RNA-seq data. However, transcriptome assembly produces a multitude of contigs, which must be clustered into genes prior to differential gene expression detection. Here we present Corset, a method that hierarchically clusters contigs using shared reads and expression, then summarizes read counts to clusters, ready for statistical testing. Using a range of metrics, we demonstrate that Corset out-performs alternative methods. Corset is available from https://code.google.com/p/corset-project/.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/90993067424d2fb196ef7b0e4a6ff69bdf12294b

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In Molecular Biology

1. Steven Kelly
2. Rob Patro
3. Julian M. Hibberd
4. David M. Emms

Citations in the corpus

• TransRate: reference-free quality assessment of de novo transcriptome assemblies (2015, methodology): “On average, Oases produced the highest mean contig scores for mouse and rice, while Trinity produced the highest mean contig scores for human and yeast (Figure 5B).”