RSEM-EVAL – for evaluating assemblies when the ground truth is unknown

De novo RNA-Seq assembly facilitates the study of transcriptomes for species without sequenced genomes, but it is challenging to select the most accurate assembly in this context. To address this challenge, a team led by researchers at the University of Wisconsin, Madison developed a model-based score, RSEM-EVAL, for evaluating assemblies when the ground truth is unknown. They show that RSEM-EVAL correctly reflects assembly accuracy, as measured by REF-EVAL, a refined set of ground-truth-based scores that the team also developed. Guided by RSEM-EVAL, the researchers assembled the transcriptome of the regenerating axolotl limb; this assembly compares favorably to a previous assembly.


RSEM-EVAL correctly selects the Trinity assembly of reads originating from a transcript of mouse geneRpl24 as the best among the default assemblies from Trinity, Oases and SOAPdenovo-Trans.

Availability – A software package implementing our methods, DETONATE, is freely available at

Li B, Fillmore N, Bai Y, Collins M, Thomson JA, Stewart R, Dewey CN. (2014) Evaluation of de novo transcriptome assemblies from RNA-Seq data. Genome Biol 15(12):553. [article]

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