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Motivation: De novo transcriptome assembly of non-model organisms is thefirst major step for many RNA-seq analysis tasks. Current methods for de novoassembly often report a large number of contiguous sequences (contigs), whichmay be fractured and incomplete sequences instead of full-length transcripts.Dealing with a large number of such contigs can slow and complicate downstreamanalysis.  Results :We present a method for clustering contigs from de novotranscriptome assemblies based upon the relationships exposed by multi-mappingsequencing fragments. Specifically, we cast the problem of clustering contigsas one of clustering a sparse graph that is induced by equivalence classes offragments that map to subsets of the transcriptome. Leveraging recentdevelopments in efficient read mapping and transcript quantification, we havedeveloped RapClust, a tool implementing this approach that is capable ofaccurately clustering most large de novo transcriptomes in a matter of minutes,while simultaneously providing accurate estimates of expression for theresulting clusters. We compare RapClust against a number of tools commonly usedfor de novo transcriptome clustering. Using de novo assemblies of organisms forwhich reference genomes are available, we assess the accuracy of thesedifferent methods in terms of the quality of the resulting clusterings, and theconcordance of differential expression tests with those based on ground truthclusters. We find that RapClust produces clusters of comparable or betterquality than existing state-of-the-art approaches, and does so substantiallyfaster. RapClust also confers a large benefit in terms of space usage, as itproduces only succinct intermediate files - usually on the order of a fewmegabytes - even when processing hundreds of millions of reads.

Accurate, Fast and Lightweight Clustering of de novo Transcriptomes using Fragment Equivalence Classes