MOTIVATION: Transcriptome sequencing has long been the favored method for quickly and inexpensively obtaining a large number of gene sequences from an organism with no reference genome. Owing to the rapid increase in throughputs and decrease in costs of next- generation sequencing, RNA-Seq in particular has become the method of choice. However, the very short reads (e.g. 2 × 90 bp paired ends) from next generation sequencing makes de novo assembly to recover complete or full-length transcript sequences an algorithmic challenge. RESULTS: Here, we present SOAPdenovo-Trans, a de novo transcriptome assembler designed specifically for RNA-Seq. We evaluated its performance on transcriptome datasets from rice and mouse. Using as our benchmarks the known transcripts from these well-annotated genomes (sequenced a decade ago), we assessed how SOAPdenovo-Trans and two other popular transcriptome assemblers handled such practical issues as alternative splicing and variable expression levels. Our conclusion is that SOAPdenovo-Trans provides higher contiguity, lower redundancy and faster execution. AVAILABILITY AND IMPLEMENTATION: Source code and user manual are available at http://sourceforge.net/projects/soapdenovotrans/.

UNLABELLED: Cloud computing offers low cost and highly flexible opportunities in bioinformatics. Its potential has already been demonstrated in high-throughput sequence data analysis. Pathway-based or gene set analysis of expression data has received relatively less attention. We developed a gene set analysis algorithm for biomarker identification in the cloud. The resulting tool, YunBe, is ready to use on Amazon Web Services. Moreover, here we compare its performance to those obtained with desktop and computing cluster solutions. AVAILABILITY AND IMPLEMENTATION: YunBe is open-source and freely accessible within the Amazon Elastic MapReduce service at s3n://lrcv-crp-sante/app/yunbe.jar. Source code and user's guidelines can be downloaded from http://tinyurl.com/yunbedownload.