MOTIVATION: Whole transcriptome shotgun sequencing data from non-normalized samples offer unique opportunities to study the metabolic states of organisms. One can deduce gene expression levels using sequence coverage as a surrogate, identify coding changes or discover novel isoforms or transcripts. Especially for discovery of novel events, de novo assembly of transcriptomes is desirable. RESULTS: Transcriptome from tumor tissue of a patient with follicular lymphoma was sequenced with 36 base pair (bp) single- and paired-end reads on the Illumina Genome Analyzer II platform. We assembled approximately 194 million reads using ABySS into 66 921 contigs 100 bp or longer, with a maximum contig length of 10 951 bp, representing over 30 million base pairs of unique transcriptome sequence, or roughly 1% of the genome. AVAILABILITY AND IMPLEMENTATION: Source code and binaries of ABySS are freely available for download at http://www.bcgsc.ca/platform/bioinfo/software/abyss. Assembler tool is implemented in C++. The parallel version uses Open MPI. ABySS-Explorer tool is implemented in Java using the Java universal network/graph framework. CONTACT: [email protected].

MOTIVATION: Analysis of array comparative genomic hybridization (aCGH) data for recurrent DNA copy number alterations from a cohort of patients can yield distinct sets of molecular signatures or profiles. This can be due to the presence of heterogeneous cancer subtypes within a supposedly homogeneous population. RESULTS: We propose a novel statistical method for automatically detecting such subtypes or clusters. Our approach is model based: each cluster is defined in terms of a sparse profile, which contains the locations of unusually frequent alterations. The profile is represented as a hidden Markov model. Samples are assigned to clusters based on their similarity to the cluster's profile. We simultaneously infer the cluster assignments and the cluster profiles using an expectation maximization-like algorithm. We show, using a realistic simulation study, that our method is significantly more accurate than standard clustering techniques. We then apply our method to two clinical datasets. In particular, we examine previously reported aCGH data from a cohort of 106 follicular lymphoma patients, and discover clusters that are known to correspond to clinically relevant subgroups. In addition, we examine a cohort of 92 diffuse large B-cell lymphoma patients, and discover previously unreported clusters of biological interest which have inspired followup clinical research on an independent cohort. AVAILABILITY: Software and synthetic datasets are available at http://www.cs.ubc.ca/ approximately sshah/acgh as part of the CNA-HMMer package. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.