Motivation: Second-generation sequencing technologies produce a massive amount of short reads in a single experiment. However, sequencing errors can cause major problems when using this approach for de novo sequencing applications. Moreover, existing error correction methods have been designed and optimized for shotgun sequencing. Therefore, there is an urgent need for the design of fast and accurate computational methods and tools for error correction of large amounts of short read data. Results: We present SHREC, a new algorithm for correcting errors in short-read data that uses a generalized suffix trie on the read data as the underlying data structure. Our results show that the method can identify erroneous reads with sensitivity and specificity of over 99% and 96% for simulated data with error rates of up to 3% as well as for real data. Furthermore, it achieves an error correction accuracy of over 80% for simulated data and over 88% for real data. These results are clearly superior to previously published approaches. SHREC is available as an efficient open-source Java implementa-tion that allows processing of ten million of short reads on a stan-dard workstation. Availability: SHREC source code in JAVA is freely available at http://www.informatik.uni-kiel.de/~jasc/Shrec/

Motivation: Most microbial species can not be cultured in the lab. Metagenomic sequencing may still yield a complete genome if the sequenced community is enriched and the sequencing coverage is high. However, the complexity in a natural population may cause the enrichment culture to contain multiple related strains. This diversity can confound existing strict assembly programs and lead to a fragmented assembly, which is unnecessary if we have a related reference genome available that can function as a scaffold. Results: Here, we map short metagenomic sequencing reads from a population of strains to a related reference genome, and compose a genome that captures the consensus of the population's sequences. We show that by iteration of the mapping and assembly procedure, the coverage increases while the similarity with the reference genome decreases. This indicates that the assembly becomes less dependent on the reference genome and approaches the consensus genome of the multi-strain population. Contact: dutilh@cmbi.ru.nl.

Summary: The shorter and vastly more numerous reads produced by second-generation sequencing technologies require new tools that can assemble massive numbers of reads in reasonable time. Existing short-read assembly tools can be classified into two categories: greedy extension-based and graph-based. While the graph-based approaches are generally superior in terms of assembly quality, the computer resources required for building and storing a huge graph are very high. In this paper, we present Taipan, an assembly algo-rithm which can be viewed as a hybrid of these two approaches. Taipan uses greedy extensions for contig construction but at each step realizes enough of the corresponding read graph to make bet-ter decisions as to how assembly should continue. We show that this approach can achieve an assembly quality at least as good as the graph-based approaches used in the popular Edena and Velvet assembly tools using a moderate amount of computing resources. Availability and Implementation: Source code in C running on Linux is freely available at http://taipan.sourceforge.net

Widespread adoption of massively parallel deoxyribonucleic acid (DNA) sequencing instruments has prompted the recent development of de novo short read assembly algorithms. A common shortcoming of the available tools is their inability to efficiently assemble vast amounts of data generated from large-scale sequencing projects, such as the sequencing of individual human genomes to catalog natural genetic variation. To address this limitation, we developed ABySS (Assembly By Short Sequences), a parallelized sequence assembler. As a demonstration of the capability of our software, we assembled 3.5 billion paired-end reads from the genome of an African male publicly released by Illumina, Inc. Approximately 2.76 million contigs ≥100 base pairs (bp) in length were created with an N50 size of 1499 bp, representing 68% of the reference human genome. Analysis of these contigs identified polymorphic and novel sequences not present in the human reference assembly, which were validated by alignment to alternate human assemblies and to other primate genomes.

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 100bp or longer, with a maximum contig length of 10,951bp, 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. Explorer tool is implemented in Java using the Java universal network/graph framework.

Shows That Teach puts on great school assemblies in California about that deal with conservation and environmental issues with fun songs.