MOTIVATION: A crucial problem in genome assembly is the discovery and correction of misassembly errors in draft genomes. We develop a method called misSEQuel that enhances the quality of draft genomes by identifying misassembly errors and their breakpoints using paired-end sequence reads and optical mapping data. Our method also fulfills the critical need for open source computational methods for analyzing optical mapping data. We apply our method to various assemblies of the loblolly pine, Francisella tularensis, rice and budgerigar genomes. We generated and used stimulated optical mapping data for loblolly pine and F.tularensis and used real optical mapping data for rice and budgerigar. RESULTS: Our results demonstrate that we detect more than 54% of extensively misassembled contigs and more than 60% of locally misassembled contigs in assemblies of F.tularensis and between 31% and 100% of extensively misassembled contigs and between 57% and 73% of locally misassembled contigs in assemblies of loblolly pine. Using the real optical mapping data, we correctly identified 75% of extensively misassembled contigs and 100% of locally misassembled contigs in rice, and 77% of extensively misassembled contigs and 80% of locally misassembled contigs in budgerigar. AVAILABILITY AND IMPLEMENTATION: misSEQuel can be used as a post-processing step in combination with any genome assembler and is freely available at http://www.cs.colostate.edu/seq/.

MOTIVATION: Assemblies of next-generation sequencing (NGS) data, although accurate, still contain a substantial number of errors that need to be corrected after the assembly process. We develop SEQuel, a tool that corrects errors (i.e. insertions, deletions and substitution errors) in the assembled contigs. Fundamental to the algorithm behind SEQuel is the positional de Bruijn graph, a graph structure that models k-mers within reads while incorporating the approximate positions of reads into the model. RESULTS: SEQuel reduced the number of small insertions and deletions in the assemblies of standard multi-cell Escherichia coli data by almost half, and corrected between 30% and 94% of the substitution errors. Further, we show SEQuel is imperative to improving single-cell assembly, which is inherently more challenging due to higher error rates and non-uniform coverage; over half of the small indels, and substitution errors in the single-cell assemblies were corrected. We apply SEQuel to the recently assembled Deltaproteobacterium SAR324 genome, which is the first bacterial genome with a comprehensive single-cell genome assembly, and make over 800 changes (insertions, deletions and substitutions) to refine this assembly. AVAILABILITY: SEQuel can be used as a post-processing step in combination with any NGS assembler and is freely available at http://bix.ucsd.edu/SEQuel/.

MOTIVATION: MicroRNAs (miRNAs) are short abundant non-coding RNAs critical for many cellular processes. Deep sequencing (next-generation sequencing) technologies are being readily used to receive a more accurate depiction of miRNA expression profiles in living cells. This type of analysis is a key step towards improving our understanding of the complexity and mode of miRNA regulation. RESULTS: miRNAkey is a software package designed to be used as a base-station for the analysis of miRNA deep sequencing data. The package implements common steps taken in the analysis of such data, as well as adds unique features, such as data statistics and multiple read determination, generating a novel platform for the analysis of miRNA expression. A user-friendly graphical interface is applied to determine the analysis steps. The tabular and graphical output contains general and detailed reports on the sequence reads and provides an accurate picture of the differentially expressed miRNAs in paired samples. AVAILABILITY AND IMPLEMENTATION: See http://ibis.tau.ac.il/miRNAkey CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.