MOTIVATION: RNA sequencing is becoming a standard for expression profiling experiments and many tools have been developed in the past few years to analyze RNA-Seq data. Numerous 'Bioconductor' packages are available for next-generation sequencing data loading in R, e.g. ShortRead and Rsamtools as well as to perform differential gene expression analyses, e.g. DESeq and edgeR. However, the processing tasks lying in between these require the precise interplay of many Bioconductor packages, e.g. Biostrings, IRanges or external solutions are to be sought. RESULTS: We developed 'easyRNASeq', an R package that simplifies the processing of RNA sequencing data, hiding the complex interplay of the required packages behind a single functionality. AVAILABILITY: The package is implemented in R (as of version 2.15) and is available from Bioconductor (as of version 2.10) at the URL: http://bioconductor.org/packages/release/bioc/html/easyRNASeq.html, where installation and usage instructions can be found. CONTACT: [email protected].

Abstract Motivation: Microarrays provide an accurate and cost-effective method for genotyping large numbers of individuals at high resolution. The resulting data permit the identification of loci at which genetic variation is associated with quantitative traits, or fine mapping of meiotic recombination, which is a key determinant of genetic diversity among individuals. Several issues inherent to short oligonucleotide arrays—cross-hybridization, or variability in probe response to target—have the potential to produce genotyping errors. There is a need for improved statistical methods for array-based genotyping. Results: We developed ssGenotyping (ssG), a multivariate, semi-supervised approach for using microarrays to genotype haploid individuals at thousands of polymorphic sites. Using a meiotic recombination dataset, we show that ssG is more accurate than existing supervised classification methods, and that it produces denser marker coverage. The ssG algorithm is able to fit probe-specific affinity differences and to detect and filter spurious signal, permitting high-confidence genotyping at nucleotide resolution. We also demonstrate that oligonucleotide probe response depends significantly on genomic background, even when the probe's specific target sequence is unchanged. As a result, supervised classifiers trained on reference strains may not generalize well to diverged strains; ssG's semi-supervised approach, on the other hand, adapts automatically. Availability: The ssGenotyping software is implemented in R. It is currently available for download (www.ebi.ac.uk/∼bourgon/yeast_genotyping/ssG) and is being submitted to Bioconductor. Contact: [email protected] Supplementary information: Supplementary data and a version including color figures are available at Bioinformatics online.

MOTIVATION: High-density DNA tiling microarrays are a powerful tool for the characterization of complete transcriptomes. The two major analytical challenges are the segmentation of the hybridization signal along genomic coordinates to accurately determine transcript boundaries and the adjustment of the sequence-dependent response of the oligonucleotide probes to achieve quantitative comparability of the signal between different probes. RESULTS: We describe a dynamic programming algorithm for finding a globally optimal fit of a piecewise constant expression profile along genomic coordinates. We developed a probe-specific background correction and scaling method that employs empirical probe response parameters determined from reference hybridizations with no need for paired mismatch probes. This combined analysis approach allows the accurate determination of dynamical changes in transcription architectures from hybridization data and will help to study the biological significance of complex transcriptional phenomena in eukaryotic genomes. AVAILABILITY: R package tilingArray at http://www.bioconductor.org.