SUMMARY: The wide range of RNA-seq applications and their high-computational needs require the development of pipelines orchestrating the entire workflow and optimizing usage of available computational resources. We present aRNApipe, a project-oriented pipeline for processing of RNA-seq data in high-performance cluster environments. aRNApipe is highly modular and can be easily migrated to any high-performance computing (HPC) environment. The current applications included in aRNApipe combine the essential RNA-seq primary analyses, including quality control metrics, transcript alignment, count generation, transcript fusion identification, alternative splicing and sequence variant calling. aRNApipe is project-oriented and dynamic so users can easily update analyses to include or exclude samples or enable additional processing modules. Workflow parameters are easily set using a single configuration file that provides centralized tracking of all analytical processes. Finally, aRNApipe incorporates interactive web reports for sample tracking and a tool for managing the genome assemblies available to perform an analysis. AVAILABILITY AND DOCUMENTATION: https://github.com/HudsonAlpha/aRNAPipe ; DOI: 10.5281/zenodo.202950. CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Abstract Summary: AStream, an R-statistical software package for the curation and identification of feature peaks extracted from liquid chromatography mass spectrometry (LC/MS) metabolomics data, is described. AStream detects isotopic, fragment and adduct patterns by identifying feature pairs that fulfill expected relational patterns. Data reduction by AStream allows compounds to be identified reliably and subsequently linked to metabolite databases. AStream provides researchers with a fast, reliable tool for summarizing metabolomic data, notably reducing curation time and increasing consistency of results. Availability: The AStream R package and a study example can be freely accessed at http://www.urr.cat/AStream/AStream.html. Contact: [email protected]; [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.

BACKGROUND: Understanding the genetic basis of disease risk in depth requires an exhaustive knowledge of the types of genetic variation. Very recently, Copy Number Variants (CNVs) have received much attention because of their potential implication in common disease susceptibility. Copy Number Polymorphisms (CNPs) are of interest as they segregate at an appreciable frequency in the general population (i.e. > 1%) and are potentially implicated in the genetic basis of common diseases. RESULTS: This paper concerns CNstream, a method for whole-genome CNV discovery and genotyping, using Illumina Beadchip arrays. Compared with other methods, a high level of accuracy was achieved by analyzing the measures of each intensity channel separately and combining information from multiple samples. The CNstream method uses heuristics and parametrical statistics to assign a confidence score to each sample at each probe; the sensitivity of the analysis is increased by jointly calling the copy number state over a set of nearby and consecutive probes. The present method has been tested on a real dataset of 575 samples genotyped using Illumina HumanHap 300 Beadchip, and demonstrates a high correlation with the Database of Genomic Variants (DGV). The same set of samples was analyzed with PennCNV, one of the most frequently used copy number inference methods for Illumina platforms. CNstream was able to identify CNP loci that are not detected by PennCNV and it increased the sensitivity over multiple other loci in the genome. CONCLUSIONS: CNstream is a useful method for the identification and characterization of CNPs using Illumina genotyping microarrays. Compared to the PennCNV method, it has greater sensitivity over multiple CNP loci and allows more powerful statistical analysis in these regions. Therefore, CNstream is a robust CNP analysis tool of use to researchers performing genome-wide association studies (GWAS) on Illumina platforms and aiming to identify CNVs associated with the variables of interest. CNstream has been implemented as an R statistical software package that can work directly from raw intensity files generated from Illumina GWAS projects. The method is available at http://www.urr.cat/cnv/cnstream.html.