SUMMARY: Anduril is an analysis and integration framework that facilitates the design, use, parallelization and reproducibility of bioinformatics workflows. Anduril has been upgraded to use Scala for pipeline construction, which simplifies software maintenance, and facilitates design of complex pipelines. Additionally, Anduril's bioinformatics repository has been expanded with multiple components, and tutorial pipelines, for next-generation sequencing data analysis. AVAILABILITYAND IMPLEMENTATION: Freely available at http://anduril.org. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SUMMARY: We have implemented a computational platform (Moksiskaan) that integrates pathway, protein-protein interaction, genome and literature mining data to result in comprehensive networks for a list of genes or proteins. Moksiskaan is able to generate hypothetical pathways for these genes or proteins as well as estimate their activation statuses using regulation information in pathway repositories. An automatically generated result document provides a detailed description of the query genes, biological processes and drug targets. Moksiskaan networks can be downloaded to Cytoscape for further analysis. To demonstrate the utility of Moksiskaan, we use gene microarray and clinical data from >200 glioblastoma multiforme primary tumor samples and translate the resulting set of 124 survival-associated genes to a network. AVAILABILITY AND IMPLEMENTATION: Moksiskaan and user guide are freely available under GNU General Public License at http://csbi.ltdk.helsinki.fi/moksiskaan/

MOTIVATION: Single nucleic polymorphisms (SNPs) are one of the most abundant genetic variations in the human genome. Recently, several platforms for high-throughput SNP analysis have become available, capable of measuring thousands of SNPs across the genome. Tools for analysing and visualizing these large genetic data sets in biologically relevant manner are rare. This hinders effective use of the SNP-array data in research on complex diseases, such as cancer. RESULTS: We describe a computational framework to analyse and visualize SNP-array data, and link the results in relevant databases. Our major objective is to develop methods for identifying DNA regions that likely harbour recessive mutations. Thus, the algorithms are designed to have high sensitivity and the identified regions are ranked using a scoring algorithm. We have also developed annotation tools that automatically query gene IDs, exon counts, microarray probe IDs, etc. In our case study, we apply the methods for identifying candidate regions for recessively inherited colorectal cancer predisposition and suggest directions for wet-lab experiments. AVAILABILITY: R-package implementation is available at http://www.ltdk.helsinki.fi/sysbio/csb/downloads/CohortComparator/