SUMMARY: We present an R based pipeline, ArrayExpressHTS, for pre-processing, expression estimation and data quality assessment of high-throughput sequencing transcriptional profiling (RNA-seq) datasets. The pipeline starts from raw sequence files and produces standard Bioconductor R objects containing gene or transcript measurements for downstream analysis along with web reports for data quality assessment. It may be run locally on a user's own computer or remotely on a distributed R-cloud farm at the European Bioinformatics Institute. It can be used to analyse user's own datasets or public RNA-seq datasets from the ArrayExpress Archive. AVAILABILITY: The R package is available at www.ebi.ac.uk/tools/rcloud with online documentation at www.ebi.ac.uk/Tools/rwiki/, also available as supplementary material.

MOTIVATION: There exist few simple and easily accessible methods to integrate ontologies programmatically in the R environment. We present ontoCAT-an R package to access ontologies in widely used standard formats, stored locally in the filesystem or available online. The ontoCAT package supports a number of traversal and search functions on a single ontology, as well as searching for ontology terms across multiple ontologies and in major ontology repositories. AVAILABILITY: The package and sources are freely available in Bioconductor starting from version 2.8: http://bioconductor.org/help/bioc-views/release/bioc/html/ontoCAT.html or via the OntoCAT website http://www.ontocat.org/wiki/r. CONTACT: [email protected]; [email protected].

BACKGROUND: Ontologies have become an essential asset in the bioinformatics toolbox and a number of ontology access resources are now available, for example, the EBI Ontology Lookup Service (OLS) and the NCBO BioPortal. However, these resources differ substantially in mode, ease of access, and ontology content. This makes it relatively difficult to access each ontology source separately, map their contents to research data, and much of this effort is being replicated across different research groups. RESULTS: OntoCAT provides a seamless programming interface to query heterogeneous ontology resources including OLS and BioPortal, as well as user-specified local OWL and OBO files. Each resource is wrapped behind easy to learn Java, Bioconductor/R and REST web service commands enabling reuse and integration of ontology software efforts despite variation in technologies. It is also available as a stand-alone MOLGENIS database and a Google App Engine application. CONCLUSIONS: OntoCAT provides a robust, configurable solution for accessing ontology terms specified locally and from remote services, is available as a stand-alone tool and has been tested thoroughly in the ArrayExpress, MOLGENIS, EFO and Gen2Phen phenotype use cases. AVAILABILITY: http://www.ontocat.org.

MOTIVATION: Describing biological sample variables with ontologies is complex due to the cross-domain nature of experiments. Ontologies provide annotation solutions; however, for cross-domain investigations, multiple ontologies are needed to represent the data. These are subject to rapid change, are often not interoperable and present complexities that are a barrier to biological resource users. RESULTS: We present the Experimental Factor Ontology, designed to meet cross-domain, application focused use cases for gene expression data. We describe our methodology and open source tools used to create the ontology. These include tools for creating ontology mappings, ontology views, detecting ontology changes and using ontologies in interfaces to enhance querying. The application of reference ontologies to data is a key problem, and this work presents guidelines on how community ontologies can be presented in an application ontology in a data-driven way. AVAILABILITY: http://www.ebi.ac.uk/efo.

Abstract Summary:ArrayExpress is one of the largest public repositories of microarray datasets. R/Bioconductor provides a comprehensive suite of microarray analysis and integrative bioinformatics software. However, easy ways for importing datasets from ArrayExpress into R/Bioconductor have been lacking. Here, we present such a tool that is suitable for both interactive and automated use. Availability: The ArrayExpress package is available from the Bioconductor project at http://www.bioconductor.org. A users guide and examples are provided with the package. Contact: [email protected] Supplementary information: Supplementary data are available Bioinformatics online.

UNLABELLED: With the production of whole genome microarray chips the ability arises to investigate whether the regulation of particular groups of genes may be influenced by their chromosomal localization. Chromosome Co-Localization probability calculator (ChroCoLoc) is a publicly available web-based tool for the analysis of co-localization of co-expressed genes identified by microarray experiments. AVAILABILITY: http://www.ebi.ac.uk/expressionprofiler/

MOTIVATION: The lack of microarray data management systems and databases is still one of the major problems faced by many life sciences laboratories. While developing the public repository for microarray data ArrayExpress we had to find novel solutions to many non-trivial software engineering problems. Our experience will be both relevant and useful for most bioinformaticians involved in developing information systems for a wide range of high-throughput technologies. RESULTS: ArrayExpress has been online since February 2002, growing exponentially to well over 10,000 hybridizations (as of September 2004). It has been demonstrated that our chosen design and implementation works for databases aimed at storage, access and sharing of high-throughput data. AVAILABILITY: The ArrayExpress database is available at http://www.ebi.ac.uk/arrayexpress/. The software is open source. CONTACT: [email protected].

MOTIVATION: Clustering is one of the most widely used methods in unsupervised gene expression data analysis. The use of different clustering algorithms or different parameters often produces rather different results on the same data. Biological interpretation of multiple clustering results requires understanding how different clusters relate to each other. It is particularly non-trivial to compare the results of a hierarchical and a flat, e.g. k-means, clustering. RESULTS: We present a new method for comparing and visualizing relationships between different clustering results, either flat versus flat, or flat versus hierarchical. When comparing a flat clustering to a hierarchical clustering, the algorithm cuts different branches in the hierarchical tree at different levels to optimize the correspondence between the clusters. The optimization function is based on graph layout aesthetics or on mutual information. The clusters are displayed using a bipartite graph where the edges are weighted proportionally to the number of common elements in the respective clusters and the weighted number of crossings is minimized. The performance of the algorithm is tested using simulated and real gene expression data. The algorithm is implemented in the online gene expression data analysis tool Expression Profiler. AVAILABILITY: http://www.ebi.ac.uk/expressionprofiler