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1.  MageComet—web application for harmonizing existing large-scale experiment descriptions 
Bioinformatics  2012;28(10):1402-1403.
Motivation: Meta-analysis of large gene expression datasets obtained from public repositories requires consistently annotated data. Curation of such experiments, however, is an expert activity which involves repetitive manipulation of text. Existing tools for automated curation are few, which bottleneck the analysis pipeline.
Results: We present MageComet, a web application for biologists and annotators that facilitates the re-annotation of gene expression experiments in MAGE-TAB format. It incorporates data mining, automatic annotation, use of ontologies and data validation to improve the consistency and quality of experimental meta-data from the ArrayExpress Repository.
Availability and implementation: Source and tutorials for MageComet are openly available at goo.gl/8LQPR under the GNU GPL v3 licenses. An implementation can be found at goo.gl/IdCuA
Contact: parkinson@ebi.ac.uk or xue.vin@gmail.com
doi:10.1093/bioinformatics/bts148
PMCID: PMC3348561  PMID: 22474121
2.  Data-driven information retrieval in heterogeneous collections of transcriptomics data links SIM2s to malignant pleural mesothelioma 
Bioinformatics  2011;28(2):246-253.
Motivation: Genome-wide measurement of transcript levels is an ubiquitous tool in biomedical research. As experimental data continues to be deposited in public databases, it is becoming important to develop search engines that enable the retrieval of relevant studies given a query study. While retrieval systems based on meta-data already exist, data-driven approaches that retrieve studies based on similarities in the expression data itself have a greater potential of uncovering novel biological insights.
Results: We propose an information retrieval method based on differential expression. Our method deals with arbitrary experimental designs and performs competitively with alternative approaches, while making the search results interpretable in terms of differential expression patterns. We show that our model yields meaningful connections between biological conditions from different studies. Finally, we validate a previously unknown connection between malignant pleural mesothelioma and SIM2s suggested by our method, via real-time polymerase chain reaction in an independent set of mesothelioma samples.
Availability: Supplementary data and source code are available from http://www.ebi.ac.uk/fg/research/rex.
Contact: samuel.kaski@aalto.fi
Supplementary Information: Supplementary data are available at Bioinformatics online.
doi:10.1093/bioinformatics/btr634
PMCID: PMC3259436  PMID: 22106335
3.  A pipeline for RNA-seq data processing and quality assessment 
Bioinformatics  2011;27(6):867-869.
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.
Contact: angela.goncalves@ebi.ac.uk
Supplementary information: Supplementary data are available at Bioinformatics online.
doi:10.1093/bioinformatics/btr012
PMCID: PMC3051320  PMID: 21233166
4.  SAIL—a software system for sample and phenotype availability across biobanks and cohorts 
Bioinformatics  2010;27(4):589-591.
Summary: The Sample avAILability system—SAIL—is a web based application for searching, browsing and annotating biological sample collections or biobank entries. By providing individual-level information on the availability of specific data types (phenotypes, genetic or genomic data) and samples within a collection, rather than the actual measurement data, resource integration can be facilitated. A flexible data structure enables the collection owners to provide descriptive information on their samples using existing or custom vocabularies. Users can query for the available samples by various parameters combining them via logical expressions. The system can be scaled to hold data from millions of samples with thousands of variables.
Availability: SAIL is available under Aferro-GPL open source license: https://github.com/sail.
Contact: gostev@ebi.ac.uk, support@simbioms.org
Supplementary information: Supplementary data are available at Bioinformatics online and from http://www.simbioms.org.
doi:10.1093/bioinformatics/btq693
PMCID: PMC3035801  PMID: 21169373
5.  Annotare—a tool for annotating high-throughput biomedical investigations and resulting data 
Bioinformatics  2010;26(19):2470-2471.
Summary: Computational methods in molecular biology will increasingly depend on standards-based annotations that describe biological experiments in an unambiguous manner. Annotare is a software tool that enables biologists to easily annotate their high-throughput experiments, biomaterials and data in a standards-compliant way that facilitates meaningful search and analysis.
Availability and Implementation: Annotare is available from http://code.google.com/p/annotare/ under the terms of the open-source MIT License (http://www.opensource.org/licenses/mit-license.php). It has been tested on both Mac and Windows.
Contact: rshankar@stanford.edu
doi:10.1093/bioinformatics/btq462
PMCID: PMC2944206  PMID: 20733062
6.  Modeling sample variables with an Experimental Factor Ontology 
Bioinformatics  2010;26(8):1112-1118.
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
Contact: malone@ebi.ac.uk
Supplementary information: Supplementary data are available at Bioinformatics online.
doi:10.1093/bioinformatics/btq099
PMCID: PMC2853691  PMID: 20200009
7.  A System for Information Management in BioMedical Studies—SIMBioMS 
Bioinformatics  2009;25(20):2768-2769.
Summary: SIMBioMS is a web-based open source software system for managing data and information in biomedical studies. It provides a solution for the collection, storage, management and retrieval of information about research subjects and biomedical samples, as well as experimental data obtained using a range of high-throughput technologies, including gene expression, genotyping, proteomics and metabonomics. The system can easily be customized and has proven to be successful in several large-scale multi-site collaborative projects. It is compatible with emerging functional genomics data standards and provides data import and export in accepted standard formats. Protocols for transferring data to durable archives at the European Bioinformatics Institute have been implemented.
Availability: The source code, documentation and initialization scripts are available at http://simbioms.org.
Contact: support@simbioms.org; mariak@ebi.ac.uk
doi:10.1093/bioinformatics/btp420
PMCID: PMC2759553  PMID: 19633095
8.  Importing ArrayExpress datasets into R/Bioconductor 
Bioinformatics  2009;25(16):2092-2094.
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: audrey@ebi.ac.uk
Supplementary information:Supplementary data are available Bioinformatics online.
doi:10.1093/bioinformatics/btp354
PMCID: PMC2723004  PMID: 19505942
9.  Probabilistic retrieval and visualization of biologically relevant microarray experiments 
Bioinformatics  2009;25(12):i145-i153.
Motivation: As ArrayExpress and other repositories of genome-wide experiments are reaching a mature size, it is becoming more meaningful to search for related experiments, given a particular study. We introduce methods that allow for the search to be based upon measurement data, instead of the more customary annotation data. The goal is to retrieve experiments in which the same biological processes are activated. This can be due either to experiments targeting the same biological question, or to as yet unknown relationships.
Results: We use a combination of existing and new probabilistic machine learning techniques to extract information about the biological processes differentially activated in each experiment, to retrieve earlier experiments where the same processes are activated and to visualize and interpret the retrieval results. Case studies on a subset of ArrayExpress show that, with a sufficient amount of data, our method indeed finds experiments relevant to particular biological questions. Results can be interpreted in terms of biological processes using the visualization techniques.
Availability: The code is available from http://www.cis.hut.fi/projects/mi/software/ismb09.
Contact: jose.caldas@tkk.fi
doi:10.1093/bioinformatics/btp215
PMCID: PMC2687969  PMID: 19477980

Results 1-9 (9)