PathEx: A novel multi factors based datasets selector web tool

Molecular Biology Research Unit, University of Namur - FUNDP, Namur, Belgium.
BMC Bioinformatics (Impact Factor: 2.58). 10/2010; 11(1):528. DOI: 10.1186/1471-2105-11-528
Source: DBLP


Microarray experiments have become very popular in life science research. However, if such experiments are only considered independently, the possibilities for analysis and interpretation of many life science phenomena are reduced. The accumulation of publicly available data provides biomedical researchers with a valuable opportunity to either discover new phenomena or improve the interpretation and validation of other phenomena that partially understood or well known. This can only be achieved by intelligently exploiting this rich mine of information.
Considering that technologies like microarrays remain prohibitively expensive for researchers with limited means to order their own experimental chips, it would be beneficial to re-use previously published microarray data. For certain researchers interested in finding gene groups (requiring many replicates), there is a great need for tools to help them to select appropriate datasets for analysis. These tools may be effective, if and only if, they are able to re-use previously deposited experiments or to create new experiments not initially envisioned by the depositors. However, the generation of new experiments requires that all published microarray data be completely annotated, which is not currently the case. Thus, we propose the PathEx approach.
This paper presents PathEx, a human-focused web solution built around a two-component system: one database component, enriched with relevant biological information (expression array, omics data, literature) from different sources, and another component comprising sophisticated web interfaces that allow users to perform complex dataset building queries on the contents integrated into the PathEx database.

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    • "A major biological topic of interest in our lab is the investigation of expression profiles to describe common mechanisms between metastasis and adaptation of cells to hypoxic conditions. PathEx [47] was queried (performed on data present in PathEx in June 2012) with the keywords “hypoxia” and “metastasis” to identify datasets available from Affymetrix HGU-133a and HGU-133Plus2 arrays. We found 7 and 9 experiments focused on hypoxia and metastasis respectively. "
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    ABSTRACT: We propose to make use of the wealth of underused DNA chip data available in public repositories to study the molecular mechanisms behind the adaptation of cancer cells to hypoxic conditions leading to the metastatic phenotype. We have developed new bioinformatics tools and adapted others to identify with maximum sensitivity those genes which are expressed differentially across several experiments. The comparison of two analytical approaches, based on either Over Representation Analysis or Functional Class Scoring, by a meta-analysis-based approach, led to the retrieval of known information about the biological situation - thus validating the model - but also more importantly to the discovery of the previously unknown implication of the spliceosome, the cellular machinery responsible for mRNA splicing, in the development of metastasis.
    Full-text · Article · Jan 2014 · PLoS ONE
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    • "To obtain information on a certain node, simply click on its name in the lower right panel {14}, displaying all the information contained in the PathEx database for this particular gene (for more information about PathEx, see [16]). "
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    ABSTRACT: The quantity of microarray data available on the Internet has grown dramatically over the past years and now represents millions of Euros worth of underused information. One way to use this data is through co-expression analysis. To avoid a certain amount of bias, such data must often be analyzed at the genome scale, for example by network representation. The identification of co-expression networks is an important means to unravel gene to gene interactions and the underlying functional relationship between them. However, it is very difficult to explore and analyze a network of such dimensions. Several programs (Cytoscape, yEd) have already been developed for network analysis; however, to our knowledge, there are no available GraphML compatible programs. We designed and developed gViz, a GraphML network visualization and exploration tool. gViz is built on clustering coefficient-based algorithms and is a novel tool to visualize and manipulate networks of co-expression interactions among a selection of probesets (each representing a single gene or transcript), based on a set of microarray co-expression data stored as an adjacency matrix. We present here gViz, a software tool designed to visualize and explore large GraphML networks, combining network theory, biological annotation data, microarray data analysis and advanced graphical features.
    Full-text · Article · Oct 2011 · BMC Research Notes
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    ABSTRACT: Correction: During the proofing stage of our article [1] we made an error by forgetting to put on the list of authors a biology expert Fabrice Beger who cross-checked the reliability and the quality of our database, tested it and gave comments on the manuscript. We apologize for any inconvenience. 1. Bareke E, Pierre M, Gaigneaux A, De Meulder B, Depiereux S, Habra N, Depiereux E: PathEx: A novel multi factors based datasets selector web tool. BMC Bioinformatics 2010, 11:528.
    Full-text · Article · Nov 2010 · BMC Bioinformatics
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