I am a researcher who is interested in bridging the gap between statistical inference, numeric computing and several areas of biology. A theme to my work is the development of models that use information integration, particularly, in the context of heterogeneous data sources.
I work extensively with large data sets derived from databases and high-throughput technologies. I am also the developer/maintainer of several software projects that aim to make available complex computational methods to the general scientific community. The subject areas that I am currently working in are: immunology, gene expression, ecology, and reproducible research.
High-throughput sequencing and microarray analysis pipelines often treat genes as essentially independent entities. For example in gene expression studies it is common that multiple individual tests are run and consequently a correction for unintended false positives is performed. The genes that are labelled as differentially expressed are then investigated in terms of the Gene Ontology, known biochemical pathways and other resources. Alternatively, the gene set approach commonly referred to as gene set analysis (GSA), reverses this dogma and begins with the construction of gene sets using these resources. Much of my effort is devoted to developing/applying statistical methods that take advantage of the modular nature of biological systems through the use of gene sets.
- Richards A.J., Herrel A. and
Bonneaud C. (2015) — htsint: a Python library for sequencing pipelines that combines data through gene set generation. BMC Bioinformatics . 18: 813–825. Download
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