Omics analysis in Caenorhabditis elegans : pattern inference and interpretation
High-throughput molecular technologies have greatly enhanced our understanding of biological processes by characterizing expression changes of genes (microarray and RNA-Seq data) and proteins (proteomics data), or transcription factor targets and epigenetics states (ChIP-chip and ChIP-Seq data). Among them, transcriptome studies based on microarrays or RNA-Seq have the ability to identify genes involved in the response to environmental change or specific stressors, thereby helping us to infer the underlying biological processes. During my PhD, I mainly focused on transcriptomic data analysis, using in most cases the nematode Caenorhabditis elegans as a model taxon. In particular, I have addressed seven specific projects: i) development of ABSSeq, an improved detection approach of differential gene expression for RNA-Seq data; ii) development of aFold, a method to fully moderate fold-change of RNA-Seq data and to improve gene ranking and visualization; iii) development of WormExp, a knowledge-based approach for interpreting gene sets in C. elegans; iv) exploration of the regulation of the C. elegans immune system using curated data sets from WormExp; v) characterization of putative major effectors (GATA transcription factors) in the C. elegans innate immune system; vi) comparison of the immune response of C. elegans at protein and transcript level. In general, our work facilitates high-throughput data analysis via improving pattern inference and interpretation, which in practice provides new insights into the immune system of C. elegans.