We developed ISoLDE (Integrative Statistics of alleLe Dependent Expression), a novel non-parametric statistical method that directly infers allelic imbalance from RNA-seq data. ISoLDE learns the distribution of a speciﬁcally designed test statistic from the data and calls genes allelically imbalanced, bi-allelically expressed or undetermined. ISoLDE is available as a Bioconductor package.
We also developped TopoFunc, a novel machine learning method to identify functional modules in gene co-expression networks and complement Gene Ontology annotations.
A comprehensive, accurate functional annotation of genes is key to systems-level approaches. Forward and reverse genetics produced a substantial amount of data on gene functions; yet, a large fraction of genes are still poorly annotated, even in model organisms. One possible approach to complement existing annotations is to analyze gene co-expression as functionally related genes tend to be co-expressed.
Gene co-expression data are represented as high-dimensional graphs in which nodes denote genes and edges denote co-expression. TopoFunc is a machine learning method that combines topological and functional information on co-expression modules. We first selected topological descriptors of gene co-expression modules that discriminate modules made of functionally related genes and modules made of randomly selected genes. Using the selected topological descriptors, we constructed a database of functional and random modules and performed Linear Discriminant Analysis to predict the type of a module. Starting from a given Gene Ontology Biological Process (GO-BP), we used a genetic algorithm to find genes whose co-expression with the largest clique of the GO-BP suggests that they may be functionally related.