Software tools developed and maintained by the Computational Biology group.


INCanTeSIMO - An integrative method for the prediction of signalling molecules inducing cellular transitions.

This method relies on the assumption that specific transcription factors (TFs) are regulated by signalling pathways and act on gene regulatory networks (GRNs), representing the interface between the two regulatory layers. Using a probabilistic approach, the effect of signalling perturbations on the interface TFs is assessed. In order to select the molecules whose activation or inhibition is more likely to trigger the desired cellular transition, exhaustive in silico perturbations of the interface TFs are performed and their effect on the GRN state is evaluated. This method requires minimum data input, in the form of one gene expression profile for each of the initial and final cellular states, therefore representing a generally applicable tool for the discovery of signalling perturbations inducing any cellular transition. The application is freely available for academic, non-profit use. (Nucleic Acid Research)[code]

Markovchain_signaling_model - A Markov chain-based computational approach to identify age-related signals induced by the niche that contribute to induction of neural stem cell quiescence.

This computational approach aims at unveiling neural stem cell-intrinsic signaling intermediates that are likely to maintain a specific cell state dictated by niche signals via a sustained effect on the downstream gene regulatory network. The approach is based on the rationale that a constant niche effect should induce sustained activation/inhibition of specific stem cell signaling pathways in most of the cells within heterogeneous populations exhibiting the same phenotype (niche determinants). This view of stem cell-niche interactions shifts the focus of the problem towards the constant niche effect on key stem cell signaling, instead of accounting for niche composition and its interaction with the stem cells explicitly. Based on this rationale, we have developed this method relying on a Markov chain model of signal transduction for the identification of niche determinants of stem cell quiescence in old mice. The method represents a novel way to model sustained signaling mediated by the niche by considering that once the signal from the niche reaches downstream transcription factors, it starts again from the niche in an iterative manner, consequently producing a sustained signal transduction. (Cell 2019) [source]


TranSyn - Transcriptional synergy as an emergent property defining cell subpopulation identity enables population shift

TransSyn is a computational tool for identifying synergistic transcriptional cores that determine cell subpopulation identities. The method is based on the notion that cell subpopulation identity is an emergent property arising from a synergistic activity of multiple transcription factors that stabilizes their gene expression levels. TransSyn uses single-cell RNA-seq data as input, and performs a dynamic search for an optimal synergistic transcriptional core that defines the subpopulation identity, using an information theoretic measure of synergy. The identified transcriptional core can be used for conversion of a subpopulation into another. The application is freely available for academic, non-profit use.
(Nature Communications 2018) [source]


SeesawPred - A Web Application for Predicting Cell-fate Determinants in Cell Differentiation

A web application that, based on a gene regulatory network (GRN) model of cell differentiation, can computationally predict cell-fate determinants from transcriptomics data. The method relies on the assumption that the stem/progenitor cell phenotype is maintained in a metastable state by the opposing cell-fate determinants, which are part of interconnected feedback loops. SeesawPred correctly predicted known cell-fate determinants on various cell differentiation examples in both mouse and human. It allows the user to upload gene expression data and does not rely on pre-compiled reference data sets, enabling its application to novel differentiation systems. The application is freely available for academic, non-profit use. (Scientific Reports 2018) [website] [source]



RefBool - A reference-based algorithm for booleanizing gene expression data

The identification of genes or molecular regulatory mechanisms implicated in biological processes often requires the discretization of gene expression data. In order to overcome the lack of certainty inherent in current methodologies and to improve the process of discretization, we developed RefBool, a reference-based algorithm for discretizing gene expression data. Instead of requiring each measurement to be classified as active or inactive, RefBool allows for the classification of a third state that can be interpreted as an intermediate expression of genes. Furthermore, each measurement is associated to a p- and q-value indicating the significance of each classification. (Bioinformatics 2017) [code]



PRUNET - Discrete Logic Modelling Optimization to Contextualize Prior Knowledge Networks

A user-friendly software tool designed to address the contextualization of a prior knowledge gene regulatory networks (PKN) using a booleanized representation of observed expression profiles corresponding to stable cellular phenotypes. PRUNET constitutes a tool for the automatic curation of a PKN to make it suitable for describing biological processes under particular experimental conditions. The general applicability of the implemented algorithm makes PRUNET suitable for a variety of biological processes, for instance cellular reprogramming or transitions between healthy and disease states.
(PLOS ONE 2015) [code]