arXiv subjects

Thomas Bataillon

Publications and source records attributed to Thomas Bataillon.

3 recordsLinked to original sources

arXiv2026

HIDDENdb: Co-dependency database reveals a plethora of genetic and protein interactions

Genetic interactions and protein co-dependencies shape cellular fitness, buffering capacity, and disease vulnerability. However, systematic integration of co-dependency relationships across heterogeneous datasets remains limited. Here, we present HIDDENdb (Harnessing Intelligent Data Discovery to Explore Gene Networks), a comprehensive database that captures genetic and protein co-dependencies inferred from large-scale perturbation screens, multi-omics datasets, and curated interaction repositories. HIDDENdb integrates genome-wide loss-of-function screens (CRISPR and shRNA) with other unbiased resources (BioGRID-ORCS and GWAS) to construct a map of co-dependency relationships across diverse biological contexts. Using robust statistical modeling and network inference approaches, we identify modules of genes and proteins exhibiting shared dependency patterns across cell lines. Notably, top-ranked gene-gene co-dependency pairs are enriched for high-confidence AlphaFold-predicted protein-protein interfaces, suggesting that a subset of inferred functional relationships may reflect underlying structural interactions. Importantly, the database enables users to explore co-dependency networks interactively. HIDDENdb is freely accessible through a web-based interface at https://bofillderoslab.shinyapps.io/hiddendb/.

q-bio.MN↗

arXiv2016

Epistasis and the structure of fitness landscapes: are experimental fitness landscapes compatible with Fisher's Geometric model?

The fitness landscape defines the relationship between genotypes and fitness in a given environment, and underlies fundamental quantities such as the distribution of selection coefficient, or the magnitude and type of epistasis. A better understanding of variation of landscape structure across species and environments is thus necessary to understand and predict how populations will adapt. An increasing number of experiments investigates the properties of fitness landscapes by identifying mutations, constructing genotypes with combinations of these mutations, and measuring the fitness of these genotypes. Yet these empirical landscapes represent a very small sample of the vast space of all possible genotypes, and this sample is often biased by the protocol used to identify mutations. Here we develop a rigorous statistical framework based on Approximate Bayesian Computation to address these concerns, and use this flexible framework to fit a broad class of phenotypic fitness models (including Fisher's model) to 26 empirical landscapes representing 9 diverse biological systems. In spite of uncertainty due to the small size of most published empirical landscapes, the inferred landscapes have similar structure in similar biological systems. Surprisingly, goodness of fit tests reveal that this class of phenotypic models, which has been successful so far in interpreting experimental data, is a plausible model in only 3 out of 9 biological systems. More precisely, although Fisher's model was able to explain several statistical properties of the landscapes - including mean and standard deviation of selection and epistasis coefficients -, it was often unable to explain the full structure of fitness landscapes.

q-bio.PE↗

arXiv2014

Properties of selected mutations and genotypic landscapes under Fisher's Geometric Model

The fitness landscape - the mapping between genotypes and fitness - determines properties of the process of adaptation. Several small genetic fitness landscapes have recently been built by selecting a handful of beneficial mutations and measuring fitness of all combinations of these mutations. Here we generate several testable predictions for the properties of these landscapes under Fisher's geometric model of adaptation (FGMA). When far from the fitness optimum, we analytically compute the fitness effect of beneficial mutations and their epistatic interactions. We show that epistasis may be negative or positive on average depending on the distance of the ancestral genotype to the optimum and whether mutations were independently selected or co-selected in an adaptive walk. Using simulations, we show that genetic landscapes built from FGMA are very close to an additive landscape when the ancestral strain is far from the optimum. However, when close to the optimum, a large diversity of landscape with substantial ruggedness and sign epistasis emerged. Strikingly, landscapes built from different realizations of stochastic adaptive walks in the same exact conditions were highly variable, suggesting that several realizations of small genetic landscapes are needed to gain information about the underlying architecture of the global adaptive landscape.

q-bio.PE↗