Biology subjects

Fontaine, J.-F.

Publications and source records attributed to Fontaine, J.-F..

5 recordsLinked to original sources

bioRxiv2026

GenesetDiseaseDrugNetwork (GDDN): a web server for disease enrichment and drug prioritization

SummaryOmics technologies profile thousands of genetic and molecular features to provide a comprehensive and quantitative measure of the cellular state. Transcriptomics and proteomics have, especially, guided discoveries of the most important biomarkers and therapeutic targets. By virtue of ongoing developments in single-cell and spatial technologies, fields of targeted therapeutics and personalized medicine are rapidly advancing. However, downstream functional analysis and disease association still remain daunting tasks in bioinformatics. We address these challenges with the GenesetDiseaseDrugNetwork (GDDN) web server. GDDN facilitates functional discovery by connecting gene-sets to enriched diseases and their corresponding therapeutics in a single step. Using a ranking system that incorporates regulatory impact, specificity, and potency, GDDN effectively prioritizes drugs with the highest clinical relevance. Our platform facilitates the interpretation of omics outputs into disease associations and personalized drug identification. Availability and ImplementationThe GDDN web server is implemented in R Shiny and is freely accessible at https://cbdm-01.zdv.uni-mainz.de/shiny/piyusmor/GDDN/, supporting all major web browsers. Contactpiyusmor@uni-mainz.de Supplementary informationSupplementary data is available at Bioinformatics online.

bioinformatics↗

bioRxiv2021

Identification of functional residues using machine learning provides insights into the evolution of odorant receptor gene families in solitary and social insects

The gene family of insect odorant receptors (ORs) has greatly expanded in the course of evolution. ORs allow insects to detect volatile chemicals and therefore play an important role in social interactions, the detection of enemies and preys, and during foraging. The sequences of several thousand ORs are known, but their specific function or ligands have been identified only for very few of them. To advance the functional characterization of ORs, we compiled, curated and aligned the sequences of 3,902 ORs from 21 insect species. We identified the amino acid positions that best predict the response to ligands using machine learning on sets of functionally characterized proteins from the fly Drosophila melanogaster, the mosquito Anopheles gambiae and the ant Harpegnathos saltator. We studied the conservation of these predicted relevant residues across all OR subfamilies and show that the subfamilies that expanded strongly in social insects exhibit high levels of conservation in their binding sites. This indicates that ORs of social insect families are typically finely tuned and exhibit a sensitivity to very similar odorants. Our novel approach provides a powerful tool to use functional information from a limited number of genes to investigate the functional evolution of large gene families.

bioinformatics↗

bioRxiv2019

SPARTA imputes sparse single-cell ChIP-seq signals leveraging epigenomic bulk ENCODE data

MotivationSingle-cell ChIP-seq (scChIP-seq) analysis is challenging due to data sparsity. High degree of data sparsity in biological high-throughput single-cell data is generally handled with imputation methods that complete the data, but specific methods for scChIP-seq are lacking. We present SIMPA, a scChIP-seq data imputation method leveraging predictive information within bulk data from ENCODE to impute missing protein-DNA interacting regions of target histone marks or transcription factors. ResultsImputations using machine learning models trained for each single cell, each target, and each genomic region accurately preserve cell type clustering and improve pathway-related gene identification on real data. Results on simulated data show that 100 input genomic regions are already enough to train single-cell specific models for the imputation of thousands of undetected regions. Furthermore, SIMPA enables the interpretation of machine learning models by revealing interaction sites of a given single cell that are most important for the imputation model trained for a specific genomic region. The corresponding feature importance values derived from promoter-interaction profiles of H3K4me3, an activating histone mark, highly correlate with co-expression of genes that are present within the cell-type specific pathways. An imputation method that allows the interpretation of the underlying models facilitates users to gain an even deeper understanding of individual cells and, consequently, of sparse scChIP-seq datasets. Availability and implementationOur interpretable imputation algorithm was implemented in Python and is available at https://github.com/salbrec/SIMPA

bioinformatics↗

bioRxiv2019

Computational identification of cell-specific variable regions in ChIP-seq data

Chromatin immunoprecipitation followed by sequencing (ChIP-seq) is used to identify genome-wide DNA regions bound by proteins. Given one ChIP-seq experiment with replicates, binding sites not observed in all the replicates will usually be interpreted as noise and discarded. However, the recent discovery of high-occupancy target (HOT) regions suggests that there are regions where binding of multiple transcription factors can be identified. To investigate ChIP-seq variability, we developed a reproducibility score and a method that identifies cell-specific variable regions in ChIP-seq data by integrating replicated ChIP-seq experiments for multiple protein targets on a particular cell type. Using our method, we found variable regions in human cell lines K562, GM12878, HepG2, MCF-7, and in mouse embryonic stem cells (mESCs). These variable-occupancy target regions (VOTs) are CG dinucleotide rich, and show enrichment at promoters and R-loops. They overlap significantly with HOT regions, but are not blacklisted regions producing non-specific binding ChIP-seq peaks. Furthermore, in mESCs, VOTs are conserved among placental species suggesting that they could have a function important for this taxon. Our method can be useful to point to such regions along the genome in a given cell type of interest, to improve the downstream interpretative analysis before follow up experiments.

bioinformatics↗

bioRxiv2019

Machine Learning in Quality Assessment of Early Stage Next-Generation Sequencing Data

Controlling quality of next generation sequencing (NGS) data files is a necessary but complex task. To address this problem, we statistically characterized common NGS quality features and developed a novel quality control procedure involving tree-based and deep learning classification algorithms. Predictive models, validated on internal data and external disease diagnostic datasets, are to some extent generalizable to data from unseen species. The derived statistical guidelines and predictive models represent a valuable resource for users of NGS data to better understand quality issues and perform automatic quality control. Our guidelines and software are available at the following URL: https://github.com/salbrec/seqQscorer.

bioinformatics↗