Biology subjects

Lecellier, C.-H.

Publications and source records attributed to Lecellier, C.-H..

4 recordsLinked to original sources

bioRxiv2026

Controlling for DNA dosage with Whole Genome Sequencing improves ATAC-seq peak calling

The Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq) is a scalable and sensitive method for profiling chromatin accessibility, enabling the identification of cis-regulatory elements (CREs) that govern gene expression in diverse cellular contexts. Although ATAC-seq is routinely applied to both bulk and single-cell samples, we reveal that its peak calling process is compromised by local biases in DNA dosage, arising not only from copy number variations (CNVs) but also from DNA replication timing (RT). These biases can distort read coverage and compromise peak detection accuracy. As part of the FANTOM consortiums efforts to elucidate genomic regulation, we propose enhancing the MACS pipeline by integrating whole-genome sequencing (WGS) data to account for local DNA dosage effects, analogous to the use of input controls in ChIP-seq analyses. By incorporating WGS data, we demonstrate an increase in both the number and width of ATAC peaks, with improved proximity to transcription start sites (TSSs). WGS-controlled ATAC peaks exhibit canonical CRE epigenetic marks and are enriched for trait- and disease-associated genetic variants. Furthermore, the number of WGS-controlled peaks correlates more strongly with gene expression levels compared to peaks called without WGS control. Collectively, these results demonstrate that integrating WGS as a control significantly enhances the accuracy of ATAC-seq peak calling. Critically, we show that even low-depth WGS data is sufficient to improve peak calling performance, making this approach both cost-effective and readily adoptable for routine analyses. To ensure accessibility and reproducibility, we implemented this method as an open-source Nextflow pipeline. By challenging the assumption of uniform genomic visibility, our approach also holds broad implications for other DNA sequencing-based technologies.

bioinformatics↗

bioRxiv2019

Sequence-level instructions direct transcription at polyT short tandem repeats.

BackgroundUsing the Cap Analysis of Gene Expression technology, the FANTOM5 consortium provided one of the most comprehensive maps of Transcription Start Sites (TSSs) in several species. Strikingly, ~72% of them could not be assigned to a specific gene and initiate at unconventional regions, outside promoters or enhancers.\n\nResultsHere, we probe these unassigned TSSs and show that, in all species studied, a significant fraction of CAGE peaks initiate at short tandem repeats (STRs) corresponding to homopolymers of thymidines (T). Additional analyse confirm that these CAGEs are truly associated with transcriptionally active chromatin marks. Furthermore, we train a sequence-based deep learning model able to predict CAGE signal at T STRs with high accuracy (~81%) Extracting features learned by this model reveals that transcription at T STRs is mostly directed by STR length but also instructions lying in the downstream sequence. Excitingly, our model also predicts that genetic variants linked to human diseases affect this STR-associated transcription.\n\nConclusionsTogether, our results extend the repertoire of non-coding transcription associated with DNA tandem repeats and complexify STR polymorphism. We also provide a new metric that can be considered in future studies of STR-related complex traits.

genomics↗

bioRxiv2017

Modeling transcription factor combinatorics in promoters and enhancers

In eukaryotic cells, transcription factors (TFs) are thought to act in a combinatorial way, by competing and collaborating to regulate common target genes. However, several questions remain regarding the conservation of these combina-tions among different gene classes, regulatory regions and cell types. We propose a new approach named TFcoop to infer the TF combinations involved in the binding of a tar-get TF in a particular cell type. TFcoop aims to predict the binding sites of the target TF upon the binding affinity of all identified cooperating TFs. The set of cooperating TFs and model parameters are learned from ChIP-seq data of the target TF. We used TFcoop to investigate the TF combina-tions involved in the binding of 106 TFs on 41 cell types and in four regulatory regions: promoters of mRNAs, lncRNAs and pri-miRNAs, and enhancers. We first assess that TFcoop is accurate and outperforms simple PWM methods for pre-dicting TF binding sites. Next, analysis of the learned models sheds light on important properties of TF combinations in different promoter classes and in enhancers. First, we show that combinations governing TF binding on enhancers are more cell-type specific than that governing binding in pro-moters. Second, for a given TF and cell type, we observe that TF combinations are different between promoters and en-hancers, but similar for promoters of mRNAs, lncRNAs and pri-miRNAs. Analysis of the TFs cooperating with the dif-ferent targets show over-representation of pioneer TFs and a clear preference for TFs with binding motif composition similar to that of the target. Lastly, our models accurately dis-tinguish promoters associated with specific biological processes.

bioinformatics↗

bioRxiv2017

Gene nucleotide composition accurately predicts expression and is linked to topological chromatin domains

Gene expression is orchestrated by distinct regulatory regions (e.g. promoters, enhancers, UTRs) to ensure a wide variety of cell types and functions. A challenge is to identify which regulatory regions are active, what are their associated features and how they work together in each cell type. Several approaches have tackled this problem by modeling gene expression based on epigenetic marks (e.g. ChIP-seq, methylation, DNase hypersensitivity), with the ultimate goal of identifying driving genomic regions and mutations that are clinically relevant in particular in precision medicine. However, these models rely on experimental data, which are limited to specific samples (even often to cell lines) and cannot be generated for all regulators and all patients. In addition, we show here that, although these approaches are accurate in predicting gene expression, their biological interpretation can be misleading. Finally these methods are not designed to capture potential regulation instructions present at the sequence level, before the binding of regulators or the opening of the chromatin. We develop here a method for predicting mRNA levels based solely on sequence features collected from distinct regulatory regions, which is as accurate as methods based on experimental data. Our approach confirms the importance of nucleotide composition in predicting gene expression and ranks regulatory regions according to their contribution. It also unveils strong influence of gene body sequence, in particular introns. We further provide evidence that the contribution of nucleotide content can be linked to co-regulations associated with genome 3D architecture and to associations of genes within topologically associated domains.

bioinformatics↗