Biology subjects

Brehelin, L.

Publications and source records attributed to Brehelin, L..

11 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↗

bioRxiv2025

A genome-wide, machine learning-guided exploration of the cis-regulatory code involved in neuronal differentiation

Gene expression is controlled by proximal and distal cis-regulatory elements (CREs), containing DNA motifs bound by various transcription factors (TFs). Other sequence features, such as specific k-mers or low complexity regions, have also been implicated [1-3]. However, in a dynamic biological process such as cell differentiation, we lack an understanding of how the transcriptional activity of CREs progressively change and what sequence features underlie these transitions, which may reflect common and/or coordinated regulatory processes. Here, we use single-cell ATAC-seq and RNA-seq to follow, at a genome scale, CREs along differentiation of induced pluripotent stem cells into cortical neurons and develop a method to automatically identify the diversity of CRE profiles and their underlying sequence features. We propose a machine-learning guided clustering algorithm, STOIC (Statistical learning TO Inform Clustering), that jointly learns an unsupervised clustering of the CREs in the space of the activity profiles and a supervised predictor associated with each cluster in the DNA-sequence space. STOIC is specifically designed to provide readily interpretable results. We show that the method identifies CRE profiles associated with highly predictive sequence features and outperforms methods solely concerned with co-activity clustering on this task. Orthogonal data collected in the same settings link the inferred CRE clusters to specific enhancer or promoter signatures. Furthermore, we show that the DNA features unveiled by STOIC reflect biologically relevant regulators and offer a valuable basis to dissect elements of the cisregulatory grammar. Finally, we demonstrate the general applicability of STOIC by analyzing five bulk CAGE datasets of human cells responding to various treatments.

bioinformatics↗

bioRxiv2025

Evidence of distal regulations orchestrated by RNAs initiating at short tandem repeats

Short Tandem Repeats (STRs), also called microsatellites, correspond to tandemly repeated short DNA motifs (1 to 6 bp) and are one of the most polymorphic and abundant repetitive elements in the human genome. Variations of their length (i.e. number of consecutive repeats) have been implicated in gene expression regulation (termed expression(e)STRs). Using captrapping followed by long read sequencing, we discovered that STRs can host transcription start sites, the presence of which depends mainly on STR flanking sequences. Here, we investigate the effect of SNPs located in these sequences and ask whether STR-initiating RNAs have regulatory potential. First, we develop fully interpretable deep learning-based models, called Modular Neural Networks, able to predict, for each STR class, the level of RNAs using 101bp-long sequences encompassing STRs. Analysis of MNN filters allows us to identify multiple regulatory elements and candidate transcription factors. Second, leveraging genome sequencing and gene expression data from the Genotype-Tissue Expression project, we use the output of our models to link the levels of STR-initiating RNAs to the expression of nearby genes. We identify 14,340 significant associations (coined RNA(r)STRs) and illustrate how this novel resource can help interpret non-coding variants associated with complex traits and diseases. Third, we unveil an intricate transcriptional interplay between STR-initiating RNAs and Alu repeats that may couple their regulatory actions, extending both the nature and the functional importance of non-coding transcription and shedding new light on the complexity of distal regulations orchestrated by repeated sequences.

genomics↗

bioRxiv2023

Optimizing data integration improves Gene Regulatory Network inference in Arabidopsis thaliana

MotivationsGene Regulatory Networks (GRN) are traditionnally inferred from gene expression profiles monitoring a specific condition or treatment. In the last decade, integrative strategies have successfully emerged to guide GRN inference from gene expression with complementary prior data. However, datasets used as prior information and validation gold standards are often related and limited to a subset of genes. This lack of complete and independent evaluation calls for new criteria to robustly estimate the optimal intensity of prior data integration in the inference process. ResultsWe address this issue for two common regression-based GRN inference models, an integrative Random Forest (weigthedRF) and a generalized linear model with stability selection estimated under a weighted LASSO penalty (weightedLASSO). These approaches are applied to data from the root response to nitrate induction in Arabidopsis thaliana. For each gene, we measure how the integration of transcription factor binding motifs influences model prediction. We propose a new approach, DIOgene, that uses model prediction error and a simulated null hypothesis for optimizing data integration strength in a hypothesis-driven, gene-specific manner. The resulting integration scheme reveals a strong diversity of optimal integration intensities between genes. In addition, it provides a good trade-off between prediction error minimization and validation on experimental interactions, while master regulators of nitrate induction can be accurately retrieved. Availability and implementationThe R code and notebooks demonstrating the use of the proposed approaches are available in the repository https://github.com/OceaneCsn/integrative_GRN_N_induction.

systems biology↗

bioRxiv2022

Systematic analysis of the genomic features involved in the binding preferences of transcription factors

Transcription factors (TFs) orchestrate gene expression and are at the core of cell-specific phenotypes and functions. One given TF can therefore have different binding sites depending on cell type and conditions. However, the TF core motif, as represented by Position Weight Matrix for instance, are often, if not invariably, cell agnostic. Likewise, paralogous TFs recognize very similar motifs while binding different genomic regions. We propose a machine learning approach called TFscope aimed at identifying the DNA features explaining the binding differences observed between two ChIP-seq experiments targeting either the same TF in two cell types or treatments or two paralogous TFs. TFscope systematically investigates differences in i) core motif, ii) nucleotide environment around the binding site and iii) presence and location of co-factor motifs. It provides the main DNA features that have been detected, and the contribution of each of these features to explain the binding differences. TFscope has been applied to more than 350 pairs of ChIP-seq. Our experiments showed that the approach is accurate and that the genomic features distinguishing TF binding in two different settings vary according to the TFs considered and/or the conditions. Several samples are presented and discussed to illustrate these findings. For TFs in different cell types or with different treatments, co-factors and nucleotide environment often explain most of the binding-site differences, while for paralogous TFs, subtle differences in the core motif seem to be the main reason for the observed differences in our experiments. The source code (python), data and results of the experiments described in this article are available at https://gite.lirmm.fr/rromero/tfscope.

bioinformatics↗

bioRxiv2020

Identification of long regulatory elements in the genome of Plasmodium falciparum and other eukaryotes

Long regulatory elements (LREs), such as CpG islands, polydA:dT tracts or AU-rich elements, are thought to play key roles in gene regulation but, as opposed to conventional binding sites of transcription factors, few methods have been proposed to formally and automatically characterize them. We present here a computational approach named DExTER dedicated to the identification of LREs and apply it to the analysis of the genomes of different eukaryotes including P. falciparum. Our analyses show that all tested genomes contain several LREs that are somewhat conserved along evolution, and that gene expression can be predicted with surprising accuracy on the basis of these long regions only. Regulation by LREs exhibits very different behaviours depending on species and conditions. On Apicomplexa organisms, the process appears highly dynamic, with different LREs involved at different phases of their life cycle. For multicellular organisms, the same LREs are involved in all tissues, but a dynamic behavior is observed along embryonic development stages. In P. falciparum, whose genome is known to be strongly depleted of transcription factors, LREs appear to be of especially high importance, and our analyses show that they are involved in both transcriptomic and post-transcriptomic regulation mechanisms. Moreover, we demonstrated the biological relevance of one the LREs discovered by DExTER in P. falciparum using an in vivo reporter assay. The source code (python) of DExTER is available at address https://gite.lirmm.fr/menichelli/DExTER.

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↗

bioRxiv2018

Accounting for ambiguity in ancestral sequence reconstruction

The reconstruction of ancestral genetic sequences from the analysis of contemporaneous data is a powerful tool to improve our understanding of molecular evolution. Various statistical criteria defined in a phylogenetic framework can be used to infer nucleotide, aminoa-cid or codon states at internal nodes of the tree, for every position along the sequence. These criteria generally select the state that maximises (or minimises) a given criterion. Although it is perfectly sensible from a statistical perspective, that strategy fails to convey useful information about the level of uncertainty associated to the inference. The present study introduces a new criterion for ancestral nucleotide reconstruction that selects a single state whenever the signal conveyed by the data is strong, and a combination of multiple states otherwise. Simulations demonstrate the benefit of this approach with a substantial increase in the accuracy of ancestral sequence reconstruction without significantly compromising on the precision of the solutions returned.

evolutionary biology↗

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↗

bioRxiv2017

Improving pairwise comparison of protein sequences with domain co-occurrence

MotivationComparing and aligning protein sequences is an essential task in bioinformatics. More specifically, local alignment tools like BLAST are widely used for identifying conserved protein sub-sequences, which likely correspond to protein domains or functional motifs. However, to limit the number of false positives, these tools are used with stringent sequence-similarity thresholds and hence can miss several hits, especially for species that are phylogenetically distant from reference organisms. A solution to this problem is then to integrate additional contextual information to the procedure.\n\nResultsHere, we propose to use domain co-occurrence to increase the sensitivity of pairwise sequence comparisons. Domain co-occurrence is a strong feature of proteins, since most protein domains tend to appear with a limited number of other domains on the same protein. We propose a method to take this information into account in a typical BLAST analysis and to construct new domain families on the basis of these results. We used Plasmodium falciparum as a case study to evaluate our method. The experimental findings showed an increase of 16% of the number of significant BLAST hits and an increase of 28% of the proteome area that can be covered with a domain. Our method identified 2473 new domains for which, in most cases, no model of the Pfam database could be linked. Moreover, our study of the quality of the new domains in terms of alignment and physicochemical properties show that they are close to that of standard Pfam domains.\n\nAvailabilitySoftware implementing the proposed approach and the Supplementary Data are available at: https://gite.lirmm.fr/menichelli/pairwise-comparison-with-cooccurrence

bioinformatics↗