Biology subjects

Ballester, B.

Publications and source records attributed to Ballester, B..

5 recordsLinked to original sources

bioRxiv2025

Exonic enhancers are a widespread class of dual-function regulatory elements

Exonic enhancers (EEs) occupy an under-appreciated niche in gene regulation. By integrating transcription factor binding, chromatin accessibility, and high-throughput enhancer-reporter assays, we demonstrate that many protein-coding exons possess enhancer activity across species. These EEs exhibit characteristic epigenomic signatures, form long-range interactions with gene promoters, and can be altered by both nonsynonymous and synonymous variants. CRISPR-mediated inactivation demonstrated the involvement of EEs in the cis-regulation of host and distal gene expression. Through large-scale cancer genome analyses, we reveal that EE mutations correlate with dysregulated target-gene expression and clinical outcomes, highlighting their potential relevance in disease. Evolutionary comparisons show that EEs exhibit both strong sequence constraint and lineage-specific plasticity, suggesting that they serve ancient regulatory functions while also contributing to species divergence. Our findings redefine the landscape of functional elements by establishing EEs as a component of gene regulation, while revealing how coding regions can simultaneously fulfil both protein-coding and cis-regulatory roles.

genomics↗

bioRxiv2024

Contextualising transcription factor binding during embryogenesis using natural sequence variation

Understanding how genetic variation impacts transcription factor (TF) binding remains a major challenge, limiting our ability to model disease-associated variants. Here, we used a highly controlled system of F1 crosses with extensive genetic diversity to profile allele-specific binding of four TFs at several embryonic time-points, using Drosophila as a model. Using a combined haplotype test, we identified 9-18% of TF bound regions impacted by genetic variation. By expanding WASP (a tool for allele-specific read mapping) to examine INDELs, we increased detection of allele imbalanced (AI) peaks by 30-50%. This fine-grained mutagenesis could reconstruct functionalized binding motifs of all factors. To prioritise potential causal variants, we trained a convolutional neural network (Basenji) to predict TF binding from DNA sequence. The model could accurately predict experimental AI for strong effect variants, providing a mechanistic interpretation for how genetic variation impacted TF binding. This revealed unexpected relationships between TFs, including potential cooperative pairs, and mechanisms of tissue specific recruitment of the ubiquitous factor CTCF.

genetics↗

bioRxiv2023

MUFFIN : A suite of tools for the analysis of functional sequencing data

The large diversity of functional genomic assays allows for the characterization of non-coding and coding events at the tissue level or at a single-cell resolution. However, this diversity also leads to protocol differences, widely varying sequencing depths, substantial disparities in sample sizes, and number of features. In this work, we have specifically designed a suite of tools for exploring the non-coding genome, particularly for identifying consensus peaks in peak-called assays, as well as linking non-coding genomic regions to genes and performing Gene Set Enrichment Analyses. We demonstrate that a generic but flexible count modelling approach can be utilised to compare different conditions across a broad range of genomic assay such as ENCODE H3K4Me3 ChIP-seq, scRNA-seq and TCGA ATAC-seq. Our Python package, MUFFIN, offers a suite of tools to address common issues associated with high-dimensional genomic data, such as normalisation, count transformation, dimensionality reduction, differential expression, and clustering. Additionally, our tool integrates with the popular Scanpy ecosystem and is available on Conda and at https://github.com/pdelangen/Muffin.

bioinformatics↗

bioRxiv2023

Normal and cancer tissues are accurately characterised by intergenic transcription at RNA polymerase 2 binding sites

Intergenic transcription in normal and cancerous tissue is pervasive and incompletely understood. To investigate this activity at a global level, we constructed an atlas of over 180,000 consensus RNA Polymerase II (RNAP2) bound intergenic regions from more than 900 RNAP2 ChIP-seq experiments across normal and cancer samples. Using unsupervised analysis, we identified 51 RNAP2 consensus clusters, many of which map to specific biotypes and identify tissue-specific regulatory signatures. We developed a meta-clustering methodology to integrate our RNAP2 atlas with active transcription across 28,797 RNA-seq samples from TCGA, GTEx and ENCODE, which revealed strong tissue- and disease-specific interconnections between RNAP2 occupancy and transcription. We demonstrate that intergenic transcription at RNAP2 bound regions are novel per-cancer and pan-cancer biomarkers showing genomic and clinically relevant characteristics including the ability to differentiate cancer subtypes and are associated with overall survival. Our results demonstrate the effectiveness of coherent data integration to uncover and characterise intergenic transcriptional activity in both normal and cancer tissues.

genomics↗

bioRxiv2018

A map of direct TF-DNA interactions in the human genome

Chromatin immunoprecipitation followed by sequencing (ChIP-seq) is the most popular assay to identify genomic regions, called ChIP-seq peaks, that are bound in vivo by transcription factors (TFs). These regions are derived from direct TF-DNA interactions, indirect binding of the TF to the DNA (through a co-binding partner), nonspecific binding to the DNA, and noise/bias/artifacts. Delineating the bona fide direct TF-DNA interactions within the ChIP-seq peaks remains challenging. We developed a dedicated software, ChIP-eat, that combines computational TF binding models and ChIP-seq peaks to automatically predict direct TF-DNA interactions. Our work culminated with predicted interactions covering >4% of the human genome, obtained by uniformly processing 1,983 ChIP-seq peak data sets from the ReMap database for 232 unique TFs. The predictions were a posteriori assessed using protein binding microarray and ChIP-exo data, and were predominantly found in high quality ChIP-seq peaks. The set of predicted direct TF-DNA interactions suggested that high-occupancy target regions are likely not derived from direct binding of the TFs to the DNA. Our predictions derived co-binding TFs supported by protein-protein interaction data and defined cis-regulatory modules enriched for disease- and trait-associated SNPs. Finally, we provide this collection of direct TF-DNA interactions and cis-regulatory modules in the human genome through the UniBind web-interface (http://unibind.uio.no).

bioinformatics↗