ReMap 2022: a database of Human, Mouse, Drosophila and Arabidopsis regulatory regions from an integrative analysis of DNA-binding sequencing experiments.

ReMap (https://remap.univ-amu.fr) aims to provide manually curated, high-quality catalogs of regulatory regions resulting from a large-scale integrative analysis of DNA-binding experiments in Human, Mouse, Fly and Arabidopsis thaliana for hundreds of transcription factors and regulators. In this 2022 update, we have uniformly processed >11 000 DNA-binding sequencing datasets from public sources across four species. The updated Human regulatory atlas includes 8103 datasets covering a total of 1210 transcriptional regulators (TRs) with a catalog of 182 million (M) peaks, while the updated Arabidopsis atlas reaches 4.8M peaks, 423 TRs across 694 datasets. Also, this ReMap release is enriched by two new regulatory catalogs for Mus musculus and Drosophila melanogaster. First, the Mouse regulatory catalog consists of 123M peaks across 648 TRs as a result of the integration and validation of 5503 ChIP-seq datasets. Second, the Drosophila melanogaster catalog contains 16.6M peaks across 550 TRs from the integration of 1205 datasets. The four regulatory catalogs are browsable through track hubs at UCSC, Ensembl and NCBI genome browsers. Finally, ReMap 2022 comes with a new Cis Regulatory Module identification method, improved quality controls, faster search results, and better user experience with an interactive tour and video tutorials on browsing and filtering ReMap catalogs.

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 built a Python package, MUFFIN, which offers a wide variety of tools suitable for a broad range of genomic assays and brings many tools that were missing from the Python ecosystem. First, MUFFIN has specialized tools for the exploration of the non-coding regions of genomes, such as a function to identify consensus peaks in peak-called assays, as well as linking genomic regions to genes and performing Gene Set Enrichment Analyses. MUFFIN also possesses a robust and flexible count table processing pipeline, comprising normalization, count transformation, dimensionality reduction, Differential Expression, and clustering. Our tools were tested on three widely different scRNA-seq, ChIP-seq and ATAC-seq datasets. MUFFIN integrates with the popular Scanpy ecosystem and is available on Conda and at https://github.com/pdelangen/Muffin.

Characterizing intergenic transcription at RNA polymerase II binding sites in normal and cancer tissues.

Intergenic transcription in normal and cancerous tissues is pervasive but incompletely understood. To investigate this, we constructed an atlas of over 180,000 consensus RNA polymerase II (RNAPII)-bound intergenic regions from 900 RNAPII chromatin immunoprecipitation sequencing (ChIP-seq) experiments in normal and cancer samples. Through unsupervised analysis, we identified 51 RNAPII consensus clusters, many of which mapped to specific biotypes and revealed tissue-specific regulatory signatures. We developed a meta-clustering methodology to integrate our RNAPII atlas with active transcription across 28,797 RNA sequencing (RNA-seq) samples from The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), and Encyclopedia of DNA Elements (ENCODE). This analysis revealed strong tissue- and disease-specific interconnections between RNAPII occupancy and transcriptional activity. We demonstrate that intergenic transcription at RNAPII-bound regions is a novel per-cancer and pan-cancer biomarker. This biomarker displays genomic and clinically relevant characteristics, distinguishing cancer subtypes and linking to overall survival. Our results demonstrate the effectiveness of coherent data integration to uncover intergenic transcriptional activity in normal and cancer tissues.