Abstract
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.
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.
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.
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•An RNAPII atlas of intergenic transcription in normal tissues and cancer samples•Intergenic atlas shows enhancer-like characteristics and transcriptional signals•Meta-clustering reveals shared transcriptional patterns among tissues and cancer types•Identified intergenic markers associated with cancer genes and survival
We developed an atlas of intergenic transcription using RNAPII binding sites to connect genomic and transcriptomic data in normal tissues and cancer samples. The atlas enables investigation of tissue specificity and core regulatory elements. Meta-clustering reveals shared transcription patterns among tissues and cancer types. We identified intergenic markers that are associated with known cancer genes and predictive of overall survival. Our study demonstrates the effectiveness of integrating diverse public datasets to characterize intergenic transcription in normal and cancer tissues, addressing limitations of previous techniques.
Radical resection for patients with oral cavity cancer remains challenging. Rapid evaporative ionization mass spectrometry (REIMS) of electrosurgical vapors has been reported for real-time ...classification of normal and tumor tissues for numerous surgical applications. However, the infiltrative pattern of invasion of oral squamous cell carcinomas (OSCC) challenges the ability of REIMS to detect low amounts of tumor cells. We evaluate REIMS sensitivity to determine the minimal amount of detected tumors cells during oral cavity cancer surgery. A total of 11 OSCC patients were included in this study. The tissue classification based on 185 REIMS ex vivo metabolic profiles from five patients was compared to histopathology classification using multivariate analysis and leave-one-patient-out cross-validation. Vapors were analyzed in vivo by REIMS during four glossectomies. Complementary desorption electrospray ionization–mass spectrometry imaging (DESI-MSI) was employed to map tissue heterogeneity on six oral cavity sections to support REIMS findings. REIMS sensitivity was assessed with a new cell-based assay consisting of mixtures of cell lines (tumor, myoblasts, keratinocytes). Our results depict REIMS classified tumor and soft tissues with 96.8% accuracy. In vivo REIMS generated intense mass spectrometric signals. REIMS detected 10% of tumor cells mixed with 90% myoblasts with 83% sensitivity and 82% specificity. DESI-MSI underlined distinct metabolic profiles of nerve features and a metabolic shift phosphatidylethanolamine PE(O-16:1/18:2))/cholesterol sulfate common to both mucosal maturation and OSCC differentiation. In conclusion, the assessment of tissue heterogeneity with DESI-MSI and REIMS sensitivity with cell mixtures characterized sensitive metabolic profiles toward in vivo tissue recognition during oral cavity cancer surgeries.
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