Biology subjects

de Langen, P.

Publications and source records attributed to de Langen, P..

2 recordsLinked to original sources

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↗