This is the code repository for the research paper Characterising intergenic transcription at RNA polymerase II binding sites in normal and cancer tissues by de Langen et al.
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.
All details on how to reproduce our results and how to re-use some parts of our code is in rerun_all/README.md. It containts the following sections :
We provide singularity environments (on Zenodo) and recipes (./env/ folder). Snakemake is required to re-run all analyses.
The input data used in this project is publicly available at Zenodo and can be found in the table below.
The output data produced by this project (RNAP2 occupancy atlas, markers, count tables...) is publicly available at Zenodo and can be found in the table below.
| Intergenic RNAP2 Atlas | Zenodo DOI |
|---|---|
input data |
 |
output data |
|
This project is licensed under the GPL-3.0 license. See the LICENSE.md file in the repository for more details.
This work was supported with ; PhD Fellowship to P.D.L. from the French Ministry of Higher Education and Research (MESR); PhD Fellowship to F.H. from the Provence-Alpes-Côte d’Azur Regional Council (Région SUD); Institut National de la Santé et de la Recherche Médicale (INSERM); The Core Cluster of the Institut Français de Bioinformatique (IFB) (ANR-11-INBS-0013) and the Centre de Calcul Intensif d’Aix-Marseille for granting access to its high performance computing resources. The results shown here are based upon data generated by the TCGA Research Network: https://www.cancer.gov/tcga, the GTEx project, the ENCODE Consortium, the ENCODE production laboratories, and independent laboratories who followed the Open Science principles and submitted raw ChIP-seq data into repositories.