This paper develops SpliceLearn, an AI/ML tool for identifying splice-switching oligonucleotide targets, and validates its effectiveness in predicting therapeutic targets in triple negative breast cancer.

bioRxiv · Oct 14, 2022 · PreprintBioinformatics

Unique genomic features and deeply-conserved functions of long non-coding RNAs in the Cancer LncRNA Census (CLC)

Joana Carlevaro-Fita, Andrés Lanzós, Lars Feuerbach, Chen Hong, David Mas-Ponte, Jakob Skou Pedersen, PCAWG Drivers and Functional Interpretation Group, Federico Abascal, Samirkumar B. Amin, Gary D. Bader, Jonathan Barenboim, Rameen Beroukhim, Johanna Bertl, Keith A. Boroevich, Søren Brunak, , S. Cenk Şahinalp

The paper introduces the Cancer LncRNA Census (CLC), a curated resource of lncRNAs with causative roles in cancer, and shows their genomic and evolutionary features.

Communications Biology · Feb 5, 2020Peer-reviewedGenomics

Integrative pathway enrichment analysis of multivariate omics data

Marta Paczkowska, Jonathan Barenboim, Nardnisa Sintupisut, Natalie S. Fox, Helen Zhu, Diala Abd-Rabbo, Miles W. Mee, Paul Christopher Boutros, PCAWG Drivers and Functional Interpretation Working Group, Federico Abascal, Samirkumar B. Amin, Gary D. Bader, Rameen Beroukhim, Johanna Bertl, Keith A. Boroevich, , Leonidas Salichos

ActivePathways integrates multivariate omics data to identify enriched pathways, revealing cancer driver genes and prognostic pathways in breast cancer.

Nature Communications · Feb 5, 2020Peer-reviewedBioinformatics

Pathway and network analysis of more than 2,500 whole cancer genomes

Matthew A. Reyna, David Haan, Marta Paczkowska, Lieven P.C. Verbeke, Miguel Vazquez, Abdullah Kahraman, Sergio Pulido-Tamayo, Jonathan Barenboim, Lina Wadi, Priyanka Dhingra, Raunak Shrestha, Gad Getz, Michael S. Lawrence, Jakob Skou Pedersen, Mark A. Rubin, , on behalf of the PCAWG Drivers and Functional Interpretation Group and the ICGC/TCGA Pan-Cancer Analysis of Whole Genome Network

The paper analyzes non-coding mutations across 2,583 cancer genomes to identify genes and pathways affected, finding clusters of interacting proteins and insights into cancer mechanisms.

bioRxiv · Aug 7, 2018 · PreprintCancer Biology

Discovery and characterization of coding and non-coding driver mutations in more than 2,500 whole cancer genomes

Esther Rheinbay, Morten Muhlig Nielsen, Federico Abascal, Grace Tiao, Henrik Hornshøj, Julian M. Hess, Randi Istrup Pedersen, Lars Feuerbach, Radhakrishnan Sabarinathan, Tobias Lynge Madsen, Jaegil Kim, Loris Mularoni, Shimin Shuai, Andrés Lanzós, Carl Herrmann, , Gad A. Getz

The paper identifies and characterizes coding and non-coding driver mutations across 2,500 cancer genomes, revealing new candidates and questioning previous findings.

bioRxiv · Dec 23, 2017 · PreprintGenomics