Nuno A. Fonseca

The paper introduces MIMt, a curated 16S rRNA database that improves species-level identification accuracy and reduces redundancy compared to existing databases.

This paper develops a gene expression signature, Cam_121, to predict long-term outcomes in primary melanoma patients, showing improved accuracy compared to clinical and existing prognostic models.

The paper provides a user guide to five online tools for exploring and visualizing PCAWG data, demonstrating their integration with external resources to enhance cancer genomics research.

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.

The paper identifies hundreds of genes deregulated by SVs in cancer, showing increased expression linked to SV breakpoints and enhancer elements.

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

This study identifies alternative promoters as key regulators in the cancer transcriptome using RNA-Seq data from 1,188 cancer samples.

The paper analyzes RNA alterations across 27 cancer types using whole-genome sequencing data, identifying gene expression associations with DNA variants and structural fusions.

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

This study analyzes whole genome and RNA-Seq data from 1,188 cancer patients to map the effects of somatic and germline genetic variation on gene expression in cancer.

This study shows that cohesin removal leads to loss of TADs and loops but preserves compartments, revealing two independent modes of chromosome organization.

The paper describes the PCAWG project's large-scale analysis of cancer whole genomes across multiple computing environments, producing validated consensus variants for downstream research.

The paper systematically characterizes gene fusions in cancer using RNA and whole genome DNA sequencing data from 1188 donors across 27 cancer types, identifying 3297 high-confidence fusions and revealing novel mechanisms.

iRAP is an RNA-seq analysis pipeline that integrates multiple tools for read mapping, expression quantification, differential expression testing, and gene set enrichment analysis, providing customizable and user-friendly workflows.