Reproducible processing of TCGA regulatory networks.

ABSTRACT:

Background

Technological advances in sequencing and computation have allowed deep exploration of the molecular basis of diseases. Biological networks have proven to be a useful framework for interrogating omics data and modeling regulatory gene and protein interactions. Large collaborative projects, such as The Cancer Genome Atlas (TCGA), have provided a rich resource for building and validating new computational methods resulting in a plethora of open-source software for downloading, pre-processing, and analyzing those data. However, for an end-to-end analysis of regulatory networks a coherent and reusable workflow is essential to integrate all relevant packages into a robust pipeline.

Findings

We developed tcga-data-nf, a Nextflow workflow that allows users to reproducibly

SUBMITTER: Fanfani V 

PROVIDER: S-EPMC11580957 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature