Biology subjects

Federico, A.

Publications and source records attributed to Federico, A..

22 records · Page 2Linked to original sources

bioRxiv2019

hypeR: An R Package for Geneset Enrichment Workflows

SummaryGeneset enrichment is a popular method for annotating high-throughput sequencing data. Existing tools fall short in providing the flexibility to tackle the varied challenges researchers face in such analyses, particularly when analyzing many signatures across multiple experiments. We present a comprehensive R package for geneset enrichment workflows that offers multiple enrichment, visualization, and sharing methods in addition to novel features such as hierarchical geneset analysis and built-in markdown reporting. hypeR is a one-stop solution to performing geneset enrichment for a wide audience and range of use cases.\n\nAvailability and implementationThe most recent version of the package is available at https://github.com/montilab/hypeR.\n\nSupplementary informationComprehensive documentation and tutorials, are available at https://montilab.github.io/hypeR-docs.

bioinformatics↗

bioRxiv2019

Yap suppresses T cell function and infiltration in the tumor microenvironment

A major challenge for cancer immunotherapy is sustaining T cell activation and recruitment in immunosuppressive solid tumors. Here we report that Yap levels are sharply induced upon activation of CD4+ and CD8+ T cells and that Yap functions as an immunosuppressive factor and inhibitor of effector differentiation. Loss of Yap in T cells results in enhanced T cell activation, differentiation and function, which translates in vivo to an improved ability for T cells to infiltrate and repress tumors. Gene expression analyses of tumor-infiltrating T cells following Yap deletion implicates Yap as a mediator of global T cell responses in the tumor microenvironment and as a key negative regulator of T cell tumor infiltration and patient survival in diverse human cancers. Collectively, our results indicate that Yap plays critical roles in T cell biology, and suggest that inhibiting Yap activity improves T cell responses in cancer.

cancer biology↗

bioRxiv2018

Pipeliner: A Nextflow-based framework for the definition of sequencing data processing pipelines

The advent of high-throughput sequencing technologies has led to the need for flexible and user-friendly data pre-processing platforms. The Pipeliner framework provides an out-of-the-box solution for processing various types of sequencing data. It combines the Nextflow scripting language and Anaconda package manager to generate modular computational workflows. We have used Pipeliner to create several pipelines for sequencing data processing including bulk RNA-seq, single-cell RNA-seq (scRNA-seq), as well as Digital Gene Expression (DGE) data. This report highlights the design methodology behind Pipeliner which enables the development of highly flexible and reproducible pipelines that are easy to extend and maintain on multiple computing environments. We also provide a quick start user guide demonstrating how to setup and execute available pipelines with toy datasets.

bioinformatics↗

bioRxiv2017

SFMetaDB: A Comprehensive Annotation of Mouse RNA Splicing Factor RNA-Seq Datasets

Although the number of RNA-Seq datasets deposited publicly has increased over the past few years, incomplete annotation of the associated metadata limits their potential use. Because of the importance of RNA splicing in diseases and biological processes, we constructed a database called SFMetaDB by curating datasets related with RNA splicing factors. Our effort focused on the RNA-Seq datasets in which splicing factors were knocked-down, knocked-out or over-expressed, leading to 75 datasets corresponding to 56 splicing factors. These datasets can be used in differential alternative splicing analysis for the identification of the potential targets of these splicing factors and other functional studies. Surprisingly, only [~]15% of all the splicing factors have been studied by loss- or gain-of-function experiments using RNA-Seq. In particular, splicing factors with domains from a few dominant Pfam domain families have not been studied. This suggests a significant gap that needs to be addressed to fully elucidate the splicing regulatory landscape. Indeed, there are already mouse models available for [~]20 of the unstudied splicing factors, and it can be a fruitful research direction to study these splicing factors in vitro and in vivo using RNA-Seq.\n\nDatabase URLhttp://sfmetadb.ece.tamu.edu/

bioinformatics↗