Author

Babita Singh

Universitat Pompeu FabraORCID 0000-0002-7989-9084Unclaimed ORCID profile · follow to get notified of new preprints

Additional preprints under this name without ORCID

Babita Singh2 papers · National Botanical Research Institute

Biology Disciplines

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Bioinformatics· 2 Cancer Biology· 1

Signature Assays, Organisms & Methods

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Research Focus:differential expression· 3 Approach:statistical model· 3 Assay:bulk RNA-seq· 2 Organism:human· 2 Research Focus:cell type annotation· 1 Assay:ChIP-seq· 1 Assay:DNA methylation· 1 Research Focus:gene regulatory network inference· 1

Frequent Co-Authors

Eduardo EyrasiD· 3 shared Amadís Pagès· 2 shared Belén Miñana· 1 shared David J. ElliottiD· 1 shared Endre Sebestyén· 1 shared Francesca Mateo· 1 shared Gerald Hysenaj· 1 shared Juan Carlos EntizneiD· 1 shared

Activity by Year

2015-2018 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 22 mutual co-author links

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3 shared papersBioinformatics

Eduardo EyrasORCID

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3 papers

Recent·Most cited

SUPPA2 provides fast, accurate, and uncertainty-aware differential splicing analysis across multiple conditions

Juan L. Trincado, Juan Carlos Entizne, Gerald Hysenaj, Babita Singh, Miha Škalič, David J. Elliott, Eduardo Eyras

Genome biology · Mar 23, 2018Peer-reviewedBioinformatics

Large-scale analysis of genome and transcriptome alterations in multiple tumors unveils novel cancer-relevant splicing networks

Endre Sebestyén, Babita Singh, Belén Miñana, Amadís Pagès, Francesca Mateo, Miguel A Pujana, Juan Valcarcel, Eduardo Eyras

The paper analyzes genome and transcriptome alterations in multiple tumors to identify novel cancer-relevant splicing networks involving RNA-binding proteins and alternative splicing changes.

bioRxiv · Feb 11, 2016Cancer Biology

A semi-supervised approach uncovers thousands of intragenic enhancers differentially activated in human cells

Juan Gonzalez-Vallinas, Amadís Pagès, Babita Singh, Eduardo Eyras

The paper introduces a semi-supervised method to identify intragenic enhancers differentially active in human cell lines and predicts thousands of such enhancers with functional properties.

bioRxiv · Jun 3, 2015Bioinformatics