Author

Wenqiang Shi

University of British ColumbiaPrimary ORCID profile (0000-0001-7599-0334 · 1 paper)Unclaimed ORCID profile · follow to get notified of new preprints

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Same name, identified by ORCID

Wenqiang ShiiD1 paper · University of Pennsylvania

Biology Disciplines

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Bioinformatics· 2 Genomics· 1

Signature Assays, Organisms & Methods

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

Frequent Co-Authors

Wyeth W. WassermaniD· 2 shared Yifeng Li· 2 shared Alistair R. R. ForrestiD· 1 shared Allison M. Matthews· 1 shared Anthony MathelieriD· 1 shared Carolyn J. BrowniD· 1 shared Chih-yu CheniD· 1 shared David J. Arenillas· 1 shared

Activity by Year

2016-2018 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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2 shared papersBioinformatics

Wyeth W. WassermanORCID

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3 papers

Recent·Most cited

Altered transcription factor binding events predict personalized gene expression and confer insight into functional cis-regulatory variants

Wenqiang Shi, Oriol Fornés, Wyeth W. Wasserman

The paper introduces TF2Exp, a framework predicting gene expression impact from cis-regulatory variants by modeling altered transcription factor binding, showing comparable performance to SNP-based models.

Bioinformatics · Dec 10, 2018Peer-reviewedBioinformatics

A role for YY1 in sex-biased transcription revealed through X-linked promoter activity and allelic binding analyses

Chih-yu Chen, Wenqiang Shi, Allison M. Matthews, Yifeng Li, David J. Arenillas, Anthony Mathelier, Masayoshi Itoh, Hideya Kawaji, Timo Lassmann, FANTOM Consortium, Yoshihide Hayashizaki, Piero Carninci, Alistair R. R. Forrest, Carolyn J. Brown, Wyeth W. Wasserman

The paper identifies YY1's role in X-linked gene escape from inactivation by analyzing promoter activity and allelic binding in human datasets.

bioRxiv · Mar 21, 2016 · PreprintGenomics

Genome-Wide Prediction of cis-Regulatory Regions Using Supervised Deep Learning Methods

Yifeng Li, Wenqiang Shi, Wyeth W Wasserman

The paper introduces DECRES, a deep learning model to identify enhancer and promoter regions in the human genome, predicting 300,000 candidate enhancers and 26,000 candidate promoters.

bioRxiv · Feb 28, 2016Bioinformatics