Hsin-Ta Wu, PhD

Hello! I'm Hsin-Ta Wu. I recently received my PhD from the Department of Computer Science and the Center for Computational Molecular Biology at Brown University. I am now working as a senior bioinformatician at Natera. My research interests are in developing algorithms and statistical models for answering important biological questions. My primary research in my PhD study focuses on computational cancer genomics, especially with respect to characterization of somatic mutations and identification of somatic mutations that drives cancer progression. For the past several years, I have developed algorithms for computational detection of large genomic alterations, structural variant, and computational identification of driver mutations. Based on experience with developing computational tools, a strong base of biological knowledge, and deep immersion in hypothesis-driven research, I am willing to touch any new fields of biological problems and come out with some methods that are efficient and scalable to solve these problems.
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PhD in Computer Science and Computational Biology

Brown University 2010 - 2016

MS in Bioinformatics

National Yang Ming University2004 - 2006

BA in Computer Science

National Cheng Chi University2000 - 2004

Awards & Competitions

Travel fellowship award for RECOMB

Warsaw, Poland 2015

Travel fellowship award for ISMB

Boston, USA 2014

BioCreAtIvE II.5 Interactor Normalization

1st place 2009

Hsun-Ruo Yin Scholarship

Best thesis award2006

National Science Council Research Award


Professional Experience

Senior Bioinformatician

Natera, San Carlos, USA Aug. 2016 - present

Participate in oncology product development. Take responsible for several oncology research projects: panel design for single nucleotide variants/small indels; tissue/plasma variant caller by deep learning approach.

PhD-level Research Internship

Pfizer, Cambridge, USA Summer 2015

Constructed a comparative analysis of NAFLD/NASH targets on transcriptomics and proteomics data. Developed an integrative analysis using network propagation algorithms and network visualization package for R.

Research Internship

Protein Information Resource, Washington DC, USA Summer 2005

Developed a rule-based literature mining system and website for protein phosphorylation. Designed a new mapping algorithm for iProLINK literature mining resource.

Computational Skills




Research projects

CoMEt pipeline

CoMEt: Combinations of Exclusive Alterations

A statistical model to identify combinations of driver mutations that are mutual exclusivity, a pattern expected for mutations in cancer pathways.

RAIG input

RAIG: Recurrent Aberrations from Interval Graph

A combinatorial approach for the problem of identifying independent and recurrent copy number aberrations, which are gains and losses of large genomic regions ranging in size from a few kilo-bases to whole chromosomes.

SV idea

Identification of structural variations from linked-reads sequencing

A statistical model to identify structural variations in a tumor more accurately by utilizing a new sequencing technology from 10X Genomics.

hotnet2 results


An algorithm to identify significant clusters of mutations in an interaction network.

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Visualization Projects

GroupNetworkD3: Improved NetowrkD3 has several new functions: grouping nodes, directed edges, more interactive functions with network.  Demo   Source code

MAGI: MAGI is a powerful web application, which displays interactive visualizations as well as crowd-sourced and text-mined mutation annotations in order to help prioritize likely driver mutations.

AML co-occurrence plot is a circle plot to display co-occurrence mutations between 200 Acute Myeloid Lukemia patients.

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The Cancer Genome Atlas (TCGA) projects

I participated in several TCGA projects, including:

Selected Publications

  • Comprehensive Pan-Genomic Characterization of Adrenocortical Carcinoma. The Cancer Genome Atlas Research Network. Cancer Cell. (2016)  
  • Reply: Co-occurrence of MYC amplification and TP53 mutations in human cancer. Leiserson, M.D.M., Vandin, F., Wu, H., Raphael, B.J. Nature Genetics. (2016)  
  • CoMEt: A Statistical Approach to Identify Combinations of Mutually Exclusive Alterations in Cancer. Leiserson, M.D.M.*, Wu, H.*+, Vandin, F., Raphael, B.J. Genome Biology / 19th Annual International Conference on Research in Computational Molecular Biology (RECOMB) . (2015) *Equal contribution. +Presenting author at RECOMB and TCGA symposium 2015 at NIH.   
  • MAGI: visualization and collaborative annotation of genomic aberrations. Leiserson, M.D.M., Gramazio, C.C., Hu J., Wu, H., Laidlaw, D.H., Raphael, B.J. Nature Methods. (2015)   
  • Pan-Cancer Network Analysis Identifies Combination of Rare Somatic Mutations across Pathways and Protein Complexes. Leiserson, M.D.M.*, Vandin, F.*, Wu, H., Dobson, J.R., Papoutsaki, A., Eldridge, J.V., Nui, B., McLellan, M, Lawrence, M.S., Gonzalez-Perez, A., Tamborero, D., Ryslik, G.A., Cheng, Y., Lopez-Bigas, N., Getz, G., Ding, L., Raphael, B.J. Nature Genetics. (2014)   
  • Comprehensive molecular characterization of gastric adenocarcinoma. The Cancer Genome Atlas Research Network. Nature. (2014)   
  • Multiplatform Analysis of 12 Cancer Types Reveals Molecular Classification within and across Tissues of Origin. The Cancer Genome Atlas Research Network. Cell. (2014)  
  • Detecting Independent and Recurrent Copy Number Aberrations using Interval Graphs. Wu, H., Hajirasouliha, I., Raphael, B.J. Bioinformatics / 22nd Annual International Conference on Intelligent Systems for Molecular Biology (ISMB). (2014)   
  • The Cancer Genome Atlas Pan-Cancer analysis project. The Cancer Genome Atlas Research Network. Nature Genetics. (2013)   
  • Comprehensive Molecular Characterization of Clear Renal Cell Carcinoma. The Cancer Genome Atlas Research Network. Nature. (2013)  
  • Genomic and Epigenomic Landscapes of Adult De Novo Acute Myeloid Leukemia The Cancer Genome Atlas Research Network. The New England Journal of Medicine. (2013)  
  • Identification of Ovarian Cancer Metastatic miRNAs. Vang, S., Wu, H., Fischer, A., Miller, D.H., MacLaughlan, S., Douglass, E., Steinhoff, M., Collins, C., Smith, P.J.S., Brard, L., Brodsky, A.S. PLOS One. (2013)   
  • An Integrative Probabilistic Model for Identification of Structural Variation in Sequencing Data. Sindi, S.S., ├ľnal, S., Peng, L.C., Wu, H., Raphael, B.J. Genome Biology. (2012)   
  • Please check my resume for more publications.