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Assistant Professor
Dept. of Genetics
Dept. of Computer Science
Stanford University
Address
Department of Genetics
240 Pasteur Dr, Room 4351
Palo Alto, CA 94304, USA [Map]

E-mail: anshul @ kundaje .net ; akundaje @ stanford .edu 
Phone: (650)-723-2353 ; Fax: (650)-725-1534



ABOUT ME

Anshul Kundaje is an Assistant Professor of Genetics and Computer Science at Stanford University. The Kundaje lab develops statistical and machine learning methods for large-scale integrative analysis of functional genomic data to decode regulatory elements and pathways across diverse cell types and tissues and understand their role in cellular function and disease. Anshul completed his Ph.D. in Computer Science in 2008 from Columbia University. As a postdoc at Stanford University from 2008-2012 and a research scientist at MIT and the Broad Institute from 2012-2014, he led the integrative analysis efforts for two of the largest functional genomics consortia - The Encyclopedia of DNA Elements (ENCODE) and The Roadmap Epigenomics Project. Dr. Kundaje is a recipient of the 2019 Chen Award of Excellence from the Human Genome Organization, 2016 NIH Director’s New Innovator Award and The 2014 Alfred Sloan Foundation Fellowship. Anshul is also a member of the NIH Director's Advisory Committee for Artificial Intelligence in Biomedical Research.

WHAT WE DO

My primary research interests are computational biology and applied machine learning with a focus on gene regulation. My lab develops statistical and machine learning methods to infer integrative models of transcriptional regulation and interpret non-coding genetic variation through the lens of gene regulation. At the core of our research is the idea that obtaining a genome-wide and system-level understanding of gene regulation is essential to decipher the causal genetic and molecular basis of disease. What makes our research unique is the ability to condense massive compendia of genomic data into interpretable and predictive computational models capable of capturing functional heterogeneity and context-specificity thereby allowing the discovery of exceptions rather than focusing only on the norms. We collaborate extensively with experimental biologists within and outside Stanford to validate hypotheses generated by our models and discover novel biology. We have led the analysis efforts of the Encyclopedia of DNA Elements (ENCODE) and The Roadmap Epigenomics Projects. We are currently working on the following key research areas.
  1. Interpretable deep learning models of protein-DNA binding, chromatin accessibility and chromatin state
  2. Learning distal regulatory interactions between regulatory elements such as enhancers and gene promoters 
  3. Leaning transcriptional regulatory networks that integrate cis-regulatory DNA sequence and activity of trans-regulators
  4. Learning dynamic regulatory models by integrating functional genomic data from temporal (e.g. differentiation/reprogramming) and perturbation (e.g. CRISPR genome engineering) experiments
  5. Prioritizing and interpreting functional genetic variation and its impact on chromatin, expression and cellular phenotypes (growth, differentiation, drug response) in healthy and disease states
  6. Early cancer detection and tissue-of-origin deconvolution from liquid biopsy (e.g. cell-free DNA) assays
For more details see our Projects. Also check out some of the lectures/talks from our lab.

Talks Playlist

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RECENT NEWS


BACKGROUND

2012-2013, I was a Research Scientist in Manolis Kellis' lab at MIT and The Broad Institute studying epigenomic and chromatin state dynamics across organisms, cell-types and individuals as part of the Roadmap Epigenomics Project and the mod/ENCODE (Encyclopedia for DNA elements) consortium. 

2008 - 2012, I was a Postdoctoral Research Associate with Serafim Batzoglou and Arend Sidow in the Computer Science Dept. at Stanford University. I served as one of the lead data coordinators and computational analysts for the ENCODE consortium. My primary focus was on deciphering heterogeneity of regulatory interactions in the human genome. I also developed ENCODE's ChIP-seq statistical data analysis pipeline.

2003-2008, I was a graduate student (PhD.) in Christina Leslie's lab in the Computer Science Dept. at Columbia University in New York. I developed Machine Learning methods for modeling transcriptional gene regulation in yeast and worm. 

2002-2003, I briefly worked at the IBM T.J. Watson Research Center in the Functional Genomics and Systems Biology group under Gustavo Stolovitzky. I developed one of the first statistical noise models for massively parallel sequencing data (MPSS) in a collaboration with The Institute of Systems Biology and Lynx Therapeutics.

In a past life, I was an Electrical Engineer (B.E from Mumbai University, 2001 and M.S. from Columbia University, 2002) and worked on computer networks and voice over IP with Henning Schulzrinne.

REPRESENTATIVE PUBLICATIONS

  • Deep learning at base-resolution reveals motif syntax of the cis-regulatory code [Code] [Videos]
    Avsec Ž, Weilert M, Shrikumar A, Alexandari A, Krueger S, Dalal K, Fropf R, McAnany C, Gagneur J, Kundaje A*, Zeitlinger J*
    bioRxiv 737981; doi: https://doi.org/10.1101/737981

  • Kipoi: accelerating the community exchange and reuse of predictive models for regulatory genomics [Website] [Code] [Preprint]
    Avsec Ž, Kreuzhuber R, Israeli J, Xu N, Cheng J, Shrikumar A, Banerjee A, Kim DS, Beier T, Urban L, Kundaje A*, Stegle O*, Gagneur J*
    Nat Biotechnol. 2019 May 28 DOI: 10.1038/s41587-019-0140-0

  • Learning Important Features Through Propagating Activation Differences [Code], [Videos], [Preprint]
    Shrikumar A, Greenside P,  Kundaje A
    Proceedings of the 34th International Conference on Machine Learning (ICML), PMLR 70:3145-3153, 2017

  • An integrated encyclopedia of DNA elements in the human genome
    Dunham I, Kundaje A, ENCODE Project Consortium
    Nature. 2012 Sep 6;489(7414):57-74. doi: 10.1038/nature11247.

  • Integrative analysis of 111 reference human epigenomes
    Roadmap Epigenomics Consortium, Kundaje A, Meuleman W, Ernst J, Bilenky M, Yen A, Heravi-Moussavi A, Kheradpour P, Zhang Z, Wang J, Ziller MJ, Amin V, Whitaker JW, Schultz MD, Ward LD, Sarkar A, Quon G, Sandstrom RS, Eaton ML, Wu YC, Pfenning AR, Wang X, Claussnitzer M, Liu Y, Coarfa C, Harris RA, Shoresh N, Epstein CB, Gjoneska E, Leung D, Xie W, Hawkins RD, Lister R, Hong C, Gascard P, Mungall AJ, Moore R, Chuah E, Tam A, Canfield TK, Hansen RS, Kaul R, Sabo PJ, Bansal MS, Carles A, Dixon JR, Farh KH, Feizi S, Karlic R, Kim AR, Kulkarni A, Li D, Lowdon R, Elliott G, Mercer TR, Neph SJ, Onuchic V, Polak P, Rajagopal N, Ray P, Sallari RC, Siebenthall KT, Sinnott-Armstrong NA, Stevens M, Thurman RE, Wu J, Zhang B, Zhou X, Beaudet AE, Boyer LA, De Jager PL, Farnham PJ, Fisher SJ, Haussler D, Jones SJ, Li W, Marra MA, McManus MT, Sunyaev S, Thomson JA, Tlsty TD, Tsai LH, Wang W, Waterland RA, Zhang MQ, Chadwick LH, Bernstein BE, Costello JF, Ecker JR, Hirst M, Meissner A, Milosavljevic A, Ren B, Stamatoyannopoulos JA, Wang T, Kellis M.
    Nature. 2015 Feb 19;518(7539):317-30. doi: 10.1038/nature14248. (PMID: 25693563)
  • Architecture of the human regulatory network derived from ENCODE data. [Website]
    Gerstein MB*, Kundaje A*, Hariharan M, Landt SG, Yan KK, Cheng C, Mu XJ, Khurana E, Rozowsky J, Alexander R, Min R, Alves P, Abyzov A, Addleman N, Bhardwaj N, Boyle AP, Cayting P, Charos A, Chen DZ, Cheng Y, Clarke D, Eastman C, Euskirchen G, Frietze S, Fu Y, Gertz J, Grubert F, Harmanci A, Jain P, Kasowski M, Lacroute P, Leng J, Lian J, Monahan H, O'Geen H, Ouyang Z, Partridge EC, Patacsil D, Pauli F, Raha D, Ramirez L, Reddy TE, Reed B, Shi M, Slifer T, Wang J, Wu L, Yang X, Yip KY, Zilberman-Schapira G, Batzoglou S, Sidow A, Farnham PJ, Myers RM, Weissman SM, Snyder M. 
    Nature. 2012 Sep 6;489(7414):91-100. doi: 10.1038/nature11245.
    *Joint First Author

  • Conserved epigenomic signals in mice and humans reveal immune basis of Alzheimer's disease
    Gjoneska E, Pfenning AR, Mathys H, Quon G, Kundaje A, Tsai LH, Kellis M.
    Nature. 2015 Feb 19;518(7539):365-9. doi: 10.1038/nature14252. (PMID: 25693568)

  • Extensive Variation in Chromatin States Across Humans [Website] [Data] 
    Kasowski M*, Kyriazopoulou-Panagiotopoulou S*, Grubert F*, Zaugg JB*, Kundaje A*, Liu Y, Boyle AP, Zhang QC, Zakharia Q, Spacek DV, Li J, Xie D, Olarerin-George A, Steinmetz LM, Hogenesch JB, Kellis M, Batzoglou S, Snyder M
    Science. 2013 Oct 17; DOI:10.1126/science.1242510
    * Equal contribution
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