Computational Biology at MSR New England

Computational Biology at MSR New England

Established: January 1, 2011

We encompass several approaches to computational biology: we try to frame the biological question under consideration in terms of more standard problems in computer science, like clustering, Steiner trees, flow problems, etc., and then use approximation algorithms motivated by statistical physics to solve these problems. One of our most successful approaches in this realm involves variants of belief- and survey propagation algorithms, but in the course of adapting our problem to this setting, we often need to derive alternative representations of the original computer science problem which might be useful when applying other algorithms as well.

We also approach many problems from the perspective of applied statistics and machine learning, making use of latent variable models and efficient operations on them to perform inference and learning. In this vein, we have tackled problems in CRISPR gene editing;  problems in statistical genetics such as effective and efficient handling of unknown confounding factors in eQTL association studies, genome-wide association studies, and analysis of methylation data; immunoinformatics such as HLA imputation and refinement, epitope prediction; problems in proteomics such as alignment of vector time series resulting from liquid-chromatography-mass-spectrometry systems.

For more information please follow the links to our individual web pages.

We run a seminar series which can be found here.


MSR NE: Post-Doctoral position in Computational Biology

A computational biology postdoc position starting July 1st, 2017 is currently open at Microsoft Research New England. Please get applications in by December 1st 2016, although we may consider later applications. Interviews will start in early 2017 and the position will be filled on a rolling basis. Most of our post-docs stay for 2 years but because many already have faculty positions lined up which they defer for a year, they also often stay only one year. 

MSR New England , located in Cambridge, MA and adjacent to the Broad Institute and MIT, is a multi-disciplinary lab which includes areas in addition to computational biology, of machine learning, theoretical computer science, economics and social media. Our researchers interested in computational biology can be found here. Our computational biology seminar series can be found here.

To submit an application follow these instructions exactly in order to ensure that we see your application:

  1. Include an academic CV including publications.
  2. Include a research statement.
  3. Include reference letters from 3-5 people familiar with your work, and who, ideally, have worked with you closely.
  4. Please apply on-line as follows (i) if you don’t have one create a Microsoft account, (ii) sign in to our careers web page at, (iii) create a career profile and save it (iv) apply for this position by selecting “PostDoctorate”, “New England, MA, U.S.”, “Computational Biology”.
  5. Please also make sure to directly email a full application (reference letters not needed, but list of references is) to, using Subject “Postdoc Applicant: FirstName LastName”.

Selected Publications

Optimized sgRNA design to maximize activity and minimize off-target effects for genetic screens with CRISPR-Cas9
JG Doench*, N Fusi*, M Sullender*, M Hegde*, EW Vaimberg*, KF Donovan, I Smith, Z Tothova, C Wilen , R Orchard , HW Virgin, J Listgarten*, DE Root, Nature Biotechnology (2016)

Warped linear mixed models for the genetic analysis of transformed phenotypes
Fusi F., Lippert C., Lawrence N., Stegle O, Nature Communications (2014)

Epigenome-wide association studies without the need for cell-type composition
Zou J, Lippert C, Heckerman D, Aryee, M, Listgarten J Nature Methods, 309–311 (2014)

FaST-LMM-Select for addressing confounding from spatial structure and rare variants
Listgarten* J, Lippert* C, Heckerman* D (*equal contributions) Nature Genetics, 45, 470-471 (2013)

Improved linear mixed models for genome-wide association studies
Listgarten J*, Lippert* C, Kadie C, Davidson B, Eskin E, Heckerman* D *(equal contributions)
Nature Methods, 2012

FaST Linear Mixed Models for Genome-Wide Association Studies
Lippert* C, Listgarten* J., Liu Y, Kadie C, Davidson R, Heckerman* D. (*equal contributions) Nature Methods, Aug. 2011

Correction for Hidden Confounders in the Genetic Analysis of Gene Expression
Listgarten J, Kadie C, Schadt E, Heckerman D
Proceedings of the National Academy of Sciences, September 1, 2010

Statistical resolution of ambiguous HLA typing data
Listgarten J, Brumme Z, Kadie C, Xiaojiang G, Walker B, Carrington M, Goulder P, Heckerman D, PLoS Computational Biology (2008)

Statistical and computational methods for comparative proteomic profiling using liquid chromatography-tandem mass spectrometry Listgarten J and Emili A, Molecular and Cellular Proteomics (2005)

Simultaneous reconstruction of multiple signaling pathways via the prize-collecting Steiner forest problem (N. Tuncbag, A. Braunstein, A. Pagnani, S.S. Huang, J. Chayes, C. Borgs, R. Zecchina, and E. Fraenkel) Journal of Computational Biology 20 (2013) 124 – 136.

Finding undetected protein associations in cell signaling by belief propagation (with M. Bailly-Bechet, C. Borgs, A. Braunstein, J. Chayes, A. Dagkessamanskaia, J. Francois, and R. Zecchina). Proceedings of the National Academy of Sciences (PNAS) 108 (2011) 882 – 887.

Statistical mechanics of Steiner trees (M. Bayati, C. Borgs, A. Braunstein, A. Ramezanpour, and R. Zecchina, Physical Review Letters 101, 037208 (2008), reprinted in Virtual Journal of Biological Physics Research 16, August 1 (2008)




Ernest Fraenkel, MIT
Ernest Fraenkel studied Chemistry and Physics as an undergraduate at Harvard College and obtained his Ph.D. in Structural Biology at MIT in the department of Biology. After doing post-doctoral work in the same field at Harvard, he turned his attention to the emerging field of Systems Biology. His research now focuses on using high-throughput techniques and computational methods to uncover the molecular pathways that are altered in disease and to identify new therapeutic strategies. Read more…

Riccardo Zecchina, Politecnico di Torino, Italy
Riccardo is Professor of Theoretical Physics at the Politecnico di Torino in Italy. His interests are in topics at the interface between Statistical Physics and Computer Science. His current research activity is focused on combinatorial and stochastic optimization, probabilistic and message-passing algorithms and interdisciplinary applications of statistical physics (in computational biology, graphical games and statistical inference). Read more…