Michael Schatz is a computational biologist and an expert at large-scale computational examination of DNA sequencing data, including the alignment, assembly, and analysis of next-generation sequencing reads. These methods have been used to reconstruct the genomes of previously unsequenced organisms, probe sequence variations, and explore a host of biological features across the tree of life. Recent improvements in sequencing technologies are challenging our capacity to store and analyze the huge volume of DNA sequence data being generated. Consequently, Schatz is particularly interested in capitalizing on the latest advances in distributed and parallel computing, especially cloud computing technologies, to advance the state of the art in bioinformatics and genomics. In a recent breakthrough, Schatz was able to create a hybrid software-based solution to eliminate errors in so-called third-generation sequencing. This makes it remarkably easier to compile, align, and analyze full-genome sequences.
2015: Sloan Foundation Research Fellowship
2012 CSHL Winship Herr Award for Excellence in Teaching
2011 CSHL Winship Herr Award for Excellence in Teaching
2010 Young Investigator of the Year, Genome Technology
The DNA tricks that gave us 100 different kinds of tomatoes
June 17, 2020
It takes 230,000 genetic differences to make 100 different varieties of tomatoes.
CSHL investigators rank among world’s most highly cited
December 11, 2019
Seven researchers affiliated with CSHL are among the scientists producing the top 1 percent of the most highly-cited research in the world.
Watson School alumni spotlights
May 19, 2019
This year, Watson School celebrates the 20th anniversary of its first entering class and looks back at some notable alumni from over the years.
Massive genome havoc in breast cancer is revealed
July 12, 2018
Researchers have made a highly detailed map of 20,000 structural variations in a cancer cell’s genome
New method can more precisely determine when a cell has ‘cashed’ RNA ‘checks’ written by active genes
January 26, 2018
CSHL scientists have designed software that enables biologists to determine with unprecedented accuracy how much protein a given cell is making.
Scientists sequence genome of worm that can regrow body parts, seek stem cell insights
September 21, 2015
Worm’s genome could lead to better understanding of its regenerative prowess and advance stem cell biology.
Mathematical ‘Gingko trees’ reveal mutations in single cells that characterize diseases
September 4, 2015
Online app could help clinicians choose the best treatments by comparing genetic fingerprints of individual cells.
The biggest beast in the Big Data forest? One field’s astonishing growth is, well, ‘genomical’!
July 6, 2015
Scientists work to figure out how to capture, store, process and interpret all that genome-encoded biological information.
Third-year student Tyler Garvin brings together engineering and medicine to help save lives
June 22, 2015
Student researches non-coding mutations responsible for changes in human gene expression.
A unique partnership
March 24, 2015
The story of a high school student working with Michael Schatz to create an app that analyzes DNA.
Stephens, Z. D. and Lee, S. Y. and Faghri, F. and Campbell, R. H. and Zhai, C. and Efron, M. J. and Iyer, R. and Schatz, M. C. and Sinha, S. and Robinson, G. E. (2015) Big Data: Astronomical or Genomical?. PLoS Biol, 13(7) pp. e1002195.
Iossifov, I. and O'Roak, B. J. and Sanders, S. J. and Ronemus, M. and Krumm, N. and Levy, D. and Stessman, H. A. and Witherspoon, K. T. and Vives, L. and Patterson, K. E. and Smith, J. D. and Paeper, B. and Nickerson, D. A. and Dea, J. and Dong, S. and Gonzalez, L. E. and Mandell, J. D. and Mane, S. M. and Murtha, M. T. and Sullivan, C. A. and Walker, M. F. and Waqar, Z. and Wei, L. and Willsey, A. J. and Yamrom, B. and Lee, Y. H. and Grabowska, E. and Dalkic, E. and Wang, Z. and Marks, S. and Andrews, P. and Leotta, A. and Kendall, J. and Hakker, I. and Rosenbaum, J. and Ma, B. and Rodgers, L. and Troge, J. and Narzisi, G. and Yoon, S. and Schatz, M. C. and Ye, K. and McCombie, W. R. and Shendure, J. and Eichler, E. E. and State, M. W. and Wigler, M. (2014) The contribution of de novo coding mutations to autism spectrum disorder. Nature, 515(7526) pp. 216-221.
Narzisi, G. and O'Rawe, Jason and Iossifov, I. and Fang, Han and Lee, Y. H. and Wang, Zihua and Wu, Yiyang and Lyon, Gholson J. and Wigler, M. H. and Schatz, M. C. (2014) Accurate de novo and transmitted indel detection in exome-capture data using microassembly. Nature Methods, 11(10) pp. 1033-1036.
Koren, S. and Schatz, M. C. and Walenz, B. P. and Martin, J. and Howard, J. T. and Ganapathy, G. and Wang, Z. and Rasko, D. A. and McCombie, W. R. and Jarvis, E. D. and Phillippy, A. M. (2012) Hybrid error correction and de novo assembly of single-molecule sequencing reads. Nature Biotechnology, 30(7) pp. 693-700.
Schatz, M. C. (2009) CloudBurst: Highly sensitive read mapping with MapReduce. Bioinformatics, 25(11) pp. 1363-1369.
Additional materials of the author atCSHL Institutional Repository