DNA sequencing efforts produce trillions and trillions of As, Cs, Gs, and Ts each minute - but what do the letters mean? My group develops novel computational methods to extract the information contained in biological sequences. We are applying these tools to discover mutations associated with cancer and autism and to reconstruct the genomes of important plants, animals, and microbes.
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
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 at
CSHL Institutional Repository