Welcome
Our main research interest is in understanding the structure and function of genomes, especially those of medical or
agricultural importance. The core strength of our research is in developing novel algorithms and computational systems
for large-scale biological sequence analysis, including leading algorithms for de novo genome assembly, variant detection,
and related –omics assays. Using these advances we have contributed to the de novo genome assemblies of dozens of species;
probed the sequence variations related to autism, cancer, and other human diseases; mapped the transcriptional and epigenetic
profiles of tomatoes, corn, and other important plant species; and explored the role of microbes in different environments.
In response to the deluge of biological sequence data we are now facing, we have also been at the forefront of distributed
and parallel computing in genomics, and have pioneered the use of cloud computing and Hadoop/MapReduce as an enabling platform to
address the big data challenges we are all facing.
Looking forward, we see ourselves at the intersection of biotechnology and algorithmics, developing systems for probing
the structure and function of genomes using the best technologies possible. Our expertise spans from low level computer
architecture, through sequencing, de novo assembly, variant identification, transcriptome & other -omics data
and up to machine learning approaches to build predictive models of diseases and treatment response. In addition to ongoing
projects in autism & other human diseases, and developmental plant biology, I was granted an NSF CAREER award
to research new approaches for analyzing single molecule sequencing, especially for genome and transcriptome analysis of crop
species. Another recent thrust has been to develop algorithms for single cell analysis, especially to use copy number variations
within individual tumor cells to examine how cancer progresses. Altogether, we intend to develop powerful new methods
for analyzing large collections of genomes to address questions of disease, development, and evolution.
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Michael Schatz
Bloomberg Distinguished
Associate Professor of
Computer Science
and Biology
Johns Hopkins University
Department of Computer Science
3400 N Charles St
Malone Hall 323
Baltimore, MD 21211
Cell: (703) 966-1987
E-mail: mschatz <at> cs.jhu.edu
Adjunct Associate Professor of
Quantitative Biology
Cold Spring Harbor Laboratory
One Bungtown Road
Koch Building 1121
Cold Spring Harbor, NY 11724
Tel: (516) 367-5218
Fax: (516) 367-8380
E-mail: mschatz <at> cshl.edu
Twitter: @mike_schatz
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