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.

Recent News
» Scikit-ribo Enables Accurate Estimation and Robust Modeling of Translation Dynamics at Codon Resolution
January 17, 2018
» Reference-quality diploid genomes without de novo assembly @ PAG
January 16, 2018
» Technology Improvements Help Elucidate Previously Indecipherable Structural Variants in GenomeWeb
January 16, 2018
» Reference quality assembly of the 3.5-Gb genome of Capsicum annuum from a single linked-read library
January 12, 2018
» LRSim: A Linked-Reads Simulator generating insights for better genome partitioning
November 9, 2017
(past news)

Upcoming Events

~~ 2018 ~~

» Advances in Genome Biology and Technology (AGBT)
Marco Island, FL. Feb 12 - 15, 2018
» The Jackson Laboratory for Genomic Medicine
Farmington, CT, April 23-25, 2018
» Biology of Genomes
CSHL, NY. May 8-12, 2018
» Biological Data Science
CSHL. Nov 7 - 10, 2018
(presentation archive)

Michael Schatz

Bloomberg Distinguished
Associate Professor of
Computer Science
and Biology

CS office and Mailing address:
Johns Hopkins University
Department of Computer Science
3400 N Charles St
Malone Hall 323
Baltimore, MD 21218

Biology office:
UTL 383

SOM office:
Johns Hopkins Medicine
Department of Oncology
Welch Library 103
1900 E. Monument Street
Baltimore, MD 21205

Adjunct Associate Professor of
Quantitative Biology

Cold Spring Harbor Laboratory
One Bungtown Road
Koch Building 1121
Cold Spring Harbor, NY 11724

Cell: (703) 966-1987
E-mail: mschatz <at> cs.jhu.edu
Twitter: @mike_schatz