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
» A robust benchmark for germline structural variant detection
June 9, 2019
» Samovar: Single-sample mosaic single-nucleotide variant calling with linked reads
May 29, 2019
» Addressing confounding artifacts in reconstruction of gene co-expression networks
May 16, 2019
» Paragraph: A graph-based structural variant genotyper for short-read sequence data
May 10, 2019
» Hypo-osmotic-like stress underlies general cellular defects of aneuploidy
May 8, 2019
(past news)

Upcoming Events

~~ 2019 ~~

» Workshop on long-read sequencing
Jackson Laboratory for Genomic Medicine, Farmington, CT. Sep 17-20, 2019
» Austrialian Genomic Technologies Association
Pullman Melbourne Albert Park, Victoria, Austrialia. Oct 7-9, 2019
» Genome Informatics
CSHL, NY. Nov 6-9, 2019
~~ 2020 ~~

» Plant and Animal Genomes Conference (PAG)
San Diego, CA. Jan 11 - 15, 2020
» Advances in Genome Biology and Technology (AGBT)
Marco Island, FL. Feb - Mar, 2020
» Biology of Genomes
CSHL, NY. May 7-11, 2020
» Biological Data Science
CSHL, NY. November 2020
(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

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