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.

Recent News
» LRSim: A Linked-Reads Simulator generating insights for better genome partitioning
November 9, 2017
» Addressing confounding artifacts in reconstruction of gene co-expression networks
October 13, 2017
» Complex rearrangements and oncogene amplifications revealed by long-read DNA and RNA sequencing of a breast cancer cell line
August 10, 2017
» Accurate detection of complex structural variations using single molecule sequencing
July 28, 2017
» Hybrid assembly with long and short reads improves discovery of gene family expansions
July 19, 2017
(past news)

Upcoming Events

~~ 2017 ~~

» Genome Informatics
CSHL. Nov 1-4, 2017
~~ 2018 ~~

» International Plant and Animal Genomes (PAG)
San Diego CA. Jan 13 - 17, 2018
» Advances in Genome Biology and Technology (AGBT)
Marco Island, FL. Feb 12 - 15, 2018
» Biological Data Science
CSHL. Nov 7 - 10, 2018
(presentation archive)



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

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