Decoding the blueprint of Life
Modern biology increasingly relies on high-throughput techniques. This trend challenges computational biologists to quickly extract as much useful information from the data as possible. In the genomic sense, this primarily implies correlating phenotypic differences with observed nucleotide sequence variations. On the protein side the challenge generally is to annotate protein function at reasonable accuracy levels. The whole organism level, then incorporates all types of evidence to annotate evolutionary history, current health conditions, and prognosed phenotypic changes.
We believe that nucleic and amino acid sequences contain a large portion of the information necessary to address both of these directions. However, we are always willing to supplement this data with other sources available for computational access. The main interest of this lab is in developing fast, accurate, and meaningful ways of analyzing the growing deluge of biological data and in bringing these developments bench- (or patient-) side. To make our predictions we rely on a number of sequence-based features (including evolutionary information, predicted structural features, and available annotations) and utilize a variety of computational methodologies (including artificial learning, network analysis and statistical methods). The active projects in the lab are described in the Projects section of this site.
We are always looking for interested/qualified individuals to join our team.
Dr. Yana Bromberg’s research focuses on deciphering the DNA “blueprints” of life’s molecular machinery. She develops novel bioinformatics techniques to find out where this machinery comes from and why/how it runs. The answers to these basic questions are important for improving our health/quality of life, preserving our environment, and, well… did we really start as green slime?!
Dr. Bromberg received her degrees from SUNY Stony Brook and Columbia University. Her work has been recognized by private and federal agencies, including NASA and NIH. She received an NSF CAREER award and is also a Fellow of the Munich Institute for Advanced Study. Her findings consistently indicate that our world functions via dependencies and interactions at all scales.
Ph.D. in Biomedical Informatics (Bioinformatics Track), 2007
Columbia University, New York, NY
M.Phil. in Biomedical Informatics, 2004
Columbia University, New York, NY
B.A. Biology / B.Eng. Computer Science (Magna Cum Laude), 2001
State University of NY (SUNY) at Stony Brook, Stony Brook, NY
Microbe-Earth system interactions, Data intensive bioinformatics, Machine and deep learning applications for ‘omics
Variant Effect Prediction, Metagenome Analysis, High-Performance Computing
Function Prediction, Metagenomics, High-Performance Computing, Network & Cluster Analysis
Probability and Statistics, Optimization, Machine Learning, Risk measures
Protein sub-cellular localizaton, structure and function prediction, Machine learning and Visualization
Selected projects & web-services
linking environmental microbes to geochemistry & mineralogy
machine learning-based classifier to evaluate deleteriousness of synonymous variants
prediction of individual predisposition to disease x through variation analysis
automated cluster load balancing accelerating computational biology workflows
Docker image of the RostLab PredictProtein pipeline
