Bromberg Lab

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.


We have open positions:

View Job Postings

updated: 05/2020

Recent Posts

BrombergLab @ Virtual ISMB 2020

The 2020 ISMB weeting will take place online due to the pandemic.

Recent Publications (10/2019)

Three manuscripts were published in NAR, Genome Medicine and frontiers in Genetics

Principal Investigator

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.


  • Protein Function and Variant Effect Prediction
  • Artificial Intelligence


  • 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

The Lab



Maximilian Miller

PostDoctoral Associate


Yannick Mahlich

PostDoctoral Associate


Ariel Aptekmann

PostDoctoral Associate

Grad Students


Zishuo Zeng

PhD Student



Chahna Patel

Undergraduate Student


Darin Mak

Undergraduate Student


Michael Gleyzer

Undergraduate Student



Chengsheng Zhu

PostDoctoral Associate


Nick Lusskin

Undergraduate Student


Yanran Wang

PhD Candidate


Adrienne Hoarfrost

PostDoctoral Fellow


Tatyana Goldberg

PhD Student


Alexis Faulborn

Undergraduate Student


Anton Molyboha

PostDoctoral Associate


Kenneth McGuinness

PostDoctoral Associate


Yannick Spreen

Visiting Scholar


Selected projects & web-services


Life & Earth - Deep Transfer Learning

linking environmental microbes to geochemistry & mineralogy


classify protein positions by type based on the expected range of mutational impacts

predicting sSNVs effects

machine learning-based classifier to evaluate deleteriousness of synonymous variants


prediction of individual predisposition to disease x through variation analysis


homolgy based protein function prediction using the HFSP measure


automated cluster load balancing accelerating computational biology workflows


annotate molecular functionality directly from sequencing read data


explore or map new genomes to database of 1,374 bacteria with available metadata


Docker image of the RostLab PredictProtein pipeline


identifying type III effectors by homology-based inference and de novo predictions


Database of SNP predicted & experimentally derived functional effects


Evaluation single amino acid substitutions effects on protein function


Expression and regulation of the mer operon in Thermus thermophilus

Mercury (Hg) is a highly toxic and widely distributed heavy metal, which some Bacteria and Archaea detoxify by the reduction of ionic …

Fingerprinting cities: differentiating subway microbiome functionality

Accumulating evidence suggests that the human microbiome impacts individual and public health. City subway systems are human-dense …

Recent & Upcoming Talks

TEDx Rutgers 2019 Conference

Yana Bromberg talks about The Big You


  • +1 (848) 932-5638
  • 76 Lipman Drive, 218 Lipman Hall, New Brunswick, NJ 08901
  • Enter Building and take stairs or elevator to 2nd Floor, Room 218
  • Monday 09:00 to 17:00
    Tuesday 09:00 to 17:00
    Wednesday 09:00 to 17:00
    Thursday 09:00 to 17:00
    Friday 09:00 to 17:00