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.

Recent Posts

The Critical Assessment of Massive Data Analysis (CAMDA) Fellowship supports participation of the CAMDA 2019 Conference in Basel, …

The Douglas E. Eveleigh Travel Award for graduate students is awarded each year by the Department of Biochemistry and Microbiology of …

The Lab

Researchers

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Adrienne Hoarfrost

NASA Astrobiology Postdoctoral Fellow

Microbe-Earth system interactions, Data intensive bioinformatics, Machine and deep learning applications for ‘omics

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Ariel Aptekmann

PostDoctoral Associate

Protein - DNA Coevolution, Metagenome Analysis, Computational Biology, Bioinformatics, Complex Systems Biology

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Chengsheng Zhu

PostDoctoral Associate

microbiome research, function prediction, machine learning

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Kenneth McGuinness

PostDoctoral Associate

Protein design/evolution, Self-assembly, Education

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Maximilian Miller

PostDoctoral Associate

Variant Effect Prediction, Metagenome Analysis, High-Performance Computing

Grad Students

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Yannick Mahlich

PhD Candidate

Function Prediction, Metagenomics, High-Performance Computing, Network & Cluster Analysis

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Yanran Wang

PhD Candidate

Precision Medicine, Machine Learning, Next Generation Sequencing

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Zishuo Zeng

PhD Student

Synonymous Variants, Machine Learning

Students

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Alexis Faulborn

Undergraduate Student

Biotechnology, Biochemistry, Molecular Genetics

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Chahna Patel

Undergraduate Student

Engineering, Machine Learning

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Nick Lusskin

Undergraduate Student

Clustering, Visualization, Metagenome Analysis

Visitors

Alumni

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Anton Molyboha

PostDoctoral Associate

Probability and Statistics, Optimization, Machine Learning, Risk measures

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Daniel Vitale

Student

Variant Effect Prediction, Machine Learning

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Srinayani Marpaka

Student

Biochemistry, Molecular Genetics

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Tatyana Goldberg

PhD Student

Protein sub-cellular localizaton, structure and function prediction, Machine learning and Visualization

Projects

Selected projects & web-services

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Life & Earth - Deep Transfer Learning

linking environmental microbes to geochemistry & mineralogy

fuNTRp

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

AVA,Dx

prediction of individual predisposition to disease x through variation analysis

HFSP

homolgy based protein function prediction using the HFSP measure

clubber

automated cluster load balancing accelerating computational biology workflows

mi-faser

annotate molecular functionality directly from sequencing read data

Predict-Protein

Docker image of the RostLab PredictProtein pipeline

fusionDB

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

pEffect

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

SNPdbe

Database of SNP predicted & experimentally derived functional effects

SNAP

Evaluation single amino acid substitutions effects on protein function

Publications

Motivation Evaluating the impact of non-synonymous genetic variants is essential for uncovering disease associations. Understanding the …

Motivation: The rapid drop in sequencing costs has produced many more (predicted) protein sequences than can feasibly be functionally …

Talks

Yana Bromberg talks about The Big You

Contact