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).
Active projects and services in the lab can be explored here.
We are always looking for interested/qualified individuals to join our team!
Two post-doctoral positions are immediately available in the lab of Dr. Yana Bromberg, in the departments of Biology and Computer Science, Emory University, Atlanta (possibly joint with the Institute of Advanced Studies, Technical University of Munich).
We are seeking highly motivated scholars to continue training in an exciting research laboratory at Emory with a focus on molecular functionality encoded in genome and metagenome data. The Bromberg lab is purely computational, studying interactions between the host and the microbiome in light of health and disease. We are also exploring biotic molecular functionality at the origins of life.
Applicants must hold a Ph.D. in Computational Biology, Bioinformatics, or related fields. Programming skills are essential, as well as some familiarity with the major bioinformatics tools/databases. Experience with high performance computing, machine learning, and whole genome and metagenome analysis is highly desired, but not required.
BrombergLab in the News
Link to publication: DOI: 10.1186/s13073-019-0670-6
The majority of microbial genomes have yet to be cultured, and most proteins identified in microbial genomes or environmental sequences cannot be functionally annotated. As a result, current computational approaches to describe microbial systems rely on incomplete reference databases that cannot adequately capture the functional diversity of the microbial tree of life, limiting our ability to model high-level features of biological sequences. Here we present LookingGlass, a deep learning model encoding contextually-aware, functionally and evolutionarily relevant representations of short DNA reads, that distinguishes reads of disparate function, homology, and environmental origin. We demonstrate the ability of LookingGlass to be fine-tuned via transfer learning to perform a range of diverse tasks: to identify novel oxidoreductases, to predict enzyme optimal temperature, and to recognize the reading frames of DNA sequence fragments. LookingGlass enables functionally relevant representations of otherwise unknown and unannotated sequences, shedding light on the microbial dark matter that dominates life on Earth.
Motivation: metal-binding proteins have a central role in maintaining life processes. Nearly one-third of known protein structures contain metal ions that are used for a variety of needs, such as catalysis, DNA/RNA binding, protein structure stability, etc. Identifying metal-binding proteins is thus crucial for understanding the mechanisms of cellular activity. However, experimental annotation of protein metal-binding potential is severely lacking, while computational techniques are often imprecise and of limited applicability.
Computational exploration of similarities among metal-binding protein structural motifs elucidates the origins of life. Biological redox reactions drive planetary biogeochemical cycles. Using a novel, structure-guided sequence analysis of proteins, we explored the patterns of evolution of enzymes responsible for these reactions. Our analysis reveals that the folds that bind transition metal–containing ligands have similar structural geometry and amino acid sequences across the full diversity of proteins. Similarity across folds reflects the availability of key transition metals over geological time and strongly suggests that transition metal–ligand binding had a small number of common peptide origins. We observe that structures central to our similarity network come primarily from oxidoreductases, suggesting that ancestral peptides may have also facilitated electron transfer reactions. Last, our results reveal that the earliest biologically functional peptides were likely available before the assembly of fully functional protein domains over 3.8 billion years ago. Thus, life is a special, very complex form of motion of matter, but this form did not always exist, and it is not separated from inorganic nature by an impassable abyss; rather, it arose from inorganic nature as a new property in the process of evolution of the world. We must study the history of this evolution if we want to solve the problem of the origin of life. [A. I. Oparin (1)]
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
Current Bromberglab members
Selected projects & web-services
machine learning-based classifier to evaluate deleteriousness of synonymous variants
Check NIH Library for comprehensive list