Yana Bromberg
Yana Brombert

Assistant Professor

2004-’06 Columbia University, Ph.D. Biomedical Informatics
2001-'04 Columbia University, M. Phil Biomedical Informatics
1997-‘01 SUNY at Stony Brook, BA Biology/B.E. Computer Science

Research Interest: Bioinformatics approaches to protein function prediction and genome variation analysis

In-depth
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. We believe that nucleic and amino acid sequences contain a large portion of the information necessary to address both of these directions.

Our main goal is to develop fast, accurate, and meaningful ways of analyzing this growing deluge of biological data and to bring these developments bench- (or patient-) side. To make our predictions we rely on a number of sequence-based features (including evolutionary information and other predictor results) and utilize a variety of methodologies (including Neural Nets, SVMs and random forests).
The active projects in the lab include:

  1. Development of an in silico mutagenesis methodology which will define functionally important residues in protein sequences. This direction addresses questions in nsSNP analysis, mutation combinatorics (possibly applicable to phylogenetics), and function prediction.
  2. Analyzing the effects of genomic SNPs (non-coding or synonymous) on the overall organism fitness. Initial steps in this direction focus on data collection and on outlining SNP characteristics that can be used to differentiate between functionally non-/important SNPs.
  3. Computational literature analysis (Natural Language Processing) to extract from free text (scientific publications, lab records, etc.) information relevant to the above two goals.

Recent publications:

  1. Zaghloul NA, Liu Y, Gerdes JM, Gascue C, Oh EC, Leitch CC, Bromberg Y, Binkley J, Leibel RL, Sidow A, Badano JL, Katsanis, N (2010). Functional analyses of variants reveal a significant role for dominant negative and common alleles in oligogenic Bardet-Biedl syndrome. Proc Natl Acad Sci U S A 107(23):10602-10607

  2. Wainreb G, Ashkenazy H, Bromberg Y, Starovolsky-Shitrit A, Haliloglu T, Ruppin E, Avraham KB, Rost B, Ben-Tal N: MuD: an interactive web server for the prediction of non-neutral substitutions using protein structural data. Nucleic Acids Res 38 Suppl:W523-528

  3. Sester M, Koebernick K, Owen D, Ao M, Bromberg Y, May E, Stock E, Andrews L, Groh V, Spies T, Steinle A et al: Conserved amino acids within the adenovirus 2 E3/19K protein differentially affect downregulation of MHC class I and MICA/B proteins. J Immunol 184(1):255-267

  4. Bromberg, Y., and Rost, B. (2009) Correlating protein function and stability through the analysis of single amino acid substitutions. BMC Bioinformatics 10 Suppl 8, S8.

  5. Bromberg, Y., Overton, J., Vaisse, C., Leibel, R. L., and Rost, B. (2009) In silico mutagenesis: a case study of the melanocortin 4 receptor. FASEB J 23, 3059-3069.

  6. Bromberg Y, Yachdav G, Ofran Y, Schneider R, Rost B (2009). New in protein structure and function annotation: hotspots, SNPs and Deep Web. Curr Opin Drug Discov Devel. 12(3):408-19.

  7. Calton MA, Ersoy BA, Zhang S, Kane JP, Malloy MJ, Pullinger CR, Bromberg Y, Pennacchio LA, Dent R, McPherson R, Ahituv N, Vaisse C (2009). Association of functionally significant Melanocortin-4 but not Melanocortin-3 receptor mutations with severe adult obesity in a large North American case-control study. Hum Mol Genet. 18(6):1140-7.

  8. Dokmanovic-Chouinard M, Chung WK, Chevre JC, Watson E, Yonan J, Wiegand B, Bromberg Y, N. Wakae N, Wright CV, Overton J, Ghosh S, Sathe GM, Ammala CE, Brown KK, Ito R, LeDuc C, Solomon K, Fischer SG, Leibel RL (2008). Positional cloning of "Lisch-Like", a candidate modifier of susceptibility to type 2 diabetes in mice. PLoS Genet. 4(7):e1000137.

  9. Bromberg Y, Yachdav G, Rost B (2008). SNAP predicts effect of mutations on protein function. Bioinformatics. 24(20):2397-8.

  10. Bromberg Y, Rost B (2008). Comprehensive in silico mutagenesis highlights functionally important residues in proteins. ECCB’08 invited presentation. Proceedings: Bioinformatics. 24(16):i207-12.

  11. Bromberg Y, Rost B (2007). SNAP: predict effect of non-synonymous polymorphisms on function. Nucleic Acids Res 35(11):3823-3835.