Etai Jacob, PhD

Bio

I am a computational biologist affiliated to the Department of Data Sciences and the Department of Medical Oncology at Dana-Farber Cancer Institute, and the Broad Institute of MIT and Harvard. My main research focus is to understand complex domains that involve large amounts of uncertainty and noise using statistical models, machine learning and computer simulations.

I like to be driven by real-world problems and therefore, most of my academic work tackles biological applications. Recently, I have focused on analyzing single cell RNA-seq data to study the effect of genome instability on RNA transcription and the impact of mutations on cancer and normal tissue developement. In my earlier work in the field, I focused more on theoretical aspects in biology. Using toy models and Monte Carlo simulations I demonstrated how certain features in protein structure could relate to the protein folding process. Later on, in my PhD work, I have focused on protein structure prediction using correlated mutation analysis, multi-domain protein folding and protein aggregation.

Before joining Dana-Farber Cancer Institute, I worked at Compugen, a clinical-stage drug discovery and development company (NASDAQ:CGEN). In compugen, I developed data mining infrastructre to integrate multi-omics data (e.g. RNA expression and ChIP-Seq data), supported new discoveries and performed on-going research of potential drug targets. I developed and utilized machine learning (e.g. Random Forests, Neural Networks and linear models) and pattern recognition methods to study protein sequence and structure (e.g. PDB, Uniprot), protein abundance and RNA expression (e.g. using microarray and NGS data along with Mass spectrometry data), biological function (e.g. using GO terms, MsigDB and PUBMED) and more.

I obtained my PhD in computational biology at the Weizmann Institute of Science and Bar-Ilan University (as a collaboration), Israel, in 2016.

Publications

Most recent publications on Google Scholar.

• Papers
• Patents

Research

• Selected
• All

Single cell RNA-seq analysis
Detection of transcriptional abnormalities and copy number variations

Correlated mutation analysis
Codon-level information improves predictions of inter-residue contacts in proteins

Multidomain protein folding
N-Terminal domains in two-domain proteins are biased to be shorter and predicted to fold faster than their C-terminal counterparts

Lattice model protein folding simulations
Why are terminal residues of proteins exposed?

Protein-chaperonin interactions
Computational studies of protein chaperone mediated folding interactions

Single cell RNA-seq analysis
Detection of transcriptional abnormalities and copy number variations

Correlated mutation analysis
Codon-level information improves predictions of inter-residue contacts in proteins

Multidomain protein folding
N-Terminal domains in two-domain proteins are biased to be shorter and predicted to fold faster than their C-terminal counterparts

Lattice model protein folding simulations
Why are terminal residues of proteins exposed?

Protein-chaperonin interactions
Computational studies of protein chaperone mediated folding interactions

Resume

• Dana-Farber Cancer Institute Sep 2016-now

  Computational Biologist
  Department of Data Sciences
• Compugen 2011-2016

  Senior Scientist
  Computational Research and Discovery
• Weizmann Institute of Science
  Bar-Ilan University 2011-2016

  Ph.D. Candidate
  Computational Biology
• Compugen 2008 - 2011

  Project leader
  Computational Research and Discovery
• Compugen 2006 - 2008

  Algorithm developer
  Algorithmic Research group
• Bar-Ilan University 2005 - 2006

  Teaching assistant
  Introduction to computing and computer aided problem solving
• Bar-Ilan University 2005 - 2006

  M.Sc. student
  Computational biology

Etai Jacob, PhD