Bio

Bio


I develop and apply Deep Learning frameworks for modeling functional genomics and non-coding variation from cost-effective data, with a primary focus on transcription factor binding imputation.

The ability to infer specific mechanisms that regulate gene expression from cost-effective data such as ATAC-seq is opening the door to a wide range of interrogations in precious samples. Our collaborators are typically experimentalist who want to 1) gain more out of their cost-effective data and/or 2) already have data form ChIP-seq or a similar assay and use our Deep Learning expertise to mine that data to the fullest extent. For collaborations on these fronts, please contact myself at jisraeli@stanford.edu or my advisor, Anshul Kundaje, at akundaje@stanford.edu

(See "Research & Scholarship" page for more details)

Honors & Awards


  • Bruce and Elizabeth Dunlevie Fellow, Bio-X Stanford Interdisciplinary Graduate Fellowship, Biophysics (2016-2019)

Education & Certifications


  • Bachelors, University of Kansas, Math (2012)
  • Master's, University of Kansas, Physics (2013)

Research & Scholarship

Current Research and Scholarly Interests


~1.5% of the human genome codes for proteins. What is the rest of the genome doing? How does it interact with human epigenomics? Does it affect disease?

The genome can be thought of as hardware specifying genes, and the epigenome is the complex software that governs how genes turn "on"‚Äč and "off". This software is driven by regulatory proteins such as transcription factors (TFs) and their interactions with DNA. These interactions can be assayed experimentally by biochemically targeting TFs bound to DNA, sequencing the DNA bound, and finding the location of that subsequence in our full DNA.

A common assay for TF binding is Chromatin Immunoprecipitation followed by Sequencing (ChIP-seq). ChIP-seq measures the genome-wide locations of a single TF, requires ~1 million cells, and >1 month of experimental work. There are hundreds of TFs and they activate and repress genes differently in hundreds of human cell types. It is not feasible to experimentally assay all of these combinations with ChIP-seq, and it would not work in precious clinically relevant samples where we cannot gather ~1 million cells. An alternative assay called ATAC-seq can map out the locations of many TFs in one experiment using <50,000 cells and only a few days of work. But ATAC-seq does not reveal which of the many proteins is present in each of the sites it detects. Working with my advisor Anshul Kundaje, I designed Deep Learning models that could overcome this limitation and predict the sites of tens of regulatory proteins from a single ATAC-seq experiment. Thus, our models can effectively infer the main results of many costly ChIP-seq experiments from a single cost-effective ATAC-seq experiment and enable these interrogations in precious samples.

The ability to infer specific mechanisms that regulate gene expression from cost-effective data such as ATAC-seq is opening the door to a wide range of interrogations in precious samples. Our collaborators are typically experimentalist who want to 1) gain more out of their cost-effective data and/or 2) already have data form ChIP-seq or a similar assay and use our Deep Learning expertise to mine that data to the fullest extent. For collaborations on these fronts, please contact myself at jisraeli@stanford.edu or my advisor, Anshul Kundaje, at akundaje@stanford.edu

Research Projects


  • How to train your DragoNN (Deep RegulAtory GenOmic Neural Network) (Scholarly Concentration Project)

    DragoNN provides a toolkit to learn how to model and interpret regulatory sequence data using deep learning.

    Location

    Stanford

    Organization

    Stanford University

    Collaborators

    • Anna Scherbina, Ph.D. Student in Biomedical Informatics, admitted Autumn 2015, School of Medicine
    • Chuang-Sheng Foo, Ph.D. Student in Computer Science, School of Engineering
    • Anshul Kundaje, Assistant Professor of Genetics and of Computer Science, Stanford University

    For More Information:

Publications

All Publications


  • Mathematical Model for Length Control by the Timing of Substrate Switching in the Type III Secretion System. PLoS computational biology Nariya, M. K., Israeli, J., Shi, J. J., Deeds, E. J. 2016; 12 (4)

    Abstract

    Type III Secretion Systems (T3SS) are complex bacterial structures that provide gram-negative pathogens with a unique virulence mechanism whereby they grow a needle-like structure in order to inject bacterial effector proteins into the cytoplasm of a host cell. Numerous experiments have been performed to understand the structural details of this nanomachine during the past decade. Despite the concerted efforts of molecular and structural biologists, several crucial aspects of the assembly of this structure, such as the regulation of the length of the needle itself, remain unclear. In this work, we used a combination of mathematical and computational techniques to better understand length control based on the timing of substrate switching, which is a possible mechanism for how bacteria ensure that the T3SS needles are neither too short nor too long. In particular, we predicted the form of the needle length distribution based on this mechanism, and found excellent agreement with available experimental data from Salmonella typhimurium with only a single free parameter. Although our findings provide preliminary evidence in support of the substrate switching model, they also make a set of quantitative predictions that, if tested experimentally, would assist in efforts to unambiguously characterize the regulatory mechanisms that control the growth of this crucial virulence factor.

    View details for DOI 10.1371/journal.pcbi.1004851

    View details for PubMedID 27078235