Homepage of Yaron Orenstein

Yaron Orenstein

I am a Senior Lecturer (Assistant Professor) at the School of Electrical and Computer Engineering at Ben-Gurion University.

I was a post-doc at MIT computer science and artificial intelligence laboratory
at MIT - Massachusetts Institue of Technology. I was a member of Prof. Bonnie Berger's group.

Formerly, I was a PhD student at the School of Computer Science at Tel Aviv University.
My advisor was Prof. Ron Shamir, and I was a member of the Algorithms in Computational Genomics
research group.

CV_Yaron_Orenstein.pdf

Orenstein Lab website


        
        wwwee.ee.bgu.ac.il/~cb/

Contact

Email:


	
	yaronore[at]bgu[dot]ac[dot]il

Research Interests

Machine learning applications to biological data.
Neural networks on DNA and RNA sequences.
Graph algorithms and NP-hard proofs.
Integer linear programming and heuristics.

Students

Shaked Naor, M.Sc. in Computer Science (co-supervised with Prof. Michal Ziv-Ukelson)
Mira Barshai, M.Sc. in Electrical Engineering
Eitamar Tripto, M.Sc. in Biomedical Engineering (co-supervied with Dr. Ilana Nisky)
Yahel Salomon, M.Sc. in Electrical Engineering (co-supervised with Dr. Ramon Birnbaum)
Yifat Shemesh, M.Sc. in Electrical Engineering (co-supervised with Dr. Kobi Cohen)
Maor Asif, M.Sc. in Electrical Engineering
Sofia Aizenshtein, M.Sc. in Electrical Engineering
Ofir Yaish, M.Sc. in Electrical Engineering
Moshe Shalom, M.Sc. in Electrical Engineering

Alumni

Maor Dan, M.Sc. in Computer Science (co-supervised with Prof. Ron Shamir, Tel-Aviv University)
Ilia Goland, M.Sc. project in Electrical Engineering

Publications

O. Adato, Y. Orenstein, J. Kopolobic, R. Juven-Gershon and R. Unger

Quantitative Analysis of Differential Expression of HOX Genes in Multiple Cancers

B. Ekim, B. Berger and Y. Orenstein

A Randomized Parallel Algorithm for Efficiently Finding Near-Optimal Universal Hitting Sets

S. Ray, D. Tillo, R.E. Boer, N. Assad, M. Barshai, G. Wu, Y. Orenstein, D. Yang, J.S. Schneekloth and C. Vinson

Custom DNA microarrays reveal diverse binding preferences of proteins and small molecules to thousands of G-quadruplexes

T. Shimko, P. Fordyce and Y. Orenstein

DeCoDe: degenerate codon design for complete protein-coding DNA libraries

M. Barshai and Y. Orenstein

Predicting G-Quadruplexes from DNA Sequences Using Multi-Kernel Convolutional Neural Networks

Y. Orenstein, Y. W. Yu and B. Berger

Joker de Bruijn: covering k-mers using joker characters

I. Ben-Bassat, B. Chor and Y. Orenstein

A deep neural network approach for learning intrinsic protein-RNA binding preferences

Y. Orenstein

Reverse de Bruijn: utilizing reverse peptide synthesis to cover all amino acid k-mers

Journal version

D. Le, T. Shimko, A. Aditham, A. Keys, S. Longwell, Y. Orenstein and P. Forydce

Comprehensive, high-resolution binding energy landscapes reveal context dependencies of transcription factor binding

Y. Orenstein, U. Ohler and B. Berger

Finding RNA structure in the unstructured RBPome

Y. Orenstein*, D. Pellow*, G. Marcais, R. Shamir and C. Kingsford

Designing small universal k-mer hitting sets for improved analysis of high-throughput sequencing

G. Marcais, D. Pellow, D. Bork, Y. Orenstein, R. Shamir and C. Kingsford

Improving the performance of minimizers and winnowing schemes

Y. Orenstein, R. Puccinelli, R. Kim, P. Fordyce and B. Berger

Optimized sequence library design for efficient in vitro interaction mapping

L. Yang*, Y. Orenstein*, A. Jolma, Y. Yimeng, J. Taipale, R. Shamir and R. Rohs

Transcription factor family-specific DNA shape readout revealed by quantitative specificity models

Top 10 papers reading list in Regulatory and Systems Genomics

D. Chen*, Y. Orenstein*, R. Golodnitsky, M. Pellach, D. Avrahami, C. Wachtel, A. Ovadia-Shochat,
H. Shir-Shapira, A. Kedmi, T. Juven-Gershon, R. Shamir, and D. Gerber

SELMAP - SELEX Affinity Landscape Mapping of transcription factor binding sites using integrated microfluidics

Y. Orenstein and R. Shamir

Modeling protein-DNA binding via high throughput in vitro technologies

Y. Orenstein, D. Pellow, G. Marcais, R. Shamir and C. Kingsford

Compact universal k-mer hitting sets

Y. Orenstein, Y. Wang and B. Berger

RCK: accurate and efficient inference of sequence- and structure-based protein-RNA binding models from RNAcompete data

ISMB best paper award

Y. Glick*, Y. Orenstein*, D. Chen, D. Avrahami, T. Zor, R. Shamir and D. Gerber

Quantitative protein interaction with DNA - an integrated microfluidic application for high-throughput measurements of transcription factor affinities

Y. Orenstein and B. Berger

Efficient design of compact unstructured RNA libraries covering all k-mers

Journal version

Y. Orenstein

ElemeNT: A computational tool for detecting core promoter elements

A. Kedmi, Y. Zehavi, Y. Glick, Y. Orenstein, D. Ideses, C. Wachtel, T. Doniger, H. Waldman,
N. Muster, J. Thompson, S. Anderson, D. Avrahami, J.R. Yates III, R. Shamir, D. Gerber and T. Gershon

Drosophila TRF2 is a preferential core promoter regulator

Y. Orenstein and R. Shamir

A comparative analysis of transcription factor binding models learned from PBM, HT-SELEX and ChIP data

Y. Orenstein and R. Shamir

Design of shortest double-stranded DNA sequences covering all k-mers with application to protein binding microarrays and synthetic enhancers

Y. Orenstein, E. Mick and R. Shamir

RAP: Accurate and fast motif finding based on protein binding microarray data

Matthew T Weirauch, Atina Cote, Raquel Norel, Matti Annala, Yue Zhao, Todd R Riley, Julio Saez-Rodriguez, Thomas Cokelaer,
Anastasia Vedenko, Shaheynoor Talukder, DREAM5 Consortium (including Y. Orenstein, C. Linhart, R. Shamir), Harmen J Bussemaker,
Quaid D Morris, Martha L Bulyk, Gustavo Stolovitzky and Timothy R Hughes

Evaluation of methods for modeling transcription factor sequence specificity

Y. Orenstein, C. Linhart and R. Shamir

Assessment of algorithms for inferring positional weight matrix motifs of transcription factor binding sites using protein binding microarray data

Y. Orenstein and D. Ron

Testing Eulerianity and connectivity in directed sparse graphs

Teaching

Software