Publications

2024

I. Vaknin, O. Willinger, J. Mandl, H. Heuberger, D. Ben-Ami, Y. Zeng, S. Goldberg, Y. Orenstein & R. Amit

Nature Communications (2024)

A universal system for boosting gene expression in eukaryotic cell-lines

O. Yaish, Y. Orenstein

Nucleic Acids Research (2024)

Generating, modeling and evaluating a large-scale set of CRISPR/Cas9 off-target sites with bulges

Deep neural networks for predicting the affinity landscape of protein-protein interactions

R.Moshe, SLA Lotati, Y. Orenstein, N.Papo‏

iScience (2024)

Mapping the sclerostin–LRP4 binding interface identifies critical interaction hotspots in loops 1 and 3 of sclerostin

S.Katchkovsky, R.Moshe, S.Lacham-Hartman, Y. Orenstein, N.Levaot, N.Papo

FEBS Letters (2024)

2023

M. Barshai, B. Engel, I. Haim and Y. Orenstein

PLoS Computational Biology (2023)

G4mismatch: Deep neural networks to predict G-quadruplex propensity based on G4-seq data

Deciphering transcription factors and their corresponding regulatory elements during inhibitory interneuron differentiation using deep neural networks

R. Alatawneh, Y. Salomon, R. Eshel, Y. Orenstein and RY. Birnbaum‏

Frontiers in Cell and Developmental Biology (2023)

An overview on nucleic-acid G-quadruplex prediction: from rule-based methods to deep neural networks

K. Elimelech-Zohar, Y. Orenstein‏

Briefings in Bioinformatics (2023)

Efficient minimizer orders for large values of k using minimum decycling sets

D. Pellow, L. Pu, B. Ekim, L. Kotlar, B. Berger, R. Shamir, Y. Orenstein

Genome Research (2023)

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SWOffinder: Efficient and versatile search of CRISPR off-targets with bulges by Smith-Waterman alignment

O. Yaish, A. Malle, E. Cohen, Y. Orenstein

iScience (2023)

2022

DeepZF: improved DNA-binding prediction of C2H2-zinc-finger proteins by deep transfer learning

S. Aizenshtein-Gazit and Y. Orenstein

Bioinformatics (2022)

Predicting the pathogenicity of bacterial genomes using widely spread protein families

Shaked Naor-Hoffmann, Dina Svetlitsky, Neta Sal-Man, Y. Orenstein and Michal Ziv-Ukelson

BMC Bioinformatics (2022)

A systematic evaluation of data processing and problem formulation of CRISPR off-target site prediction

O. Yaish, M. Asif and Y. Orenstein

Briefings in Bioinformatics (2022)

DeepCRISTL: deep transfer learning to predict CRISPR/Cas9 functional and endogenous on-target editing efficiency

S. Elkayam and Y. Orenstein

Bioinformatics (2022)

Computational modeling of mRNA degradation dynamics using deep neural networks

O. Yaish and Y. Orenstein

Bioinformatics (2022)

rG4detector, a novel RNA G-quadruplex predictor, uncovers their impact on stress granule formation

M.Turner, YM. Danino, M.Barshai, NS. Yacovzada, Y.Cohen, T.Olender, R.Rotkopf, D. Monchaud, E.Hornstein, Y.Orenstein

Nucleic acids research (2022)

Efficient minimizer orders for large values of k using minimum decycling sets

D.Pellow, L.Pu, B.Ekim, L.Kotlar, B.Berger, R.Shamir, Y.Orenstein‏

bioRxiv(2022)

rG4detector: convolutional neural network to predict RNA G-quadruplex propensity based on rG4-seq data

M Turner, M Barshai, Y Orenstein‏

Proceedings of the 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics(2022)

2021

MultiRBP: multi-task neural network for protein-RNA binding prediction

J. Karin, H. Michel and Y. Orenstein

Proceedings of the 12th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics (2021)

Improving the efficiency of de Bruijn graph construction using compact universal hitting sets

Y. Ben-Ari, D. Flomin, L. Pu, Y. Orenstein and R. Shamir

Proceedings of the 12th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics (2021)

A comparative analysis of RNA-binding proteins binding models learned from RNAcompete, RNA Bind-n-Seq and eCLIP data

E. Tripto and Y. Orenstein

Briefings in Bioinformatics (2021)

G4detector: Convolutional Neural Network to Predict DNA G-quadruplexes

M. Barshai*, A. Aubert* and Y. Orenstein

IEEE/ACM Transactions on Computational Biology and Bioinformatics (2021)

Overcoming the design, build, test bottleneck for synthesis of nonrepetitive protein-RNA cassettes

N. Katz, E. Tripto, N. Granik, S. Goldberg, O. Atar, Z. Yakhini, Y. Orenstein and R. Amit

Nature Communications (2021)

Improved Analysis of High-Throughput Sequencing Data Using Small Universal k-Mer Hitting Sets

Y. Orenstein

Methods in Molecular Biology (2021)

2020

DeepSELEX: inferring DNA-binding preferences from HT-SELEX data using multi-class CNNs

M. Asif, Y. Orenstein

Bioinformatics (2022)

Predicting the pathogenicity of bacterial genomes using widely spread protein families

M. Barshai*, E. Tripto*, Y. Orenstein

Annual Review of Biomedical Data Science (2020)

Quantitative Analysis of Differential Expression of HOX Genes in Multiple Cancers

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

Cancers (2020)

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

B. Ekim, B. Berger and Y. Orenstein

Proceedings of the International Conference on Research in Computational Molecular Biology (RECOMB) 2020

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

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

ACS Chemical Biology (2020)

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

T. Shimko, P. Fordyce and Y. Orenstein

Bioinformatics (2020)

Reverse de Bruijn: Utilizing Reverse Peptide Synthesis to Cover All Amino Acid k-mers

Y. Orenstein

Journal of Computational Biology (2020)

2019

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

M. Barshai and Y. Orenstein

Proceedings of the 10th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics (2019)

2018

Joker de Bruijn: covering k-mers using joker characters

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

Journal of Computational Biology (2018)

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

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

Bioinformatics (2018)

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

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

Proceedings of the National Academy of Sciences (2018)

Finding RNA structure in the unstructured RBPome

Y. Orenstein, U. Ohler and B. Berger

BMC Genomics (2018)

2017

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

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

* Equal contribution

PLoS Computational Biology (2017)

Improving the performance of minimizers and winnowing schemes

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

Bioinformatics (2017)

Optimized sequence library design for efficient in vitro interaction mapping

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

Cell Systems (2017)

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

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

* Equal contribution

Molecular Systems Biology (2017), Top 10 papers reading list in Regulatory and Systems Genomics

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

DD.Le, TC.Shimko, AK.Aditham, AM.Keys, Y.Orenstein, PM.Fordyce‏

BioRxiv (2017)

Computational Modeling of Protein-RNA Interactions

Y.Orenstein‏

Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (2017)

Joker de Bruijn: Sequence Libraries to Cover All k-mers Using Joker Characters.

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

RECOMB (2017)

Modeling protein–DNA binding via high-throughput in vitro technologies

Yaron Orenstein, Ron Shamir

Briefings in functional genomics (2017)

2016

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

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

* Equal contribution

Scientific Reports (2016)

Integrated microfluidic approach for quantitative high-throughput measurements of transcription factor binding affinities

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

Nucleic Acids Research (2016)

Compact universal k-mer hitting sets

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

Proceedings of the 15th Annual Workshop on Algorithms in Bioinformatics (WABI) (2016)

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

Y. Orenstein and B. Berger

Journal of Computational Biology (2016)

Sequence biases in CLIP experimental data are incorporated in protein RNA-binding models

Y.Orenstein, R.Hosur, S.Simmons, J.Bienkoswka, B.Berger

BioRxiv (2016)

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

Y.Orenstein, Y.Wang, B.Berger‏

Bioinformatics (2016)

2015

HTS-IBIS: fast and accurate inference of binding site motifs from HT-SELEX data

Y.Orenstein, R.Shamir‏

BioRxiv (2015)

ElemeNT: A computational tool for detecting core promoter elements

A. Sloutskin, Y. Danino, Y. Orenstein, Y. Zehavi, T. Doniger, R. Shamir and T. Gershon

Transcription (2015)

2014

Drosophila TRF2 is a preferential core promoter regulator

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

Genes and Development (2014)

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

Y. Orenstein and R. Shamir

Nucleic Acids Research (2014)

2013

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

Y. Orenstein and R. Shamir

Bioinformatics (2013)

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

Y. Orenstein, E. Mick and R. Shamir

Journal of Computational Biology (2013)

Evaluation of methods for modeling transcription factor sequence specificity

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

Nature Biotechnology (2013)

2012

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

Y. Orenstein, C. Linhart and R. Shamir

PLoS ONE (2012)

2011

Testing Eulerianity and connectivity in directed sparse graphs

Y. Orenstein and D. Ron

Theoretical Computer Science (2011)