| Evaluation of methods for modeling transcription factor sequence specificity MT Weirauch, A Cote, R Norel, M Annala, Y Zhao, TR Riley, ... Nature biotechnology 31 (2), 126-134, 2013 | 304 | 2013 |
| A comparative analysis of transcription factor binding models learned from PBM, HT-SELEX and ChIP data Y Orenstein, R Shamir Nucleic acids research 42 (8), e63-e63, 2014 | 90 | 2014 |
| Transcription factor family‐specific DNA shape readout revealed by quantitative specificity models L Yang, Y Orenstein, A Jolma, Y Yin, J Taipale, R Shamir, R Rohs Molecular systems biology 13 (2), 910, 2017 | 69 | 2017 |
| Drosophila TRF2 is a preferential core promoter regulator A Kedmi, Y Zehavi, Y Glick, Y Orenstein, D Ideses, C Wachtel, T Doniger, ... Genes & development 28 (19), 2163-2174, 2014 | 48 | 2014 |
| ElemeNT: a computational tool for detecting core promoter elements A Sloutskin, YM Danino, Y Orenstein, Y Zehavi, T Doniger, R Shamir, ... Transcription 6 (3), 41-50, 2015 | 40 | 2015 |
| Comprehensive, high-resolution binding energy landscapes reveal context dependencies of transcription factor binding DD Le, TC Shimko, AK Aditham, AM Keys, SA Longwell, Y Orenstein, ... Proceedings of the National Academy of Sciences 115 (16), E3702-E3711, 2018 | 39 | 2018 |
| RCK: accurate and efficient inference of sequence-and structure-based protein–RNA binding models from RNAcompete data Y Orenstein, Y Wang, B Berger Bioinformatics 32 (12), i351-i359, 2016 | 39 | 2016 |
| A deep neural network approach for learning intrinsic protein-RNA binding preferences I Ben-Bassat, B Chor, Y Orenstein Bioinformatics 34 (17), i638-i646, 2018 | 30 | 2018 |
| Assessment of algorithms for inferring positional weight matrix motifs of transcription factor binding sites using protein binding microarray data Y Orenstein, C Linhart, R Shamir PLoS One 7 (9), e46145, 2012 | 25 | 2012 |
| Improving the performance of minimizers and winnowing schemes G Marçais, D Pellow, D Bork, Y Orenstein, R Shamir, C Kingsford Bioinformatics 33 (14), i110-i117, 2017 | 21 | 2017 |
| RAP: accurate and fast motif finding based on protein-binding microarray data Y Orenstein, E Mick, R Shamir Journal of computational biology 20 (5), 375-382, 2013 | 19 | 2013 |
| Designing small universal k-mer hitting sets for improved analysis of high-throughput sequencing Y Orenstein, D Pellow, G Marçais, R Shamir, C Kingsford PLoS computational biology 13 (10), e1005777, 2017 | 17 | 2017 |
| Modeling protein–DNA binding via high-throughput in vitro technologies Y Orenstein, R Shamir Briefings in functional genomics 16 (3), 171-180, 2017 | 15 | 2017 |
| 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, ... Scientific reports 6 (1), 1-13, 2016 | 14 | 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 44 (6), e51-e51, 2016 | 14 | 2016 |
| Design of shortest double-stranded DNA sequences covering all k-mers with applications to protein-binding microarrays and synthetic enhancers Y Orenstein, R Shamir Bioinformatics 29 (13), i71-i79, 2013 | 14 | 2013 |
| Compact universal k-mer hitting sets Y Orenstein, D Pellow, G Marçais, R Shamir, C Kingsford International Workshop on Algorithms in Bioinformatics, 257-268, 2016 | 13 | 2016 |
| Testing eulerianity and connectivity in directed sparse graphs Y Orenstein, D Ron Theoretical Computer Science 412 (45), 6390-6408, 2011 | 13 | 2011 |
| Custom DNA microarrays reveal diverse binding preferences of proteins and small molecules to thousands of G-quadruplexes S Ray, D Tillo, RE Boer, N Assad, M Barshai, G Wu, Y Orenstein, D Yang, ... ACS chemical biology 15 (4), 925-935, 2020 | 10 | 2020 |
| HTS-IBIS: fast and accurate inference of binding site motifs from HT-SELEX data Y Orenstein, R Shamir bioRxiv, 022277, 2015 | 7 | 2015 |
