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Items: 21

1.

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

Ben-Bassat I, Chor B, Orenstein Y.

Bioinformatics. 2018 Sep 1;34(17):i638-i646. doi: 10.1093/bioinformatics/bty600.

PMID:
30423078
2.

Joker de Bruijn: Covering k-Mers Using Joker Characters.

Orenstein Y, Yu YW, Berger B.

J Comput Biol. 2018 Nov;25(11):1171-1178. doi: 10.1089/cmb.2018.0032. Epub 2018 Aug 17.

3.

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

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

Res Comput Mol Biol. 2017 May;10229:389-390. doi: 10.1007/978-3-319-56970-3. No abstract available.

4.

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

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

Proc Natl Acad Sci U S A. 2018 Apr 17;115(16):E3702-E3711. doi: 10.1073/pnas.1715888115. Epub 2018 Mar 27.

5.

Finding RNA structure in the unstructured RBPome.

Orenstein Y, Ohler U, Berger B.

BMC Genomics. 2018 Feb 20;19(1):154. doi: 10.1186/s12864-018-4540-1.

6.

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

Orenstein Y, Pellow D, Marçais G, Shamir R, Kingsford C.

PLoS Comput Biol. 2017 Oct 2;13(10):e1005777. doi: 10.1371/journal.pcbi.1005777. eCollection 2017 Oct.

7.

Optimized Sequence Library Design for Efficient In Vitro Interaction Mapping.

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

Cell Syst. 2017 Sep 27;5(3):230-236.e5. doi: 10.1016/j.cels.2017.07.006.

8.

Improving the performance of minimizers and winnowing schemes.

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

Bioinformatics. 2017 Jul 15;33(14):i110-i117. doi: 10.1093/bioinformatics/btx235.

9.

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

Yang L, Orenstein Y, Jolma A, Yin Y, Taipale J, Shamir R, Rohs R.

Mol Syst Biol. 2017 Feb 6;13(2):910. doi: 10.15252/msb.20167238.

10.

SELMAP - SELEX affinity landscape MAPping of transcription factor binding sites using integrated microfluidics.

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

Sci Rep. 2016 Sep 15;6:33351. doi: 10.1038/srep33351.

11.

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

Orenstein Y, Shamir R.

Brief Funct Genomics. 2017 May 1;16(3):171-180. doi: 10.1093/bfgp/elw030.

12.

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

Orenstein Y, Wang Y, Berger B.

Bioinformatics. 2016 Jun 15;32(12):i351-i359. doi: 10.1093/bioinformatics/btw259.

13.

Efficient Design of Compact Unstructured RNA Libraries Covering All k-mers.

Orenstein Y, Berger B.

J Comput Biol. 2016 Feb;23(2):67-79. doi: 10.1089/cmb.2015.0179. Epub 2015 Dec 29.

14.

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

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

Nucleic Acids Res. 2016 Apr 7;44(6):e51. doi: 10.1093/nar/gkv1327. Epub 2015 Dec 3.

15.

ElemeNT: a computational tool for detecting core promoter elements.

Sloutskin A, Danino YM, Orenstein Y, Zehavi Y, Doniger T, Shamir R, Juven-Gershon T.

Transcription. 2015;6(3):41-50. doi: 10.1080/21541264.2015.1067286.

16.

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

Orenstein Y, Shamir R.

Bioinformatics. 2015 Apr 15;31(8):1340. doi: 10.1093/bioinformatics/btv084. Epub 2015 Mar 24. No abstract available.

17.

Drosophila TRF2 is a preferential core promoter regulator.

Kedmi A, Zehavi Y, Glick Y, Orenstein Y, Ideses D, Wachtel C, Doniger T, Waldman Ben-Asher H, Muster N, Thompson J, Anderson S, Avrahami D, Yates JR 3rd, Shamir R, Gerber D, Juven-Gershon T.

Genes Dev. 2014 Oct 1;28(19):2163-74. doi: 10.1101/gad.245670.114. Epub 2014 Sep 15.

18.

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

Orenstein Y, Shamir R.

Nucleic Acids Res. 2014 Apr;42(8):e63. doi: 10.1093/nar/gku117. Epub 2014 Feb 5.

19.
20.

RAP: accurate and fast motif finding based on protein-binding microarray data.

Orenstein Y, Mick E, Shamir R.

J Comput Biol. 2013 May;20(5):375-82. doi: 10.1089/cmb.2012.0253. Epub 2013 Mar 6.

21.

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

Orenstein Y, Linhart C, Shamir R.

PLoS One. 2012;7(9):e46145. doi: 10.1371/journal.pone.0046145. Epub 2012 Sep 28.

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