171 related articles for article (PubMed ID: 27307637)
1. 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; 32(12):i351-i359. PubMed ID: 27307637
[TBL] [Abstract][Full Text] [Related]
2. Integrating thermodynamic and sequence contexts improves protein-RNA binding prediction.
Su Y; Luo Y; Zhao X; Liu Y; Peng J
PLoS Comput Biol; 2019 Sep; 15(9):e1007283. PubMed ID: 31483777
[TBL] [Abstract][Full Text] [Related]
3. Finding RNA structure in the unstructured RBPome.
Orenstein Y; Ohler U; Berger B
BMC Genomics; 2018 Feb; 19(1):154. PubMed ID: 29463232
[TBL] [Abstract][Full Text] [Related]
4. A comparative analysis of RNA-binding proteins binding models learned from RNAcompete, RNA Bind-n-Seq and eCLIP data.
Tripto E; Orenstein Y
Brief Bioinform; 2021 Nov; 22(6):. PubMed ID: 34017982
[TBL] [Abstract][Full Text] [Related]
5. RNAcompete-S: Combined RNA sequence/structure preferences for RNA binding proteins derived from a single-step in vitro selection.
Cook KB; Vembu S; Ha KCH; Zheng H; Laverty KU; Hughes TR; Ray D; Morris QD
Methods; 2017 Aug; 126():18-28. PubMed ID: 28651966
[TBL] [Abstract][Full Text] [Related]
6. Rapid and systematic analysis of the RNA recognition specificities of RNA-binding proteins.
Ray D; Kazan H; Chan ET; Peña Castillo L; Chaudhry S; Talukder S; Blencowe BJ; Morris Q; Hughes TR
Nat Biotechnol; 2009 Jul; 27(7):667-70. PubMed ID: 19561594
[TBL] [Abstract][Full Text] [Related]
7. RNAcontext: a new method for learning the sequence and structure binding preferences of RNA-binding proteins.
Kazan H; Ray D; Chan ET; Hughes TR; Morris Q
PLoS Comput Biol; 2010 Jul; 6(7):e1000832. PubMed ID: 20617199
[TBL] [Abstract][Full Text] [Related]
8. RNAcompete methodology and application to determine sequence preferences of unconventional RNA-binding proteins.
Ray D; Ha KCH; Nie K; Zheng H; Hughes TR; Morris QD
Methods; 2017 Apr; 118-119():3-15. PubMed ID: 27956239
[TBL] [Abstract][Full Text] [Related]
9. GraphProt: modeling binding preferences of RNA-binding proteins.
Maticzka D; Lange SJ; Costa F; Backofen R
Genome Biol; 2014 Jan; 15(1):R17. PubMed ID: 24451197
[TBL] [Abstract][Full Text] [Related]
10. A deep neural network approach for learning intrinsic protein-RNA binding preferences.
Ben-Bassat I; Chor B; Orenstein Y
Bioinformatics; 2018 Sep; 34(17):i638-i646. PubMed ID: 30423078
[TBL] [Abstract][Full Text] [Related]
11. ssHMM: extracting intuitive sequence-structure motifs from high-throughput RNA-binding protein data.
Heller D; Krestel R; Ohler U; Vingron M; Marsico A
Nucleic Acids Res; 2017 Nov; 45(19):11004-11018. PubMed ID: 28977546
[TBL] [Abstract][Full Text] [Related]
12. Inferring RNA sequence preferences for poorly studied RNA-binding proteins based on co-evolution.
Yang S; Wang J; Ng RT
BMC Bioinformatics; 2018 Mar; 19(1):96. PubMed ID: 29529991
[TBL] [Abstract][Full Text] [Related]
13. RBPBind: Quantitative Prediction of Protein-RNA Interactions.
Gaither J; Lin YH; Bundschuh R
J Mol Biol; 2022 Jun; 434(11):167515. PubMed ID: 35662470
[TBL] [Abstract][Full Text] [Related]
14. Accurate prediction of RNA nucleotide interactions with backbone k-tree model.
Ding L; Xue X; LaMarca S; Mohebbi M; Samad A; Malmberg RL; Cai L
Bioinformatics; 2015 Aug; 31(16):2660-7. PubMed ID: 25886978
[TBL] [Abstract][Full Text] [Related]
15. Structure-based prediction of protein- peptide binding regions using Random Forest.
Taherzadeh G; Zhou Y; Liew AW; Yang Y
Bioinformatics; 2018 Feb; 34(3):477-484. PubMed ID: 29028926
[TBL] [Abstract][Full Text] [Related]
16. A combined sequence and structure based method for discovering enriched motifs in RNA from in vivo binding data.
Polishchuk M; Paz I; Kohen R; Mesika R; Yakhini Z; Mandel-Gutfreund Y
Methods; 2017 Apr; 118-119():73-81. PubMed ID: 28274760
[TBL] [Abstract][Full Text] [Related]
17. SSMART: sequence-structure motif identification for RNA-binding proteins.
Munteanu A; Mukherjee N; Ohler U
Bioinformatics; 2018 Dec; 34(23):3990-3998. PubMed ID: 29893814
[TBL] [Abstract][Full Text] [Related]
18. Predicting pseudoknotted structures across two RNA sequences.
Sperschneider J; Datta A; Wise MJ
Bioinformatics; 2012 Dec; 28(23):3058-65. PubMed ID: 23044552
[TBL] [Abstract][Full Text] [Related]
19. A deep boosting based approach for capturing the sequence binding preferences of RNA-binding proteins from high-throughput CLIP-seq data.
Li S; Dong F; Wu Y; Zhang S; Zhang C; Liu X; Jiang T; Zeng J
Nucleic Acids Res; 2017 Aug; 45(14):e129. PubMed ID: 28575488
[TBL] [Abstract][Full Text] [Related]
20. Protein-specific prediction of mRNA binding using RNA sequences, binding motifs and predicted secondary structures.
Livi CM; Blanzieri E
BMC Bioinformatics; 2014 Apr; 15():123. PubMed ID: 24780077
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]