93 related articles for article (PubMed ID: 28651966)
1. 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]
2. 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]
3. 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]
4. 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]
5. 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]
6. 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]
7. 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]
8. 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]
9. 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]
10. 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]
11. Genome-wide map of RNA degradation kinetics patterns in dendritic cells after LPS stimulation facilitates identification of primary sequence and secondary structure motifs in mRNAs.
Kumagai Y; Vandenbon A; Teraguchi S; Akira S; Suzuki Y
BMC Genomics; 2016 Dec; 17(Suppl 13):1032. PubMed ID: 28155712
[TBL] [Abstract][Full Text] [Related]
12. 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]
13. Protein Interaction Profile Sequencing (PIP-seq).
Foley SW; Gregory BD
Curr Protoc Mol Biol; 2016 Oct; 116():27.5.1-27.5.15. PubMed ID: 27723083
[TBL] [Abstract][Full Text] [Related]
14. SMARTIV: combined sequence and structure de-novo motif discovery for in-vivo RNA binding data.
Polishchuk M; Paz I; Yakhini Z; Mandel-Gutfreund Y
Nucleic Acids Res; 2018 Jul; 46(W1):W221-W228. PubMed ID: 29800452
[TBL] [Abstract][Full Text] [Related]
15. Easier, Better, Faster, Stronger: Improved Methods for RNA-Protein Interaction Studies.
Haque N; Hogg JR
Mol Cell; 2016 Jun; 62(5):650-1. PubMed ID: 27259196
[TBL] [Abstract][Full Text] [Related]
16. Leveraging cross-link modification events in CLIP-seq for motif discovery.
Bahrami-Samani E; Penalva LO; Smith AD; Uren PJ
Nucleic Acids Res; 2015 Jan; 43(1):95-103. PubMed ID: 25505146
[TBL] [Abstract][Full Text] [Related]
17. Computational analysis of CLIP-seq data.
Uhl M; Houwaart T; Corrado G; Wright PR; Backofen R
Methods; 2017 Apr; 118-119():60-72. PubMed ID: 28254606
[TBL] [Abstract][Full Text] [Related]
18. Increase in backbone mobility of the VTS1p-SAM domain on binding to SRE-RNA.
Ravindranathan S; Oberstrass FC; Allain FH
J Mol Biol; 2010 Feb; 396(3):732-46. PubMed ID: 20004205
[TBL] [Abstract][Full Text] [Related]
19. Using RNA secondary structures to guide sequence motif finding towards single-stranded regions.
Hiller M; Pudimat R; Busch A; Backofen R
Nucleic Acids Res; 2006; 34(17):e117. PubMed ID: 16987907
[TBL] [Abstract][Full Text] [Related]
20. CRISPR/Cas9-mediated integration enables TAG-eCLIP of endogenously tagged RNA binding proteins.
Van Nostrand EL; Gelboin-Burkhart C; Wang R; Pratt GA; Blue SM; Yeo GW
Methods; 2017 Apr; 118-119():50-59. PubMed ID: 28003131
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
