Abstract
Untranslated regions of mRNA (UTRs) are involved in defining the fate of the transcript through processes such as mRNA localization, degradation, translation initiation regulation, and several others: the action of trans-factors such as RNA-binding proteins and non-coding RNAs, combined with the presence of defined sequence and structural cis-elements, ultimately determines protein synthesis levels. Identifying functional regions in UTRs and uncovering post-transcriptional regulators acting upon these is thus of paramount importance to understand this regulatory layer: these tasks can now be approached computationally to reduce the testable hypothesis space and drive the experimental validation in a more effective way.
This chapter will focus on presenting databases and tools allowing to study the various aspects of post-transcriptional regulation, including the profiling of actively translated mRNAs, regulatory network analysis (e.g., RBP and ncRNA binding sites), trans-factor binding sites prediction, motif search (sequence and secondary structure), and other aspects of this regulatory layer: two potential analysis pipelines are also presented as practical examples of how these tools could be integrated and effectively employed.
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References
Metzker ML (2010) Sequencing technologies—the next generation. Nat Rev Genet 11:31–46
Ingolia NT (2014) Ribosome profiling: new views of translation, from single codons to genome scale. Nat Rev Genet 15:205–213
Milek M, Wyler E, Landthaler M (2012) Transcriptome-wide analysis of protein-RNA interactions using high-throughput sequencing. Semin Cell Dev Biol 23:206–212
Arava Y (2003) Isolation of polysomal RNA for microarray analysis. Methods Mol Biol 224:79–87
Ule J, Jensen KB, Ruggiu M et al (2003) CLIP identifies Nova-regulated RNA networks in the brain. Science 302:1212–1215
Chi SW, Zang JB, Mele A, Darnell RB (2009) Argonaute HITS-CLIP decodes microRNA–mRNA interaction maps. Nature 460:479–486
Hafner M, Landthaler M, Burger L et al (2010) Transcriptome-wide identification of RNA-binding protein and microRNA target sites by PAR-CLIP. Cell 141:129–141
König J, Zarnack K, Rot G et al (2010) iCLIP reveals the function of hnRNP particles in splicing at individual nucleotide resolution. Nat Struct Mol Biol 17:909–915
Linder B, Grozhik AV, Olarerin-George AO et al (2015) Single-nucleotide-resolution mapping of m6A and m6Am throughout the transcriptome. Nat Methods 12:767–772
Van Nostrand EL, Pratt GA, Shishkin AA et al (2016) Robust transcriptome-wide discovery of RNA-binding protein binding sites with enhanced CLIP (eCLIP). Nat Methods 13:508–514
Zarnegar BJ, Flynn RA, Shen Y et al (2016) irCLIP platform for efficient characterization of protein–RNA interactions. Nat Methods 13:489–492
Kargapolova Y, Levin M, Lackner K, Danckwardt S (2017) sCLIP—an integrated platform to study RNA–protein interactomes in biomedical research: identification of CSTF2tau in alternative processing of small nuclear RNAs. Nucleic Acids Res 45:6074–6086
George H, Ule J, Hussain S (2017) Illustrating the epitranscriptome at nucleotide resolution using methylation-iCLIP (miCLIP). Methods Mol Biol 1562:91–106
Ray D, Kazan H, Chan ET et al (2009) Rapid and systematic analysis of the RNA recognition specificities of RNA-binding proteins. Nat Biotechnol 27:667–670
Cook KB, Vembu S, Ha KCH et al (2017) RNAcompete-S: combined RNA sequence/structure preferences for RNA binding proteins derived from a single-step in vitro selection. Methods 126:18–28
Lambert N, Robertson A, Jangi M et al (2014) RNA Bind-n-Seq: quantitative assessment of the sequence and structural binding specificity of RNA binding proteins. Mol Cell 54:887–900
Campbell ZT, Bhimsaria D, Valley CT et al (2012) Cooperativity in RNA-protein interactions: global analysis of RNA binding specificity. Cell Rep 1:570–581
Lin C, Miles WO (2019) Beyond CLIP: advances and opportunities to measure RBP-RNA and RNA-RNA interactions. Nucleic Acids Res 47:5490–5501
Larsson O, Sonenberg N, Nadon R (2011) anota: analysis of differential translation in genome-wide studies. Bioinformatics 27:1440–1441
Tebaldi T, Dassi E, Kostoska G et al (2014) tRanslatome: an R/Bioconductor package to portray translational control. Bioinformatics 30:289–291
Olshen AB, Hsieh AC, Stumpf CR et al (2013) Assessing gene-level translational control from ribosome profiling. Bioinformatics 29:2995–3002
Xiao Z, Zou Q, Liu Y, Yang X (2016) Genome-wide assessment of differential translations with ribosome profiling data. Nat Commun 7:11194
Zhong Y, Karaletsos T, Drewe P et al (2017) RiboDiff: detecting changes of mRNA translation efficiency from ribosome footprints. Bioinformatics 33:139–141
Li W, Wang W, Uren PJ et al (2017) Riborex: fast and flexible identification of differential translation from Ribo-seq data. Bioinformatics 33:1735–1737
Oertlin C, Lorent J, Murie C et al (2019) Generally applicable transcriptome-wide analysis of translation using anota2seq. Nucleic Acids Res 47:e70–e70
Ernlund AW, Schneider RJ, Ruggles KV (2018) RIVET: comprehensive graphic user interface for analysis and exploration of genome-wide translatomics data. BMC Genomics 19:809
Stocks MB, Moxon S, Mapleson D et al (2012) The UEA sRNA workbench: a suite of tools for analysing and visualizing next generation sequencing microRNA and small RNA datasets. Bioinformatics 28:2059–2061
Wan C, Gao J, Zhang H et al (2017) CPSS 2.0: a computational platform update for the analysis of small RNA sequencing data. Bioinformatics 33:3289–3291
Giurato G, De Filippo MR, Rinaldi A et al (2013) iMir: an integrated pipeline for high-throughput analysis of small non-coding RNA data obtained by smallRNA-Seq. BMC Bioinformatics 14:362
Sun Z, Evans J, Bhagwate A et al (2014) CAP-miRSeq: a comprehensive analysis pipeline for microRNA sequencing data. BMC Genomics 15:423
Vitsios DM, Enright AJ (2015) Chimira: analysis of small RNA sequencing data and microRNA modifications. Bioinformatics 31:3365–3367
Shi J, Dong M, Li L et al (2015) mirPRo—a novel standalone program for differential expression and variation analysis of miRNAs. Sci Rep 5:14617
Barturen G, Rueda A, Hamberg M et al (2014) sRNAbench: profiling of small RNAs and its sequence variants in single or multi-species high-throughput experiments. Methods Next Gen Seq 1:21–31
Aparicio-Puerta E, Lebrón R, Rueda A et al (2019) sRNAbench and sRNAtoolbox 2019: intuitive fast small RNA profiling and differential expression. Nucleic Acids Res 47:W530–W535
Fehlmann T, Backes C, Kahraman M et al (2017) Web-based NGS data analysis using miRMaster: a large-scale meta-analysis of human miRNAs. Nucleic Acids Res 45:8731–8744
Wu X, Kim T-K, Baxter D et al (2017) sRNAnalyzer—a flexible and customizable small RNA sequencing data analysis pipeline. Nucleic Acids Res 45:12140–12151
Pogorelcnik R, Vaury C, Pouchin P et al (2018) sRNAPipe: a Galaxy-based pipeline for bioinformatic in-depth exploration of small RNAseq data. Mob DNA 9:25
Kuksa PP, Amlie-Wolf A, Katanic Ž et al (2018) SPAR: small RNA-seq portal for analysis of sequencing experiments. Nucleic Acids Res 46:W36–W42
Lu Y, Baras AS, Halushka MK (2018) miRge 2.0 for comprehensive analysis of microRNA sequencing data. BMC Bioinformatics 19:275
Desvignes T, Batzel P, Sydes J et al (2019) miRNA analysis with Prost! Reveals evolutionary conservation of organ-enriched expression and post-transcriptional modifications in three-spined stickleback and zebrafish. Sci Rep 9:3913
Li J, Kho AT, Chase RP et al (2020) COMPSRA: a COMprehensive Platform for Small RNA-Seq data Analysis. Sci Rep 10:4552
Uren PJ, Bahrami-Samani E, Burns SC et al (2012) Site identification in high-throughput RNA-protein interaction data. Bioinformatics 28:3013–3020
Kucukural A, Özadam H, Singh G et al (2013) ASPeak: an abundance sensitive peak detection algorithm for RIP-Seq. Bioinformatics 29:2485–2486
Li Y, Zhao DY, Greenblatt JF, Zhang Z (2013) RIPSeeker: a statistical package for identifying protein-associated transcripts from RIP-seq experiments. Nucleic Acids Res 41:e94
Lovci MT, Ghanem D, Marr H et al (2013) Rbfox proteins regulate alternative mRNA splicing through evolutionarily conserved RNA bridges. Nat Struct Mol Biol 20:1434–1442
Corcoran DL, Georgiev S, Mukherjee N et al (2011) PARalyzer: definition of RNA binding sites from PAR-CLIP short-read sequence data. Genome Biol 12:R79
Sievers C, Schlumpf T, Sawarkar R et al (2012) Mixture models and wavelet transforms reveal high confidence RNA-protein interaction sites in MOV10 PAR-CLIP data. Nucleic Acids Res 40:e160
Wang T, Chen B, Kim M et al (2014) A model-based approach to identify binding sites in CLIP-Seq data. PLoS One 9:e93248
Blankenberg D, Von Kuster G, Coraor N et al (2010) Galaxy: a web-based genome analysis tool for experimentalists. Curr Protoc Mol Biol. https://doi.org/10.1002/0471142727.mb1910s89
Chen B, Yun J, Kim MS et al (2014) PIPE-CLIP: a comprehensive online tool for CLIP-seq data analysis. Genome Biol 15:R18
Krakau S, Richard H, Marsico A (2017) PureCLIP: capturing target-specific protein–RNA interaction footprints from single-nucleotide CLIP-seq data. Genome Biol 18:240
Drewe-Boss P, Wessels H-H, Ohler U (2018) omniCLIP: probabilistic identification of protein-RNA interactions from CLIP-seq data. Genome Biol 19:183
Liu Q, Shvarts T, Sliz P, Gregory RI (2020) RiboToolkit: an integrated platform for analysis and annotation of ribosome profiling data to decode mRNA translation at codon resolution. Nucleic Acids Res 48:W218–W229
Fang H, Huang Y-F, Radhakrishnan A et al (2018) Scikit-ribo enables accurate estimation and robust modeling of translation dynamics at codon resolution. Cell Syst 6:180–191.e4
Khorshid M, Rodak C, Zavolan M (2011) CLIPZ: a database and analysis environment for experimentally determined binding sites of RNA-binding proteins. Nucleic Acids Res 39:D245–D252
Ray D, Kazan H, Cook KB et al (2013) A compendium of RNA-binding motifs for decoding gene regulation. Nature 499:172–177
Cook KB, Kazan H, Zuberi K et al (2011) RBPDB: a database of RNA-binding specificities. Nucleic Acids Res 39:D301–D308
Giudice G, Sánchez-Cabo F, Torroja C, Lara-Pezzi E (2016) ATtRACT—a database of RNA-binding proteins and associated motifs. Database. https://doi.org/10.1093/database/baw035
Zhu Y, Xu G, Yang YT et al (2019) POSTAR2: deciphering the post-transcriptional regulatory logics. Nucleic Acids Res 47:D203–D211
Yang Y-CT, Di C, Hu B et al (2015) CLIPdb: a CLIP-seq database for protein-RNA interactions. BMC Genomics 16:51
Benoit Bouvrette LP, Bovaird S, Blanchette M, Lécuyer E (2020) oRNAment: a database of putative RNA binding protein target sites in the transcriptomes of model species. Nucleic Acids Res 48:D166–D173
Dassi E, Re A, Leo S et al (2014) AURA 2: empowering discovery of post-transcriptional networks. Translation 2:e27738
Blin K, Dieterich C, Wurmus R et al (2014) DoRiNA 2.0—upgrading the doRiNA database of RNA interactions in post-transcriptional regulation. Nucleic Acids Res 43:D160–D167
Li J-H, Liu S, Zhou H et al (2014) starBase v2.0: decoding miRNA-ceRNA, miRNA-ncRNA and protein-RNA interaction networks from large-scale CLIP-Seq data. Nucleic Acids Res 42:D92–D97
Grillo G, Turi A, Licciulli F et al (2010) UTRdb and UTRsite (RELEASE 2010): a collection of sequences and regulatory motifs of the untranslated regions of eukaryotic mRNAs. Nucleic Acids Res 38:D75–D80
Kozomara A, Birgaoanu M, Griffiths-Jones S (2019) miRBase: from microRNA sequences to function. Nucleic Acids Res 47:D155–D162
Chen Y, Wang X (2020) miRDB: an online database for prediction of functional microRNA targets. Nucleic Acids Res 48:D127–D131
Hsu S-D, Chu C-H, Tsou A-P et al (2008) miRNAMap 2.0: genomic maps of microRNAs in metazoan genomes. Nucleic Acids Res 36:D165–D169
Huang H-Y, Lin Y-C-D, Li J et al (2019) miRTarBase 2020: updates to the experimentally validated microRNA–target interaction database. Nucleic Acids Res 48:D148–D154
Cho S, Jang I, Jun Y et al (2013) MiRGator v3.0: a microRNA portal for deep sequencing, expression profiling and mRNA targeting. Nucleic Acids Res 41:D252–D257
Andrés-León E, González Peña D, Gómez-López G, Pisano DG (2015) miRGate: a curated database of human, mouse and rat miRNA–mRNA targets. Database. https://doi.org/10.1093/database/bav035
Dweep H, Gretz N (2015) miRWalk2.0: a comprehensive atlas of microRNA-target interactions. Nat Methods 12:697
Rennie W, Kanoria S, Liu C et al (2016) STarMirDB: a database of microRNA binding sites. RNA Biol 13:554–560
Karagkouni D, Paraskevopoulou MD, Chatzopoulos S et al (2017) DIANA-TarBase v8: a decade-long collection of experimentally supported miRNA–gene interactions. Nucleic Acids Res 46:D239–D245
Karagkouni D, Paraskevopoulou MD, Tastsoglou S et al (2019) DIANA-LncBase v3: indexing experimentally supported miRNA targets on non-coding transcripts. Nucleic Acids Res 48:D101–D110
Quek XC, Thomson DW, Maag JLV et al (2015) lncRNAdb v2.0: expanding the reference database for functional long noncoding RNAs. Nucleic Acids Res 43:D168–D173
Bu D, Yu K, Sun S et al (2012) NONCODE v3.0: integrative annotation of long noncoding RNAs. Nucleic Acids Res 40:D210–D215
Cheng L, Wang P, Tian R et al (2019) LncRNA2Target v2.0: a comprehensive database for target genes of lncRNAs in human and mouse. Nucleic Acids Res 47:D140–D144
Zhang X, Wu D, Chen L et al (2014) RAID: a comprehensive resource for human RNA-associated (RNA-RNA/RNA-protein) interaction. RNA 20:989–993
Teng X, Chen X, Xue H et al (2020) NPInter v4.0: an integrated database of ncRNA interactions. Nucleic Acids Res 48:D160–D165
Burge SW, Daub J, Eberhardt R et al (2013) Rfam 11.0: 10 years of RNA families. Nucleic Acids Res 41:D226–D232
Chang T-H, Huang H-Y, Hsu JB-K et al (2013) An enhanced computational platform for investigating the roles of regulatory RNA and for identifying functional RNA motifs. BMC Bioinformatics 14(Suppl 2):S4
Müller S, Rycak L, Afonso-Grunz F et al (2014) APADB: a database for alternative polyadenylation and microRNA regulation events. Database. https://doi.org/10.1093/database/bau076
Bakheet T, Hitti E, Khabar KSA (2018) ARED-Plus: an updated and expanded database of AU-rich element-containing mRNAs and pre-mRNAs. Nucleic Acids Res 46:D218–D220
Fallmann J, Sedlyarov V, Tanzer A et al (2016) AREsite2: an enhanced database for the comprehensive investigation of AU/GU/U-rich elements. Nucleic Acids Res 44:D90–D95
Mokrejs M, Masek T, Vopálensky V et al (2010) IRESite—a tool for the examination of viral and cellular internal ribosome entry sites. Nucleic Acids Res 38:D131–D136
Zhao J, Li Y, Wang C et al (2020) IRESbase: a comprehensive database of experimentally validated internal ribosome entry sites. Genomics Proteomics Bioinformatics 18:129–139
Kolekar P, Pataskar A, Kulkarni-Kale U et al (2016) IRESPred: web server for prediction of cellular and viral internal ribosome entry site (IRES). Sci Rep 6:27436
Castellano S, Gladyshev VN, Guigó R, Berry MJ (2008) SelenoDB 1.0 : a database of selenoprotein genes, proteins and SECIS elements. Nucleic Acids Res 36:D332–D338
Campillos M, Cases I, Hentze MW, Sanchez M (2010) SIREs: searching for iron-responsive elements. Nucleic Acids Res 38:W360–W367
Jacobs GH, Chen A, Stevens SG et al (2009) Transterm: a database to aid the analysis of regulatory sequences in mRNAs. Nucleic Acids Res 37:D72–D76
Liu Y, Sun S, Bredy T et al (2017) MotifMap-RNA: a genome-wide map of RBP binding sites. Bioinformatics 33:2029–2031
Agostini F, Zanzoni A, Klus P et al (2013) catRAPID omics: a web server for large-scale prediction of protein-RNA interactions. Bioinformatics 29:2928–2930
Armaos A, Cirillo D, Gaetano Tartaglia G (2017) omiXcore: a web server for prediction of protein interactions with large RNA. Bioinformatics 33:3104–3106
Cirillo D, Blanco M, Armaos A et al (2016) Quantitative predictions of protein interactions with long noncoding RNAs. Nat Methods 14:5–6
Muppirala UK, Honavar VG, Dobbs D (2011) Predicting RNA-protein interactions using only sequence information. BMC Bioinformatics 12:489
Paz I, Kosti I, Ares M Jr et al (2014) RBPmap: a web server for mapping binding sites of RNA-binding proteins. Nucleic Acids Res 42:W361–W367
Biswas A, Brown CM (2014) Scan for Motifs: a webserver for the analysis of post-transcriptional regulatory elements in the 3′ untranslated regions (3' UTRs) of mRNAs. BMC Bioinformatics 15:174
Pan X, Rijnbeek P, Yan J, Shen H-B (2018) Prediction of RNA-protein sequence and structure binding preferences using deep convolutional and recurrent neural networks. BMC Genomics 19:511
Yu H, Wang J, Sheng Q et al (2019) beRBP: binding estimation for human RNA-binding proteins. Nucleic Acids Res 47:e26
Yan Z, Hamilton WL, Blanchette M (2020) Graph neural representational learning of RNA secondary structures for predicting RNA-protein interactions. Bioinformatics 36:i276–i284
Deng L, Yang W, Liu H (2019) PredPRBA: prediction of Protein-RNA binding affinity using gradient boosted regression trees. Front Genet 10:637
Lam JH, Li Y, Zhu L et al (2019) A deep learning framework to predict binding preference of RNA constituents on protein surface. Nat Commun 10:4941
Qiu J, Bernhofer M, Heinzinger M et al (2020) ProNA2020 predicts protein–DNA, protein–RNA, and protein–protein binding proteins and residues from sequence. J Mol Biol 432:2428–2443
Lewis BP, Burge CB, Bartel DP (2005) Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets. Cell 120:15–20
Vejnar CE, Blum M, Zdobnov EM (2013) miRmap web: comprehensive microRNA target prediction online. Nucleic Acids Res 41:W165–W168
Krüger J, Rehmsmeier M (2006) RNAhybrid: microRNA target prediction easy, fast and flexible. Nucleic Acids Res 34:W451–W454
Wang X, El Naqa IM (2008) Prediction of both conserved and nonconserved microRNA targets in animals. Bioinformatics 24:325–332
Linsley PS, Schelter J, Burchard J et al (2007) Transcripts targeted by the microRNA-16 family cooperatively regulate cell cycle progression. Mol Cell Biol 27:2240–2252
Sturm M, Hackenberg M, Langenberger D, Frishman D (2010) TargetSpy: a supervised machine learning approach for microRNA target prediction. BMC Bioinformatics 11:292
Mitra R, Bandyopadhyay S (2011) MultiMiTar: a novel multi objective optimization based miRNA-target prediction method. PLoS One 6:e24583
Bandyopadhyay S, Saha S, Maulik U, Deb K (2008) A simulated annealing-based multiobjective optimization algorithm: AMOSA. IEEE Trans Evol Comput 12:269–283
Paraskevopoulou MD, Georgakilas G, Kostoulas N et al (2013) DIANA-microT web server v5.0: service integration into miRNA functional analysis workflows. Nucleic Acids Res 41:W169–W173
Wolstencroft K, Haines R, Fellows D et al (2013) The Taverna workflow suite: designing and executing workflows of Web Services on the desktop, web or in the cloud. Nucleic Acids Res 41:W557–W561
Oulas A, Karathanasis N, Louloupi A et al (2012) A new microRNA target prediction tool identifies a novel interaction of a putative miRNA with CCND2. RNA Biol 9:1196–1207
Coronnello C, Benos PV (2013) ComiR: combinatorial microRNA target prediction tool. Nucleic Acids Res 41:W159–W164
Ding J, Li X, Hu H (2016) TarPmiR: a new approach for microRNA target site prediction. Bioinformatics 32:2768–2775
John B, Enright AJ, Aravin A et al (2004) Human MicroRNA targets. PLoS Biol 2:e363
Helwak A, Kudla G, Dudnakova T, Tollervey D (2013) Mapping the human miRNA interactome by CLASH reveals frequent noncanonical binding. Cell 153:654–665
Krek A, Grün D, Poy MN et al (2005) Combinatorial microRNA target predictions. Nat Genet 37:495–500
Kertesz M, Iovino N, Unnerstall U et al (2007) The role of site accessibility in microRNA target recognition. Nat Genet 39:1278–1284
Sabarinathan R, Tafer H, Seemann SE et al (2013) The RNAsnp web server: predicting SNP effects on local RNA secondary structure. Nucleic Acids Res 41:W475–W479
Halvorsen M, Martin JS, Broadaway S, Laederach A (2010) Disease-associated mutations that alter the RNA structural ensemble. PLoS Genet 6:e1001074
He F, Wei R, Zhou Z et al (2019) Integrative analysis of somatic mutations in non-coding regions altering RNA secondary structures in cancer genomes. Sci Rep 9:8205
Corley M, Solem A, Qu K et al (2015) Detecting riboSNitches with RNA folding algorithms: a genome-wide benchmark. Nucleic Acids Res 43:1859–1868
Yao Z, Weinberg Z, Ruzzo WL (2006) CMfinder—a covariance model based RNA motif finding algorithm. Bioinformatics 22:445–452
Hiller M, Pudimat R, Busch A, Backofen R (2006) Using RNA secondary structures to guide sequence motif finding towards single-stranded regions. Nucleic Acids Res 34:e117
Bailey TL, Boden M, Buske FA et al (2009) MEME SUITE: tools for motif discovery and searching. Nucleic Acids Res 37:W202–W208
Rabani M, Kertesz M, Segal E (2008) Computational prediction of RNA structural motifs involved in posttranscriptional regulatory processes. Proc Natl Acad Sci U S A 105:14885–14890
Goodarzi H, Najafabadi HS, Oikonomou P et al (2012) Systematic discovery of structural elements governing stability of mammalian messenger RNAs. Nature 485:264–268
Fukunaga T, Ozaki H, Terai G et al (2014) CapR: revealing structural specificities of RNA-binding protein target recognition using CLIP-seq data. Genome Biol 15:R16
Zambelli F, Pavesi G (2015) De novo secondary structure motif discovery using RNAProfile. Methods Mol Biol 1269:49–62
Pietrosanto M, Adinolfi M, Casula R et al (2018) BEAM web server: a tool for structural RNA motif discovery. Bioinformatics 34:1058–1060
Maticzka D, Lange SJ, Costa F, Backofen R (2014) GraphProt: modeling binding preferences of RNA-binding proteins. Genome Biol 15:R17
Bahrami-Samani E, Penalva LOF, Smith AD, Uren PJ (2015) Leveraging cross-link modification events in CLIP-seq for motif discovery. Nucleic Acids Res 43:95–103
Polishchuk M, Paz I, Yakhini Z, Mandel-Gutfreund Y (2018) SMARTIV: combined sequence and structure de-novo motif discovery for in-vivo RNA binding data. Nucleic Acids Res 46:W221–W228
Munteanu A, Mukherjee N, Ohler U (2018) SSMART: sequence-structure motif identification for RNA-binding proteins. Bioinformatics 34:3990–3998
Kazan H, Morris Q (2013) RBPmotif: a web server for the discovery of sequence and structure preferences of RNA-binding proteins. Nucleic Acids Res 41:W180–W186
Kazan H, Ray D, Chan ET et al (2010) RNAcontext: a new method for learning the sequence and structure binding preferences of RNA-binding proteins. PLoS Comput Biol 6:e1000832
Cereda M, Pozzoli U, Rot G et al (2014) RNAmotifs: prediction of multivalent RNA motifs that control alternative splicing. Genome Biol 15:R20
Conesa A, Madrigal P, Tarazona S et al (2016) A survey of best practices for RNA-seq data analysis. Genome Biol 17:13
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Destefanis, E., Dassi, E. (2022). Introduction to Bioinformatics Resources for Post-transcriptional Regulation of Gene Expression . In: Dassi, E. (eds) Post-Transcriptional Gene Regulation. Methods in Molecular Biology, vol 2404. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-1851-6_1
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