ArticleNucleic acids research2017
ssHMM: extracting intuitive sequence-structure motifs from high-throughput RNA-binding protein data.
Article in Nucleic acids research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Towards in silico CLIP-seq: predicting protein-RNA interaction via sequence-to-signal learning.Genome biology · 2023Article
- RNANetMotif: Identifying sequence-structure RNA network motifs in RNA-protein binding sites.PLoS computational biology · 2022Article
- Dual Attention Mechanisms and Feature Fusion Networks Based Method for Predicting LncRNA-Disease Associations.Interdisciplinary sciences, computational life sciences · 2022Article
- RNALigands: a database and web server for RNA-ligand interactions.RNA (New York, N.Y.) · 2022Article
- The nuclear transcription factor, TAF7, is a cytoplasmic regulator of protein synthesis.Science advances · 2021Article
- Spatial correlation statistics enable transcriptome-wide characterization of RNA structure binding.Cell reports methods · 2021Article
- HMGB1 coordinates SASP-related chromatin folding and RNA homeostasis on the path to senescence.Molecular systems biology · 2021Article
- Motif Discovery from CLIP Experiments.Methods in molecular biology (Clifton, N.J.) · 2021Article
- Zooming in on protein-RNA interactions: a multi-level workflow to identify interaction partners.Biochemical Society transactions · 2020Review
- Proximity-CLIP and Expedited Non-Radioactive Library Preparation of Small RNA Footprints for Next-Generation Sequencing.Current protocols in molecular biology · 2020Article
- Deep neural networks for interpreting RNA-binding protein target preferences.Genome research · 2020Article
- Functional impacts of non-coding RNA processing on enhancer activity and target gene expression.Journal of molecular cell biology · 2019Review
- Article
- Discovering sequence and structure landscapes in RNA interaction motifs.Nucleic acids research · 2019Article
- Practical considerations on performing and analyzing CLIP-seq experiments to identify transcriptomic-wide RNA-protein interactions.Methods (San Diego, Calif.) · 2019Review
- Computational approaches for the analysis of RNA-protein interactions: A primer for biologists.The Journal of biological chemistry · 2019Review
- Identification and characterization of novel conserved RNA structures in Drosophila.BMC genomics · 2018Article
- Proximity-CLIP provides a snapshot of protein-occupied RNA elements in subcellular compartments.Nature methods · 2018Article
- pysster: classification of biological sequences by learning sequence and structure motifs with convolutional neural networks.Bioinformatics (Oxford, England) · 2018Article
- Data Science Issues in Studying Protein-RNA Interactions with CLIP Technologies.Annual review of biomedical data science · 2018Article
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5 authors.
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Abstract
RNA-binding proteins (RBPs) play an important role in RNA post-transcriptional regulation and recognize target RNAs via sequence-structure motifs. The extent to which RNA structure influences protein binding in the presence or absence of a sequence motif is still poorly understood. Existing RNA motif finders either take the structure of the RNA only partially into account, or employ models which are not directly interpretable as sequence-structure motifs. We developed ssHMM, an RNA motif finder based on a hidden Markov model (HMM) and Gibbs sampling which fully captures the relationship between RNA sequence and secondary structure preference of a given RBP. Compared to previous methods which output separate logos for sequence and structure, it directly produces a combined sequence-structure motif when trained on a large set of sequences. ssHMM's model is visualized intuitively as a graph and facilitates biological interpretation. ssHMM can be used to find novel bona fide sequence-structure motifs of uncharacterized RBPs, such as the one presented here for the YY1 protein. ssHMM reaches a high motif recovery rate on synthetic data, it recovers known RBP motifs from CLIP-Seq data, and scales linearly on the input size, being considerably faster than MEMERIS and RNAcontext on large datasets while being on par with GraphProt. It is freely available on Github and as a Docker image.
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