ReviewBiology direct2015
RNA motif discovery: a computational overview.
Review in Biology direct, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 21 citations in OpenAlex.
- Predicting Capsid Protein Binding Sites in Single-Stranded RNA Viruses Using Machine Learning from Local Geometric Features.bioRxiv : the preprint server for biology · 2026Article
- Computational prediction of disease related lncRNAs using machine learning.Scientific reports · 2023Article
- Influence of the mRNA initial region on protein production: a case study using recombinant detoxified pneumolysin as a model.Frontiers in bioengineering and biotechnology · 2023Article
- Interpretation of deep learning in genomics and epigenomics.Briefings in bioinformatics · 2021Review
- Identifying Trypanosome Protein-RNA Interactions Using RIP-Seq.Methods in molecular biology (Clifton, N.J.) · 2020Article
- RNA secondary structure prediction using an ensemble of two-dimensional deep neural networks and transfer learning.Nature communications · 2019Article
- Responsive self-assembly of tectoRNAs with loop-receptor interactions from the tetrahydrofolate (THF) riboswitch.Nucleic acids research · 2019Article
- Genomic data mining for functional annotation of human long noncoding RNAs.Journal of Zhejiang University. Science. B · 2019Review
- Identification of 15 candidate structured noncoding RNA motifs in fungi by comparative genomics.BMC genomics · 2017Article
- Know Your Enemy: Successful Bioinformatic Approaches to Predict Functional RNA Structures in Viral RNAs.Frontiers in microbiology · 2017Review
- A novel method for the identification of conserved structural patterns in RNA: From small scale to high-throughput applications.Nucleic acids research · 2016Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Genomic studies have greatly expanded our knowledge of structural non-coding RNAs (ncRNAs). These RNAs fold into characteristic secondary structures and perform specific-structure dependent biological functions. Hence RNA secondary structure prediction is one of the most well studied problems in computational RNA biology. Comparative sequence analysis is one of the more reliable RNA structure prediction approaches as it exploits information of multiple related sequences to infer the consensus secondary structure. This class of methods essentially learns a global secondary structure from the input sequences. In this paper, we consider the more general problem of unearthing common local secondary structure based patterns from a set of related sequences. The input sequences for example could correspond to 3(') or 5(') untranslated regions of a set of orthologous genes and the unearthed local patterns could correspond to regulatory motifs found in these regions. These sequences could also correspond to in vitro selected RNA, genomic segments housing ncRNA genes from the same family and so on. Here, we give a detailed review of the various computational techniques proposed in literature attempting to solve this general motif discovery problem. We also give empirical comparisons of some of the current state of the art methods and point out future directions of research.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.