ReviewFrontiers in bioengineering and biotechnology2015
Computational Prediction of miRNA Genes from Small RNA Sequencing Data.
Review in Frontiers in bioengineering and biotechnology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
16 citing papers in PubMed, 43 citations in OpenAlex.
- Description Generation Using Variational Auto-Encoders for Precursor microRNA.Entropy (Basel, Switzerland) · 2024Article
- Bladder cancer: non-coding RNAs and exosomal non-coding RNAs.Functional & integrative genomics · 2024Review
- Distinct small RNAs are expressed at different stages ofFrontiers in genetics · 2024Article
- MicroRNA and Hemophilia-A Disease: Bioinformatics Prediction and Experimental Analysis.Cell journal · 2021Article
- Review
- MicroRNAs and target molecules in bladder cancer.Medical oncology (Northwood, London, England) · 2020Review
- Small RNA-Seq reveals novel miRNAs shaping the transcriptomic identity of rat brain structures.Life science alliance · 2018Article
- FLI1 and PKC co-activation promote highly efficient differentiation of human embryonic stem cells into endothelial-like cells.Cell death & disease · 2018Article
- A comprehensive, cell specific microRNA catalogue of human peripheral blood.Nucleic acids research · 2017Article
- miRCat2: accurate prediction of plant and animal microRNAs from next-generation sequencing datasets.Bioinformatics (Oxford, England) · 2017Article
- miRmine: a database of human miRNA expression profiles.Bioinformatics (Oxford, England) · 2017Article
- Article
- Tools4miRs - one place to gather all the tools for miRNA analysis.Bioinformatics (Oxford, England) · 2016Article
- A Comprehensive Prescription for Plant miRNA Identification.Frontiers in plant science · 2016Article
- Applications of Next-generation Sequencing in Systemic Autoimmune Diseases.Genomics, proteomics & bioinformatics · 2015Review
- Editorial: Bioinformatics of Non-Coding RNAs with Applications to Biomedicine: Recent Advances and Open Challenges.Frontiers in bioengineering and biotechnology · 2015Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Next-generation sequencing now for the first time allows researchers to gage the depth and variation of entire transcriptomes. However, now as rare transcripts can be detected that are present in cells at single copies, more advanced computational tools are needed to accurately annotate and profile them. microRNAs (miRNAs) are 22 nucleotide small RNAs (sRNAs) that post-transcriptionally reduce the output of protein coding genes. They have established roles in numerous biological processes, including cancers and other diseases. During miRNA biogenesis, the sRNAs are sequentially cleaved from precursor molecules that have a characteristic hairpin RNA structure. The vast majority of new miRNA genes that are discovered are mined from small RNA sequencing (sRNA-seq), which can detect more than a billion RNAs in a single run. However, given that many of the detected RNAs are degradation products from all types of transcripts, the accurate identification of miRNAs remain a non-trivial computational problem. Here, we review the tools available to predict animal miRNAs from sRNA sequencing data. We present tools for generalist and specialist use cases, including prediction from massively pooled data or in species without reference genome. We also present wet-lab methods used to validate predicted miRNAs, and approaches to computationally benchmark prediction accuracy. For each tool, we reference validation experiments and benchmarking efforts. Last, we discuss the future of the field.
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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.