ArticleNucleic acids research2017
Mirnovo: genome-free prediction of microRNAs from small RNA sequencing data and single-cells using decision forests.
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 32 papers.
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Who cites it
32 citing papers in PubMed.
- DB-IRES: a deep learning model based on ensemble learning for predicting internal ribosome entry sites.BMC bioinformatics · 2026Article
- Technical considerations and review of urinary microRNAs as biomarkers for chronic kidney disease in dogs and cats.Veterinary clinical pathology · 2025Review
- Review
- Class-agnostic annotation of small RNAs balances sensitivity and specificity in diverse organisms.Computational and structural biotechnology journal · 2025Article
- Analysis ofbioRxiv : the preprint server for biology · 2024Article
- Analysis ofFrontiers in cellular and infection microbiology · 2024Article
- MyBrain-Seq: A Pipeline for MiRNA-Seq Data Analysis in Neuropsychiatric Disorders.Biomedicines · 2023Article
- Non-coding RNAs in human health and disease: potential function as biomarkers and therapeutic targets.Functional & integrative genomics · 2023Review
- BrumiR: A toolkit for de novo discovery of microRNAs from sRNA-seq data.GigaScience · 2022Article
- The Multiverse of Plant Small RNAs: How Can We Explore It?International journal of molecular sciences · 2022Review
- Biogenesis, Functions, Interactions, and Resources of Non-Coding RNAs in Plants.International journal of molecular sciences · 2022Review
- Differences in PLAToxins · 2022Article
- gga-miRNOME, a microRNA-sequencing dataset from chick embryonic tissues.Scientific data · 2022Article
- A Unified Computational Framework for a Robust, Reliable, and Reproducible Identification of Novel miRNAs From the RNA Sequencing Data.Frontiers in bioinformatics · 2022Article
- MicroRNA-mediated bioengineering for climate-resilience in crops.Bioengineered · 2021Review
- Article
- Evaluation and application of tools for the identification of known microRNAs in plants.Applications in plant sciences · 2021Review
- Article
- Fast and accurate microRNA search using CNN.BMC bioinformatics · 2019Article
- A semi-supervised machine learning framework for microRNA classification.Human genomics · 2019Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
The discovery of microRNAs (miRNAs) remains an important problem, particularly given the growth of high-throughput sequencing, cell sorting and single cell biology. While a large number of miRNAs have already been annotated, there may well be large numbers of miRNAs that are expressed in very particular cell types and remain elusive. Sequencing allows us to quickly and accurately identify the expression of known miRNAs from small RNA-Seq data. The biogenesis of miRNAs leads to very specific characteristics observed in their sequences. In brief, miRNAs usually have a well-defined 5' end and a more flexible 3' end with the possibility of 3' tailing events, such as uridylation. Previous approaches to the prediction of novel miRNAs usually involve the analysis of structural features of miRNA precursor hairpin sequences obtained from genome sequence. We surmised that it may be possible to identify miRNAs by using these biogenesis features observed directly from sequenced reads, solely or in addition to structural analysis from genome data. To this end, we have developed mirnovo, a machine learning based algorithm, which is able to identify known and novel miRNAs in animals and plants directly from small RNA-Seq data, with or without a reference genome. This method performs comparably to existing tools, however is simpler to use with reduced run time. Its performance and accuracy has been tested on multiple datasets, including species with poorly assembled genomes, RNaseIII (Drosha and/or Dicer) deficient samples and single cells (at both embryonic and adult stage).
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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.