ArticleBriefings in bioinformatics2013
Detecting miRNAs in deep-sequencing data: a software performance comparison and evaluation.
Article in Briefings in bioinformatics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
What it found
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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.
The trial behind it
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
20 citing papers in PubMed.
- A semi-supervised machine learning framework for microRNA classification.Human genomics · 2019Article
- miPIE: NGS-based Prediction of miRNA Using Integrated Evidence.Scientific reports · 2019Article
- Article
- Small RNA-Seq reveals novel miRNAs shaping the transcriptomic identity of rat brain structures.Life science alliance · 2018Article
- Plasma small ncRNA pair panels as novel biomarkers for early-stage lung adenocarcinoma screening.BMC genomics · 2018Article
- miRCat2: accurate prediction of plant and animal microRNAs from next-generation sequencing datasets.Bioinformatics (Oxford, England) · 2017Article
- Evaluation of microRNA alignment techniques.RNA (New York, N.Y.) · 2016Article
- miARma-Seq: a comprehensive tool for miRNA, mRNA and circRNA analysis.Scientific reports · 2016Article
- miRNA Digger: a comprehensive pipeline for genome-wide novel miRNA mining.Scientific reports · 2016Article
- SePIA: RNA and small RNA sequence processing, integration, and analysis.BioData mining · 2016Article
- miRA: adaptable novel miRNA identification in plants using small RNA sequencing data.BMC bioinformatics · 2015Article
- MiR expression profiles of paired primary colorectal cancer and metastases by next-generation sequencing.Oncogenesis · 2015Article
- Computational Prediction of miRNA Genes from Small RNA Sequencing Data.Frontiers in bioengineering and biotechnology · 2015Review
- New Approaches to Comparative and Animal Stress Biology Research in the Post-genomic Era: A Contextual Overview.Computational and structural biotechnology journal · 2014Review
- MicroRNA expression profiling of the fifth-instar posterior silk gland of Bombyx mori.BMC genomics · 2014Article
- miRdentify: high stringency miRNA predictor identifies several novel animal miRNAs.Nucleic acids research · 2014Article
- Accuracy of microRNA discovery pipelines in non-model organisms using closely related species genomes.PloS one · 2014Article
- iMir: an integrated pipeline for high-throughput analysis of small non-coding RNA data obtained by smallRNA-Seq.BMC bioinformatics · 2013Article
- MicroRNA discovery by similarity search to a database of RNA-seq profiles.Frontiers in genetics · 2013Article
- Finding microRNA targets in plants: current status and perspectives.Genomics, proteomics & bioinformatics · 2012Review
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Deep sequencing has become a popular tool for novel miRNA detection but its data must be viewed carefully as the state of the field is still undeveloped. Using three different programs, miRDeep (v1, 2), miRanalyzer and DSAP, we have analyzed seven data sets (six biological and one simulated) to provide a critical evaluation of the programs performance. We selected these software based on their popularity and overall approach toward the detection of novel and known miRNAs using deep-sequencing data. The program comparisons suggest that, despite differing stringency levels they all identify a similar set of known and novel predictions. Comparisons between the first and second version of miRDeep suggest that the stringency level of each of these programs may, in fact, be a result of the algorithm used to map the reads to the target. Different stringency levels are likely to affect the number of possible novel candidates for functional verification, causing undue strain on resources and time. With that in mind, we propose that an intersection across multiple programs be taken, especially if considering novel candidates that will be targeted for additional analysis. Using this approach, we identify and performed initial validation of 12 novel predictions in our in-house data with real-time PCR, six of which have been previously unreported.
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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.