ArticleRNA (New York, N.Y.)2010
Computational prediction of Caenorhabditis box H/ACA snoRNAs using genomic properties of their host genes.
Article in RNA (New York, N.Y.), 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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
5 citing papers in PubMed, 8 citations in OpenAlex.
- Article
- Sequencing of individual barcoded cDNAs using Pacific Biosciences and Oxford Nanopore Technologies reveals platform-specific error patterns.Genome research · 2022Article
- H/ACA Small Ribonucleoproteins: Structural and Functional Comparison Between Archaea and Eukaryotes.Frontiers in microbiology · 2021Review
- Family size and turnover rates among several classes of small non-protein-coding RNA genes in Caenorhabditis nematodes.Genome biology and evolution · 2012Article
- The nucleolus of Caenorhabditis elegans.Journal of biomedicine & biotechnology · 2012Review
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
Identification of small nucleolar RNAs (snoRNAs) in genomic sequences has been challenging due to the relative paucity of sequence features. Many current prediction algorithms rely on detection of snoRNA motifs complementary to target sites in snRNAs and rRNAs. However, recent discovery of snoRNAs without apparent targets requires development of alternative prediction methods. We present an approach that combines rule-based filters and a Bayesian Classifier to identify a class of snoRNAs (H/ACA) without requiring target sequence information. It takes advantage of unique attributes of their genomic organization and improved species-specific motif characterization to predict snoRNAs that may otherwise be difficult to discover. Searches in the genomes of Caenorhabditis elegans and the closely related Caenorhabditis briggsae suggest that our method performs well compared to recent benchmark algorithms. Our results illustrate the benefits of training gene discovery engines on features restricted to particular phylogenetic groups and the utility of incorporating diverse data types in gene prediction.
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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.