Evidence map›Paper›PMID 37077974›Full record

ArticleQuantitative plant biology2022

NCodR: A multi-class support vector machine classification to distinguish non-coding RNAs in Viridiplantae.

Chandran Nithin, Sunandan Mukherjee, Jolly Basak, Ranjit Prasad Bahadur

Abstract read
In one paragraph

Article in Quantitative plant biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Chandran NithinComputational Structural Biology Lab, Department of Biotechnology, Indian Institute of Technology, Kharagpur 721302, India.ORCID https://orcid.org/0000-0001-8212-6093
Sunandan MukherjeeComputational Structural Biology Lab, Department of Biotechnology, Indian Institute of Technology, Kharagpur 721302, India.ORCID https://orcid.org/0000-0002-4361-0103
Jolly BasakDepartment of Biotechnology, Visva-Bharati, Santiniketan, 731235, India.
Ranjit Prasad BahadurComputational Structural Biology Lab, Department of Biotechnology, Indian Institute of Technology, Kharagpur 721302, India.ORCID https://orcid.org/0000-0002-6705-1713

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Non-coding RNAs (ncRNAs) are major players in the regulation of gene expression. This study analyses seven classes of ncRNAs in plants using sequence and secondary structure-based RNA folding measures. We observe distinct regions in the distribution of AU content along with overlapping regions for different ncRNA classes. Additionally, we find similar averages for minimum folding energy index across various ncRNAs classes except for pre-miRNAs and lncRNAs. Various RNA folding measures show similar trends among the different ncRNA classes except for pre-miRNAs and lncRNAs. We observe different k-mer repeat signatures of length three among various ncRNA classes. However, in pre-miRs and lncRNAs, a diffuse pattern of k-mers is observed. Using these attributes, we train eight different classifiers to discriminate various ncRNA classes in plants. Support vector machines employing radial basis function show the highest accuracy (average F1 of ~96%) in discriminating ncRNAs, and the classifier is implemented as a web server, NCodR.

Indexed as

k-mer repeatsncRNA predictionnon-coding RNARNA foldingSVM classifier

Identifiers

PMID37077974
PMCPMC10095871

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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.