Evidence map›Paper›PMID 29059285›Full record

ReviewBriefings in bioinformatics2019

Computational tools for plant small RNA detection and categorization.

Lionel Morgado, Frank Johannes

Abstract readReview
In one paragraph

Review in Briefings in bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing 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

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.

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

13 citing papers in PubMed.

  1. Review
  2. Article
  3. Review
  4. Review
  5. Article
  6. Article
  7. Interactive Web-based Annotation of Plant MicroRNAs with iwa-miRNA.Genomics, proteomics & bioinformatics · 2022
    Article
  8. Role of phasiRNAs in plant-pathogen interactions: molecular perspectives and bioinformatics tools.Physiology and molecular biology of plants : an international journal of functional plant biology · 2022
    Review
  9. Understanding small ORF diversity through a comprehensive transcription feature classification.DNA research : an international journal for rapid publication of reports on genes and genomes · 2021
    Review
  10. Review
  11. Article
  12. Article
  13. Review
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

2 authors.

Lionel Morgado
Frank Johannes

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Small RNAs (sRNAs) are important short-length molecules with regulatory functions essential for plant development and plasticity. High-throughput sequencing of total sRNA populations has revealed that the largest share of sRNA remains uncategorized. To better understand the role of sRNA-mediated cellular regulation, it is necessary to create accurate and comprehensive catalogues of sRNA and their sequence features, a task that currently relies on nontrivial bioinformatic approaches. Although a large number of computational tools have been developed to predict features of sRNA sequences, these tools are mostly dedicated to microRNAs and none integrates the functionalities necessary to describe units from all sRNA pathways thus far discovered in plants. Here, we review the different classes of sRNA found in plants and describe available bioinformatics tools that can help in their detection and categorization.

Indexed as

AlgorithmsComputational BiologyHigh-Throughput Nucleotide SequencingMachine LearningMicroRNAsPlantsRNA, PlantRNA, Small InterferingSequence Analysis, RNASoftwareMicroRNAsRNA, PlantRNA, Small Interferingsmall RNA categorizationsRNA function predictionsRNA sequencingsRNA structural features

Identifiers

PMID29059285
PMCPMC6781577

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

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.