Evidence map›Paper›PMID 29722882›Full record

ArticleBioinformatics (Oxford, England)2018

Modeling one thousand intron length distributions with fitild.

Osamu Gotoh

Abstract read
In one paragraph

Article in Bioinformatics (Oxford, England), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Article
  2. Article
  3. GINGER: an integrated method for high-accuracy prediction of gene structure in higher eukaryotes at the gene and exon level.DNA research : an international journal for rapid publication of reports on genes and genomes · 2023
    Article
  4. Article
  5. Article
  6. Article
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

1 author.

Osamu GotohArtificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology (AIST), AIST Tokyo Waterfront Bio-IT Research Building, Koto-ku, Tokyo, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Motivation: Intron length distribution (ILD) is a specific feature of a genome that exhibits extensive species-specific variation. Whereas ILD contributes to up to 30% of the total information content for intron recognition in some species, rendering it an important component of computational gene prediction, very few studies have been conducted to quantitatively characterize ILDs of various species. Results: We developed a set of computer programs (fitild, compild, etc.) to build statistical models of ILDs and compare them with one another. Each ILD of more than 1000 genomes was fitted with fitild to a statistical model consisting of one, two, or three components of Frechet distributions. Several measures of distances between ILDs were calculated by compild. A theoretical model was presented to better understand the origin of the observed shape of an ILD. Availability and implementation: The C++ source codes are available at https://github.com/ogotoh/fitild.git/. Supplementary information: Supplementary data are available at Bioinformatics online.

Indexed as

GenomeIntronsSoftwareComputational BiologyModels, Statistical

Identifiers

PMID29722882
PMCPMC6157073

What OpenQuestion holds

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LicenceCC BY-NC
Read underepoch 390

Registered trials

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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.