Evidence map›Paper›PMID 31080962›Full record

ArticleACM-BCB ... ... : the ... ACM Conference on Bioinformatics, Computational Biology and Biomedicine. ACM Conference on Bioinformatics, Computational Biology and Biomedicine

ULTRA: A Model Based Tool to Detect Tandem Repeats.

Daniel Olson, Travis Wheeler

Abstract read
In one paragraph

Article in ACM-BCB ... ... : the ... ACM Conference on Bioinformatics, Computational Biology and Biomedicine. ACM Conference on Bioinformatics, Computational Biology and Biomedicine. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.

0numbers the graph read from it
0cells of the map it votes in
25citing 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

25 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Article
  6. Article
  7. ULTRA-Effective Labeling of Repetitive Genomic Sequence.bioRxiv : the preprint server for biology · 2024
    Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Review
  17. Article
  18. Review
  19. Article
  20. 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.

Daniel OlsonUniversity of Montana, Missoula, Montana daniel.olson@umontana.edu.
Travis WheelerUniversity of Montana, Missoula, Montana travis.wheeler@umontana.edu.

Funding

Surveillance genome sequencing to detect SARS-CoV-2 virus variants in MontanaP20GM103546 · NIGMS · UNIVERSITY OF MONTANA · PI BOWLER, BRUCE E · 2012 to 2021
$19.2M
Improved protein-DNA models for translated sequence search with profile HMMsR15GM123487 · NIGMS · UNIVERSITY OF MONTANA · PI WHEELER, TRAVIS JOHN · 2017 to 2017
$426k
NIGMS NIH HHS P20 GM103546NIGMS NIH HHS R15 GM123487
6 · The paper itself

Abstract

In biological sequences, tandem repeats consist of tens to hundreds of residues of a repeated pattern, such as atgatgatgatgatg ('atg' repeated), often the result of replication slippage. Over time, these repeats decay so that the original sharp pattern of repetition is somewhat obscured, but even degenerate repeats pose a problem for sequence annotation: when two sequences both contain shared patterns of similar repetition, the result can be a false signal of sequence homology. We describe an implementation of a new hidden Markov model for detecting tandem repeats that shows substantially improved sensitivity to labeling decayed repetitive regions, presents low and reliable false annotation rates across a wide range of sequence composition, and produces scores that follow a stable distribution. On typical genomic sequence, the time and memory requirements of the resulting tool (

Indexed as

Annotation ErrorSequence AlignmentTandem Repeats

Identifiers

PMID31080962
PMCPMC6508075

What OpenQuestion holds

Textmetadata
LicenceTDM
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.