Evidence map›Paper›PMID 39575229›Full record

ArticleBioinformatics advances2024

ULTRA-effective labeling of tandem repeats in genomic sequence.

Daniel R Olson, Travis J Wheeler

Abstract read
In one paragraph

Article in Bioinformatics advances, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

  1. Article
  2. Article
  3. Finding low-complexity DNA sequences with longdust.Bioinformatics (Oxford, England) · 2026
    Article
  4. Article
  5. Article
  6. Article
  7. NEAR: neural embeddings for amino acid relationships.Bioinformatics (Oxford, England) · 2025
    Article
  8. Article
  9. NEAR: Neural Embeddings for Amino acid Relationships.bioRxiv : the preprint server for biology · 2025
    Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

2 authors.

Daniel R OlsonDepartment of Computer Science, University of Montana, Missoula, MT 59812, United States.ORCID https://orcid.org/0000-0002-3681-5944
Travis J WheelerDepartment of Computer Science, University of Montana, Missoula, MT 59812, United States.ORCID https://orcid.org/0000-0003-2004-1785

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
Maintenance and Development of RepeatMasker and RepeatModelerR01HG002939 · NHGRI · INSTITUTE FOR SYSTEMS BIOLOGY · PI Robert MacDonald Hubley, Arian Fredericus Anthonius Smit · 2003 to 2026
$9.1M
Dfam: sustainable growth, curation support, and improved quality for mobile element annotationU24HG010136 · NHGRI · INSTITUTE FOR SYSTEMS BIOLOGY · PI Robert MacDonald Hubley, Arian Fredericus Anthonius Smit · 2018 to 2026
$5.2M
Machine learning approaches for improved accuracy and speed in sequence annotation: supplement for software enhancementR01GM132600 · NIGMS · UNIVERSITY OF MONTANA · PI WHEELER, TRAVIS JOHN · 2019 to 2022
$1.4M
NHGRI NIH HHS R01 HG002939NHGRI NIH HHS U24 HG010136NIGMS NIH HHS P20 GM103546NIGMS NIH HHS R01 GM132600
6 · The paper itself

Abstract

In the age of long read sequencing, genomics researchers now have access to accurate repetitive DNA sequence (including satellites) that, due to the limitations of short read-sequencing, could previously be observed only as unmappable fragments. Tools that annotate repetitive sequence are now more important than ever, so that we can better understand newly uncovered repetitive sequences, and also so that we can mitigate errors in bioinformatic software caused by those repetitive sequences. To that end, we introduce the 1.0 release of our tool for identifying and annotating locally repetitive sequence, Availability and implementation: ULTRA is released under an open source license, and is available for download at https://github.com/TravisWheelerLab/ULTRA.

Identifiers

PMID39575229
PMCPMC11580682

What OpenQuestion holds

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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.