Evidence map›Paper›PMID 40115535›Full record

ReviewComputational and structural biotechnology journal2025

Fast detection of unique genomic regions.

Beatriz Vieira Mourato, Bernhard Haubold

Abstract readReview
In one paragraph

Review in Computational and structural biotechnology journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

2 authors.

Beatriz Vieira MouratoResearch Group Bioinformatics, Max-Planck-Institute for Evolutionary Biology, August-Thienemann-Str. 2, 24306, Plön, Germany.
Bernhard HauboldResearch Group Bioinformatics, Max-Planck-Institute for Evolutionary Biology, August-Thienemann-Str. 2, 24306, Plön, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Unique genomic regions are of particular interest in two scenarios: When extracted from a single mammalian target genome, they are highly enriched for developmental genes. When extracted from target genomes compared to closely related neighbor genomes, they are highly enriched for diagnostic markers. Despite their biological importance and potential economic value, unique regions remain difficult to detect from whole genome sequences. In this review we survey three efficient programs for the detection of unique regions at scale, genmap, macle, and fur. We explain these programs and demonstrate their application by analyzing simulated and real data. Example scripts for searching for unique regions are available from the Github repository evolbioinf/sure as part of a detailed tutorial.

Identifiers

PMID40115535
PMCPMC11925158

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.