Evidence map›Paper›PMID 42026820›Full record

ArticleMolecular ecology resources2026

SPrUCE: Utilizing Ultraconserved Elements of DNA for Population-Level Genetic Diversity Estimation.

Daira Melendez, Ali Osman Berk Şapcı, Vineet Bafna, Siavash Mirarab

Abstract read
In one paragraph

Article in Molecular ecology resources, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. DipSkmer: Reference-free population genomics with diploid genome skims.bioRxiv : the preprint server for biology · 2026
    Article
  2. 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

4 authors.

Daira MelendezBioinformatics and Systems Biology Graduate Program, UC San Diego, San Diego, California, USA.ORCID https://orcid.org/0000-0002-4264-2138
Ali Osman Berk ŞapcıBioinformatics and Systems Biology Graduate Program, UC San Diego, San Diego, California, USA.
Vineet BafnaDepartment of Computer Science and Engineering, UC San Diego, San Diego, California, USA.
Siavash MirarabDepartment of Electrical and Computer Engineering, UC San Diego, San Diego, California, USA.ORCID https://orcid.org/0000-0001-5410-1518

Funding

Computational methods for detecting patterns of complex genomic variationR01GM114362 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI Vineet Bafna · 2016 to 2026
$3.1M
Minderoo FoundationNIGMS NIH HHS R01 GM114362
6 · The paper itself

Abstract

Ultraconserved elements (UCEs) provide ideal candidates for targeted sequencing and cost-effective acquisition of genome-wide data. While UCEs have been widely used in phylogenetic studies to reconstruct evolutionary relationships, their use in population-level research has been limited. This limited application stems from uncertainty over whether UCEs can capture the levels of genetic variation needed to answer population genomic questions central to ecology and biodiversity research. The concern is that, by definition, UCEs are highly conserved and may therefore lack sufficient within-species variation. The more variable flanking regions (400-750 bp from the UCE core) contain informative polymorphisms, though diversity decreases near the core. Thus, any naive estimator of genetic diversity that ignores this conservation will have an underestimation bias. In this paper, we introduce SPrUCE: Sigmoid Pi requiring UCEs, a reference-free method that estimates nucleotide diversity

Indexed as

Computational BiologyConserved SequenceGenetics, PopulationGenetic VariationAnimalsInvertebratesSequence Analysis, DNAVertebratesconservation geneticsnucleotide diversitypopulation genomicsultraconserved elements

Identifiers

PMID42026820
PMCPMC13106921

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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.