Evidence map›Paper›PMID 33528497›Full record

ArticleMolecular biology and evolution2021

Genome-Scale Profiling Reveals Noncoding Loci Carry Higher Proportions of Concordant Data.

Robert Literman, Rachel Schwartz

Abstract read
In one paragraph

Article in Molecular biology and evolution, 2021. 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. Accurate, scalable, and fully automated inference of species trees from raw genome assemblies using ROADIES.Proceedings of the National Academy of Sciences of the United States of America · 2025
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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.

Robert LitermanDepartment of Biological Sciences, University of Rhode Island, South Kingstown, RI, USA.
Rachel SchwartzDepartment of Biological Sciences, University of Rhode Island, South Kingstown, RI, USA.ORCID 0000-0001-7414-9893

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Many evolutionary relationships remain controversial despite whole-genome sequencing data. These controversies arise, in part, due to challenges associated with accurately modeling the complex phylogenetic signal coming from genomic regions experiencing distinct evolutionary forces. Here, we examine how different regions of the genome support or contradict well-established relationships among three mammal groups using millions of orthologous parsimony-informative biallelic sites (PIBS) distributed across primate, rodent, and Pecora genomes. We compared PIBS concordance percentages among locus types (e.g. coding sequences (CDS), introns, intergenic regions), and contrasted PIBS utility over evolutionary timescales. Sites derived from noncoding sequences provided more data and proportionally more concordant sites compared with those from CDS in all clades. CDS PIBS were also predominant drivers of tree incongruence in two cases of topological conflict. PIBS derived from most locus types provided surprisingly consistent support for splitting events spread across the timescales we examined, although we find evidence that CDS and intronic PIBS may, respectively and to a limited degree, inform disproportionately about older and younger splits. In this era of accessible wholegenome sequence data, these results:1) suggest benefits to more intentionally focusing on noncoding loci as robust data for tree inference and 2) reinforce the importance of accurate modeling, especially when using CDS data.

Indexed as

GenomePhylogenyAnimalsHumansMammalsRNA, UntranslatedWhole Genome SequencingRNA, Untranslatedbioinformaticsgenomicsphylogenetics

Identifiers

PMID33528497
PMCPMC8136493

What OpenQuestion holds

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LicenceCC BY-NC
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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.