Evidence map›Paper›PMID 26923229›Full record

ArticleBMC evolutionary biology2016

Profiles of low complexity regions in Apicomplexa.

Fabia U Battistuzzi, Kristan A Schneider, Matthew K Spencer, David Fisher, Sophia Chaudhry, Ananias A Escalante

Open access · goldAbstract read
In one paragraph

Article in BMC evolutionary biology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
1.9field-weighted citation impact, top 14% of its field
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

15 citing papers in PubMed, 27 citations in OpenAlex.

  1. Review
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  9. Review
  10. Article
  11. Article
  12. Review
  13. Genomics of apicomplexan parasites.Critical reviews in biochemistry and molecular biology · 2017
    Review
  14. Evolution of the merozoite surface protein 7 (msp7) family in Plasmodium vivax and P. falciparum: A comparative approach.Infection, genetics and evolution : journal of molecular epidemiology and evolutionary genetics in infectious diseases · 2017
    Article
  15. 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

6 authors at 5 institutions in 2 countries.

Fabia U BattistuzziDepartment of Biological Sciences, Oakland University, Rochester, MI, USA. battistu@oakland.edu.
Kristan A SchneiderDepartment of MNI, University of Applied Sciences Mittweida, Mittweida, Germany. schneid2@hs-mittweida.de.
Matthew K SpencerDepartment of Geology and Physics, Lake Superior State University, Sault Ste. Marie, MI, USA. mspencer1@lssu.edu.
David FisherDavid Eccles School of Business, University of Utah, Salt Lake City, UT, USA. david.fisher@hsc.utah.edu.
Sophia ChaudhryDepartment of Biological Sciences, Oakland University, Rochester, MI, USA. sophia.chaudhry@wayne.edu.
Ananias A EscalanteInstitute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA, USA. ananias.escalante@temple.edu.
Oakland University · USHochschule Mittweida · DELake Superior State University · USTemple University · USUniversity of Utah · US

Funding

Evolution of Plasmodium vivax and Asian Macaque MalariasR01GM080586 · NIGMS · ARIZONA STATE UNIVERSITY-TEMPE CAMPUS · PI ESCALANTE, ANANIAS ALBERTO · 2008 to 2011
$1.2M
NIGMS NIH HHS R01 GM080586NIGMS NIH HHS R01GM080586
6 · The paper itself

Abstract

backgroundLow complexity regions (LCRs) are a ubiquitous feature in genomes and yet their evolutionary history and functional roles are unclear. Previous studies have shown contrasting evidence in favor of both neutral and selective mechanisms of evolution for different sets of LCRs suggesting that modes of identification of these regions may play a role in our ability to discern their evolutionary history. To further investigate this issue, we used a multiple threshold approach to identify species-specific profiles of proteome complexity and, by comparing properties of these sets, determine the influence that starting parameters have on evolutionary inferences.

resultsWe find that, although qualitatively similar, quantitatively each species has a unique LCR profile which represents the frequency of these regions within each genome. Inferences based on these profiles are more accurate in comparative analyses of genome complexity as they allow to determine the relative complexity of multiple genomes as well as the type of repetitiveness that is most common in each. Based on the multiple threshold LCR sets obtained, we identified predominant evolutionary mechanisms at different complexity levels, which show neutral mechanisms acting on highly repetitive LCRs (e.g., homopolymers) and selective forces becoming more important as heterogeneity of the LCRs increases.

conclusionsOur results show how inferences based on LCRs are influenced by the parameters used to identify these regions. Sets of LCRs are heterogeneous aggregates of regions that include homo- and heteropolymers and, as such, evolve according to different mechanisms. LCR profiles provide a new way to investigate genome complexity across species and to determine the driving mechanism of their evolution.

Indexed as

Evolution, MolecularGenome, ProtozoanApicomplexaBase CompositionComputational BiologyLinear ModelsRepetitive Sequences, Nucleic AcidSpecies Specificity

Identifiers

PMID26923229
PMCPMC4770516
OpenAlexW2291280894

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.