Evidence map›Paper›PMID 34215180›Full record

ArticleBMC genomics2021

Modelling segmental duplications in the human genome.

Eldar T Abdullaev, Iren R Umarova, Peter F Arndt

Open access · goldAbstract read
In one paragraph

Article in BMC genomics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
3.1field-weighted citation impact, top 9% 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

12 citing papers in PubMed, 21 citations in OpenAlex.

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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

3 authors at 2 institutions in 2 countries.

Eldar T AbdullaevDepartment of Computational Molecular Biology, Max Planck Institute for Molecular Genetics, Ihnestraße 63/73, Berlin, 14195, Germany. abdullae@molgen.mpg.de.ORCID http://orcid.org/0000-0001-9432-664X
Iren R UmarovaFaculty of Computational Mathematics and Cybernetics, Moscow State University, Leninskiye Gory 1-52, Moscow, 119991, Russia.
Peter F ArndtDepartment of Computational Molecular Biology, Max Planck Institute for Molecular Genetics, Ihnestraße 63/73, Berlin, 14195, Germany.
Max Planck Institute for Molecular Genetics · DELomonosov Moscow State University · RU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSegmental duplications (SDs) are long DNA sequences that are repeated in a genome and have high sequence identity. In contrast to repetitive elements they are often unique and only sometimes have multiple copies in a genome. There are several well-studied mechanisms responsible for segmental duplications: non-allelic homologous recombination, non-homologous end joining and replication slippage. Such duplications play an important role in evolution, however, we do not have a full understanding of the dynamic properties of the duplication process.

resultsWe study segmental duplications through a graph representation where nodes represent genomic regions and edges represent duplications between them. The resulting network (the SD network) is quite complex and has distinct features which allow us to make inference on the evolution of segmantal duplications. We come up with the network growth model that explains features of the SD network thus giving us insights on dynamics of segmental duplications in the human genome. Based on our analysis of genomes of other species the network growth model seems to be applicable for multiple mammalian genomes.

conclusionsOur analysis suggests that duplication rates of genomic loci grow linearly with the number of copies of a duplicated region. Several scenarios explaining such a preferential duplication rates were suggested.

Indexed as

Genome, HumanSegmental Duplications, GenomicAnimalsEvolution, MolecularGene DuplicationGenomicsHumansComplex networksSDsSegmental duplications

Identifiers

PMID34215180
PMCPMC8254307
OpenAlexW3173731554

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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.