Evidence map›Paper›PMID 20615949›Full record

ArticleProceedings of the National Academy of Sciences of the United States of America2010

A time-invariant principle of genome evolution.

Subhajyoti De, M Madan Babu

Abstract read
In one paragraph

Article in Proceedings of the National Academy of Sciences of the United States of America, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.

0numbers the graph read from it
0cells of the map it votes in
26citing 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

26 citing papers in PubMed.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

2 authors.

Subhajyoti DeMedical Research Council Laboratory of Molecular Biology, Cambridge CB2 0QH, United Kingdom. sde@mrc-lmb.cam.ac.uk
M Madan Babu

Funding

Medical Research Council MC_U105185859
6 · The paper itself

Abstract

Uncovering general principles of genome evolution that are time-invariant and that operate in germ and somatic cells has implications for genome-wide association studies (GWAS), gene therapy, and disease genomics. Here we investigate the relationship between structural alterations (e.g., insertions and deletions) and single-nucleotide substitutions by comparing the following genomes that diverged at different times across germ- and somatic-cell lineages: (i) the reference human and chimpanzee genome (in million years), (ii) the reference human and personal genomes (in tens of thousands of years), and (iii) structurally altered regions in cancer and genetically engineered cells (in days). At the species level, genes with structural alteration in nearby regions show increased single-nucleotide changes and tend to evolve faster. In personal genomes, the single-nucleotide substitution rate is higher near sites of structural alteration and decreases with increasing distance. In human cancer cell populations and in cells genetically engineered using zinc-finger nucleases, single-nucleotide changes occur frequently near sites of structural alterations. We present evidence that structural alteration induces single-nucleotide changes in nearby regions and discuss possible molecular mechanisms that contribute to this phenomenon. We propose that the low fidelity of nonreplicative error-prone repair polymerases, which are used during insertion or deletion, result in break-repair-induced single-nucleotide mutations in the vicinity of structural alteration. Thus, in the mutational landscape, structural alterations are linked to single-nucleotide changes across different time scales in both somatic- and germ-cell lineages. We discuss implications for genome evolution, GWAS, disease genomics, and gene therapy and emphasize the need to investigate both types of mutations within a single framework.

Indexed as

Evolution, MolecularAnimalsBase PairingGenetics, PopulationGenomeHumansMutationPan troglodytesPolymorphism, Single NucleotideSpecies SpecificityTime Factors

Identifiers

PMID20615949
PMCPMC2919972

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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.