Evidence map›Paper›PMID 20144222›Full record

ArticleGenome biology2010

2x genomes--depth does matter.

Michel C Milinkovitch, Raphaël Helaers, Eric Depiereux, Athanasia C Tzika, Toni Gabaldón

Abstract read
In one paragraph

Article in Genome biology, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.

0numbers the graph read from it
0cells of the map it votes in
36citing 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

36 citing papers in PubMed.

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  5. Ensembl comparative genomics resources.Database : the journal of biological databases and curation · 2016
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors.

Michel C MilinkovitchLaboratory of Artificial and Natural Evolution (LANE), Department of Zoology and Animal Biology, Sciences III, 30, Quai Ernest-Ansermet, 1211 Geneva 4, Switzerland. michel.milinkovitch@unige.ch
Raphaël Helaers
Eric Depiereux
Athanasia C Tzika
Toni Gabaldón

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundGiven the availability of full genome sequences, mapping gene gains, duplications, and losses during evolution should theoretically be straightforward. However, this endeavor suffers from overemphasis on detecting conserved genome features, which in turn has led to sequencing multiple eutherian genomes with low coverage rather than fewer genomes with high-coverage and more even distribution in the phylogeny. Although limitations associated with analysis of low coverage genomes are recognized, they have not been quantified.

resultsHere, using recently developed comparative genomic application systems, we evaluate the impact of low-coverage genomes on inferences pertaining to gene gains and losses when analyzing eukaryote genome evolution through gene duplication. We demonstrate that, when performing inference of genome content evolution, low-coverage genomes generate not only a massive number of false gene losses, but also striking artifacts in gene duplication inference, especially at the most recent common ancestor of low-coverage genomes. We show that the artifactual gains are caused by the low coverage of genome sequence per se rather than by the increased taxon sampling in a biased portion of the species tree.

conclusionsWe argue that it will remain difficult to differentiate artifacts from true changes in modes and tempo of genome evolution until there is better homogeneity in both taxon sampling and high-coverage sequencing. This is important for broadening the utility of full genome data to the community of evolutionary biologists, whose interests go well beyond widely conserved physiologies and developmental patterns as they seek to understand the generative mechanisms underlying biological diversity.

Indexed as

Comparative Genomic HybridizationEvolution, MolecularPhylogenyAnimalsCatsDatabases, GeneticDogsFishesGene DuplicationGenomeGuinea PigsHumansInsectaMiceModels, GeneticPrimates

Identifiers

PMID20144222
PMCPMC2872876

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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.