Evidence map›Paper›PMID 20836842›Full record

ArticleBMC evolutionary biology2010

An efficient method to find potentially universal population genetic markers, applied to metazoans.

Anne Chenuil, Thierry B Hoareau, Emilie Egea, Gwilherm Penant, Caroline Rocher, Didier Aurelle, Kenza Mokhtar-Jamai, John D D Bishop, Emilie Boissin, Angie Diaz and 5 more

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Article in BMC evolutionary biology, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

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3 · Its place in the literature

Who cites it

8 citing papers in PubMed.

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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

15 authors.

Anne ChenuilAix-Marseille Université, Laboratoire DIMAR, CNRS UMR6540, rue de la batterie des Lions, 13007 Marseille, France. anne.chenuil-maurel@univmed.fr
Thierry B Hoareau
Emilie Egea
Gwilherm Penant
Caroline Rocher
Didier Aurelle
Kenza Mokhtar-Jamai
John D D Bishop
Emilie Boissin
Angie Diaz
Manuela Krakau
Pieternella C Luttikhuizen
Francesco P Patti
Nicolas Blavet
Sylvain Mousset

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDespite the impressive growth of sequence databases, the limited availability of nuclear markers that are sufficiently polymorphic for population genetics and phylogeography and applicable across various phyla restricts many potential studies, particularly in non-model organisms. Numerous introns have invariant positions among kingdoms, providing a potential source for such markers. Unfortunately, most of the few known EPIC (Exon Primed Intron Crossing) loci are restricted to vertebrates or belong to multigenic families.

resultsIn order to develop markers with broad applicability, we designed a bioinformatic approach aimed at avoiding multigenic families while identifying intron positions conserved across metazoan phyla. We developed a program facilitating the identification of EPIC loci which allowed slight variation in intron position. From the Homolens databases we selected 29 gene families which contained 52 promising introns for which we designed 93 primer pairs. PCR tests were performed on several ascidians, echinoderms, bivalves and cnidarians. On average, 24 different introns per genus were amplified in bilaterians. Remarkably, five of the introns successfully amplified in all of the metazoan genera tested (a dozen genera, including cnidarians). The influence of several factors on amplification success was investigated. Success rate was not related to the phylogenetic relatedness of a taxon to the groups that most influenced primer design, showing that these EPIC markers are extremely conserved in animals.

conclusionsOur new method now makes it possible to (i) rapidly isolate a set of EPIC markers for any phylum, even outside the animal kingdom, and thus, (ii) compare genetic diversity at potentially homologous polymorphic loci between divergent taxa.

Indexed as

AnimalsComputational BiologyGenetic MarkersIntronsPhylogenyPolymerase Chain ReactionPolymorphism, GeneticGenetic Markers

Identifiers

PMID20836842
PMCPMC2949868

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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.