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Evaluation of MC1R high-throughput nucleotide sequencing data generated by the 1000 Genomes Project

Overview of attention for article published in Genetics and Molecular Biology, May 2017
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Title
Evaluation of MC1R high-throughput nucleotide sequencing data generated by the 1000 Genomes Project
Published in
Genetics and Molecular Biology, May 2017
DOI 10.1590/1678-4685-gmb-2016-0180
Pubmed ID
Authors

Leonardo Arduino Marano, Letícia Marcorin, Erick da Cruz Castelli, Celso Teixeira Mendes-Junior

Abstract

The advent of next-generation sequencing allows simultaneous processing of several genomic regions/individuals, increasing the availability and accuracy of whole-genome data. However, these new approaches may present some errors and bias due to alignment, genotype calling, and imputation methods. Despite these flaws, data obtained by next-generation sequencing can be valuable for population and evolutionary studies of specific genes, such as genes related to how pigmentation evolved among populations, one of the main topics in human evolutionary biology. Melanocortin-1 receptor (MC1R) is one of the most studied genes involved in pigmentation variation. As MC1R has already been suggested to affect melanogenesis and increase risk of developing melanoma, it constitutes one of the best models to understand how natural selection acts on pigmentation. Here we employed a locally developed pipeline to obtain genotype and haplotype data for MC1R from the raw sequencing data provided by the 1000 Genomes FTP site. We also compared such genotype data to Phase 3 VCF to evaluate its quality and discover any polymorphic sites that may have been overlooked. In conclusion, either the VCF file or one of the presently described pipelines could be used to obtain reliable and accurate genotype calling from the 1000 Genomes Phase 3 data.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 14 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 14 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 3 21%
Student > Doctoral Student 2 14%
Professor > Associate Professor 2 14%
Student > Ph. D. Student 2 14%
Student > Bachelor 1 7%
Other 2 14%
Unknown 2 14%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 7 50%
Agricultural and Biological Sciences 3 21%
Arts and Humanities 1 7%
Social Sciences 1 7%
Chemistry 1 7%
Other 0 0%
Unknown 1 7%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 02 August 2017.
All research outputs
#19,951,180
of 25,382,440 outputs
Outputs from Genetics and Molecular Biology
#526
of 772 outputs
Outputs of similar age
#235,209
of 324,786 outputs
Outputs of similar age from Genetics and Molecular Biology
#10
of 16 outputs
Altmetric has tracked 25,382,440 research outputs across all sources so far. This one is in the 18th percentile – i.e., 18% of other outputs scored the same or lower than it.
So far Altmetric has tracked 772 research outputs from this source. They receive a mean Attention Score of 4.8. This one is in the 28th percentile – i.e., 28% of its peers scored the same or lower than it.
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We're also able to compare this research output to 16 others from the same source and published within six weeks on either side of this one. This one is in the 37th percentile – i.e., 37% of its contemporaries scored the same or lower than it.