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Prioritising positively selected variants in whole-genome sequencing data using FineMAV

Prioritising positively selected variants in whole-genome sequencing data using FineMAV

https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_d7507879d4cf4a00bf3c99ca75b07b84

Prioritising positively selected variants in whole-genome sequencing data using FineMAV

About this item

Full title

Prioritising positively selected variants in whole-genome sequencing data using FineMAV

Publisher

England: BioMed Central Ltd

Journal title

BMC bioinformatics, 2021-12, Vol.22 (1), p.604-604, Article 604

Language

English

Formats

Publication information

Publisher

England: BioMed Central Ltd

More information

Scope and Contents

Contents

In population genomics, polymorphisms that are highly differentiated between geographically separated populations are often suggestive of Darwinian positive selection. Genomic scans have highlighted several such regions in African and non-African populations, but only a handful of these have functional data that clearly associates candidate variations driving the selection process. Fine-Mapping of Adaptive Variation (FineMAV) was developed to address this in a high-throughput manner using population based whole-genome sequences generated by the 1000 Genomes Project. It pinpoints positively selected genetic variants in sequencing data by prioritizing high frequency, population-specific and functional derived alleles.
We developed a stand-alone software that implements the FineMAV statistic. To graphically visualise the FineMAV scores, it outputs the statistics as bigWig files, which is a common file format supported by many genome browsers. It is available as a command-line and graphical user interface. The software was tested by replicating the FineMAV scores obtained using 1000 Genomes Project African, European, East and South Asian populations and subsequently applied to whole-genome sequencing datasets from Singapore and China to highlight population specific variants that can be subsequently modelled. The software tool is publicly available at https://github.com/fadilla-wahyudi/finemav .
The software tool described here determines genome-wide FineMAV scores, using low or high-coverage whole-genome sequencing datasets, that can be used to prioritize a list of population specific, highly differentiated candidate variants for in vitro or in vivo functional screens. The tool displays these scores on the human genome browsers...

Alternative Titles

Full title

Prioritising positively selected variants in whole-genome sequencing data using FineMAV

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Primary Identifiers

Record Identifier

TN_cdi_doaj_primary_oai_doaj_org_article_d7507879d4cf4a00bf3c99ca75b07b84

Permalink

https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_d7507879d4cf4a00bf3c99ca75b07b84

Other Identifiers

ISSN

1471-2105

E-ISSN

1471-2105

DOI

10.1186/s12859-021-04506-9

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