ArticleEcology and evolution2018
PMERGE: Computational filtering of paralogous sequences from RAD-seq data.
Article in Ecology and evolution, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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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.
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Who cites it
4 citing papers in PubMed, 18 citations in OpenAlex.
- Widespread Deviant Patterns of Heterozygosity in Whole-Genome Sequencing Due to Autopolyploidy, Repeated Elements, and Duplication.Genome biology and evolution · 2023Article
- A population-level statistic for assessing Mendelian behavior of genotyping-by-sequencing data from highly duplicated genomes.BMC bioinformatics · 2022Article
- Evidence for Glacial Refugia of the Forest Understorey SpeciesFrontiers in plant science · 2021Article
- PMERGE: Computational filtering of paralogous sequences from RAD-seq data.Ecology and evolution · 2018Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Restriction-site associated DNA sequencing (RAD-seq) can identify and score thousands of genetic markers from a group of samples for population-genetics studies. One challenge of de novo RAD-seq analysis is to distinguish paralogous sequence variants (PSVs) from true single-nucleotide polymorphisms (SNPs) associated with orthologous loci. In the absence of a reference genome, it is difficult to differentiate true SNPs from PSVs, and their impact on downstream analysis remains unclear. Here, we introduce a network-based approach, PMERGE that connects fragments based on their DNA sequence similarity to identify probable PSVs. Applying our method to de novo RAD-seq data from 150 Atlantic salmon (
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