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Accuracy of RNAseq based SNP discovery and genotyping in Populusnigra. | LitMetric

AI Article Synopsis

  • - The study focuses on Populus nigra, an important tree species, and aims to utilize RNA sequencing to identify and categorize Single Nucleotide Polymorphisms (SNPs) in its natural populations for genomic analysis.
  • - Researchers developed a bioinformatics pipeline for SNP identification from RNAseq data, validating its accuracy by comparing it to existing genetic data sources and confirming its reliability through genetic analyses.
  • - The findings highlight RNA sequencing as an effective method for accurately genotyping SNPs in P. nigra, paving the way for future studies in quantitative and population genomics.

Article Abstract

Backgroud: Populus nigra is a major tree species of ecological and economic importance for which several initiatives have been set up to create genomic resources. In order to access the large number of Single Nucleotide Polymorphisms (SNPs) typically needed to carry out a genome scan, the present study aimed at evaluating RNA sequencing as a tool to discover and type SNPs in genes within natural populations of P. nigra.

Results: We have devised a bioinformatics pipeline to call and type SNPs from RNAseq reads and applied it to P. nigra transcriptomic data. The accuracy of the resulting RNAseq-based SNP calling and typing has been evaluated by (i) comparing their position and alleles to those previously reported in candidate genes, (ii) assessing their genotyping accuracy with respect to a previously available SNP chip and (iii) evaluating their inter-annual repeatability. We found that a combination of several callers yields a good compromise between the number of variants type and the accuracy of genotyping. We further used the resulting genotypic data to carry out basic genetic analyses whose results confirm the quality of the RNAseq-based SNP dataset.

Conclusions: We demonstrated the potential and accuracy of RNAseq as an efficient way to genotype SNPs in P. nigra. These results open prospects towards the use of this technology for quantitative and population genomics studies.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6291945PMC
http://dx.doi.org/10.1186/s12864-018-5239-zDOI Listing

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