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The GATK joint genotyping workflow is appropriate for calling variants in RNA-q experiments
成人高考英语试题Jean-Simon Brouard 1,Flavio Schenkel 2,Andrew Marete 1and Nathalie Bissonnette 1*
2020江苏高考英语Main text
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在线翻释Mainly designed to quantify gene expression,the next-generation quencing (NGS)of RNA samples (RNA quencing,or RNA-q)also offers new opportunities for the efficient detection of transcriptome variants (SNPs and short indels).RNA-Seq notably reprents a powerful approach for discovering causal mutations underlying quantitative trait loci [1].Recent examples include the transcriptome analysis of the bovine pituitary gland [2],bovine blastocysts [3],pig hypothalamus and liver [4].RNA-q can also generate a large number of genotypes
required to test the association of polymorphisms with traits of economic importance [5].However,veral pre-cautions must be taken when calling variants from RNA-q data.The main challenges include handling splice junctions,detecting variants in low-expresd re-gions,and managing duplicated reads [6,7].Many of the numerous strategies and tools propod to overcome the challenges rely on the Genome Analysis Toolkit (GATK),which is a popular t of programs for discover-ing and genotyping variants from next-generation quen-cing (NGS)data [8,9].The group behind GATK published the GATK Best Practices for variant calling,which are esntially a number osplitter
f optional steps that were proven to increa the quality of NGS-derived variants,steps either upstream (preparatory)or
downstream金针菇的英文
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*Correspondence:nathalie.bissonnette@canada.ca 1
Sherbrooke Rearch and Development Centre,Agriculture and Agri-Food Canada,Sherbrooke,QC J1M 0C8,Canadadlt
Full list of author information is available at the end of the
article
Brouard et al.Journal of Animal Science and Biotechnology doi/10.1186/s40104-019-0359-0
