A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff. We describe a new computer program, SnpEff, for rapidly categorizing the effects of variants in genome sequences. Once a genome is sequenced, SnpEff annotates variants based on their genomic locations and predicts coding effects. Annotated genomic locations include intronic, untranslated region, upstream, downstream, splice site, or intergenic regions. Coding effects such as synonymous or non-synonymous amino acid replacement, start codon gains or losses, stop codon gains or losses, or frame shifts can be predicted. Here the use of SnpEff is illustrated by annotating 356,660 candidate SNPs in 117 Mb unique sequences, representing a substitution rate of 1/305 nucleotides, between the Drosophila melanogaster w1118; iso-2; iso-3 strain and the reference y1; cn1 bw1 sp1 strain. We show that 15,842 SNPs are synonymous and 4,467 SNPs are non-synonymous (N/S 0.28). The remaining SNPs are in other categories, such as stop codon gains (38 SNPs), stop codon losses (8 SNPs), and start codon gains (297 SNPs) in the 5′UTR. We found, as expected, that the SNP frequency is proportional to the recombination frequency (i.e., highest in the middle of chromosome arms). We also found that start-gain or stop-lost SNPs in Drosophila melanogaster often result in additions of N-terminal or C-terminal amino acids that are conserved in other Drosophila species. It appears that the 5′ and 3′ UTRs are reservoirs for genetic variations that changes the termini of proteins during evolution of the Drosophila genus. As genome sequencing is becoming inexpensive and routine, SnpEff enables rapid analyses of whole-genome sequencing data to be performed by an individual laboratory.
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References in zbMATH (referenced in 3 articles )
Showing results 1 to 3 of 3.
- Kim, Juhyun; Shen, Judong; Wang, Anran; Mehrotra, Devan V.; Ko, Seyoon; Zhou, Jin J.; Zhou, Hua: VCSEL: Prioritizing SNP-set by penalized variance component selection (2021)
- Wolff, Alexander: Analysis of expression profile and gene variation via development of methods for next generation sequencing data (2018)
- Carugo, Oliviero (ed.); Eisenhaber, Frank (ed.): Data mining techniques for the life sciences (2016)