Frequently, it may be of interest to mine a NGS data set for the presence of one or more single nucleotide variants of interest; for example, variants that have known associations to specific traits or genetic disorders.
With the growing number of sequencing efforts revealing ever more single nucleotide variants in the non-coding regions of the genome, accurate algorithms predicting the structural consequences of these mutations are likely to play an important role in genomic interpretation.
There is no direct evidence for multiple hits on the Cave Bear dataset (no site with more than two nucleotide variants), which is probably due to the high transition transversion bias thereby making such multiple mutations on a site not readily apparent.
These positions correspond to what are commonly known as SNPs or more precisely, single nucleotide variants.
This kit detects more single-nucleotide variants in the untranslated regions (UTRs) than the other platforms (Clark et al. 2011), although comparisons of performance with NimbleGen and Agilent's "+UTR" kits have yet to be performed.
For the purpose of SNP-based diagnostics in tall fescue, therefore, it may be more reliable to use nucleotide variants identified previously in conserved regions of the chloroplast DNA-derived gene matK and the internal transcribed spacer region of ribosomal DNA, as implemented in a previous study [ 26].
These length and nucleotide variants constitute more than 50% of the total EBV miRNA reads, and many sequences show significant deviation from the miRBase reference sequence.
Randomly selected sites accumulated nucleotide variants much more rapidly with SD values double those found for the conserved sites (Table 2).
Our study identified more than 14 million potential single nucleotide variants and validated ~3 million of these variants.
This is becoming ever more evident as less single nucleotide variants are found to be associated with genetic diseases that affect the protein sequence than found in regulatory regions [ 15].
