Data Mining to Detect Common, Unique, and Polymorphic Simple Sequence Repeats

Abstract

Nowadays computational data mining of biological data is of paramount importance to discover patterns in large data generated through sequencing and other efforts. The extracted information can be used in various ways to get new insights about subject organism. Simple sequence repeats (SSRs) consist of 1–6 nucleotides and can be characterized in wet laboratory as well as mined through computational approaches. These repeats help in the genetic mapping, breeding experiments, phylogeny and can also be used to develop molecular markers. In view of their usefulness, various specialized biological databases of SSRs were developed. In this chapter, a case study is presented which used in silico mined nucleotide sequence data to further detect putative polymorphic, common, and unique SSRs in chloroplast genomes of genus Triticum. Earlier, SSRs were detected in several organisms; however, in silico detection of unique, common, and putative polymorphic SSRs is a recent development which can be used in various ways including the identification of species.