Key-string algorithm--novel approach to computational analysis of repetitive sequences in human centromeric DNA.

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• Noncoding DNA contains large repetitive sequences and higher-order repeats (HORs) that are challenging to analyze.
• Existing computational tools may not effectively identify these complex structures.

Purpose of the Study:

• To introduce and validate the Key-string Algorithm (KSA), a novel computational approach for identifying and analyzing large repetitive DNA sequences and HORs.
• To demonstrate KSA's utility in analyzing complex genomic regions, specifically alpha satellites and HORs in human chromosome 7.

Main Methods:

• Developed a suite of seven KSA-related software programs for sequence analysis.
• Applied KSA segmentation using specific key strings (DCCGTTT, GTA, TTTC) to human genetic sequence AC017075.8.
• Utilized graphical displays, eye inspection, and modest computations for analysis.

Main Results:

• Identified 55 copies of 2734-bp 16mer HORs and a distinct start-string.
• KSA successfully identified HORs in AC017075.8, outperforming RepeatMasker and Tandem Repeat Finder.
• Described centromere folding as an effect of HORs and super-HORs, and demonstrated novel KSA-based analytical methods.

Conclusions:

• The KSA approach provides a simple, robust, and computationally modest framework for investigating repetitive DNA and HORs.
• KSA facilitates easier identification of HORs compared to their constituent monomers and aids in detecting mutations.
• The method has potential applications in forensic medicine, disease diagnosis, evolutionary biology, and paleontology.