TRStalker: an efficient heuristic for finding fuzzy tandem repeats.

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• Eukaryotic genomes contain abundant repetitive sequences, including tandem repeats (TRs).
• TRs are crucial for molecular regulation and implicated in diseases like trinucleotide repeat disorders.
• Detecting highly divergent TRs, or fuzzy TRs, remains a challenge for current bioinformatics tools.

Purpose of the Study:

• To develop an efficient algorithm for detecting fuzzy tandem repeats (TRs).
• To improve the understanding of TRs' roles in biological regulation and disease.
• To provide insights into genome evolution through the identification of divergent repetitive elements.

Main Methods:

• Developed TRStalker, an algorithm employing heuristics for a Steiner version of the generalized median string problem.
• Addressed high divergence in TRs caused by base substitutions, insertions, and deletions.
• Utilized a motif string not necessarily present in the input for fuzziness measurement.

Main Results:

• TRStalker efficiently detects hard-to-find fuzzy tandem repeats.
• Experimental results show superior performance compared to state-of-the-art methods for fuzzy TR detection.
• Confirmed the presence of detected fuzzy TRs in significant biological sequences.

Conclusions:

• TRStalker offers an effective solution for identifying highly divergent tandem repeats.
• The algorithm enhances the study of TRs' functional and pathological significance.
• TRStalker will be integrated into the TReaDS web service for broader accessibility.