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A new algorithm for detecting low-complexity regions in protein sequences.

Sung W Shin¹, Sam M Kim

¹Department of Computer Engineering, Kyungpook National University Daegu 702-701, Korea. swshin@bioinfo.knu.ac.kr

Bioinformatics (Oxford, England)

|August 31, 2004

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This study introduces a novel algorithm to detect and mask low-complexity regions (LCRs) in protein sequences. By analyzing repeating subsequences, this method enhances the accuracy of protein database searches and functional predictions.

Area of Science:

Bioinformatics
Computational Biology
Structural Bioinformatics

Background:

Pair-wise alignment and local similarity searches in protein sequences often yield false positives due to compositionally biased regions, known as low-complexity regions (LCRs).
Filtering these LCRs is crucial for improving the reliability of homology searches and subsequent functional predictions.
Existing algorithms primarily rely on statistical approaches.

Purpose of the Study:

To investigate the structural properties of LCRs in biological sequences.
To develop a novel algorithm for effectively detecting and masking LCRs.

Main Methods:

Developed an algorithm based on complexity analysis of subsequences delimited by repeating subsequences.
Utilized suffix tree construction to identify repeating subsequences within protein sequences.

Related Experiment Videos

Iteratively tested subsequences against defined criteria to identify LCRs.

Main Results:

The algorithm successfully detects and masks LCRs in protein sequences.
Repeating subsequences were identified as reliable indicators of LCRs.
Testing on 1000 proteins demonstrated the algorithm's effectiveness, competing strongly with existing methods.

Conclusions:

The proposed algorithm improves the quality of protein database searches by effectively handling LCRs.
The structural approach based on repeating subsequences offers a competitive alternative to statistical methods for LCR detection.