DRIMust: a web server for discovering rank imbalanced motifs using suffix trees

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• Molecular recognition in cellular regulation relies on identifying sequence patterns in DNA, RNA, and proteins.
• Statistical and computational tools are crucial for discovering and understanding these sequence motifs.

Purpose of the Study:

• To introduce DRIMust, a novel web application for de novo motif discovery.
• To provide an efficient and accurate tool for identifying over-represented sequence motifs in ranked lists.

Main Methods:

• The DRIMust algorithm employs a minimum hypergeometric statistical framework.
• Suffix trees are utilized for efficient enumeration of potential motif candidates.
• Input consists of ranked sequences in FASTA format, with a data-driven threshold for defining the top list.

Main Results:

• DRIMust identifies motifs over-represented at the top of ranked sequence lists.
• Results include individual motifs with P-values and Position Specific Scoring Matrices.
• Comparative analysis shows DRIMust outperforms state-of-the-art tools in accuracy and speed.

Conclusions:

• DRIMust offers a unique combination of efficient large-scale data searching and rigorous P-value assessment for motif discovery.
• The tool provides significant advantages in result accuracy and computational efficiency.