Association of nucleotide patterns with gene function classes: application to human 3' untranslated sequences

Area of Science:

• Genomics
• Bioinformatics
• Molecular Biology

Background:

• Gene expression relies on transcription factors and mRNA translation regulation.
• Post-transcriptional control, particularly in the 3' untranslated region (3' UTR), is crucial but not fully understood.
• Specific nucleotide sequences in 3' UTRs are associated with certain protein function classes.

Purpose of the Study:

• To develop and apply a new association rule mining method for discovering nucleotide sequence patterns in human 3' UTRs.
• To identify significant associations between these patterns and protein function classes.
• To provide statistical validation for known regulatory elements and discover novel ones.

Main Methods:

• Association rule mining algorithm applied to a human 3' UTR sequence database.
• Identification of nucleotide patterns occurring more frequently than expected within specific protein function classes.

Main Results:

• The AU-Rich Element (ARE) was statistically validated in the 3' UTR of cytokines.
• Novel GC-rich patterns were discovered in the 3' UTR of homeodomain transcription factors and nuclear proteins.
• The method demonstrated effectiveness in discovering sequence-function associations.

Conclusions:

• The developed method successfully identifies biologically relevant nucleotide sequence patterns in 3' UTRs.
• This approach offers a powerful tool for discovering regulatory elements and understanding gene expression control.
• Findings contribute to the understanding of post-transcriptional regulation and its link to protein function.