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Methods for assessing the statistical significance of molecular sequence features by using general scoring schemes
1Department of Mathematics, Stanford University, CA 94305.
Summary
Identifying significant sequence patterns in nucleic acids and proteins is crucial. This study presents a statistical theory to assess if these patterns are chance occurrences, aiding in the discovery of biologically relevant features.
Area of Science:
- Bioinformatics
- Computational Biology
- Genomics
Background:
- Biological significance is often found in unusual nucleic acid or protein sequence patterns.
- Determining if these patterns arise by chance is essential for biological interpretation.
Purpose of the Study:
- To develop a statistical theory for assessing the significance of sequence patterns.
- To provide methods for identifying biologically relevant patterns in nucleic acid and protein sequences.
Main Methods:
- Assigning scoring values to individual residues or sets of residues based on biophysical properties or sequence similarity.
- Utilizing an appropriate random model to derive precise numerical formulas for statistical significance.
- Analyzing high-scoring segments to identify optimal scoring systems.
Main Results:
- Precise formulas for assessing the statistical significance of high-aggregate-score regions.
- Characterization of high-scoring segments, enabling the selection of optimal scoring systems.
- Application of the theory to protein sequences, revealing biologically significant features like charge regions, hydrophobic segments, and subalignments.
Conclusions:
- The developed theory provides a robust framework for evaluating the statistical significance of sequence patterns.
- This approach aids in distinguishing biologically relevant patterns from random occurrences.
- The findings have practical applications in identifying key features in various proteins and genes, including those related to transcription factors, receptors, and cystic fibrosis.