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Creating and Applying a Reference to Facilitate the Discussion and Classification of Proteins in a Diverse Group
Published on: August 16, 2017
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DEFLATE compression algorithm corrects for overestimation of phylogenetic diversity by Grantham approach to
Arran Schlosberg1, Brian Y H Lam2, Giles S H Yeo3
1Kolling Institute of Medical Research, Royal North Shore Hospital, Pacific Hwy, St Leonards, NSW 2065, Australia. asch5328@uni.sydney.edu.au.
International Journal of Molecular Sciences
|May 16, 2014
Summary
New computational methods improve the classification of non-synonymous single nucleotide polymorphisms (nsSNPs) linked to disease. This approach enhances accuracy by accounting for sequence variation, aiding genetic variant analysis.
Area of Science:
- Genomics
- Bioinformatics
- Computational Biology
Background:
- Advances in genome sequencing yield numerous non-synonymous single nucleotide polymorphisms (nsSNPs) associated with diseases.
- Laboratory classification and familial co-segregation are often infeasible for large numbers of nsSNPs.
- Existing in-silico tools like Align-GVGD may underestimate deleterious effects, especially with increasing sequence data.
Purpose of the Study:
- To develop an improved in-silico method for classifying nsSNPs.
- To address the limitations of existing tools in handling large multiple-species sequence alignments (MSAs).
- To provide a quantitative pathogenicity score for inter-gene variant comparison.
Main Methods:
- Utilized the DEFLATE compression algorithm to adjust Grantham measures for species variation.
- Developed a quantitative clustering approach for known neutral and deleterious nsSNPs within genes.
- Applied the clustering method for binary classification of novel variants.
Main Results:
- The adjusted Grantham measure improved classification accuracy compared to Align-GVGD.
- The method demonstrated reduced sensitivity to large MSAs.
- A novel pathogenicity score was derived for comparing variants across different genes.
Conclusions:
- The developed approach offers a more accurate and robust method for nsSNP classification.
- This tool aids in the triage of genetic variants, particularly in the context of disease association studies.
- Open-source code and a web server are available for broader application.
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