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LS-SNP: large-scale annotation of coding non-synonymous SNPs based on multiple information sources.
Rachel Karchin1, Mark Diekhans, Libusha Kelly
1Department of Biopharmaceutical Sciences, University of California at San Francisco, San Francisco, CA 94143, USA. rachelk@salilab.org
Bioinformatics (Oxford, England)
|April 14, 2005
Summary
LS-SNP is a new pipeline annotating non-synonymous single nucleotide polymorphisms (nsSNPs). It maps nsSNPs to proteins and pathways, predicting their impact on protein stability and human health.
Area of Science:
- Genomics
- Bioinformatics
- Computational Biology
Background:
- The NCBI dbSNP database contains millions of single nucleotide polymorphisms (SNPs) with limited functional annotation.
- Non-synonymous SNPs (nsSNPs) are crucial for understanding individual variations, disease susceptibility, and drug responses.
Purpose of the Study:
- To develop a comprehensive genomic-scale software pipeline, LS-SNP, for annotating nsSNPs.
- To predict the functional impact of nsSNPs on protein structure, function, and human health.
Main Methods:
- Developed LS-SNP, a software pipeline for annotating nsSNPs.
- Mapped nsSNPs to protein sequences, functional pathways, and protein structure models.
- Predicted nsSNP effects on protein stability, domain interactions, and ligand binding.
Main Results:
- LS-SNP annotates 28,043 validated nsSNPs in human proteins from SwissProt/TrEMBL.
- The pipeline predicts nsSNP impact on protein stability, domain interfaces, ligand binding, and overall human health.
- Annotations are accessible via a web interface, allowing analysis by genomic region, gene, protein, or pathway.
Conclusions:
- LS-SNP provides comprehensive annotation for nsSNPs, aiding in the identification of functional variants.
- The tool facilitates the investigation of molecular mechanisms underlying the functional consequences of nsSNPs.
- Results are valuable for identifying candidate functional SNPs and understanding disease-related genetic variations.