The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
LS-SNP: large-scale annotation of coding non-synonymous SNPs based on multiple information sources
Abstract
MOTIVATION: The NCBI dbSNP database lists over 9 million single nucleotide polymorphisms (SNPs) in the human genome, but currently contains limited annotation information. SNPs that result in amino acid residue changes (nsSNPs) are of critical importance in variation between individuals, including disease and drug sensitivity. RESULTS: We have developed LS-SNP, a genomic scale software pipeline to annotate nsSNPs. LS-SNP comprehensively maps nsSNPs onto protein sequences, functional pathways and comparative protein structure models, and predicts positions where nsSNPs destabilize proteins, interfere with the formation of domain-domain interfaces, have an effect on protein-ligand binding or severely impact human health. It currently annotates 28,043 validated SNPs that produce amino acid residue substitutions in human proteins from the SwissProt/TrEMBL database. Annotations can be viewed via a web interface either in the context of a genomic region or by selecting sets of SNPs, genes, proteins or pathways. These results are useful for identifying candidate functional SNPs within a gene, haplotype or pathway and in probing molecular mechanisms responsible for functional impacts of nsSNPs. AVAILABILITY: http://www.salilab.org/LS-SNP CONTACT: rachelk@salilab.org SUPPLEMENTARY INFORMATION: http://salilab.org/LS-SNP/supp-info.pdf.
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Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421
Kabsch, W., & Sander, C. (1983) · Biopolymers · doi:10.1002/bip.360221211 · Cited by 15,320
<tt>BLAT</tt> —The <tt>BLAST</tt> -Like Alignment Tool
Kent, W. J. (2002) · Genome Research · doi:10.1101/gr.229202 · Cited by 8,480
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Self-citationComparative protein structure modeling using MODELLER
Webb, B., & Sali, A. (2016) · Current Protocols in Protein Science · doi:10.1002/cpps.20 · Cited by 730
“…in testing and improving a sequence-structure alignment (Wolf et al., 1998); in annotating single nucleotide polymorphisms (Mirkovic et al., 2004; Karchin et al., 2005); in structural characterization of large complexes by docking to low-resolution cryo-electron density maps (Spahn et al.,…”
MethodPerformance of mutation pathogenicity prediction methods on missense variants
Thusberg, J., Olatubosun, A., & Vihinen, M. (2011) · Human Mutation · doi:10.1002/humu.21445 · Cited by 537
Xu, J., Yang, P., Xue, S., Sharma, B., Sanchez-Martin, M., Wang, F., Beaty, K. A., Dehan, E., & Parikh, B. (2019) · Human Genetics · doi:10.1007/s00439-019-01970-5 · Cited by 305
Zamdborg, L., LeDuc, R. D., Glowacz, K. J., Kim, Y.-B., Viswanathan, V., Spaulding, I. T., Early, B. P., Bluhm, E. J., Babai, S., & Kelleher, N. L. (2007) · Nucleic Acids Research · doi:10.1093/nar/gkm371 · Cited by 251
Metadata
- Authors
- Karchin, R., Diekhans, M., Kelly, L., Thomas, D. J., Pieper, U., Eswar, N., Haussler, D., & Sali, A.
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2005-04-12
- Volume
- 21
- Issue
- 12
- Pages
- 2814-2820
- ISSN
- 0266-7061 (ISSN-L)
- 1367-4803
- 1460-2059
- OpenAlex
- W2148830266
- Cited by
- 228
- References
- 3
- FWCI
- 10.59
- Type
- journal article
- Language
- en
- Open access
- closed
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