| Title |
SNPdbe: constructing an nsSNP functional impacts database
|
|---|---|
| Published in |
Bioinformatics, December 2011
|
| DOI | 10.1093/bioinformatics/btr705 |
| Pubmed ID | |
| Authors |
Christian Schaefer, Alice Meier, Burkhard Rost, Yana Bromberg |
| Abstract |
Many existing databases annotate experimentally characterized single nucleotide polymorphisms (SNPs). Each non-synonymous SNP (nsSNP) changes one amino acid in the gene product (single amino acid substitution;SAAS). This change can either affect protein function or be neutral in that respect. Most polymorphisms lack experimental annotation of their functional impact. Here, we introduce SNPdbe-SNP database of effects, with predictions of computationally annotated functional impacts of SNPs. Database entries represent nsSNPs in dbSNP and 1000 Genomes collection, as well as variants from UniProt and PMD. SAASs come from >2600 organisms; 'human' being the most prevalent. The impact of each SAAS on protein function is predicted using the SNAP and SIFT algorithms and augmented with experimentally derived function/structure information and disease associations from PMD, OMIM and UniProt. SNPdbe is consistently updated and easily augmented with new sources of information. The database is available as an MySQL dump and via a web front end that allows searches with any combination of organism names, sequences and mutation IDs. |
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X Demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Germany | 1 | 33% |
| United Kingdom | 1 | 33% |
| Unknown | 1 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 2 | 67% |
| Members of the public | 1 | 33% |
Mendeley demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Germany | 4 | 6% |
| United States | 3 | 5% |
| Sweden | 1 | 2% |
| India | 1 | 2% |
| United Kingdom | 1 | 2% |
| Spain | 1 | 2% |
| Canada | 1 | 2% |
| Brazil | 1 | 2% |
| Unknown | 52 | 80% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 16 | 25% |
| Researcher | 16 | 25% |
| Student > Bachelor | 7 | 11% |
| Student > Master | 5 | 8% |
| Professor | 4 | 6% |
| Other | 12 | 18% |
| Unknown | 5 | 8% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 37 | 57% |
| Biochemistry, Genetics and Molecular Biology | 9 | 14% |
| Computer Science | 8 | 12% |
| Medicine and Dentistry | 2 | 3% |
| Immunology and Microbiology | 1 | 2% |
| Other | 1 | 2% |
| Unknown | 7 | 11% |