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F-SNP: computationally predicted functional SNPs for disease association studies.

Lee PH, Shatkay H - Nucleic Acids Res. (2007)

Bottom Line: These effects are predicted and indicated at the splicing, transcriptional, translational and post-translational level.As such, the database helps identify and focus on SNPs with potential deleterious effect to human health.Users can also identify non-synonymous SNPs that may have deleterious effects on protein structure or function, interfere with protein translation or impede post-translational modification.

View Article: PubMed Central - PubMed

Affiliation: Computational Biology and Machine Learning Lab, School of Computing, Queen's University, Kingston, ON, Canada. lee@cs.queensu.ca

ABSTRACT
The Functional Single Nucleotide Polymorphism (F-SNP) database integrates information obtained from 16 bioinformatics tools and databases about the functional effects of SNPs. These effects are predicted and indicated at the splicing, transcriptional, translational and post-translational level. As such, the database helps identify and focus on SNPs with potential deleterious effect to human health. In particular, users can retrieve SNPs that disrupt genomic regions known to be functional, including splice sites and transcriptional regulatory regions. Users can also identify non-synonymous SNPs that may have deleterious effects on protein structure or function, interfere with protein translation or impede post-translational modification. A web interface enables easy navigation for obtaining information through multiple starting points and exploration routes (e.g. starting from SNP identifier, genomic region, gene or target disease). The F-SNP database is available at http://compbio.cs.queensu.ca/F-SNP/.

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Decision procedure for functional SNP assessment. Each SNP is examined for deleterious effects with respect to each functional category (i.e. protein coding, splicing regulation, transcriptional regulation and post-translation—as shown in the top part of the figure). For each category, a series of tests is executed to determine whether the SNP has a functional impact. First the type (coding, intronic, etc.) of the genomic region is identified, using data from dbSNP (3) and Ensembl (4). Once this is determined, other tests are performed. For example, to assess if a SNP has a deleterious effect on protein coding, it first must be located on a coding region. Ensembl (4) is used to examine if this is a nonsense mutation, in which case the SNP is considered to be deleterious. Otherwise—if the SNP is a missense mutation, it is further tested by five different tools [PolyPhen (6), SIFT (7), SNPeffect (8), SNPs3D (9) and LS-SNP (10)] to check if the non-synonymous substitution is deleterious. A majority vote among these tools concludes the process, and identifies the SNP as either having a potentially deleterious functional impact (denoted ‘functional’ in the figure) or not.
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Figure 1: Decision procedure for functional SNP assessment. Each SNP is examined for deleterious effects with respect to each functional category (i.e. protein coding, splicing regulation, transcriptional regulation and post-translation—as shown in the top part of the figure). For each category, a series of tests is executed to determine whether the SNP has a functional impact. First the type (coding, intronic, etc.) of the genomic region is identified, using data from dbSNP (3) and Ensembl (4). Once this is determined, other tests are performed. For example, to assess if a SNP has a deleterious effect on protein coding, it first must be located on a coding region. Ensembl (4) is used to examine if this is a nonsense mutation, in which case the SNP is considered to be deleterious. Otherwise—if the SNP is a missense mutation, it is further tested by five different tools [PolyPhen (6), SIFT (7), SNPeffect (8), SNPs3D (9) and LS-SNP (10)] to check if the non-synonymous substitution is deleterious. A majority vote among these tools concludes the process, and identifies the SNP as either having a potentially deleterious functional impact (denoted ‘functional’ in the figure) or not.

Mentions: Figure 1 illustrates the assessment process. We note that in the case of SNPs within regulatory regions, for instance, ‘transcription factor binding site’ or ‘exonic splicing regulatory regions’ (as shown in the two middle boxes in Figure 1), we additionally examine whether the region is conserved across multiple species (chimp/dog/mouse/rat/chicken/zebrafish/fugu) to determine whether the SNP is functional. This strategy is mainly used because there is a high rate of false positive findings by in silico prediction tools due to the short length of such sequences (typically 6–8-mer) (12). The additional information about conserved regions across multiple species is thus used as a way to filter out possible false-positive predictions (2,11–14).Figure 1.


F-SNP: computationally predicted functional SNPs for disease association studies.

Lee PH, Shatkay H - Nucleic Acids Res. (2007)

Decision procedure for functional SNP assessment. Each SNP is examined for deleterious effects with respect to each functional category (i.e. protein coding, splicing regulation, transcriptional regulation and post-translation—as shown in the top part of the figure). For each category, a series of tests is executed to determine whether the SNP has a functional impact. First the type (coding, intronic, etc.) of the genomic region is identified, using data from dbSNP (3) and Ensembl (4). Once this is determined, other tests are performed. For example, to assess if a SNP has a deleterious effect on protein coding, it first must be located on a coding region. Ensembl (4) is used to examine if this is a nonsense mutation, in which case the SNP is considered to be deleterious. Otherwise—if the SNP is a missense mutation, it is further tested by five different tools [PolyPhen (6), SIFT (7), SNPeffect (8), SNPs3D (9) and LS-SNP (10)] to check if the non-synonymous substitution is deleterious. A majority vote among these tools concludes the process, and identifies the SNP as either having a potentially deleterious functional impact (denoted ‘functional’ in the figure) or not.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2238878&req=5

Figure 1: Decision procedure for functional SNP assessment. Each SNP is examined for deleterious effects with respect to each functional category (i.e. protein coding, splicing regulation, transcriptional regulation and post-translation—as shown in the top part of the figure). For each category, a series of tests is executed to determine whether the SNP has a functional impact. First the type (coding, intronic, etc.) of the genomic region is identified, using data from dbSNP (3) and Ensembl (4). Once this is determined, other tests are performed. For example, to assess if a SNP has a deleterious effect on protein coding, it first must be located on a coding region. Ensembl (4) is used to examine if this is a nonsense mutation, in which case the SNP is considered to be deleterious. Otherwise—if the SNP is a missense mutation, it is further tested by five different tools [PolyPhen (6), SIFT (7), SNPeffect (8), SNPs3D (9) and LS-SNP (10)] to check if the non-synonymous substitution is deleterious. A majority vote among these tools concludes the process, and identifies the SNP as either having a potentially deleterious functional impact (denoted ‘functional’ in the figure) or not.
Mentions: Figure 1 illustrates the assessment process. We note that in the case of SNPs within regulatory regions, for instance, ‘transcription factor binding site’ or ‘exonic splicing regulatory regions’ (as shown in the two middle boxes in Figure 1), we additionally examine whether the region is conserved across multiple species (chimp/dog/mouse/rat/chicken/zebrafish/fugu) to determine whether the SNP is functional. This strategy is mainly used because there is a high rate of false positive findings by in silico prediction tools due to the short length of such sequences (typically 6–8-mer) (12). The additional information about conserved regions across multiple species is thus used as a way to filter out possible false-positive predictions (2,11–14).Figure 1.

Bottom Line: These effects are predicted and indicated at the splicing, transcriptional, translational and post-translational level.As such, the database helps identify and focus on SNPs with potential deleterious effect to human health.Users can also identify non-synonymous SNPs that may have deleterious effects on protein structure or function, interfere with protein translation or impede post-translational modification.

View Article: PubMed Central - PubMed

Affiliation: Computational Biology and Machine Learning Lab, School of Computing, Queen's University, Kingston, ON, Canada. lee@cs.queensu.ca

ABSTRACT
The Functional Single Nucleotide Polymorphism (F-SNP) database integrates information obtained from 16 bioinformatics tools and databases about the functional effects of SNPs. These effects are predicted and indicated at the splicing, transcriptional, translational and post-translational level. As such, the database helps identify and focus on SNPs with potential deleterious effect to human health. In particular, users can retrieve SNPs that disrupt genomic regions known to be functional, including splice sites and transcriptional regulatory regions. Users can also identify non-synonymous SNPs that may have deleterious effects on protein structure or function, interfere with protein translation or impede post-translational modification. A web interface enables easy navigation for obtaining information through multiple starting points and exploration routes (e.g. starting from SNP identifier, genomic region, gene or target disease). The F-SNP database is available at http://compbio.cs.queensu.ca/F-SNP/.

Show MeSH