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ASSIMILATOR: a new tool to inform selection of associated genetic variants for functional studies.

Martin P, Barton A, Eyre S - Bioinformatics (2011)

Bottom Line: These are likely to identify a number of putative causal variants, which cannot be separated further in terms of strength of genetic association due to linkage disequilibrium.The challenge will be selecting which variant to prioritize for subsequent expensive functional studies.A wealth of functional information generated from wet lab experiments now exists but cannot be easily interrogated by the user.

View Article: PubMed Central - PubMed

Affiliation: Arthritis Research UK Epidemiology Unit, Manchester Academic Health Science Centre, The University of Manchester, Manchester, UK. paul.martin-2@manchester.ac.uk

ABSTRACT

Motivation: Fine-mapping experiments from genome-wide association studies (GWAS) are underway for many complex diseases. These are likely to identify a number of putative causal variants, which cannot be separated further in terms of strength of genetic association due to linkage disequilibrium. The challenge will be selecting which variant to prioritize for subsequent expensive functional studies. A wealth of functional information generated from wet lab experiments now exists but cannot be easily interrogated by the user. Here, we describe a program designed to quickly assimilate this data called ASSIMILATOR and validate the method by interrogating two regions to show its effectiveness.

Availability: http://www.medicine.manchester.ac.uk/musculoskeletal/research/arc/genetics/bioinformatics/assimilator/.

Show MeSH
Examples of ASSIMILATOR output showing results for (a) Pomerantz et al. with the causal SNP highlighted and (b) Gaulton et al. showing the evidence that the SNP is in a region of open chromatin. In addition, an example of results for a SNP without an rs number, as might be the case for novel SNPs identified via the 1000 Genomes project (http://www.1000genomes.org), is shown.
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Figure 1: Examples of ASSIMILATOR output showing results for (a) Pomerantz et al. with the causal SNP highlighted and (b) Gaulton et al. showing the evidence that the SNP is in a region of open chromatin. In addition, an example of results for a SNP without an rs number, as might be the case for novel SNPs identified via the 1000 Genomes project (http://www.1000genomes.org), is shown.

Mentions: The output can be viewed in a standard web browser and allows the user to quickly identify SNPs, which could be functionally important. To add extra functionality, the ability to view selected SNPs in NCBIs dbSNP (Sherry et al., 2001) and in the UCSC Genome Browser has been incorporated into the output. To efficiently display features for a SNP in the UCSC Genome Browser, only tracks that contain features in the SNP region are displayed. The user interface has been designed to allow further mining of the output (Fig. 1) to display information from the multiple cell types and links to external data. This includes the ability to view the detailed experimental data thereby allowing users to assess the biological relevance of the results in the context of the thresholds and criteria used. ASSIMILATOR automatically queries any new tracks appearing from the ENCODE project on UCSC and includes these in the analysis. To further ensure ASSIMILATOR stays up to date, an option is available, which searches all UCSC database versions for ENCODE tracks and automatically uses the latest suitable version [currently March. 2006 (NCBI36/hg18)]. The ENCODE data release policy places restrictions on the publication of ENCODE data; therefore, the date at which the data becomes unrestricted is also displayed to aid the user.Fig. 1.


ASSIMILATOR: a new tool to inform selection of associated genetic variants for functional studies.

Martin P, Barton A, Eyre S - Bioinformatics (2011)

Examples of ASSIMILATOR output showing results for (a) Pomerantz et al. with the causal SNP highlighted and (b) Gaulton et al. showing the evidence that the SNP is in a region of open chromatin. In addition, an example of results for a SNP without an rs number, as might be the case for novel SNPs identified via the 1000 Genomes project (http://www.1000genomes.org), is shown.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC3008640&req=5

Figure 1: Examples of ASSIMILATOR output showing results for (a) Pomerantz et al. with the causal SNP highlighted and (b) Gaulton et al. showing the evidence that the SNP is in a region of open chromatin. In addition, an example of results for a SNP without an rs number, as might be the case for novel SNPs identified via the 1000 Genomes project (http://www.1000genomes.org), is shown.
Mentions: The output can be viewed in a standard web browser and allows the user to quickly identify SNPs, which could be functionally important. To add extra functionality, the ability to view selected SNPs in NCBIs dbSNP (Sherry et al., 2001) and in the UCSC Genome Browser has been incorporated into the output. To efficiently display features for a SNP in the UCSC Genome Browser, only tracks that contain features in the SNP region are displayed. The user interface has been designed to allow further mining of the output (Fig. 1) to display information from the multiple cell types and links to external data. This includes the ability to view the detailed experimental data thereby allowing users to assess the biological relevance of the results in the context of the thresholds and criteria used. ASSIMILATOR automatically queries any new tracks appearing from the ENCODE project on UCSC and includes these in the analysis. To further ensure ASSIMILATOR stays up to date, an option is available, which searches all UCSC database versions for ENCODE tracks and automatically uses the latest suitable version [currently March. 2006 (NCBI36/hg18)]. The ENCODE data release policy places restrictions on the publication of ENCODE data; therefore, the date at which the data becomes unrestricted is also displayed to aid the user.Fig. 1.

Bottom Line: These are likely to identify a number of putative causal variants, which cannot be separated further in terms of strength of genetic association due to linkage disequilibrium.The challenge will be selecting which variant to prioritize for subsequent expensive functional studies.A wealth of functional information generated from wet lab experiments now exists but cannot be easily interrogated by the user.

View Article: PubMed Central - PubMed

Affiliation: Arthritis Research UK Epidemiology Unit, Manchester Academic Health Science Centre, The University of Manchester, Manchester, UK. paul.martin-2@manchester.ac.uk

ABSTRACT

Motivation: Fine-mapping experiments from genome-wide association studies (GWAS) are underway for many complex diseases. These are likely to identify a number of putative causal variants, which cannot be separated further in terms of strength of genetic association due to linkage disequilibrium. The challenge will be selecting which variant to prioritize for subsequent expensive functional studies. A wealth of functional information generated from wet lab experiments now exists but cannot be easily interrogated by the user. Here, we describe a program designed to quickly assimilate this data called ASSIMILATOR and validate the method by interrogating two regions to show its effectiveness.

Availability: http://www.medicine.manchester.ac.uk/musculoskeletal/research/arc/genetics/bioinformatics/assimilator/.

Show MeSH