SOFTWARE & DATA

Marigorta and Navarro (2013) “High Trans-ethnic Replicability of GWAS Results Implies Common Causal Variants”
A compressed file containing the database of associated SNPs and replication attempts performed on Europeans, East Asians and Africans is available (Table S5). Also the Supplementary Materials used in the paper by Marigorta and Navarro are provided (Tables 1 to 11).

Gazave et al. Genome Research (2011) “Copy Number Variation Analysis In The Great Apes Reveals Species-Specific Patterns Of Structural Variation
A compressed file containing the aCGH data used in the paper by Gazave et al (2011)

GWASpi

GWASpi is a Genome Wide Association study application.

GWASpi provides the tools and integrated know-how to quickly and safely process your GWAS data, all in a single, easy to use application. Once your genotype data is loaded into GWASpi’s hierarchical database (netCDF), you will be able to manage, manipulate and transform it, as well as perform basic quality controls and association studies. GWASpi will also generate charts, plots and reports for you.

SNPator

SNPator was originally designed to help CeGen users to handle, retrieve, transform and analyze the genetic data generated by the genotyping facilities of the institution. Hoewever, SNPator is also open to external users who may want to upload their own genotyped data in order to take advantage its data processing features.

Users, depending on their interests, will be able to perform a set of operations which may range from very simple format transformations (e.g. creating input files from their data for different bioinformatics software) to some complex biostatistical calculations that may help them to mine their datasets without having to be familiar with the statistical software usually involved in such studies.

SYSNPs

SYSNPs (which stands for Select Your SNPs) is the first web server implementing algorithms that allow for efficient and simultaneous consideration of all the relevant criteria in order to obtain tag-SNPs that appropriately cover large sets of genes, genomic regions or Gene Ontology (GO) terms.

SYSNPs allows the user to compile information about all the SNPs in any human gene or genome region of interest and to select some of them according to different criteria, such as functional properties, technological information and tagging information from their choice populations. After the criteria are established, SYSNPs provides a set of tag-SNPs that fulfill them, as well as all the information about the SNPs associated with those tag-SNPs.

Therefore, SYSNPs can be used at least with two main objectives. Firstly, it helps users to select optimal sets of tag-SNPs, producing, among several other goodies, a list that is ready to be sent to the genotyping platform. Secondly, after association studies have been conducted, SYSNPs allows the user to annotate SNP variants in the genomic regions where the associations have been found. A file can be downloaded with the corresponding annotations.

ACD

Whole genome scans analyze large sets of genetic markers, mainly single nucleotide polymorphisms, over the entire genome in order to find variants and regions associated with complex traits so these can be further investigated. Analyzing the results of such scans becomes difficult due to multiple testing problems and to the genomic distributions of recombination, linkage disequilibrium and true associations, which generate an extremely complex network of dependences between markers. Here we present Association Cluster Detector (ACD), a simple tool aiming to ease the analysis of the results of whole genome scans. ACD facilitates correction for multiple tests using several standard procedures and implements a sliding-window heuristic method that helps in detecting potentially interesting candidate regions by exploiting the property of non-random distribution of significantly associated markers.

Marques-Bonet T., Oscar Lao, Robert Goertsches, Manuel Comabella, Xavier Montalban, Arcadi Navarro. Association Cluster Detector: a tool for heuristic detection of association clusters in whole-genome scans.

Bioinformatics 2005 , 21 (s2):180-181.