BiERapp ( BioInformatic for Rare Diseases Application) allows finding genes affected by deleterious variants that segregate along family pedigrees , case-controls or sporadic samples .
PupaSuite is a tool for the selection of SNPs with potential phenotypic effect to support the design of large-scale genotyping projects and the characterization of new SNPs from next generation technologies.
MCDGPA is proposed to identify disease-related genes. MCDGPA is divided into three steps: module partition, genes prioritization in each disease-associated module, and rank fusion for the global ranking. When applied to the prostate cancer and breast cancer network, MCDGPA significantly improves previous algorithms in terms of cross-validation and disease-related genes prediction. In addition, the improvement is robust to the selection of gene prioritization methods when implementing prioritization in each disease-associated module and module partition algorithms when implementing network partition. In this sense MCDGPA is a general framework that allows integrating many previous gene prioritization methods and improving predictive accuracy.