:: DESCRIPTION
HapCART is a convenient analysis tool for detecting disease-related haplotype-haplotype interactions. HapCART overcome high-dimensional issues which combine the advantages of data mining with the concept of haplotypes and consider haplotype uncertainty.
::DEVELOPER
Cathy S.J. Fann lab,Institute of Biomedical Informatics, National Yang-Ming University, Taipei
:: SCREENSHOTS
N/A
:: REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
:: DESCRIPTION
SCANSTAT considers a number of marker loci in the genome. At each marker, genotypes are available for two types of observations
::DEVELOPER
:: SCREENSHOTS
N/A
:: REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
Citation
Scan statistics to scan markers for susceptibility genes.
Hoh J, Ott J.
Proc Natl Acad Sci U S A. 2000 Aug 15;97(17):9615-7.
:: DESCRIPTION
GWAS Pathway Identifier combines GWAS(Genome-Wide Accociation Studies) and pathway data as well as known and predicted protein-interaction data to identify disease specific SNP sets.
::DEVELOPER
the Interfaculty Institute for Biomedical Informatics (IBMI)
:: SCREENSHOTS
:: REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
:: DESCRIPTION
VAAST (the Variant Annotation, Analysis and Search Tool) is a probabilistic search tool for identifying damaged genes and their disease-causing variants in personal genome sequences. VAAST builds upon existing amino acid substitution (AAS) and aggregative approaches to variant prioritization, combining elements of both into a single unified likelihood-framework that allows users to identify damaged genes and deleterious variants with greater accuracy, and in an easy-to-use fashion. VAAST can score both coding and non-coding variants, evaluating the cumulative impact of both types of variants simultaneously. VAAST can identify rare variants causing rare genetic diseases, and it can also use both rare and common variants to identify genes responsible for common diseases. VAAST thus has a much greater scope of use than any existing methodology.
::DEVELOPER
:: SCREENSHOTS
N/A
:: REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
Citation:
VAAST 2.0: improved variant classification and disease-gene identification using a conservation-controlled amino acid substitution matrix
Hu H Huff CD Moore B Flygare S Reese MG Yandell M
Genet Epidemiol. 2013 37(6):622-34.
A probabilistic disease-gene finder for personal genomes
Yandell M Huff CD Hu H Singleton M Moore B Xing J Jorde L Reese MG
Genome Res. 2011 doi:10.1101/gr.123158.111
:: DESCRIPTION
DigSee is a text mining search engine to provide evidence sentences describing that “genes” are involved in the development of “disease” through “biological events”. Biological events such as gene expression, regulation, phosphorylation, localization, and protein catabolism play important roles in the development of diseases. Understanding the association between diseases and genes can be enhanced with the identification of involved biological events in this association. With input of (disease, genes, events), users can obtain Medline abstracts with highlighted evidence sentences.
::DEVELOPER
Data Mining & Computational Biology Lab
:: SCREENSHOTS
N/A
:: REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
Citation
Kim J, So S, Lee HJ, Park JC, Kim JJ, Lee H.
DigSee: Disease gene search engine with evidence sentences (version cancer).
Nucleic Acids Res. 2013 Jul;41(Web Server issue):W510-7. doi: 10.1093/nar/gkt531. Epub 2013 Jun 12. PMID: 23761452; PMCID: PMC3692119.
:: DESCRIPTION
GeneCOST is a novel scoring based method to evaluate every gene for its disease association.
::DEVELOPER
Advanced Genomics and Bioinformatic Research Group, İGBAM
:: SCREENSHOTS
N/A
:: REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
Citation
Ozer B, Sağıroğlu M, Demirci H.
GeneCOST: a novel scoring-based prioritization framework for identifying disease causing genes.
Bioinformatics. 2015 Nov 15;31(22):3715-7. doi: 10.1093/bioinformatics/btv424. Epub 2015 Jul 21. PMID: 26203168.
:: DESCRIPTION
DAPPLE looks for significant physical connectivity among proteins encoded for by genes in loci associated to disease according to protein-protein interactions reported in the literature. The hypothesis behind DAPPLE is that causal genetic variation affects a limited set of underlying mechanisms that are detectable by protein-protein interactions.
::DEVELOPER
:: SCREENSHOTS
N/A
:: REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
Citation
Rossin EJ, Lage K, Raychaudhuri S, Xavier RJ, Tartar D, IIBDGC, Cotsapas C, Daly MJ. 2011
Proteins Encoded in Genomic Regions Associated with Immune-Mediated Disease Physically Interact and Suggest Underlying Biology.
PLoS Genetics 7(1): e1001273
:: DESCRIPTION
gTDT implemented gene-based or group-wise TDT for rare variant aggregation analysis. Currently gTDT implemented haplotype-based tests for 6 models, M1-M6. It takes as input a ped file and a dat file that specify the relationships, and a VCF file that stores genotype data.
::DEVELOPER
:: SCREENSHOTS
N/A
::REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
Citation
A haplotype-based framework for group-wise transmission/disequilibrium tests for rare variant association analysis.
Chen R, Wei Q, Zhan X, Zhong X, Sutcliffe J, Cox N, Cook EH, Li C, Chen W, Li B.
Bioinformatics. 2015 Jan 6. pii: btu860.
:: DESCRIPTION
diSNPselection is a Matlab code for disease informative SNP (diSNP) selection for gene-based association tests.
::DEVELOPER
:: SCREENSHOTS
N/A
:: REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
Citation:
Brief Bioinform. 2014 Mar;15(2):279-91. doi: 10.1093/bib/bbs087. Epub 2013 Jan 15.
Boosting signals in gene-based association studies via efficient SNP selection.
Wu C1, Cui Y.
:: DESCRIPTION
LRASSOC suite deals with the situation where we have a case-control sample of affected and unaffected individuals with their marker genotypes for 2 biallelic marker loci. These 2 marker loci may be in linkage disequilibrium with 1 or 2 biallelic disease susceptibility loci and therefore affect disease risk through association or may themselves be disease susceptibility loci. We are interested in modelling the effects of the genotype on the probability of disease risk in order to draw conclusions regarding the nature of the joint effect of the loci. Among the issues we may wish to investigate are whether either of the 2 loci actually has an effect on disease risk, the strength and statistical significance of any effect, the nature of such an effect e.g is the effect additive on some scale or do the alleles at the same loci interact in a dominance effect. We also want to compare single and joint locus models to investigate how the strength and significance of the effect of each locus is affected by the presence or absence of the other in a model and, a related point, whether the additive and dominance effects of two loci are independent or whether they interact (often called epistasis in this context).
::DEVELOPER
:: SCREENSHOTS
Command Line
:: REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
Citation:
North B.V., Sham P.C. and Curtis D.
“Application of logistic regression to case-control association studies involving two causative loci“,
Human Heredity (2005) 59: 79-87.
