DNA-Chip Analyzer (dChip) is a Windows software package for probe-level (e.g. Affymetrix platform) and high-level analysis of gene expression microarrays and SNP microarrays.
Gene expression or SNP data from various microarray platforms can also be analyzed by importing as external dataset. At the probe level, dChip can display and normalize the CEL files, and the model-based approach allows pooling information across multiple arrays and automatic probe selection to handle cross-hybridization and image contamination. High-level analysis in dChip includes comparing samples, hierarchical clustering, view expression and SNP data along chromosome, LOH and copy number analysis of SNP arrays, and linkage analysis. In these functions the gene information and sample information are correlated with the analysis results.
Started in Wing Wong Lab , Developed & Maintained by Cheng Li Lab.
The GEC (Genetic Type I error calculator) is a Java-based application developed to address multiple-testing issue with dependent Single-nucleotide polymorphisms (SNPs). Based on this new measure, several popular multiple-testing methods including Bonferroni, Holm, Simes correction was improved to evaluate significance level of SNP p-values in genome-wide association studies.
SNPdat (SNP Data Analysis Tool) is a high throughput analysis tool that can provide a comprehensive annotation of both novel and known single nucleotide polymorphisms (SNPs). It is specifically designed for use with organisms which are either not supported by other tools or have a small number of annotated SNPs available, however it can also be used to analyse datasets from organisms which are densely sampled for SNPs. It can be used for analysis of any organism with a draft sequence and annotation. SNPdat makes possible analyses involving non-model organisms that are not supported by the vast majority of SNP annotation tools currently available.
Syzygy is a targeted sequencing post processing analysis tool that allows: 1. SNP and indel detection; 2. Allele frequency estimation; 3. Single-marker association test; 4. Group-wise marker test association; 5. Experimental QC summary (%dbSNP, Ts/Tv, Ns/S); 6. Power to detect variant.