The tools listed below are all unsupported tools that have been developed internally at Thermo Fisher Scientific. They are not validated products and are provided as is and without warranty. Use of these tools is offered to those customers who understand and accept the associated terms and conditions and wish to take advantage of their potential to help manage and analyze GeneChip™ data. If any of these tools are to be used in a production environment it is the responsibility of the end-user to perform the required validation.
Whenever possible, documentation is also provided to help provide guidance on use of the tools.
SNPolisher is an R package for post-process analyses of Axiom™ genotyping array results. SNPolisher generates cluster plots and density plots for each SNP to evaluate quality; genotypes OTV SNPs to produce AA, AB, BB, and OTV clusters; changes SNP calls during post-processing; tests for intensity shifts between batches; reformats Axiom output for use with fitTetra; and reformats fitTetra output for classification and visualization in SNPolisher. The input files are the standard output files from Axiom arrays.
Both R and perl (64-bit) must be installed for SNPolisher to run. To install SNPolisher, download the zipped package file. This folder contains the SNPolisher R package file, the User's Guide, the Quick Reference Card, and several example data sets. Unzip the folder, and then install the R package (SNPolisher_3.0.tar.gz). Follow the detailed instructions in the User Guide to install perl, R, and the SNPolisher package.
seg-limo detects regions of differential expression from multiple transcription tiling array data sets at a user specified False Discovery Rate. It outputs the regions together with differential expression estimates in egr format for convenient visualization in the Integrated Genome Browser. It is written in ANSI C++ and has been tested on Intel Linux, both 32 and 64 bit. The source code is being distributed under the GPL license.
ld_compare (version 1.0) is a tool for fast calculation of single-marker and multi-marker SNP correlations on a whole-genome scale. It is ideally suited for determination of genetic coverage for whole genome SNP panels and for generating raw SNP correlation data useful for selection of tag SNPs or evaluation of SNP tagging strategies.
For Research Use Only. Not for use in diagnostic procedures.