AnyExpress: Integrated toolkit for analysis of cross-platform gene expression data using a fast interval matching algorithm

Background: Cross-platform analysis of gene express data requires multiple, intricate processes at different layers with various platforms. However, existing tools handle only a single platform and are not flexible enough to support custom changes, which arise from the new statistical methods, updated versions of reference data, and better platforms released every month or year. Current tools are so tightly coupled with reference information, such as reference genome, transcriptome database, and SNP, which are often erroneous or outdated, that the output results are incorrect and misleading. Results: We developed AnyExpress, a software package that combines cross-platform gene expression data using a fast interval-matching algorithm. Supported platforms include next-generation-sequencing technology, microarray, SAGE, MPSS, and more. Users can define custom target transcriptome database references for probe/read mapping in any species, as well as criteria to remove undesirable probes/r...

• Type: Journal
• Year: 2011
• Where: BMCBI
• Authors: Jihoon Kim, Kiltesh Patel, Hyunchul Jung, Winston Patrick Kuo, Lucila Ohno-Machado
• Keywords: Artificial Intelligence, BMCBI 2011, Correlation Coefficients, Cross-platform Gene Expression, Gene Expression Data