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with reference information, such as reference genome, transcriptome database, and SNP, which are often erroneous or outdated, that the output results are incorrect and misleading.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/reads. AnyExpress offers scalable processing features such as binding, normalization, and summarization that are not present in existing software tools. As a case study, we applied AnyExpress to published Affymetrix microarray and Illumina NGS RNA-Seq data from human kidney and liver. The mean of within-platform correlation coefficient was 0.98 for within-platform samples in kidney and liver, respectively. The mean of cross-platform correlation coefficients was 0.73. These results confirmed those of the original and secondary studies. Applying filtering produced higher agreement between microarray and NGS, according to an agreement index calculated from differentially expressed genes. CONCLUSION: AnyExpress can combine cross-platform gene expression data, process data from both open- and closed-platforms, select a custom target reference, filter out undesirable probes or reads based on custom-defined biological features, and perform quantile-normalization with a large number of microarray samples. AnyExpress is fast, comprehensive, flexible, and freely available at http://anyexpress.sourceforge.net.

Effect of data combination on predictive modeling: a study using gene expression data.

BACKGROUND: The quality of predictive modeling in biomedicine depends on the amount of data available for model building. OBJECTIVE: To study the effect of combining microarray data sets on feature selection and predictive modeling performance. METHODS: Empirical evaluation of stability of feature selection and discriminatory power of classifiers using three previously published gene expression data sets, analyzed both individually and in combination. RESULTS: Feature selection was not robust for the individual as well as for the combined data sets. The classification performance of models built on individual and combined data sets was heavily dependent on the data set from which the features were extracted. CONCLUSION: We identified volatility of feature selection as contributing factor to some of the problems faced by predictive modeling using microarray data.

PositionMatcher: A Fast Custom-Annotation Tool for Short DNA Sequences.

Microarray probes and reads from massively parallel sequencing technologies are two most widely used genomic tags for a transcriptome study. Names and underlying technologies might differ, but expression technologies share a common objective-to obtain mRNA abundance values at the gene level, with high sensitivity and specificity. However, the initial tag annotation becomes obsolete as more insight is gained into biological references (genome, transcriptome, SNP, etc.). While novel alignment algorithms for short reads are being released every month, solutions for rapid annotation of tags are rare. We have developed a generic matching algorithm that uses genomic positions for rapid custom-annotation of tags with a time complexity O(nlogn). We demonstrate our algorithm on the custom annotation of Illumina massively parallel sequencing reads and Affymetrix microarray probes and identification of alternatively spliced regions.