ArticleBMC bioinformatics2011
AnyExpress: integrated toolkit for analysis of cross-platform gene expression data using a fast interval matching algorithm.
Article in BMC bioinformatics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Predictive three-biomarker panel in peripheral blood mononuclear cells for detecting hepatocellular carcinoma.Scientific reports · 2024Article
- Revealing post-transcriptional microRNA-mRNA regulations in Alzheimer's disease through ensemble graphs.BMC genomics · 2018Article
- A regression framework for assessing covariate effects on the reproducibility of high-throughput experiments.Biometrics · 2018Article
- Genomic Approaches to Posttraumatic Stress Disorder: The Psychiatric Genomic Consortium Initiative.Biological psychiatry · 2018Review
- A semi-parametric statistical model for integrating gene expression profiles across different platforms.BMC bioinformatics · 2016Article
- AmalgamScope: merging annotations data across the human genome.BioMed research international · 2014Article
- virtualArray: a R/bioconductor package to merge raw data from different microarray platforms.BMC bioinformatics · 2013Article
- Microarray profiling reveals the integrated stress response is activated by halofuginone in mammary epithelial cells.BMC research notes · 2011Article
- iDASH: integrating data for analysis, anonymization, and sharing.Journal of the American Medical Informatics Association : JAMIAArticle
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Authors and funding
5 authors.
Funding
Abstract
backgroundCross-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with reference information, such as reference genome, transcriptome database, and SNP, which are often erroneous or outdated, that the output results are incorrect and misleading.
resultsWe 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.
conclusionAnyExpress 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.
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Registered trials
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