title: Integrative analysis of independent transcriptome data for rare diseases.
abstract: High-throughput technologies used to interrogate transcriptomes have been generating a great amount of publicly available gene expression data. For rare diseases that lack of clinical samples and research funding, there is a practical benefit to jointly analyze existing data sets commonly related to a specific rare disease. In this study, we collected a number of independently generated transcriptome data sets from four species: human, fly, mouse and worm. All data sets included samples with both normal and abnormal mitochondrial function. We reprocessed each data set to standardize format, scale and gene annotation and used HomoloGene database to map genes between species. Standardized procedure was also applied to compare gene expression profiles of normal and abnormal mitochondrial function to identify differentially expressed genes. We further used meta-analysis and other integrative analyses to recognize patterns across data sets and species. Novel insights related to mitochondrial dysfunction was revealed via these analyses, such as a group of genes consistently dysregulated by impaired mitochondrial function in multiple species. This study created a template for the study of rare diseases using genomic technologies and advanced statistical methods. All data and results generated by this study are freely available and stored at http://goo.gl/nOGWC2, to support further data mining.
authors: Center for Biomedical Informatics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA. Electronic address: zhangz@email.chop.edu.
authors: Zhe
authors: Z
authors: Zhang
authors: Department of Computer Science, Wayne State University, 5057 Woodward Avenue, Suite 3010, Detroit, MI 48202, USA. Electronic address: zmhailat@wayne.edu.
authors: Zeyad
authors: Z
authors: Hailat
authors: Division of Human Genetics, The Children's Hospital of Philadelphia and University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA. Electronic address: falkm@email.chop.edu.
authors: Marni J
authors: MJ
authors: Falk
authors: Department of Computer Science, Wayne State University, 5057 Woodward Avenue, Suite 3010, Detroit, MI 48202, USA. Electronic address: xuewen.chen@wayne.edu.
authors: Xue-wen
authors: XW
authors: Chen
mesh_terms: D000818:Animals; D019295:Computational Biology / Q000379:methods*; D057225:Data Mining; D030541:Databases, Genetic; D006801:Humans; D020407:Internet; D051379:Mice; D008928:Mitochondria / Q000235:genetics* / Q000473:pathology; D058977:Molecular Sequence Annotation; D035583:Rare Diseases / Q000235:genetics* / Q000473:pathology; D059467:Transcriptome / Q000235:genetics*
keywords: Integrative analysis; Mitochondrial dysfunction; Transcriptome data