title: Partitioning the human transcriptome using HKera, a novel classifier of housekeeping and tissue-specific genes.
abstract: High-throughput transcriptomic experiments have made it possible to classify genes that are ubiquitously expressed as housekeeping (HK) genes and those expressed only in selective tissues as tissue-specific (TS) genes. Although partitioning a transcriptome into HK and TS genes is conceptually problematic owing to the lack of precise definitions and gene expression profile criteria for the two, information whether a gene is an HK or a TS gene can provide an initial clue to its cellular and/or functional role. Consequently, the development of new and novel HK (TS) classification methods has been a topic of considerable interest in post-genomics research. Here, we report such a development. Our method, called HKera, differs from the others by utilizing a novel property of HK genes that we have previously uncovered, namely that the ranking order of their expression levels, as opposed to the expression levels themselves, tends to be preserved from one tissue to another. Evaluated against multiple benchmark sets of human HK genes, including one recently derived from second generation sequencing data, HKera was shown to perform significantly better than five other classifiers that use different methodologies. An enrichment analysis of pathway and gene ontology annotations showed that HKera-predicted HK and TS genes have distinct functional roles and, together, cover most of the ontology categories. These results show that HKera is a good transcriptome partitioner that can be used to search for, and obtain useful expression and functional information for, novel HK (TS) genes.
authors.affiliation: Bioinformatics Program, Taiwan International Graduate Program, Institute of Information Science, Academia Sinica, Taipei, Taiwan ; Institute of BioMedical Informatics, NationalYang-MingUniversity, Taipei, Taiwan ; Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan.
authors.forename: Austin W T
authors.initials: AW
authors.lastname: Chiang
authors.affiliation: Institute of BioMedical Informatics, NationalYang-MingUniversity, Taipei, Taiwan ; Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan.
authors.forename: Grace T W
authors.initials: GT
authors.lastname: Shaw
authors.affiliation: Bioinformatics Program, Taiwan International Graduate Program, Institute of Information Science, Academia Sinica, Taipei, Taiwan ; Institute of BioMedical Informatics, NationalYang-MingUniversity, Taipei, Taiwan ; Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan.
authors.forename: Ming-Jing
authors.initials: MJ
authors.lastname: Hwang
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D020869:Gene Expression Profiling / Q000706:statistics & numerical data*; D020043:Genes, Essential; D023281:Genomics / Q000379:methods / Q000706:statistics & numerical data; D006801:Humans; D058977:Molecular Sequence Annotation; D009928:Organ Specificity; D059467:Transcriptome