Mining housekeeping genes with a Naive Bayes classifier

Ferrari, Luna De; Aitken, Stuart

doi:10.1186/1471-2164-7-277

Cited by 69 publications

(47 citation statements)

References 26 publications

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“…C, D) Boxplots that display different degree of autosomal dosage compensation of the genes that are classified as housekeeping, or non-housekeeping based on the Naïve Bayes Classifier [40]. p values are from Mann-Whitney U tests.…”

Section: Resultsmentioning

confidence: 99%

Effects of Gene Dose, Chromatin, and Network Topology on Expression in Drosophila melanogaster

et al. 2016

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Deletions, commonly referred to as deficiencies by Drosophila geneticists, are valuable tools for mapping genes and for genetic pathway discovery via dose-dependent suppressor and enhancer screens. More recently, it has become clear that deviations from normal gene dosage are associated with multiple disorders in a range of species including humans. While we are beginning to understand some of the transcriptional effects brought about by gene dosage changes and the chromosome rearrangement breakpoints associated with them, much of this work relies on isolated examples. We have systematically examined deficiencies of the left arm of chromosome 2 and characterize gene-by-gene dosage responses that vary from collapsed expression through modest partial dosage compensation to full or even over compensation. We found negligible long-range effects of creating novel chromosome domains at deletion breakpoints, suggesting that cases of gene regulation due to altered nuclear architecture are rare. These rare cases include trans de-repression when deficiencies delete chromatin characterized as repressive in other studies. Generally, effects of breakpoints on expression are promoter proximal (~100bp) or in the gene body. Effects of deficiencies genome-wide are in genes with regulatory relationships to genes within the deleted segments, highlighting the subtle expression network defects in these sensitized genetic backgrounds.

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Section: Resultsmentioning

confidence: 99%

Effects of Gene Dose, Chromatin, and Network Topology on Expression in Drosophila melanogaster

et al. 2016

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“…Actually, compared to all other islands in the SOM, we found that ES100 shows a more than 10-fold enrichment in segments that have a housekeeping probability 0.75 according to De Ferrari et al [34]. By contrast genes associated with high CpG-density and bivalent chromatin (H3K4me3 and H3K27me3 modified) in ESCs have been linked to genes with more complex expression patterns among them key developmental genes [11].…”

Section: Resultsmentioning

confidence: 70%

A Global Genome Segmentation Method for Exploration of Epigenetic Patterns

et al. 2012

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Current genome-wide ChIP-seq experiments on different epigenetic marks aim at unraveling the interplay between their regulation mechanisms. Published evaluation tools, however, allow testing for predefined hypotheses only. Here, we present a novel method for annotation-independent exploration of epigenetic data and their inter-correlation with other genome-wide features. Our method is based on a combinatorial genome segmentation solely using information on combinations of epigenetic marks. It does not require prior knowledge about the data (e.g. gene positions), but allows integrating the data in a straightforward manner. Thereby, it combines compression, clustering and visualization of the data in a single tool. Our method provides intuitive maps of epigenetic patterns across multiple levels of organization, e.g. of the co-occurrence of different epigenetic marks in different cell types. Thus, it facilitates the formulation of new hypotheses on the principles of epigenetic regulation. We apply our method to histone modification data on trimethylation of histone H3 at lysine 4, 9 and 27 in multi-potent and lineage-primed mouse cells, analyzing their combinatorial modification pattern as well as differentiation-related changes of single modifications. We demonstrate that our method is capable of reproducing recent findings of gene centered approaches, e.g. correlations between CpG-density and the analyzed histone modifications. Moreover, combining the clustered epigenetic data with information on the expression status of associated genes we classify differences in epigenetic status of e.g. house-keeping genes versus differentiation-related genes. Visualizing the distribution of modification states on the chromosomes, we discover strong patterns for chromosome X. For example, exclusively H3K9me3 marked segments are enriched, while poised and active states are rare. Hence, our method also provides new insights into chromosome-specific epigenetic patterns, opening up new questions how “epigenetic computation” is distributed over the genome in space and time.

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“…[15], [18], [28]). The HK set identified by Ramskold et al [6] was also treated as gold-standard, since it was derived from RNAseq experiments, which can detect expression signals more comprehensively and at a higher resolution than conventional microarray experiments [34].…”

Section: Methodsmentioning

confidence: 99%

“…A lower TSI indicates a lower tendency for the gene to be TS (or a higher tendency for it to be HK). The 0.1 threshold was chosen following [16].eFor each gene, the Näive Bayes classifier [18] calculates a probability ( P ) of it being an HK gene; in this study, we choose those with a P value greater than 0.8 to be classified as HK genes.fAccording to [6], those genes with an RPKM (reads per kilobase of exon model per million mapped reads) score greater than 0.3 were classified as HK genes.…”

Section: Methodsmentioning

confidence: 99%

Partitioning the Human Transcriptome Using HKera, a Novel Classifier of Housekeeping and Tissue-Specific Genes

2013

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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.

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Mining housekeeping genes with a Naive Bayes classifier

Abstract: Background: Traditionally, housekeeping and tissue specific genes have been classified using direct assay of mRNA presence across different tissues, but these experiments are costly and the results not easy to compare and reproduce.

Cited by 69 publications

References 26 publications

Effects of Gene Dose, Chromatin, and Network Topology on Expression in Drosophila melanogaster

Effects of Gene Dose, Chromatin, and Network Topology on Expression in Drosophila melanogaster

A Global Genome Segmentation Method for Exploration of Epigenetic Patterns

Partitioning the Human Transcriptome Using HKera, a Novel Classifier of Housekeeping and Tissue-Specific Genes

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