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Journal ArticleDOI

Large scale comparison of global gene expression patterns in human and mouse

23 Dec 2010-Genome Biology (BioMed Central)-Vol. 11, Iss: 12, pp 1-11
TL;DR: The results indicate that the global patterns of tissue-specific expression of orthologous genes are conserved in human and mouse.
Abstract: It is widely accepted that orthologous genes between species are conserved at the sequence level and perform similar functions in different organisms. However, the level of conservation of gene expression patterns of the orthologous genes in different species has been unclear. To address the issue, we compared gene expression of orthologous genes based on 2,557 human and 1,267 mouse samples with high quality gene expression data, selected from experiments stored in the public microarray repository ArrayExpress. In a principal component analysis (PCA) of combined data from human and mouse samples merged on orthologous probesets, samples largely form distinctive clusters based on their tissue sources when projected onto the top principal components. The most prominent groups are the nervous system, muscle/heart tissues, liver and cell lines. Despite the great differences in sample characteristics and experiment conditions, the overall patterns of these prominent clusters are strikingly similar for human and mouse. We further analyzed data for each tissue separately and found that the most variable genes in each tissue are highly enriched with human-mouse tissue-specific orthologs and the least variable genes in each tissue are enriched with human-mouse housekeeping orthologs. The results indicate that the global patterns of tissue-specific expression of orthologous genes are conserved in human and mouse. The expression of groups of orthologous genes co-varies in the two species, both for the most variable genes and the most ubiquitously expressed genes.

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Citations
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Journal ArticleDOI
TL;DR: Li et al. as discussed by the authors proposed the cross-species gene set enrichment problem (XGSEP) to identify gene sets with statistically significant difference between cases and controls against a large gene set.
Abstract: MOTIVATION Gene set enrichment analysis (GSEA) has been widely used to identify gene sets with statistically significant difference between cases and controls against a large gene set. GSEA needs both phenotype labels and expression of genes. However, gene expression are assessed more often for model organisms than minor species. Also, importantly gene expression are not measured well under specific conditions for human, due to high risk of direct experiments, such as non-approved treatment or gene knockout, and then often substituted by mouse. Thus, predicting enrichment significance (on a phenotype) of a given gene set of a species (target, say human), by using gene expression measured under the same phenotype of the other species (source, say mouse) is a vital and challenging problem, which we call CROSS-species gene set enrichment problem (XGSEP). RESULTS For XGSEP, we propose the CROSS-species gene set enrichment analysis (XGSEA), with three steps of: (1) running GSEA for a source species to obtain enrichment scores and $p$-values of source gene sets; (2) representing the relation between source and target gene sets by domain adaptation; and (3) using regression to predict $p$-values of target gene sets, based on the representation in (2). We extensively validated the XGSEA by using five regression and one classification measurements on four real data sets under various settings, proving that the XGSEA significantly outperformed three baseline methods in most cases. A case study of identifying important human pathways for T -cell dysfunction and reprogramming from mouse ATAC-Seq data further confirmed the reliability of the XGSEA. AVAILABILITY Source code of the XGSEA is available through https://github.com/LiminLi-xjtu/XGSEA.

8 citations

Dissertation
15 Dec 2011
TL;DR: In this paper, a nouvelle mesure de similarite semantique et fonctionnelle (IntelliGO) entre les genes, which exploitite au mieux les annotations fonoctionnelles issues of l'ontologie GO ('Gene Ontology'), is presented.
Abstract: L'analyse bioinformatique des donnees de transcriptomique a pour but d'identifier les genes qui presentent des variations d'expression entre differentes situations, par exemple entre des echantillons de tissu sain et de tissu malade et de caracteriser ces genes a partir de leurs annotations fonctionnelles. Dans ce travail de these, je propose quatre contributions pour la prise en compte des connaissances du domaine dans ces methodes. Tout d'abord je definis une nouvelle mesure de similarite semantique et fonctionnelle (IntelliGO) entre les genes, qui exploite au mieux les annotations fonctionnelles issues de l'ontologie GO ('Gene Ontology'). Je montre ensuite, grâce a une methodologie d'evaluation rigoureuse, que la mesure IntelliGO est performante pour la classification fonctionnelle des genes. En troisieme contribution je propose une approche differentielle avec affectation floue pour la construction de profils d'expression differentielle (PED). Je definis alors un algorithme d'analyse de recouvrement entre classes fonctionnelles et ensemble des references, ici les PEDs, pour mettre en evidence des genes ayant a la fois les memes variations d'expression et des annotations fonctionnelles similaires. Cette methode est appliquee a des donnees experimentales produites a partir d'echantillons de tissus sains, de tumeur colo-rectale et de lignee cellulaire cancereuse. Finalement, la mesure de similarite IntelliGO est generalisee a d'autres vocabulaires structures en graphe acyclique dirige et enracine (rDAG) comme l'est l'ontologie GO, avec un exemple d'application concernant la reduction semantique d'attributs avant la fouille.

7 citations

Posted ContentDOI
22 Jun 2017-bioRxiv
TL;DR: These findings enhance the understanding of the role of WGD in genome evolution and highlights cases of functional divergence of Ss4R duplicates, possibly related to a niche shift in early salmonid evolution.
Abstract: Whole genome duplication (WGD) has been a major evolutionary driver of increased genomic complexity in vertebrates, yet little is known about how selection operates on the resulting gene duplicates. Here, we present a draft genome assembly of a salmonid species, European grayling (Thymallus thymallus) and use comparative genomics and transcriptomics to understand evolutionary consequences of WGD in the genome of salmonid ancestor ~80 million years ago (Ss4R). We find evidence for lineage-specific rates in rediploidization and that ~60% of the Ss4R ohnologs have experienced different types of non-neutral evolution of tissue-specific gene expression regulation. Distinct selective pressures were associated with tissue type, biological function and selection pressure on protein coding sequence. Finally, our results indicate the role of adaptive divergence of Ss4R duplicates in the evolution of salmonid metabolism and identifies loss of purifying selection on one Ss4R ohnolog encoding a key chloride pump linked to the evolution of anadromy.

6 citations


Cites background from "Large scale comparison of global ge..."

  • ...A strong expression conservation pattern in brain related genes has been described across vertebrates [42, 43, 44, 45]....

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Journal ArticleDOI
TL;DR: By applying Episo to public human and mouse RNA-BisSeq data, it is found that the RNA m5C is not evenly distributed among the transcript isoforms, implying the m5c may subject to be regulated at isoform level.
Abstract: MOTIVATION RNA 5-methylcytosine (m5C) is a type of post-transcriptional modification that may be involved in numerous biological processes and tumorigenesis. RNA m5C can be profiled at single-nucleotide resolution by high-throughput sequencing of RNA treated with bisulfite (RNA-BisSeq). However, the exploration of transcriptome-wide profile and potential function of m5C in splicing remains to be elucidated due to lack of isoform level m5C quantification tool. RESULTS We developed a computational package to quantify Epitranscriptomal RNA m5C at the transcript isoform level (named Episo). Episo consists of three tools: mapper, quant and Bisulfitefq, for mapping, quantifying and simulating RNA-BisSeq data, respectively. The high accuracy of Episo was validated using an improved m5C-specific methylated RNA immunoprecipitation (meRIP) protocol, as well as a set of in silico experiments. By applying Episo to public human and mouse RNA-BisSeq data, we found that the RNA m5C is not evenly distributed among the transcript isoforms, implying the m5C may subject to be regulated at isoform level. AVAILABILITY AND IMPLEMENTATION Episo is released under the GNU GPLv3+ license. The resource code Episo is freely accessible from https://github.com/liujunfengtop/Episo (with Tophat/cufflink) and https://github.com/liujunfengtop/Episo/tree/master/Episo_Kallisto (with Kallisto). SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

6 citations

References
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Journal ArticleDOI
TL;DR: The Gene Set Enrichment Analysis (GSEA) method as discussed by the authors focuses on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation.
Abstract: Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.

34,830 citations

Journal ArticleDOI
TL;DR: There is no obvious downside to using RMA and attaching a standard error (SE) to this quantity using a linear model which removes probe-specific affinities, and the exploratory data analyses of the probe level data motivate a new summary measure that is a robust multi-array average (RMA) of background-adjusted, normalized, and log-transformed PM values.
Abstract: SUMMARY In this paper we report exploratory analyses of high-density oligonucleotide array data from the Affymetrix GeneChip R � system with the objective of improving upon currently used measures of gene expression. Our analyses make use of three data sets: a small experimental study consisting of five MGU74A mouse GeneChip R � arrays, part of the data from an extensive spike-in study conducted by Gene Logic and Wyeth’s Genetics Institute involving 95 HG-U95A human GeneChip R � arrays; and part of a dilution study conducted by Gene Logic involving 75 HG-U95A GeneChip R � arrays. We display some familiar features of the perfect match and mismatch probe ( PM and MM )v alues of these data, and examine the variance–mean relationship with probe-level data from probes believed to be defective, and so delivering noise only. We explain why we need to normalize the arrays to one another using probe level intensities. We then examine the behavior of the PM and MM using spike-in data and assess three commonly used summary measures: Affymetrix’s (i) average difference (AvDiff) and (ii) MAS 5.0 signal, and (iii) the Li and Wong multiplicative model-based expression index (MBEI). The exploratory data analyses of the probe level data motivate a new summary measure that is a robust multiarray average (RMA) of background-adjusted, normalized, and log-transformed PM values. We evaluate the four expression summary measures using the dilution study data, assessing their behavior in terms of bias, variance and (for MBEI and RMA) model fit. Finally, we evaluate the algorithms in terms of their ability to detect known levels of differential expression using the spike-in data. We conclude that there is no obvious downside to using RMA and attaching a standard error (SE) to this quantity using a linear model which removes probe-specific affinities. ∗ To whom correspondence should be addressed

10,711 citations


"Large scale comparison of global ge..." refers methods in this paper

  • ...The resulting 1,323 CEL files were pre-processed using Bioconductor’s RMA package [32] to create an integrated, normalized data matrix....

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Journal ArticleDOI
TL;DR: In this paper, high-density oligonucleotide arrays offer the opportunity to examine patterns of gene expression on a genome scale, and the authors have designed custom arrays that interrogate the expression of the vast majority of proteinencoding human and mouse genes and have used them to profile a panel of 79 human and 61 mouse tissues.
Abstract: The tissue-specific pattern of mRNA expression can indicate important clues about gene function. High-density oligonucleotide arrays offer the opportunity to examine patterns of gene expression on a genome scale. Toward this end, we have designed custom arrays that interrogate the expression of the vast majority of protein-encoding human and mouse genes and have used them to profile a panel of 79 human and 61 mouse tissues. The resulting data set provides the expression patterns for thousands of predicted genes, as well as known and poorly characterized genes, from mice and humans. We have explored this data set for global trends in gene expression, evaluated commonly used lines of evidence in gene prediction methodologies, and investigated patterns indicative of chromosomal organization of transcription. We describe hundreds of regions of correlated transcription and show that some are subject to both tissue and parental allele-specific expression, suggesting a link between spatial expression and imprinting.

3,513 citations


"Large scale comparison of global ge..." refers background or result in this paper

  • ...While studies suggested that orthologous genes do not share similar expression patterns [1-5], other groups reported the opposite observations [6-9]....

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  • ...Alternatively, many other studies made use of species-specific arrays to identify coexpressed groups of orthologous genes [4-6,16,17]....

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Journal ArticleDOI
TL;DR: The ability of the trained ANN models to recognize SRBCTs is demonstrated, and the potential applications of these methods for tumor diagnosis and the identification of candidate targets for therapy are demonstrated.
Abstract: The purpose of this study was to develop a method of classifying cancers to specific diagnostic categories based on their gene expression signatures using artificial neural networks (ANNs). We trained the ANNs using the small, round blue-cell tumors (SRBCTs) as a model. These cancers belong to four distinct diagnostic categories and often present diagnostic dilemmas in clinical practice. The ANNs correctly classified all samples and identified the genes most relevant to the classification. Expression of several of these genes has been reported in SRBCTs, but most have not been associated with these cancers. To test the ability of the trained ANN models to recognize SRBCTs, we analyzed additional blinded samples that were not previously used for the training procedure, and correctly classified them in all cases. This study demonstrates the potential applications of these methods for tumor diagnosis and the identification of candidate targets for therapy.

2,683 citations


"Large scale comparison of global ge..." refers methods in this paper

  • ...PCA has been often used to study high-dimensional data generated by genome-wide gene expression studies [22-25]....

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Book
27 Jan 2006
TL;DR: In this article, the authors present a detailed case study of R algorithms with publicly available data, and a major section of the book is devoted to fully worked case studies, with a companion website where readers can reproduce every number, figure and table on their own computers.
Abstract: Full four-color book. Some of the editors created the Bioconductor project and Robert Gentleman is one of the two originators of R. All methods are illustrated with publicly available data, and a major section of the book is devoted to fully worked case studies. Code underlying all of the computations that are shown is made available on a companion website, and readers can reproduce every number, figure, and table on their own computers.

2,625 citations

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