Large scale comparison of global gene expression patterns in human and mouse
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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01 Mar 2001
TL;DR: Using singular value decomposition in transforming genome-wide expression data from genes x arrays space to reduced diagonalized "eigengenes" x "eigenarrays" space gives a global picture of the dynamics of gene expression, in which individual genes and arrays appear to be classified into groups of similar regulation and function, or similar cellular state and biological phenotype.
Abstract: ‡We describe the use of singular value decomposition in transforming genome-wide expression data from genes 3 arrays space to reduced diagonalized ‘‘eigengenes’’ 3 ‘‘eigenarrays’’ space, where the eigengenes (or eigenarrays) are unique orthonormal superpositions of the genes (or arrays). Normalizing the data by filtering out the eigengenes (and eigenarrays) that are inferred to represent noise or experimental artifacts enables meaningful comparison of the expression of different genes across different arrays in different experiments. Sorting the data according to the eigengenes and eigenarrays gives a global picture of the dynamics of gene expression, in which individual genes and arrays appear to be classified into groups of similar regulation and function, or similar cellular state and biological phenotype, respectively. After normalization and sorting, the significant eigengenes and eigenarrays can be associated with observed genome-wide effects of regulators, or with measured samples, in which these regulators are overactive or underactive, respectively.
1,815 citations
TL;DR: The utility of the gene expression data that are in the public domain and how researchers are making use of these data are discussed and recommendations are provided that can improve the utility of such data.
Abstract: Our understanding of gene expression has changed dramatically over the past decade, largely catalysed by technological developments. High-throughput experiments - microarrays and next-generation sequencing - have generated large amounts of genome-wide gene expression data that are collected in public archives. Added-value databases process, analyse and annotate these data further to make them accessible to every biologist. In this Review, we discuss the utility of the gene expression data that are in the public domain and how researchers are making use of these data. Reuse of public data can be very powerful, but there are many obstacles in data preparation and analysis and in the interpretation of the results. We will discuss these challenges and provide recommendations that we believe can improve the utility of such data.
335 citations
TL;DR: It is demonstrated that PhenomeNET can identify orthologous genes, genes involved in the same pathway and gene–disease associations through the comparison of mutant phenotypes, and is applied to prioritize genes for rare and orphan diseases for which the molecular basis is unknown.
Abstract: Phenotypes are investigated in model organisms to understand and reveal the molecular mechanisms underlying disease. Phenotype ontologies were developed to capture and compare phenotypes within the context of a single species. Recently, these ontologies were augmented with formal class definitions that may be utilized to integrate phenotypic data and enable the direct comparison of phenotypes between different species. We have developed a method to transform phenotype ontologies into a formal representation, combine phenotype ontologies with anatomy ontologies, and apply a measure of semantic similarity to construct the PhenomeNET cross-species phenotype network. We demonstrate that PhenomeNET can identify orthologous genes, genes involved in the same pathway and gene–disease associations through the comparison of mutant phenotypes. We provide evidence that the Adam19 and Fgf15 genes in mice are involved in the tetralogy of Fallot, and, using zebrafish phenotypes, propose the hypothesis that the mammalian homologs of Cx36.7 and Nkx2.5 lie in a pathway controlling cardiac morphogenesis and electrical conductivity which, when defective, cause the tetralogy of Fallot phenotype. Our method implements a whole-phenome approach toward disease gene discovery and can be applied to prioritize genes for rare and orphan diseases for which the molecular basis is unknown.
221 citations
Cites background from "Large scale comparison of global ge..."
...As orthologous genes tend to be associated with related phenotypes and share common patterns of gene expression across species (15,16), the former provides a useful validation of the PhenomeNET approach....
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TL;DR: The Gene Expression Atlas is an added-value database providing information about gene expression in different cell types, organism parts, developmental stages, disease states, sample treatments and other biological/experimental conditions.
Abstract: Gene Expression Atlas (http://www.ebi.ac.uk/gxa) is an added-value database providing information about gene expression in different cell types, organism parts, developmental stages, disease states, sample treatments and other biological/experimental conditions. The content of this database derives from curation, re-annotation and statistical analysis of selected data from the ArrayExpress Archive and the European Nucleotide Archive. A simple interface allows the user to query for differential gene expression either by gene names or attributes or by biological conditions, e.g. diseases, organism parts or cell types. Since our previous report we made 20 monthly releases and, as of Release 11.08 (August 2011), the database supports 19 species, which contains expression data measured for 19,014 biological conditions in 136,551 assays from 5598 independent studies.
166 citations
Cites methods from "Large scale comparison of global ge..."
...The Functional Genomics group has now produced two more data sets of this nature, including a global mouse data re-analysis (7) and an order-of-magnitude expansion of the human Affymetrix data set (unpublished data)....
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TL;DR: Cross-species comparisons of genomes, transcriptomes and gene regulation are now feasible at unprecedented resolution and throughput, enabling the comparison of human and mouse biology at the molecular level.
Abstract: Cross-species comparisons of genomes, transcriptomes and gene regulation are now feasible at unprecedented resolution and throughput, enabling the comparison of human and mouse biology at the molecular level. Insights have been gained into the degree of conservation between human and mouse at the level of not only gene expression but also epigenetics and inter-individual variation. However, a number of limitations exist, including incomplete transcriptome characterization and difficulties in identifying orthologous phenotypes and cell types, which are beginning to be addressed by emerging technologies. Ultimately, these comparisons will help to identify the conditions under which the mouse is a suitable model of human physiology and disease, and optimize the use of animal models.
158 citations
References
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TL;DR: By assembling these links into a gene-coexpression network, this work found several components that were animal-specific as well as interrelationships between newly evolved and ancient modules.
Abstract: To elucidate gene function on a global scale, we identified pairs of genes that are coexpressed over 3182 DNA microarrays from humans, flies, worms, and yeast. We found 22,163 such coexpression relationships, each of which has been conserved across evolution. This conservation implies that the coexpression of these gene pairs confers a selective advantage and therefore that these genes are functionally related. Manyof these relationships provide strong evidence for the involvement of new genes in core biological functions such as the cell cycle, secretion, and protein expression. We experimentallyconfirmed the predictions implied bysome of these links and identified cell proliferation functions for several genes. By assembling these links into a gene-coexpression network, we found several components that were animal-specific as well as interrelationships between newly evolved and ancient modules.
2,210 citations
"Large scale comparison of global ge..." refers background in this paper
...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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TL;DR: In this article, singular value decomposition is used to transform genome-wide expression data from genes 3 arrays space to reduced diagonalized eigengenes 3 eigenarrays space.
Abstract: ‡We describe the use of singular value decomposition in transforming genome-wide expression data from genes 3 arrays space to reduced diagonalized ‘‘eigengenes’’ 3 ‘‘eigenarrays’’ space, where the eigengenes (or eigenarrays) are unique orthonormal superpositions of the genes (or arrays). Normalizing the data by filtering out the eigengenes (and eigenarrays) that are inferred to represent noise or experimental artifacts enables meaningful comparison of the expression of different genes across different arrays in different experiments. Sorting the data according to the eigengenes and eigenarrays gives a global picture of the dynamics of gene expression, in which individual genes and arrays appear to be classified into groups of similar regulation and function, or similar cellular state and biological phenotype, respectively. After normalization and sorting, the significant eigengenes and eigenarrays can be associated with observed genome-wide effects of regulators, or with measured samples, in which these regulators are overactive or underactive, respectively.
1,824 citations
01 Mar 2001
TL;DR: Using singular value decomposition in transforming genome-wide expression data from genes x arrays space to reduced diagonalized "eigengenes" x "eigenarrays" space gives a global picture of the dynamics of gene expression, in which individual genes and arrays appear to be classified into groups of similar regulation and function, or similar cellular state and biological phenotype.
Abstract: ‡We describe the use of singular value decomposition in transforming genome-wide expression data from genes 3 arrays space to reduced diagonalized ‘‘eigengenes’’ 3 ‘‘eigenarrays’’ space, where the eigengenes (or eigenarrays) are unique orthonormal superpositions of the genes (or arrays). Normalizing the data by filtering out the eigengenes (and eigenarrays) that are inferred to represent noise or experimental artifacts enables meaningful comparison of the expression of different genes across different arrays in different experiments. Sorting the data according to the eigengenes and eigenarrays gives a global picture of the dynamics of gene expression, in which individual genes and arrays appear to be classified into groups of similar regulation and function, or similar cellular state and biological phenotype, respectively. After normalization and sorting, the significant eigengenes and eigenarrays can be associated with observed genome-wide effects of regulators, or with measured samples, in which these regulators are overactive or underactive, respectively.
1,815 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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TL;DR: Principal component analysis is often incorporated into genome-wide expression studies, but what is it and how can it be used to explore high-dimensional data?
Abstract: Principal component analysis is often incorporated into genome-wide expression studies, but what is it and how can it be used to explore high-dimensional data?
1,538 citations
TL;DR: A comprehensive gene orientated phylogenetic resource, EnsemblCompara GeneTrees, based on a computational pipeline to handle clustering, multiple alignment, and tree generation, including the handling of large gene families, is developed.
Abstract: The use of phylogenetic trees to describe the evolution of biological processes was established in the 1950s (Hennig 1952) and remains a fundamental approach to understanding the evolution of individual genes through to complete genomes; for example, in the mouse (Mouse Genome Sequencing Consortium 2002), rat (Gibbs et al. 2004), chicken (International Chicken Genome Sequencing Consortium 2004), and monodelphis (Mikkelsen et al. 2007) genome papers, and numerous papers on individual sequences. Now routine, the determination of vertebrate genome sequences provides a rich data source to understand evolution, and using phylogenetic trees of the genes is one of the best ways to organize these data. However, the increased set of genomes makes the compute and engineering tasks to form all the gene trees progressively more complex and harder for individual groups to use. The Ensembl project provides an accurate and consistent protein-coding gene set for all vertebrate genomes (International Human Genome Sequencing Consortium 2001; Dehal et al. 2002; Mouse Genome Sequencing Consortium 2002; Gibbs et al. 2004; Xie et al. 2005; Mikkelsen et al. 2007; Rhesus Macaque Genome Sequencing and Analysis Consortium 2007). Previously (until April 2006), Ensembl provided a basic method for tracing orthologs via the Best Reciprocal BLAST method, similar to approaches used in other genome analyses, such as Drosophila melanogaster (Adams et al. 2000) or human (International Human Genome Sequencing Consortium 2001). In June 2006 (Hubbard et al. 2007), we replaced this system with a phylogenetically sound, gene tree-based approach, providing a complete set of phylogenetic trees spanning 91% of genes across vertebrates. In addition to the vertebrates we have included a few important non-vertebrate species (fly, worm, and yeast) to act both as out groups and provide links to these model organisms. In this paper we provide the motivation, implementation, and benchmarking of this method and document the display and access methods for these trees.
There have been a number of methods proposed for routine generation of genomewide orthology descriptions, including Inparanoid (Remm et al. 2001), MSOAR (Fu et al. 2007), OrthoMCL (Li et al. 2003), HomoloGene (Wheeler et al. 2008), TreeFam (Li et al. 2006), PhyOP (Goodstadt and Ponting 2006), and PhiGs (Dehal and Boore 2006). The first four, Inparanoid, MSOAR, OrthoMCL, and HomoloGene, focus on providing clusters (or linked clusters) of genes, without an explicit tree topology. PhyOP (Goodstadt and Ponting 2006) uses a tree-based method, but between pairs of closely related species, resolving paralogs accurately by using neutral substitution (as measured by d S, the synonymous substitution rate). TreeFam provides an explicit gene tree across multiple species, using both d S, d N (nonsynonymous substitution rate), nucleotide and protein distance measures, and the standard species tree to balance duplications vs. deletions to inform the tree construction, using the program TreeBeST (http://treesoft.sourceforge.net/treebest.shtml; L. Heng, A.J. Vilella, E. Birney, and R. Durbin, in prep.).
The PhiGs method (Dehal and Boore 2006) is a leading phylogenetic-based method that produced a comprehensive phylogenetic resource for the genomes at the time it was run, and the basic outline of its analysis, which was clustering of protein sequences, followed by phylogenetic trees, is similar to the method presented here. However, the PhiGs resource covered a smaller number of species (23 vs. 45) and has been difficult to keep up to date with the advances in gene sets and genomes. Another major difference between PhiG-based phylogenetic trees and the phylogenetic trees presented here is that the former was calculated using a single maximum likelihood method based on protein evolution. In contrast, the Ensembl gene trees are calculated using a new method, TreeBeST, which integrates multiple tree topologies, in particular both DNA level and protein level models and combines this with a species-tree aware penalization of topologies, which are inconsistent with known species relationships. We show in this paper that this method produces trees that are more consistent with synteny relationships and less anomalous topologies than single protein-based phylogenetic methods.
There are also many single phylogenetic tree-building approaches, many of them based on maximum likelihood methods; one leading method is PhyML (Guindon and Gascuel 2003). It is unclear what is the best method to use, in particular in the context of genome-wide tree building with constraints on computational costs and the need to robustly handle many complex scenarios usually involving large families with heterogeneous phylogenetic depths. In this paper, we benchmark in vertebrates the tree programs TreeBeST and PhyML, and the resulting trees to basic best reciprocal hit (BRH) methods, and cluster frameworks, in particular Inparanoid and HomoloGene. We also benchmark to a recent PhyOP data set. The PhyOP pipeline has recently switched to use the same tree-building program (TreeBeST) that we use, but differs in its input clusters. Although we adopted this same tree-building method, we describe here considerable novel engineering in the deployment of these methods across all vertebrates. Similar to the PhiGs resource, we have used the dense coverage of genomes to provide topologically based timings (i.e., the standard use of outgroups vs. subsequent lineages to bracket a duplication), in order to label duplication events.
1,135 citations
"Large scale comparison of global ge..." refers methods in this paper
...The pairing of these orthologous probesets was done based on gene orthologs obtained from Ensembl Compara [33]....
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