Cross species analysis of microarray expression data

Lu, Yong; Huggins, Peter; Bar‐Joseph, Ziv

doi:10.1093/bioinformatics/btp247

Cited by 91 publications

(85 citation statements)

References 62 publications

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“…To compare expression data between different species, microarrays can be processed in three different manners (Lu et al, 2009). One approach combines multiple microarray experiments to identify differentially expressed genes in each species independently and then compare these genes among different species (Mustroph et al, 2010).…”

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confidence: 99%

“…Another approach hybridizes samples from different but closely related species to the same microarray and requires similar experimental conditions as well as orthology information. Finally, separate arrays can be used to sample similar experimental conditions for different species, and all of them are analyzed together to investigate expression evolution of orthologs, paralogs, or specific functional categories (Lu et al, 2009). The latter approach was used, for example, to investigate species-specific gene duplications in human and mouse (Huminiecki and Wolfe, 2004).…”

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confidence: 99%

“…Based on graph theory, transcriptional networks were inferred revealing that highly connected genes are often conserved and have essential functional rules. Sets of genes that are conserved at both sequence and expression levels among multiple species are expected to play a key role in biological responses (Stuart et al, 2003;Lu et al, 2009).…”

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Comparative Network Analysis Reveals That Tissue Specificity and Gene Function Are Important Factors Influencing the Mode of Expression Evolution in Arabidopsis and Rice

2011

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Microarray experiments have yielded massive amounts of expression information measured under various conditions for the model species Arabidopsis (Arabidopsis thaliana) and rice (Oryza sativa). Expression compendia grouping multiple experiments make it possible to define correlated gene expression patterns within one species and to study how expression has evolved between species. We developed a robust framework to measure expression context conservation (ECC) and found, by analyzing 4,630 pairs of orthologous Arabidopsis and rice genes, that 77% showed conserved coexpression. Examples of nonconserved ECC categories suggested a link between regulatory evolution and environmental adaptations and included genes involved in signal transduction, response to different abiotic stresses, and hormone stimuli. To identify genomic features that influence expression evolution, we analyzed the relationship between ECC, tissue specificity, and protein evolution. Tissue-specific genes showed higher expression conservation compared with broadly expressed genes but were fast evolving at the protein level. No significant correlation was found between protein and expression evolution, implying that both modes of gene evolution are not strongly coupled in plants. By integration of cis-regulatory elements, many ECC conserved genes were significantly enriched for shared DNA motifs, hinting at the conservation of ancestral regulatory interactions in both model species. Surprisingly, for several tissue-specific genes, patterns of concerted network evolution were observed, unveiling conserved coexpression in the absence of conservation of tissue specificity. These findings demonstrate that orthologs inferred through sequence similarity in many cases do not share similar biological functions and highlight the importance of incorporating expression information when comparing genes across species.

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Comparative Network Analysis Reveals That Tissue Specificity and Gene Function Are Important Factors Influencing the Mode of Expression Evolution in Arabidopsis and Rice

2011

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“…However results have to be interpreted carefully because of variance in efficiency of probe-transcript hybridization, caused by differences in sequence similarities or e.g. number of gene copies, due to species-specific duplication events (Lu et al 2009). Unlike classical microarray experiments, RNA-seq does not require genome sequence information ), neither a priori knowledge of gene functions.…”

Section: Top-down Approachmentioning

confidence: 99%

Mechanisms of vegetative propagation in bulbs : a molecular approach

Moreno-Pachon

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“…Variation in hybridization efficiencies, due to species-inherent sequence differences in the targeted parts of mRNAs, and other technical issues made direct interpretations difficult. A number of normalization and analytics procedures helped to bypass these problems [36,37]. However, most of these shortcomings can now easily be avoided, thanks to the development of high-throughput next-generation sequencing (NGS) techniques [38].…”

Section: Homology and Gene Expression: Kernels Character Identity Nementioning

confidence: 99%

Deep homology in the age of next-generation sequencing

Tschopp

Tabin

2017

Phil. Trans. R. Soc. B

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One contribution of 17 to a theme issue 'Evodevo in the genomics era, and the origins of morphological diversity'. The principle of homology is central to conceptualizing the comparative aspects of morphological evolution. The distinctions between homologous or non-homologous structures have become blurred, however, as modern evolutionary developmental biology (evo-devo) has shown that novel features often result from modification of pre-existing developmental modules, rather than arising completely de novo. With this realization in mind, the term 'deep homology' was coined, in recognition of the remarkably conserved gene expression during the development of certain animal structures that would not be considered homologous by previous strict definitions. At its core, it can help to formulate an understanding of deeper layers of ontogenetic conservation for anatomical features that lack any clear phylogenetic continuity. Here, we review deep homology and related concepts in the context of a gene expression-based homology discussion. We then focus on how these conceptual frameworks have profited from the recent rise of high-throughput nextgeneration sequencing. These techniques have greatly expanded the range of organisms amenable to such studies. Moreover, they helped to elevate the traditional gene-by-gene comparison to a transcriptome-wide level. We will end with an outlook on the next challenges in the field and how technological advances might provide exciting new strategies to tackle these questions.This article is part of the themed issue 'Evo-devo in the genomics era, and the origins of morphological diversity'.

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Cross species analysis of microarray expression data

Cited by 91 publications

References 62 publications

Comparative Network Analysis Reveals That Tissue Specificity and Gene Function Are Important Factors Influencing the Mode of Expression Evolution in Arabidopsis and Rice

Comparative Network Analysis Reveals That Tissue Specificity and Gene Function Are Important Factors Influencing the Mode of Expression Evolution in Arabidopsis and Rice

Mechanisms of vegetative propagation in bulbs : a molecular approach

Deep homology in the age of next-generation sequencing

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