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

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TLDR
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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A Comprehensive Mouse Transcriptomic BodyMap across 17 Tissues by RNA-seq

TL;DR: A comprehensive mouse transcriptomic BodyMap across 17 tissues of six-weeks old C57BL/6JJcl mice using RNA-seq is constructed, finding different expression patterns between protein-coding and non-c coding genes.
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Enteric glia express proteolipid protein 1 and are a transcriptionally unique population of glia in the mammalian nervous system

TL;DR: Enteric glia are transcriptionally unique and distinct from other cell types in the nervous system, suggesting that a combinatorial code of molecular markers can be used to identify distinct subtypes.
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Systems medicine and metabolic modelling

TL;DR: Mardinoglu A, Nielsen J (University of Technology, Gothenburg, Sweden).
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A comparison of human and mouse gene co-expression networks reveals conservation and divergence at the tissue, pathway and disease levels

TL;DR: Conservation of co-expression is a powerful approach to identify gene targets and processes with potential similarity and divergence between mice and humans, which has implications for drug testing and other studies employing the mouse as a model organism.
References
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Journal ArticleDOI

Exploration, normalization, and summaries of high density oligonucleotide array probe level data

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.
Journal ArticleDOI

A gene atlas of the mouse and human protein-encoding transcriptomes

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.
Journal ArticleDOI

Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks

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.
Book

Bioinformatics and Computational Biology Solutions Using R and Bioconductor

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.
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