ArticleBMC bioinformatics2013
A novel method for cross-species gene expression analysis.
Article in BMC bioinformatics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 3 of them syntheses that pooled it.
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Who cites it
28 citing papers in PubMed, 3 syntheses or guidelines pooled it.
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- Meta- and cross-species analyses of insulin resistance based on gene expression datasets in human white adipose tissues.Scientific reports · 2018Pooled it
- Rethinking Meta-Analysis: Applications for Air Pollution Data and Beyond.Risk analysis : an official publication of the Society for Risk Analysis · 2015Pooled it
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- Improved integration of single-cell transcriptome data demonstrates common and unique signatures of heart failure in mice and humans.GigaScience · 2024Article
- CoSIA: an R Bioconductor package for CrOss Species Investigation and Analysis.Bioinformatics (Oxford, England) · 2023Article
- CoSIA: an R Bioconductor package for CrOss Species Investigation and Analysis.bioRxiv : the preprint server for biology · 2023Article
- A Phylogenetic Framework to Simulate Synthetic Interspecies RNA-Seq Data.Molecular biology and evolution · 2023Article
- Family dinner: Transcriptional plasticity of five Noctuidae (Lepidoptera) feeding on three host plant species.Ecology and evolution · 2022Article
- Bipartite tight spectral clustering (BiTSC) algorithm for identifying conserved gene co-clusters in two species.Bioinformatics (Oxford, England) · 2021Article
- Meta-Analysis-Assisted Detection of Gravity-Sensitive Genes in Human Vascular Endothelial Cells.Frontiers in cell and developmental biology · 2021Article
- A hybrid multi-objective whale optimization algorithm for analyzing microarray data based on Apache Spark.PeerJ. Computer science · 2021Article
- Cross-Species Annotation of Expressed Genes and Detection of Different Functional Gene Modules Between 10 Cold- and 10 Hot-Propertied Chinese Herbal Medicines.Frontiers in genetics · 2020Article
- Comparative transcriptomics sheds light on differential adaptation and species diversification between twoAoB PLANTS · 2019Article
- A statistical normalization method and differential expression analysis for RNA-seq data between different species.BMC bioinformatics · 2019Article
- Dissecting clinical outcome of porcine circovirus type 2 with in vivo derived transcriptomic signatures of host tissue responses.BMC genomics · 2018Article
- Inference and Evolutionary Analysis of Genome-Scale Regulatory Networks in Large Phylogenies.Cell systems · 2017Article
- Comprehensive identification of sexually dimorphic genes in diverse cattle tissues using RNA-seq.BMC genomics · 2016Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAnalysis of gene expression from different species is a powerful way to identify evolutionarily conserved transcriptional responses. However, due to evolutionary events such as gene duplication, there is no one-to-one correspondence between genes from different species which makes comparison of their expression profiles complex.
resultsIn this paper we describe a new method for cross-species meta-analysis of gene expression. The method takes the homology structure between compared species into account and can therefore compare expression data from genes with any number of orthologs and paralogs. A simulation study shows that the proposed method results in a substantial increase in statistical power compared to previously suggested procedures. As a proof of concept, we analyzed microarray data from heat stress experiments performed in eight species and identified several well-known evolutionarily conserved transcriptional responses. The method was also applied to gene expression profiles from five studies of estrogen exposed fish and both known and potentially novel responses were identified.
conclusionsThe method described in this paper will further increase the potential and reliability of meta-analysis of gene expression profiles from evolutionarily distant species. The method has been implemented in R and is freely available at http://bioinformatics.math.chalmers.se/Xspecies/.
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