SynthesisBMC genomics2011
Meta-analysis of muscle transcriptome data using the MADMuscle database reveals biologically relevant gene patterns.
Synthesis in BMC genomics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 2 of them syntheses that pooled it.
What it found
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Who cites it
10 citing papers in PubMed, 2 syntheses or guidelines pooled it, 22 citations in OpenAlex.
- A common gene signature across multiple studies relate biomarkers and functional regulation in tolerance to renal allograft.Kidney international · 2015Pooled it
- Transcriptional assessment by microarray analysis and large-scale meta-analysis of the metabolic capacity of cardiac and skeletal muscle tissues to cope with reduced nutrient availability in Gilthead Sea Bream (Sparus aurata L.).Marine biotechnology (New York, N.Y.) · 2014Pooled it
- Integrative genomics analysis of nasal intestinal-type adenocarcinomas demonstrates the major role of CACNA1C and paves the way for a simple diagnostic tool in male woodworkers.Clinical epigenetics · 2021Article
- Implication of molecular vascular smooth muscle cell heterogeneity among arterial beds in arterial calcification.PloS one · 2018Article
- Prokineticin receptor-1-dependent paracrine and autocrine pathways control cardiac tcf21Scientific reports · 2017Article
- Article
- Pharmacologic management of Duchenne muscular dystrophy: target identification and preclinical trials.ILAR journal · 2014Review
- Article
- Article
- Immune response and mitochondrial metabolism are commonly deregulated in DMD and aging skeletal muscle.PloS one · 2011Article
Corrections and comments
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Authors and funding
12 authors at 6 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDNA microarray technology has had a great impact on muscle research and microarray gene expression data has been widely used to identify gene signatures characteristic of the studied conditions. With the rapid accumulation of muscle microarray data, it is of great interest to understand how to compare and combine data across multiple studies. Meta-analysis of transcriptome data is a valuable method to achieve it. It enables to highlight conserved gene signatures between multiple independent studies. However, using it is made difficult by the diversity of the available data: different microarray platforms, different gene nomenclature, different species studied, etc. DESCRIPTION: We have developed a system tool dedicated to muscle transcriptome data. This system comprises a collection of microarray data as well as a query tool. This latter allows the user to extract similar clusters of co-expressed genes from the database, using an input gene list. Common and relevant gene signatures can thus be searched more easily. The dedicated database consists in a large compendium of public data (more than 500 data sets) related to muscle (skeletal and heart). These studies included seven different animal species from invertebrates (Drosophila melanogaster, Caenorhabditis elegans) and vertebrates (Homo sapiens, Mus musculus, Rattus norvegicus, Canis familiaris, Gallus gallus). After a renormalization step, clusters of co-expressed genes were identified in each dataset. The lists of co-expressed genes were annotated using a unified re-annotation procedure. These gene lists were compared to find significant overlaps between studies.
conclusionsApplied to this large compendium of data sets, meta-analyses demonstrated that conserved patterns between species could be identified. Focusing on a specific pathology (Duchenne Muscular Dystrophy) we validated results across independent studies and revealed robust biomarkers and new pathways of interest. The meta-analyses performed with MADMuscle show the usefulness of this approach. Our method can be applied to all public transcriptome data.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.