ArticleMethods (San Diego, Calif.)2008
Identification of mitochondrial disease genes through integrative analysis of multiple datasets.
Article in Methods (San Diego, Calif.), 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
3 citing papers in PubMed.
- A Correlation between 3'-UTR ofJournal of fungi (Basel, Switzerland) · 2023Article
- Systems Biology Approaches Toward Understanding Primary Mitochondrial Diseases.Frontiers in genetics · 2019Review
- Necrotizing pneumonia and empyema in an immunocompetent patient caused by Nocardia cyriacigeorgica and identified by 16S rRNA and secA1 sequencing.Annals of laboratory medicine · 2014Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Determining the genetic factors in a disease is crucial to elucidating its molecular basis. This task is challenging due to a lack of information on gene function. The integration of large-scale functional genomics data has proven to be an effective strategy to prioritize candidate disease genes. Mitochondrial disorders are a prevalent and heterogeneous class of diseases that are particularly amenable to this approach. Here we explain the application of integrative approaches to the identification of mitochondrial disease genes. We first examine various datasets that can be used to evaluate the involvement of each gene in mitochondrial function. The data integration methodology is then described, accompanied by examples of common implementations. Finally, we discuss how gene networks are constructed using integrative techniques and applied to candidate gene prioritization. Relevant public data resources are indicated. This report highlights the success and potential of data integration as well as its applicability to the search for mitochondrial disease genes.
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