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Metamoodics: meta-analysis and bioinformatics resource for mood disorders

Molecular Psychiatry volume 19, pages 748–749 (2014)Cite this article

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Mood disorders, including major depression (MD) and bipolar disorder (BP), are among the most common psychiatric disorders.1 Although genetic factors have an important role in their etiology,2, 3 the heritability of these disorders remains largely unexplained. Owing to advances in high-throughput genomic technologies, it is becoming increasingly feasible to investigate the genetic architecture of these disorders at an unprecedented resolution. The challenge is how to make sense of the enormous flood of data generated by the diverse genomic studies that use these new technologies. It is essential to integrate the findings from such studies in order to develop a more comprehensive understanding of the genetic contribution to these disorders that can be readily translated into new and more rational therapeutic interventions. Toward this end, we have developed a bioinformatics resource called Metamoodics (www.metamoodics.org), which integrate results from various genomic studies of mood disorders and enhance their interpretation by placing them within an enriched genomic context. Metamoodics was created as a resource for the research community to explore what is currently known about the genetic contribution to mood disorders.

Metamoodics includes a variety of online applications and tools for visualization and further analysis of the genomics data. Data visualization make use of NCBI SeqView API and a local mirror of the UCSC genome browser. The results can be queried by gene or chromosomal position to obtain either a gene level view via chromosomal or regional plots, or a more global view via the genome browser to investigate the genomic context of the findings. User-defined data can be uploaded to the genome browser using the custom-track option to visualize alongside the Metamoodics tracks. Analysis tools include SVAw9 for surrogate variable analysis, METAw for meta-analysis and a gene expression package for individual and mega-analysis of expression data.

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Authors and Affiliations

  1. Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, USA

    M Pirooznia, F Seifuddin, J Judy, F S Goes & P P Zandi

  2. Department of Psychiatry, University of Iowa Carver College of Medicine, Iowa City, IA, USA

    J B Potash

  3. Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA

    P P Zandi

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  1. M Pirooznia
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  2. F Seifuddin
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  3. J Judy
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  6. P P Zandi
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Correspondence to P P Zandi.

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Pirooznia, M., Seifuddin, F., Judy, J. et al. Metamoodics: meta-analysis and bioinformatics resource for mood disorders. Mol Psychiatry 19, 748–749 (2014). https://doi.org/10.1038/mp.2013.118

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