November 4, 2014
An article published in Methods have tackled the problem of lack of clinical samples and low research funding to study rare diseases by utilizing currently available transcriptome data and performing an integrative analysis on it to reveal novel information of mitochondrial disease. Zhang et al. created the Transcriptome of Mitochondrial Dysfunction (ToMD) from publicly shared data archive including 30 independent data sets and about 500 biological samples collected from human tissue and cell lines, worm, fruit fly and mouse. In each dataset the authors included a comparison of samples with primary mitochondrial dysfunction – caused by pathogenic gene mutations in patients, knockdown/knockout of key mitochondrial genes in cultured cells or model animals, or exposure to chemicals such as Rotenone and Rapamycin – to those with normal or rescued mitochondrial function. The authors then describe the bioinformatics methods they used to “evaluate data quality, standardize gene annotation, and perform integrative analysis”, which according to them was mostly automated but needed some human supervision to ensure data quality and sample labeling.
Not only did the analysis of the transcriptome derived from various sources highlighted the central players of mitochondrial dysfunction, but it also provided novel information on therapeutic targets. The authors believe that this study provides a method by which researchers can “integrate transcriptome data sets sharing a common link to a rare disease, but generated from different platforms, cell types, and even species” with limited resources. All data sets and analysis results within ToMD are freely available at https://mseqdr.org/data/tomd/.