"Diabetes Associated Genes from the Dark Matter of the Human Proteome"

Delgado, Ana Paula; Br, Pamela; Ao,; Narayanan, Ramaswamy

doi:10.15406/mojpb.2014.01.00020

MOJPB

2014

DOI: 10.15406/mojpb.2014.01.00020

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"Diabetes Associated Genes from the Dark Matter of the Human Proteome"

Ana Paula Delgado¹,

Pamela Br²,

Ao Ao³

et al.

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Cited by 7 publications

(1 citation statement)

References 87 publications

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“…Our own results as well as several other reports support such a lack of correlation. [30][31][32][33][34] During the early stages of microarray technology, vast data sets were generated with no standards. The establishment of Minimum Information About a Microarray Experiment, MIAME, 35 greatly reduced the noise in subsequent array-based datasets.…”

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Transcriptome and Proteome Analysis: A Perspective on Correlation

Narayanan¹,

Ven²

2014

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and proteomics Db. The last two mentioned studies together account for approximately 84% of the total annotated protein-coding genes in humans. Together, these two studies identified >18,000 proteins encoded by both known genes and uncharacterized Open Reading Frames. Over 20,000 protein isoforms expressions were also characterized. 25,26 Numerous normal tissues, body fluids and cell lines were used in these two studies. These recently described protein expression databases greatly expand our ability to establish correlative evidence for mRNA and protein expression for most of the human proteome.Based on mRNA expression using a high throughput transcriptome analysis, interpretations are often made about the functional relevance, pathways, interacting proteins and the possibility of drug therapy use. Protein expression is often inferred, but frequently a correlation of mRNA versus protein is missing in studies. The complex regulation of protein expression at the level of noncoding RNAs, DNA methylation, epigenetic changes, gene amplifications, copy number variations, mutations, post translational modifications such as acetylation, amidation, myristylation, phosphorylation, sumoylation, Ubiquitination etc., stability and degradation as well as the interacting proteins adds to the complexity of transcriptome-based interpretations. 27,28

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Transcriptome and Proteome Analysis: A Perspective on Correlation

Narayanan¹,

Ven²

2014

MOJPB

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Healthy Genome: A Myth or A Paradigm Shift in Bioinformatics Research?

Narayanan¹

2014

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Ebola-Associated Genes in the Human Genome: Implications for Novel Targets

Narayanan¹

2014

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"Diabetes Associated Genes from the Dark Matter of the Human Proteome"

Cited by 7 publications

References 87 publications

Transcriptome and Proteome Analysis: A Perspective on Correlation

Transcriptome and Proteome Analysis: A Perspective on Correlation

Healthy Genome: A Myth or A Paradigm Shift in Bioinformatics Research?

Ebola-Associated Genes in the Human Genome: Implications for Novel Targets

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