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 New strategies for predicting epigenetically modified regions 
The data analysis required for detecting epigenetically modified regions is complex requiring several analytical steps and the ability to recognize false calls. In this study, researchers analyzed the utility of data fusion for features like sequence conservation and histone coverage to improve the prioritization of candidate modified regions. The results suggested 1) there is major advantage in using a wide range of features versus just an enrichment score, 2) a novel feature, histone coverage, contributed the highest amount information for prediction while the enrichment score seemed to be the worst feature, and 3) the Random Forest algorithm (a machine learning method) , provided a high quality prioritized list of candidates useful for wet-lab validation. To help get data for the study, the researchers used Diagenode's well-cited anti-H3K4me3 antibody.
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 Forward feedback regulation between GIV and STAT3 and 
 therapeutic implications for cancer 
Gα-interacting vesicle-associated protein (GIV) modulates key signaling pathways during biological processes such as wound healing, tumor angiogenesis, and cancer metastasis. GIV is a therapeutic target and a biomarker for prognostication in cancer patients. The researchers here reported transcription factor STAT3 (a central regulator of tumor metastasis) upregulates GIV transcription by binding directly to its promoter and that GIV enhances STAT3 activation via positively autoregulating its own transcription. Immunohistochemical analysis of breast carcinomas showed significant correlation between STAT3 activation and elevated GIV expression. The forward feedback regulation between GIV and STAT3 may have therapeutic implications for cancer and epithelial regeneration. As part of obtaining data for this study, the researchers used the Diagenode Bioruptor for chromatin shearing.
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