SYGNIS AG Signs Distribution Agreement with Chinese Nanodigmbio Co. Ltd.
News Aug 22, 2015
SYGNIS AG has announced that it has signed a distribution agreement with Nanodigmbio Co. Ltd. for the commercialization of SYGNIS’ proprietary product portfolio in China. China is set to become a crucial player in the healthcare and life sciences industry and is expected to become the global leader in drug discovery and innovation within a decade.
According to the Ministry of Health, the Chinese government took various initiatives to promote genomics research, and plans to invest more than $300 billion in biotechnology in a five-year program ending in 2017.
With this agreement, SYGNIS grants Nanodigmbio the rights to promote, market and sell all existing as well as future products including the Company’s revolutionary TruePrime™ products for primer-free whole genome amplification (WGA) as well as SunScript™ thermostable reverse transcriptase kits for the translation of RNA into DNA to researchers in molecular biology, oncology and clinical diagnostics in China.
As a leading solution provider, Nanodigmbio is offering a broad portfolio of quality research reagents as well as high-tech instruments in the wide fields of molecular biology, protein research, cell biology and diagnostics to customers all over China.
“We are very pleased about this distribution agreement for our proprietary product portfolio with Nanodigmbio, a leading distributor in China. With its wide customer network focused on molecular biology and diagnostics, Nanodigmbio is best positioned to leverage the benefits of our existing as well as future products to customers in genomics research especially in the fast growing field of NGS in China,” Pilar de la Huerta, CEO and CFO of SYGNIS commented.
Huerta continued, “China is a key market for us and together with Nanodigmbio we are persuing an aggressive strategy to enter this market. In parallel we are negotiating with key sequencing companies in China to provide them with our kits for their sequencing services.”
Computer scientists at Carnegie Mellon University say neural networks and supervised machine learning techniques can efficiently characterize cells that have been studied using single cell RNA-sequencing (scRNA-seq). This finding could help researchers identify new cell subtypes and differentiate between healthy and diseased cells.