Compugen Ltd. (NASDAQ: CGEN) announced today the development of "NexGen," a new platform designed to significantly enhance Compugen’s overall infrastructure for predictive drug and drug target discovery. NexGen is designed to analyze Next Generation Sequencing data which is now beginning to be generated worldwide through RNA-Seq methodology.
RNA-Seq is a new and powerful ultra-high throughput approach to provide raw data for transcriptome analysis and expression profiling. Although this new approach provides a massive amount of data in the form of very short partial transcript sequences, it also creates an extremely challenging environment for obtaining meaningful and accurate information.
Compugen's NexGen Platform, which incorporates advanced algorithms and other proprietary tools, is designed to efficiently and accurately integrate and analyze this vast amount of short sequence data. The integration of this capability with Compugen's discovery infrastructure, mainly its predictive transcriptome and proteome, is expected to provide the Company with both enhanced identification of novel genes and splice variants, and a broader view of the expression levels of RNA transcripts, facilitating new associations to pathological or healthy conditions.
These new integrated capabilities should provide Compugen with further substantial advantages in predictive discovery of potential drugs and drug targets, and also in the discovery of potential diagnostic product candidates.
The proprietary insights obtained by Compugen’s decade-long pioneering research focused on predictive transcriptome and expression analysis, which resulted in the Company's LEADS and MED platforms, were fundamental in overcoming the technological challenges in building the NexGen platform.
In turn, by enabling the integration and analysis of this enormous quantity of additional data, the NexGen platform adds significant advantages to LEADS and MED, the infrastructure platforms that support all of Compugen’s drug and target product candidate discovery platforms.
Similar to the LEADS and MED platforms, NexGen is broadly applicable across essentially all therapeutic and diagnostic areas, but its primary use will be in support of the Company's focus areas of novel therapeutic proteins and monoclonal antibodies for oncology and immunology, highly complex therapeutic areas with significant unmet medical needs.
Dr. Zurit Levine, Compugen’s VP of Research and Discovery, stated, “New RNA-seq high-throughput sequencing techniques are beginning to generate vast quantities of data that provide a potentially invaluable, but difficult to utilize, resource for use in the discovery of drugs and diagnostics.
This is particularly true with respect to complex diseases such as cancer and immune-related pathologies. Therefore, our successful development of the NexGen platform, built on, extending and enhancing our existing predictive capabilities, represents an important addition to our infrastructure for internal discovery as well as for discovery collaborations with others.”
Dr. Anat Cohen-Dayag, President and CEO of Compugen, added, “NexGen is an excellent example of how Compugen benefits on an ongoing basis from our decade-long and continuing pioneering efforts to create deeper scientific understandings, algorithms, and approaches for predictive modeling of key biological phenomena at the molecular level. Built from the “bottom up,” our unique predictive discovery infrastructure can readily integrate new technologies such as RNA-seq, as part of the process by which this infrastructure is continuously enhanced and expanded.”
About Next Generation Sequencing
The high demand for low-cost sequencing has provided strong incentives to develop “next generation sequencing” technologies. These technologies rely on various methods to parallelize the sequencing process, thus producing millions of sequences simultaneously, with exceptional speed, yield and specificity.
Such high-throughput sequencing technologies, known as parallel deep sequencing technologies, are lowering the cost and increasing the output of DNA and RNA sequencing (RNA-Seq) by orders of magnitude beyond what is possible with standard dye-terminator methods.
Furthermore, these technologies in theory should facilitate various types of analysis, such as transcriptome analyses, de-novo assembly of genomes, identification of single nucleotide DNA mutations, and more. However, the massive amount of raw data generated in this form also imposes huge analytical challenges to obtain meaningful and accurate information.
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