Biomarker discovery research is an important stepping stone towards new targeted treatment methods for complex diseases, such as cancer. GENALICE LINK is a unique and open correlation platform integrating molecular data and diagnostic data from different sources. LINK is an enabler of accelerated translational molecular research and biomarker discovery studies in particular.
Product features and benefits
- First open correlation platform capable of linking all relevant biomedical domains (DNA, RNA, protein and phenotypic data) with curated public data & validated knowledge.
- Provides a platform for basic molecular and translational researchers that enables data sharing and collaboration, facilitates discovery, and accelerates the diffusion of new knowledge throughout the healthcare ecosystem and into common clinical practice.
- Enables users to apply data analysis and interpretation in novel ways to accelerate biomarker discovery, translational research, and clinical trials: fine-grain, fully differentiated and no masking.
- Through the use of groundbreaking technologies, the 3 V’s of Big Data are fully mastered:
- Volume: 1000-fold reduction of the storage/processing footprint.
- Velocity: analyzes large datasets in a matter of seconds on a single CPU standard hardware configuration. Conventional methods easily take days or weeks to produce the same results in a big datacenter.
- Variety: integrates biomedical data from any source, including NGS data at highest level of detail (SNPs, INDELs etc.).
- Processes up to 1 million DNA samples from more than 150,000 patients simultaneously on a single CPU standard hardware configuration.
- Combines the robustness of a world-class database with the performance and flexibility of customized software and provides open ‘logic’ entry.
- Uses five new techniques: ‘Uniform format’, ‘Data selection’, ‘Data movement’, ‘Causality/Correlation’ and ‘NB micro scheduler’.
GENALICE LINK has been validated on various datasets, including the TCGA ovarian cancer datasets as published in Nature in 2011, using the DNA copy number variation and mRNA datasets.
The outcome of the analysis showed approximately 68% overlap in the findings between LINK and TCGA:
The observed differences can be explained by the nature of the two analysis methods: TCGA did a course-grain (at chromosome and focal area – groups of genes – level), while GENALICE LINK is capable of analyzing the data at gene level. As a result, TCGA analysis had two effects on gene level: Drag-along and Leveling-off.
When the downstream effects of DNA CNV changes are being analyzed on groups of genes level, it can be the case that one gene with a negative CNV drags-along the other genes within the same group. In the adjacent example, gene 3 drags genes 1, 2, 4 to n into the same basket – while GENALICE LINK can disqualify these genes.
In case the downstream effects of DNA CNV changes are being analyzed on groups of genes level and there is one gene within this group with a negative CNV and one with a positive CNV, the overall effect of the group can be neutral or levelled-off. GENALICE LINK can make fine-grain observations and unmask both.
GENALICE LINK: Better differentiation and no masking
GENALICE LINK can correlate NGS data with any other biomedical dataset at the highest level of granularity: Gene level, exon level or even SNP and INDEL level. This makes LINK a powerful open correlation platform for e.g. better and faster biomarker discovery studies.
*For more information on the validations and product availability in your country, please contact us directly.