synomics2017-05-19T08:43:39+00:00

Synomics Studio

multi-SNP network association studies in hours or days, not months
  • rapidly identifying biomarker networks with >20 genomic, phenotypic and clinical factors acting in combination

  • comprehensive, reproducible and interpretable results in hours or days on a single 4 GPU machine

  • integrated validation and biological annotation & interpretation aiding discovery of new, more specific and useful biomarker networks

  • enabling detailed patient stratification in clinical trials design, healthcare analytics & clinical decision support

  • affordable multi-dimensional, hyper-combinatorial analysis of disease population scale genomic study data

Synomics Studio is a powerful new analytical approach to multi-dimensional, hyper-combinatorial association studies. Synomics Studio enables large scale datasets including genomic, phenotypic and clinical data to be fully analyzed for associations that meet stringent user-defined criteria for p-value, penetrance and SNP network size. Synomics uses a massively parallelized algorithm, tuned for multiple GPU compute devices, to give results in minutes, hours or days, rather than weeks or months.

Synomics Studio makes it easy for genomics and precision medicine researchers to quickly identify, validate and understand the metabolic relevance of disease related features in novel biomarker networks by combining multi-factor association analysis with integrated validation and biological annotation and interpretation features. These include a full bioinformatics knowledge graph containing SNP, gene & pathway annotations and systems pharmacology models.

The study below was performed on a population of 15,000 people, all of whom had that are associated with elevated breast, ovarian and other cancer risks due to abnormal DNA repair and recombination functionality. The cohort is split into those ‘affected’ individuals who have or have had cancer and ‘unaffected’ who have not.

The graph above shows that even for people with disease risk mutants, there are no biomarker networks containing less than 6 SNPs in combination that are represented only in the affected population and not in the unaffected. This helps explain the lack of performance of traditional GWAS (which can combine only a maximum of a couple of SNPs at a time) in complex diseases, as networks with combinations that appear in the control set may well be random observations.

The most populous biomarker networks, with specific combinations present in 103 affected people and 0 controls, contain 17 SNPs acting in combination – a complexity of analysis and level of insight into the disease and personal risk that only Synomics can achieve.

Synomics Studio runs on various architectures including IBM Minsky, and is featured in the IBM Reference Architecture for High Performance Analytics in Healthcare and Life Science