Golden Helix is located in Bozeman, MT about 90 miles north of
Yellowstone National Park, nestled in the heart of the Rocky
Mountains. Google Map to GHI
Founded in 1998 in Bozeman, Montana by Dr. Christophe Lambert, Golden Helix is a leading bioinformatics company that enables the world’s leading researchers to find or diagnose the genetic causes of disease, drug safety, and drug efficacy – advancing the promises of personalized medicine. Using Golden Helix’ genetic analysis software and analytic services, these researchers are able to make sense of complex, expensive genetic data, turning it into novel, actionable findings. Further, leveraging core competencies in genetic analysis and predictive analytics, Golden Helix develops complex genetic diagnostic and cytogenetic solutions that enable the diagnosis or prediction of disease. Golden Helix’ clients include several of the top ten pharmaceutical companies (including GlaxoSmithKline, Johnson & Johnson, and Roche), numerous biotechs, and over 100 academic and government research organizations around the world.
Genetic research scientists who work with massive amounts of complex data generated by very sophisticated, very expensive devices, are typically handicapped when it comes to interpreting or getting maximal value out of their data. Though there is no shortage of the rough, specific-purpose software routines that precipitate out of academic research projects, these programs lack the very features their users need to accelerate research and make breakthroughs: an intuitive, high-performance interface that enables researchers to use the software effectively; robust analysis and visualization tools that help them make and understand findings; comprehensive technical support for when they have challenges; and cross-platform compatibility that facilitates work with collaborators and disparate tools. Without a product with these qualities, researchers are forced to spend an inordinate amount of time and money building expertise in computer science instead of their research.
Golden Helix’s SNP & Variation Suite (SVS) is a unique set of software tools created specifically for genetic research. SVS helps researchers easily analyze, visualize, and publish important discoveries on genetic, phenotypic, and clinical data, getting them past the frustration of having to use incompatible, difficult programs instead of focusing on their research, making important findings, and publishing their results.
SVS is packaged for SNP, copy number, and sequence data analyses on both population and family-based cohorts, including very sensitive algorithms for finding copy number breakpoints on individual samples in clinical cytogenetics. Further, SVS is compatible with all major genotyping and sequencing platforms and is used by hundreds of genetic and pharmacogenetic researchers in the industry, government, and academia the world over. With hundreds of citations in peer reviewed articles, SVS enjoys a reputation for performance, usability, reliability, and fast time to results.
In 2012, Golden Helix introduced GenomeBrowse, a free tool for visualizing sequence data. With over 1,000 downloads in just a few short months, GenomeBrowse rose the bar on the experience of explorinrg and finding key insights into genomic data.
Even with optimal tools at their disposal, many researchers realize they don’t have the time, the expertise, or the desire to analyze their data. Some simply recognize the analysis phase of their project as the most critical portion of their study and feel it is too important to risk missing a critical finding simply because they didn’t know the best or most current methods for teasing significant results out of genetic data.
It is in addressing this challenge that Golden Helix has earned a reputation for helping researchers get the greatest value from their data in a cost-effective, time-efficient manner. Providing best-in-class analysis services on complex data, Golden Helix employs leading software tools, state-of-the-art methods, statistical expertise, and custom development capabilities to uncover hidden relationships in complex data. With a staff of computer scientists, statistical geneticists, bioinformaticians, and statisticians, Golden Helix approaches analytic challenges creatively and from several directions, with the result being novel, statistically significant and, most importantly, reproducible findings. Service offerings are comprehensive, beginning with initial study design and progressing through data preparation, quality assurance, segmentation, analysis, reporting, and assistance in documentation and publication.
In 2007, FDA approved In Vitro Diagnostic Multi-Variant Index Assay (IVDMIA), Mammaprint, a diagnostic involving a signature of 70 genes run through a complex predictive algorithm to assess a patient’s risk of the spread of breast cancer. The predictive improvement over the single gene approaches of past prognostics/diagnostics was dramatic, representing a true paradigm shift in diagnostic capability.
By analyzing terabytes of whole-genome genetic information and working in collaboration with partners around the world, Golden Helix is utilizing a similar strategy to develop diagnostics to accurately diagnose or even predict disease. Empowered with this knowledge, potentially years in advance of symptoms, people can take proactive steps before they become ill, thus leading longer, healthier, and more fulfilling lives.
The future of genetic diagnostics is analyzing multiple markers, utilizing complex predictive models, and involving a combination of gene expression, genotype, copy number, and other genomic, proteomic, and metabolomic information. Golden Helix is taking advantage of this opportunity with the analytic IP to build models using all of this data combined and is one of several commercial organizations involved in the FDA Micro Array Quality Consortium (MAQC) efforts to develop standards for complex genetic diagnostics and prognostics. Further, Golden Helix’ CEO is the co-chair of the FDA Genome Wide Copy Number Variation Data Analysis Team, helping set the standards for predictive modeling in this new field.