“We do have the idea of being gender equal…but we have a long way to go before we are gender equal,” argues Anneli Häyren, a researcher at the Centre for Gender Research at Uppsala University in Sweden. “I think it will take quite a lot of time - another 50 years at least - until we get there - and that is only if we keep working at it.”
Numerous innovations have hit life science labs in the past decade, such as new techniques for designing DNA and editing genomes. Yet, researchers still rely on older tools — such as paper notebooks, Excel spreadsheets, and email — to manage data collected from those innovations. This means time is wasted organizing, finding, and duplicating information before even starting new experiments.
Even though DNA sequencing technology is becoming increasingly accessible, it is still difficult to glean helpful information from an individual’s DNA. That is because there is a lack of reference data compiled from other people’s DNA. Most reference data is currently curated by academic institutions and is often compiled in different formats, making it difficult for doctors and researchers to use.
As hospitals and public health organizations switch to using genomic data for testing, searching through genomic data can still take some time. Y Combinator-backed startup, One Codex, wants to help researchers, clinicians and public health officials, who have sequenced more than 100,000 genomes and created petabytes of data, to search this data.
Through its Genomic Medicine Initiative (GMI), UCSF has integrated data from a comprehensive cancer genetic testing program into the electronic medical records of patients at the UCSF Medical Center. Not only does it allow for continuity of care with all testing and treatment results tied to the same electronic record, but it also allows physicians and researchers to identify larger patterns in the data that can lead to the development of better treatments – which is known as precision medicine.
“Our vision is about closing the design-build-test-and-evolve loop,” said CEO Mike Fero, who was a researcher at Stanford focusing on protein localization and who was previously a vice president at a computational genomics company called Neomorphics that was sold to Affymetrix in 2000. “We want to shorten the time frame it takes to get your DNA built and run more experiments.”
"Xcell Biosciences created the Technology Access Program (TAP) to accelerate the discovery of novel applications using our proprietary Primary Cell Control Systems in the field of Immunotherapy. We launched the TAP to collaborate with researchers and commercial partners in advancing the technology frontier of Human Microenvironments (HME) including Tumor Microenvironments (TME). We will provide the instrumentation and technical support necessary for partners to jointly invent and validate new discovery technology," said Janette Phi, COO. "Our solutions focus on clinical researchers developing therapies at the convergence of precision medicine, stem cell technology, and immunotherapy."
Predictive Oncology has appointed Dan Handley to its board of directors. Handley is a professor and director of the Clinical and Translational Genome Research Institute of Southern California University. Previously, he was chief scientific officer of the Clinical and Translational Genome Research Institute, a Florida-based non-profit. He also previously served as chief scientific officer for Advanced Healthcare Technology Solutions; as a senior researcher at Procter & Gamble; a senior administrator, researcher, and laboratory manager at the David Geffen UCLA School of Medicine; and as a founding biotechnology inventor for the National Genetics Institute.
OnRamp.Bio's flagship product, ROSALIND™, enables researchers, drug developers and bench scientists to analyze raw genomics data by providing a transformative experience through point-and-click experiment set up, interactive data visualization and interpretation. This new approach increases productivity by freeing up time for the bioinformatician to focus on more challenging workloads while making bioinformatic analysis more accessible for the scientist to do more discovery with their data.
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