Although data-sharing is crucial for making the best use of genetic data in diagnosing disease, many individuals who might donate data are concerned about privacy. Jagadeesh et al. describe a solution that combines a protocol from modern cryptography with frequency-based clinical genetics used to diagnose causal disease mutations in patients with monogenic disorders. This framework correctly identified the causal gene in cases involving actual patients, while protecting more than 99% of individual participants' most private variants.
This spring I was invited to a global meeting about cancer research – how tumor data should be gathered, integrated and interpreted. It brought together specialists from medicine, biology, chemistry, mathematics and computer science for an extensive multi-disciplinary exploration. On the long trans-Atlantic flight back, to distract myself, I casually pulled out a movie from the in-flight entertainment with an intriguing title, "Collateral Beauty." To my great surprise, the movie touched on cancer- it was about the devastating effect on the hapless family of a glioblastoma multiforme (GBM) victim. About a hundred thousand cases of brain tumors are diagnosed a year in the US, and a quarter of these are gliomas, or tumors of the supportive tissue of the brain.
Medicine has traditionally been a science of observation and experience. For thousands of years, clinicians have integrated the knowledge of preceding generations with their own life-long experiences to treat patients according to the oath of Hippocrates; mostly based on trial and error. Knowledge generation is changing dramatically. The digitalization of medicine allows the comparison of disease progression or treatment responses from patients worldwide. Whole-genome sequencing allows searching and comparing one's own genome to millions and soon billions of other human genomes.
The aim of this book is to provide the fundamentals for data analysis for genomics. We developed this book based on the computational genomics courses we are giving every year. We have had invariably an interdisciplinary audience with backgrounds from physics, biology, medicine, math, computer science or other quantitative fields. We want this book to be a starting point for computational genomics students and a guide for further data analysis in more specific topics in genomics. This is why we tried to cover a large variety of topics from programming to basic genome biology.
Piece by piece, Illumina Inc. is assembling a great growth company . In May, the company announced it spent $100 million to acquire Edico Genome. This San Diego startup makes gear to speed up the processing of bioinformation. This isn't just news; it's a clear signal that Illumina is actively planning the next phase of its growth. And it's going to be explosive.