Genome sequencing is now at the "cusp of routine clincial care" according to Nature, but researchers are struggling to cope with the sheer volume of data collected. Part of the problem is overcollection of data: scientists collecting data that they do not know how to interpret, a problem Russ Altman, a bioinformatician at Stanford School of Medicine, describes as "shooting yourself in the foot". At the current level of production, gene sequences account for 40 petabytes yearly, certainly a large number, but storage itself is not a concern according to Paul Flicek, a computational genomics specialist at the European Molecular Biology Laboratory's European Bioinformatics Institute in Cambridge, UK. The real issue is the exponential scaling of computation when looking at many different variables in a diverse data set. Other issues facing genome data science at scale include raw processing power (although much of this is offloaded to the cloud), and the lure of talent to Genomics England.
Genome sequencing is now at the "cusp of routine clincial care" according to Nature, but researchers are struggling to cope with the sheer volume of data collected. Part of the problem is overcollection of data: scientists collecting data that they do not know how to interpret, a problem Russ Altman, a bioinformatician at Stanford School of Medicine, describes as "shooting yourself in the foot". At the current level of production, gene sequences account for 40 petabytes yearly, certainly a large number, but storage itself is not a concern according to Paul Flicek, a computational genomics specialist at the European Molecular Biology Laboratory's European Bioinformatics Institute in Cambridge, UK. The real issue is the exponential scaling of computation when looking at many different variables in a diverse data set. Other issues facing genome data science at scale include raw processing power (although much of this is offloaded to the cloud), and the lure of talent to Silicon Valley.