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Sequence data, the “backbone” of genomics, is in transformation, and this transformation may well change what genomes are. The changes described in this essay concern how biological processes associated with genomes are modeled. As predictive models based on machine learning techniques are brought to bear on genomic data, they increasingly target aspects of genomes—such as their variation and their manifold spatial and temporal relationality in biological processes and function—that seem most distant and difficult to derive from the relatively stable, monolithic and hence tractable forms of order found in DNA sequences. This essay poses the growing importance of predictive models for genomes as a touchstone for wider transformations in many sectors of science, industry, commerce, media and government.

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