The Human Genome Project changed everything

Parallel transformations
HGP participants trusted their own power to innovate 
but also hoped for other developments to leverage the 
programme. While the project unfolded, a revolution 
occurred in computation. In the late 1980s, the only 
computers in the laboratories of genomicists were the 
earliest PCs and Apple products. By 2000, we had all 
been connected by the internet, bandwidth was ade-
quate to move the genome data, and adequate process-
ing power was accessible. A strength of the HGP and its 
participants was that these parallel developments were 
rapidly incorporated into the framework of biology. 
Necessity speeds invention — and the need to manage 
copious amounts of digital genome data was the real 
driver of the growth of computational biology, ahead 
of the demands of physiologists or structural biologists. 
Most importantly, a generation of bioinformatics experts 
and computational biologists emerged who brought the 
genome data to the widest audiences.
The power of advances in genomics and computers 
was revealed in the spectacular series of post-HGP 
projects that were of comparable scale. After multiple 
mammalian genome projects, programmes including 
the Haplotype Mapping (HapMap) Project
9
, the 1000 
Genomes Project
10
 and The Cancer Genome Atlas 
(TCGA) progressively illustrated the advancement of 
knowledge by more sophisticated data sharing, compar-
ison and analysis. As these and other projects unfolded, 
new constituencies were engaged and more scientists 
and clinicians became ‘digital’ and ‘genomic’. The pro-
jects were emblematic of the advancement of scaling, 
digitization and sharing that was sparked by the HGP.
Some still tally the success of the HGP from lists of 
new drugs or therapies and argue that world-changing 
examples in biology, such as the spectacular advances 
of gene editing tools or the expansion of cancer thera-
peutics through targeted immunotherapy, are largely 
based on microbial, cellular and animal studies rather 
than genomics. This argument misses the point. These 
are among the myriad of discoveries that occurred in the 
backdrop of a new era. New ideas and primary discovery 
may still be the ‘quiet conversation with nature’ of the 
experimental biologist — but validation, contextualiza-
tion, deployment and translation are all streamlined by 
the fruits of the HGP.
It is a vastly different world today in 2020, com-
pared with 1990. Human genome sequences cost less 
than US$1,000 per genome, all trainees in experimen-
tal biology and genetics are pressed to be proficient in 
computer languages, and easy access to mountains of 
primary and derived data has come to be expected. As 
the recent coronavirus pandemic emerged, thousands 
of trainees, forced to remain out of the wet-lab, pivoted 
to computational studies; 30 years ago they would have 
been lost. The real fruits of the HGP lie in the contrast 
between the primitive state of digital biology in the late 
1980s and the current ease with which all scholars can 
access, harness and analyse biological data.
1
. 
Lander, E. S. et al. Initial sequencing and analysis of the human 
genome. 
Nature