Clark Center Room E300
318, Campus Drive
Stanford University
Stanford, CA, 94305
USA
Email: fabio.zanini@stanford.edu

September 24, 2017
To the attention of the Early Career Clinical Research Symbiont Award Committee
Dear Sir or Madam:
As a graduate student in the group of Richard Neher at the Max Planck Institute for Developmental
Biology in Tübingen, Germany, I have collected a comprehensive dataset on genome evolutionof the Human Immunodeficiency Virus (HIV). In collaboration with Prof. Jan Albert at theKarolinska Institute in Stockholm, Sweden, we have processed more than 70 plasma samples from
10 untreated HIV human subjects: each infection is followed longitudinally for up to 15 years.
We then deep sequenced the virus population from each sample, describing to an unprecedented
level of detail the massive genetic changes that occur during natural HIV infections. The dataset isexceptional because nowadays antiviral treatment is administered immediately upon HIV diagnosis,
lowering viral titers to undetectable levels; this makes it impossible to study the genetic mutations
accumulated by the virus, the very same mutations that make HIV so difficult to eradicate. The
dataset is also remarkably complete, since it covers the whole viral genome with great sequencing
depth; this allows for many kinds of secondary analyses beyond the scope of the initial publication(DOI: 10.7554/eLife.11282).
The publication sparkled interest in a mixed audience spanning from medical doctors to computational data scientists. Traditionally, the 100,000,000 sequencing reads would be deposited
into a sequence read archive (SRA). However, the complex structure of the dataset – different
patients, time points, more than 10,000 genetic sites of interest, a number of clinical metadata –is largely obscured in this process, creating a high barrier for downstream research. To facilitatesharing, in addition to uploading the reads to an SRA, I also developed a web application, hosted
at the URL https://hiv.biozentrum.unibas.ch/, that greatly simplifies data exploration. The
website allows both human interaction, for instance via animated plots, and automated miningof data and metadata via a coherent programming interface. It is anecdotal but revealing thatI have been able to leverage the same website to discuss with both wet lab virologists and machine learning experts. The application is designed such that everything is an endpoint; thismakes it trivial to source the data from any connected device, from a personal laptop to a virtualmachine in the cloud. Moreover, the backend powering the website has a modular architechture that allows easy expansion for derived projects – for instance, it has since be used to estimate time of infection of HIV patients. The source code of the application is also available athttps://github.com/iosonofabio/hivwholeweb.
Several secondary analyses accessing the web application have been reported. I attach the bioRxiv
preprint of Hartl. et al. (2016): the authors scan the dataset extensively to understand the molecular mechanisms driving natural selection in HIV evolution. In absence of the web application,
they would have had to access the sequence read archive, download all the reads, reconstruct thestructure of data and metadata, and recall all the single nucleotide polymorphisms. Data cleaning
would have been especially tedious but critical, because the naturally high variability of the HIV
genome is entangled with sequencing errors. Thanks to the web application, however, Hartl. etal. could download all mutation abundance tables simply by directing their web browser to theappropriate URLs.

As more and more complex analyses are developed, I hope that both dataset and web application
will continue to be used to deepen our understanding of HIV and as a springboard to study andtreat other human viruses.

URL: https://hiv.biozentrum.unibas.ch/
Attachment: Hartl et al 2016.pdf

Yours Faithfully,

Fabio Zanini
Postdoc, Quake lab
Stanford University2