#+TITLE: Senior Research Scientist: Bioinformatics
#+AUTHOR: Brad Chapman
#+DATE:
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* Job Description

As a member of the Harvard T.H. Chan School of Public Health Bioinformatics
Core, the Senior Research Scientist will provide custom bioinformatics analysis and
software development for researchers in the school and wider biolandcal
community, with a focus on high throughput sequence data management and
analysis. This includes:

- Developing research collaborations and new sources of funding, providing bioinformatics support
  for ongoing investigations.

- Building reusable analysis tools resulting from collaborations, leading to
  widely useful software infrastructure.

- Integration of developed analyses within open source and commercial
  frameworks, allowing wide usage across a diverse set of research needs.
, Google Compute Engine and Microsoft Azure
- Designing custom reporting and visualization of computational results, to
  communicate these results through publication and conference presentation.

- Stewardship to the wider scientific community by providing software and
  training. Collaborative mentoring with other bioinformaticians in the core.

The ideal candidate will integrate within the Bioinformatics Core team and
initiate self-directed research and development.

** Requirements

- PhD in biological sciences and bioinformatics
- 15+ years of programming experience
- Demonstrated utilization of best-practice software methodologies
  including revision control, testing frameworks and reproducible
  development practices
- Strong background in the data management, processing and analysis of
  sequencing experiments (Exome/Whole Genome Sequencing, RNA-Seq and
  other variants)
- Experience building and maintaining large scale analysis tools on distributed
  platforms
- History of providing reproducible analysis environments on cloud computing
  infrastructure like Amazon Web Services, Google Compute Engine and Microsoft
  Azure
- Ability to communicate results effectively, both formally and informally

* Activities Statement

The prevalence of high throughput sequencing data in public health research
requires validated, scalable infrastructure able to effectively process,
prioritize and distribute results to biological collaborators. Researchers
want to take advantage of high throughput sequencing for existing cohorts like
the Nurses' Health Study and need ready-to-run tools and expertise to
effectively process, quality control and analyze their data.

The necessary infrastructure is a complex coordination of rapidly changing open
source software tools from the scientific community. Improvement and support for
this infrastructure requires regular maintenance and development work. This
often conflicts with project budgets and timelines which focus on the underlying
biological questions.

My work enables science at the Harvard Chan School by developing
re-usable infrastructure for use by the Bioinformatics Core and the wider
scientific community. The key component of my job is understanding the needs of
existing projects and providing a high quality, easy-to-use implementation that
improves our ability to collaborate with research scientists. For example,
by having infrastructure that reliably calls variants on large scale whole
genome sequencing projects, we can devote core hours to understanding the underlying biology through downstream analysis and
presentation of data, instead of putting together ad hoc tools to
process each new incoming project.

This infrastructure builds on expertise and available tools from the wider
bioinformatics community. We aggressively re-use existing software and also
contribute back to that software in the form of bug reports, fixes and
validation. Additionally, we support our analysis tools in a wide variety of
computational environments, ranging from local HPC to cloud infrastructure to
commercial analysis platforms. This maximizes re-use and interoperability,
making the software development process as efficient as possible. By being an
active and respected member of the community, we are able to establish
collaborative relationships outside of the immediate Harvard community, driving
the development of new areas of research and continued building of
infrastructure.

My work involves three major areas of focus:

- Building external collaborations to develop infrastructure and expertise
  that we can re-use on multiple core projects.

- Developing tooling for software interoperability.

- Establishing scientific standards for validation.


** Scientific collaborations

High quality, scalable and validated infrastructure is essential to working
effectively in biological research. A major challenge is that this work is not
well funded or rewarded in the context of traditional academic research, which
emphasizes answering specific biological questions. My personal goal is to
continue to develop this critical re-usable infrastructure by establishing larger
collaborations. These aim to improve our expertise in research areas essential
to public health research, such as germline variant calling in large populations
and somatic variant detection in heterogeneous tumor samples. These projects
continue to develop expertise in personalized genomics and make the tools
available to analyses in the core as well as the larger research
community.

I would like to highlight two projects where external collaborations helped the
Harvard Chan School establish local expertise while simultaneously solving
difficult scientific problems in the scope of the work. These projects
led to re-usable tools that are in daily use by myself and other members of the
Bioinformatics Core.

The first is an ongoing collaboration with AstraZeneca Oncology Research and
Development. This is a multi-year project focused around improving calling for
low frequency somatic variants and handling difficult data types. Recent work
allowed use of Unique Molecular Indexes (UMIs), which helps effectively sequence
to high depths for identification of low frequency variants in somatic tissue or
circulating tumor DNA. Coupled with filters for identifying artifacts caused by
oxidative damage or FFPE deamination, this allows us to work on hard to analyze
samples. At Harvard Chan School, this work supports exome FFPE analysis of
Nurses' Health Study samples with Peter Kraft.

- Validation and improvement of cancer calling: http://bcb.io/2016/04/04/vardict-filtering/
- DNA damage filters: https://github.com/bcbio/bcbio.github.io/blob/master/_posts/2017-01-31-damage-filters.md

The second collaboration, with the University of Melbourne Center for Cancer
Research, develops automated pipelines for cancer analysis. This extends somatic
calling to clinical patient samples, requiring additional automation, validation
and accreditation. By allocating effort to improving single sample turnaround
and provenance, we build the reproducibility workflow components essential for
both research work and clinical applications.

** Interoperability

The largest barrier to efficient software development is integrating
a wide variety of specialized open source scientific software. As part of our
work in the core we make bcbio -- our analysis toolkit named after Blue
Collar Bioinformatics, a blog where I discuss on-going development work --
available to the community. Extensive reuse of open source tools allows us
continually improve, maintain and support bcbio.

While we've effectively been able to integrate tools within bcbio, a second
challenge has been to include bcbio inside other platforms. Users
increasingly want to run the same analyses on local HPC environments;
on cloud environments like Amazon Web Services, Google Compute Engine and
Microsoft Azure; and on commercial providers like DNAnexus, SevenBridges and
Arvados.

To avoid the support overhead of maintaining bcbio in multiple environment, I've
become actively involved with the Common Workflow Language (CWL) community. This
is a community effort initiated at the Open Bioinformatics Codefest, a free two
day coding session prior to the Bioinformatics Open Source Conference (BOSC).
I've been an organizer of Codefest and BOSC since 2010 and this mentoring and
development workshop brings new members into the community through individual
work with existing programmers, as well as encouraging development of new
cross-project collaborations.

Emerging from this collaborative environment, the Common Workflow Language
became a widely used community standard supported by both open source workflow
tools, commercial analysis providers and the Global Alliance for Genomics and
Health (GA4GH). Practically, it provides a way to run bcbio within multiple
computational environments. To ensure this, we contributed bcbio workflows to
the GA4GH-DREAM workflow execution challenge, which ensures our community built
tools run reliably for multiple users. This provides flexibility for research
analysis at Harvard, allowing us to choose between local and remote compute
solutions based on cost and turnaround requirements.

We implemented bcbio within these environments through collaborations with
commercial analysis providers: Veritas Genetics, Seven Bridges Genomics and
DNAnexus. These projects integrate CWL pipelines generated by bcbio into their
workflow environments. This demonstrates the usefulness of bcbio, and Harvard's
community contributions, to a wide audience of researchers.

- bcbio, our analysis framework --  https://bcb.io
- Common Workflow Language: http://www.commonwl.org/
- Codefest: http://www.open-bio.org/wiki/Codefest_2017
- Bioinformatics Open Source Conference: http://www.open-bio.org/wiki/BOSC_2017
- GA4GH-DREAM Workflow Execution Challenge https://www.synapse.org/#!Synapse:syn8507133/wiki/415976

** Validation

As we've continued to expand the reach and usage of bcbio, it becomes
increasingly critical to ensure we have high confidence methods for validating
workflows. High throughput analysis work is rapidly changing and requires
continuous integration of updated methods, while simultaneously expanding to
incorporate new types of input data and assays. As part of providing service in
collaborations, the core needs to be able to assess sensitivity and precision of
methods to understand both the outputs of analysis processes as well as cases
where we cannot reliably detect biological signal.

As part of this validation work, my work in the core has becoming increasingly
involved with the global community developing reference standards. We continued
our work with the National Institute of Standards and Technology's Genome in a
Bottle and GA4GH benchmarking teams on expanding reference standard for human
genome sequencing. This includes providing ethnically diverse test datasets
which ensure methods work on the type of patient populations we investigate at
Harvard. We've also provided test datasets and validations for moving to the
more accurate Human Genome Build 38, and helped to build validation sets for
difficult biological problems like structural variant calling. In the context of
bcbio, this has included expanding our structural variant calling capabilities
as well as integrating efficient gVCF based germline variant calling.

The key component of this work is establishing reproducible, measurable metrics
to assess how well high throughput sequencing approaches identify variants. Our
involvement in these initiatives establishes the Harvard Chan School as a key
contributor to developing fully transparent standards and open source software
for the ongoing transition to personalized, precision medicine. This is critical
for patient care, establishing Harvard as a center of expertise for clinical
variant assessment.

- Validation of human genome build 38: http://bcb.io/2015/09/17/hg38-validation/
- NIST Genome in a Bottle: http://genomeinabottle.org/
- GA4GH benchmarking: https://genomicsandhealth.org/working-groups/benchmarking

Community based collaborative development provides a strong statement from
Harvard Chan School about our concern for making our research tools and results
widely accessible. My work in the Bioinformatics core thus produces both great
science within our research groups, and also enables this same great science in
the broader scientific community.
