At Arcadia, one of our central tenets is that our science should be maximally useful. Computational tasks are embedded in all aspects of our scientific inquiry, so we need our computing strategies to be maximally useful too. What does this mean? In addition to aligning with scientific priorities across Arcadia, we believe that useful computing is innovative, usable, reproducible, and timely (jump to see how we define these key concepts).
This is easier said than done. We don’t yet know how to balance all these distinct but interdependent needs. Below we discuss our goals and most pressing challenges along with our current approaches to solve them. Over time, we expect to iterate on our solutions as we learn what works and what doesn’t. While we don’t yet have the answers, we are committed to identifying and sharing solutions that work for our organization. We hope that you’ll weigh in on our pubs to better inform our strategy and help us understand how these efforts might help you too.
Ensure that computational creativity and accountability permeate the scientific process.
Arcadia’s software team could take on most computational tasks for the rest of our scientists. But we believe that the scientists who directly create and contribute to a project are best positioned to analyze that project’s data because of their domain expertise. Further, computational thinking has the potential to transform biology as a discipline if we’re able to integrate it well.
CHALLENGE: How can we help individual scientists build and maintain agency in computational work?
Not all scientists can be expert in all things, nor should they be. Our researchers have a wide array of scientific backgrounds, so we’re equipping them with a baseline vocabulary and skills to support a dialogue about computational science across the organization. We are also actively collaborating with scientists on their computational inquiries. The learning curve is steep, so we started an internal training group that offers short workshops and office hours for extra help as well as a Slack channel dedicated to software questions.
Design our technologies to spur new opportunities.
If done well, some of our computational technologies will broaden our imagination and spur new hypotheses. Some scientific computation will happen after projects begin and goals are already set, but we also want it to spark totally new questions and actionable ideas.
CHALLENGE: How do we create space for innovation so that we’re able to swiftly identify and pursue new leads?
Our software team is inherently collaborative, working directly with our other scientists to write and apply code for their projects. Beyond this more supportive work, we are experimenting with the best ways to foster space for exploratory, curiosity-driven computational innovation. We are starting by creating cultural clarity around this priority and making sure the software team allocates bandwidth effectively between maintenance and creative work. We are also growing Arcadia slowly so that we have the flexibility to hire as needed to test computationally-generated hypotheses in the lab.
Make our work reproducible and usable.
At minimum, we want other researchers to obtain the same results as us if they run the same code using the same data and methods. We also want others to easily adapt, extend, and use our products to answer similar biological questions.
CHALLENGE: How do we scale reproducibility and usability?
Ensuring these qualities takes significant effort. For each effort, we work to define the minimum viable product, or “MVP” — the lowest threshold for reproducibility. This helps us ensure reusability while still prioritizing our time and staying on track with our big-picture science and financial sustainability goals. To determine how to make a computational product reproducible and easily usable, we consider whether there are pre-existing methods, how much tinkering is required, how often we expect to use the methodology, the size of the user base, and the infrastructure needed to run the code. In essence, we align MVPs to the impact and maturity of the problem. We work hard to make sure we don’t let perfect get in the way of good (and useful!).
We guide our decision-making about potential solutions to our challenges using the following operating principles:
Explore, experiment, and formalize. When possible, we explore our decision space, conduct small experiments, and formalize our decisions using the data and feedback we collect.
Maximize utility of computational products. Our decisions will be driven by what makes our products most usable both within and outside of Arcadia. We will iterate on this in a data-driven way.
Ship focused computational products quickly and iteratively. We encourage decisions that support fast and iterative approaches to scientific computation.
Remember that no decision is truly irreversible. We recognize that decisions can be changed if our original strategy no longer serves our goals. We keep this in mind to minimize analysis paralysis as we progress.
We’ve come up with a shared vocabulary that has helped us discuss and iterate on solutions for our challenges. We define some of the most important words here.
Computational products: Scripts, notebooks, workflows, software packages, and/or repositories that we encode and their accompanying documentation.
Useful: Our computational products are helpful in addressing biological problems.
Usable: The ability for someone to use our computational products with relatively little activation effort – for example, by following the documentation, and without a substantial time or monetary investment, someone inside or outside of Arcadia can use our code.
Repeatable: The ability for someone to repeat the scientific analysis we have encoded and achieve the exact same results as we produced.
Reproducible: The ability for someone to repeat the scientific analysis we encoded on similar but new data and achieve a similar biological result, or to use an orthogonal analysis on the same data and achieve a similar biological result.
Open: Computational products, data, and explanations that are freely available with clear licenses for reuse, redistribution, and reproduction. To maximize the usefulness of our science, our goal is to make our tools as open as possible while respecting the licenses of the underlying software and our translational goals.
Iterative: A style of computational product development where the product is improved by repeated review, testing, and deployment on real problems.
To support usability and reproducibility in our computing projects, we created baseline guidelines to aid in the development of clear and organized code, proper data management, and effective documentation. These guidelines are meant to be simple and approachable, meeting our scientists where they are.
To reduce the burden of following these guidelines, we rely heavily on templates and training. We have implemented an internal training program called the “Arcadia Users Group” (AUG) to roll these guidelines out and help computationally level-up our organization. We’re working on a perspective piece that will go into more detail about this work.
A key strategy for streamlining repeatable computational tasks for our scientists is to use a workflow orchestration framework. We selected Nextflow to help create reproducible pipelines for our standardized computational tasks. We highlight our decision-making process in a perspective pub:
In practice, each project has taken a tailored approach to creating computational products that other scientists can use and that can reproduce the results we present in pubs.
For example, a method pub about isolating phage DNA and identifying nucleosides had an accompanying GitHub repository that contained a rendered analysis notebook, pointers to the input data, and the software versions used to run the analysis. The repository showed how to process mass spectrometry data to find nucleosides.
Another research effort that demonstrated how to compare amoeboid crawling behavior took a similar approach by documenting the analysis in notebooks, but further created a linked Binder to enable others to interact with and re-run the available code. Binders are ephemeral compute environments that allow researchers to execute or change code drawn from GitHub repositories that themselves do not change.
Lastly, a computational project explored sequence attributes that correlate with protein annotations using actin as a model protein. We encoded the pipeline as a snakemake workflow and adapted it so that researchers can upload their own actin protein sequences to see how they score for different annotation attributes.
Many of our computational tasks are routine and need to be performed multiple times by different people across Arcadia — for example, sequencing data quality control, genome assembly, and building phylogenetic trees. We chose to standardize these routine pipelines as Nextflow workflows deployed through AWS Batch. We’ll release a pub soon that describes how we came to this decision.
Our first Nextflow workflow encodes a pipeline for quickly assessing the quality of new sequencing data. Our goal with this workflow is to let scientists quickly and confidently post new sequencing data to public repositories before formal biological analysis, thereby allowing other researchers to access it as soon as possible. To do this, we need to ensure that our new sequencing data doesn’t contain errors that should preclude its deposition in a public database. The seqqc workflow produces an interpretable report to assess sequence data quality.
Learn more about this resource:
Both notebooks and workflows have proven to be helpful tools for documenting and executing analyses across the organization. However, for some tasks, we found that we ended up writing similar notebooks multiple times. When we encountered this, we pulled the code out into new software tools to make it more usable and better tested.
Our first software tool is a new R package, sourmashconsumr, which provides a set of functions to analyze and visualize the outputs of the sourmash package. Our goal was to allow scientists to easily work with the outputs of sourmash in R and to provide a set of default plots to more quickly understand the content of sequencing data.
Learn more about what the sourmashconsumr package does:
As we continue using computation to address biological questions, we will keep our focus on making our approach and products useful. As we iterate and find the approaches that work best for our organization, we’ll keep documenting the products we make, how those products are used within and outside of Arcadia, and the thoughts behind our decisions.
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