Today we’re introducing Andrew Riha who recently was awarded one of our project grants for his tool lineage. With lineage Andrew will make the genetic data you store on Open Humans even more useful, by enabling Ancestry analyses!

Hey Andrew, please give our blog readers a quick introduction about who you are!

I’m a systems engineer at an aerospace company in Southern California. I studied at Iowa State University, the University of Newcastle, and Delft University of Technology, and I have a B.S. and M.S. in computer engineering. A few years ago, I became interested in direct-to-consumer DNA testing after a friend told me about his experience with 23andMe. This interest developed into a passion, and I’m currently pursuing a graduate certificate in bioinformatics. My hobbies include running, traveling, and backpacking.

When and how did you come to Open Humans?

Director of Research, Bastian, introduced me to the Open Humans platform in early 2018. I had mentioned to Bastian that I wanted to turn my hobby open source Python project lineage into a web app, so he suggested I consider applying for a project grant.

Have you been involved in any projects on Open Humans so far, either as a participant or even running your own?

This is my first project with Open Humans. I’m looking forward to learning from others and further developing and integrating lineage into the Open Humans ecosystem as a great open source web app!

Your project lineage was awarded one of the Open Humans project grants. Can you explain us what the project is about?

lineage is a framework for analyzing genotype files (e.g., raw data files from 23andMe, Ancestry, etc.), primarily for the purposes of genetic genealogy and ancestry analysis. It can identify DNA and genes shared between individuals, and it provides other useful capabilities such as merging raw data files from different testing companies, identifying discrepant and discordant SNPs, and remapping SNPs to different assemblies / builds.

How did you come up with the idea behind lineage?

After my friend told me about his experience with 23andMe, I started researching how to get tested and found the International Society of Genetic Genealogy’s wiki very helpful and informative. The wiki led me to an excellent paper by Whit Athey that discussed using genotype files to phase the chromosomes of a family group and “reverse engineer” the DNA of a missing parent in the process! So, for a CS50 final project, I challenged myself to implement Whit’s algorithm in Python, using scientific libraries and vectorized programming in order to efficiently handle and analyze the large datasets involved.

The initial algorithm implementation was successful, and lineage had begun. But, I soon realized the need for other capabilities, such as comparing / merging files from different testing companies and determining what DNA is shared between individuals so that it could be used to guide the phasing algorithm. So, lineage grew into the framework that exists today, and I eventually want to return to implementing Whit’s algorithm, applying the bioinformatics and visualization concepts that I’ve learned along the way.

Is there anything important that we didn’t cover so far that you’d like to add?

lineage wouldn’t have been possible without the knowledge and help graciously provided by so many people. It is in that spirit that I’d like to encourage others to create and contribute to open source projects – sharing your ideas and passions with the world can be a very rewarding endeavor!

Oh, and thanks Mom, Dad, grandmas, and grandpas for the genes. 🙂

How can we make it easy to add data to Open Humans?

Open Humans lives through its community of members and the projects they design. That’s why there’s a large number of tools that make the creation of these projects possible: Projects can be run right on-site, use the Python command line interface library or use generic OAuth2-based API-methods to interact with Open Humans. But one simple need remained painful: simply enabling Open Humans members to upload file(s) into your own project.

Doing this needed some fiddling. Even if you code, setting up your own website can be time-consuming and often is something you don’t want to spend a lot of time on. Along with Mad – and the great help of some of our prospective Outreachy interns – I’ve been busy to reduce this pain…

Meet the oh_data_uploader template! All you need to allow Open Humansmembers to upload data into your project, with a one-click deployment to Heroku, for free! All of the project configuration can be done right in your browser, no assembly or coding required.

Now the process boils down to a simple 5-step guide and instead of taking some hours to set up your own data source it should now take between 5-10 minutes. Just use the administrative backend to fill out the configuration parameters, add the file meta data you expect and edit the copy-text of your project website using Markdown in the same way and you’re good to go. You can click here to see how it looks like out of the box (just ask if you want to have the demo password 😊).

I made already good use of this template myself, because it is what I used to quickly deploy the FamilyTreeDNA integration into Open Humans. What new data source will you add to Open Humans today?

Dear Open Humans community,

Our nonprofit organization, Open Humans Foundation, will be having its annual election next month for its Board of Directors. Three of our nine seats will be up for election and, while current board members are invited to re-apply and continue their service, not all are planning to do so.

I’m also delighted to share that we have restructured our organization’s governance to create “community seats”. One of the three seats this round will be chosen by Open Humans members!

Anyone may apply to our board. The process involves a self-nomination, and nominees should be seconded by a current member of the Board of Directors.

Being a director of this organization is a position of trust. While I manage day-to-day operations, the board is our highest tier of governance – our ultimate decision-making authority. Many board members also contribute to the organization as officers, e.g. as secretary or treasurer. As Executive Director, I serve at the board’s behest.

You can learn more about our organization’s governance – including current board members, bylaws, and standing rules – by visiting the organization website here: http://openhumansfoundation.org/

Please self-nominate by completing our self-nomination form. Our deadline for self-nominations is March 12.

We expect our board-elected seats to be determined at our annual meeting on March 26, and we look forward to holding a community election following this. Please stay tuned!

Sincerely,

Mad Price Ball
Executive Director, Open Humans Foundation

Today we’re interviewing Kevin Arvai. Kevin is a bioinformatician with an interest in personal genetic data and he was awarded a project grant to implement a project that will bring genotype imputation to the Open Humans community.

Kevin, please give our blog readers a quick introduction about who you are!

I am a data scientist at a clinical genetics company in Maryland. My background and formal education is in biology, however I completed a master’s degree in computational biology and bioinformatics. Like many, I’m riding the wave of data that our generation has found itself immersed in by competing in data science competitions and contributing to “open-” (source, science, data) projects. I’m particularly interested in machine learning and human genetics but looking forward to learning new skills by building Imputer.

When and how did you come to Open Humans?

I came to Open Humans in February 2018 after working on a project with the Director of Research, Bastian, at a hackathon hosted by NCBI.

Have you been involved in any projects on Open Humans so far, either as a participant or even running your own

Not only is this my first project working with Open Humans, this is my first project as part of a open source community. Open Humans was a welcoming and collaborative group of people that encouraged my ideas, so it seemed like a perfect fit to start contributing.

Your project Imputer was awarded one of the Open Humans project grants. Can you explain us what the project is about?

The goal of Imputer is to provide users with a more comprehensive picture of their genome. Direct to consumer genetics companies, like 23andMe, only genotype a small fraction of the genome. Researchers are finding new genetic locations associated with traits and diseases at a rapid pace. Users might be interested in knowing their genotype status for these new associations, but the locations may be in regions that direct to consumer tests are not genotyping. Imputer leverages the vast amount of genotype data made available by 1000 genomes project and by the Haplotype Research Consortium to provide Open Humans users with genotype estimates at additional locations in their genome.

How did you come up with the idea behind Imputer?

The genesis of Imputer was spawned from long conversation over lunch with Bastian.

Is there anything important that we didn’t cover so far that you’d like to add?

I’d like to encourage others who are “interested in, but anxious about” contributing to open source projects to take the leap! If you’ve found this post, Open Humans is a great place to start!

Kevin’s encouragement motivated you to take action? The Open Humans project grants are ongoing and you can apply for one too!

President Bartlet of The West Wing is calling his famous “What’s next” to his secretary after managing a task.

I just defended my PhD last week, and one question from virtually every person who attended and stayed for the after-party: What’s Next? Which initially felt a bit weird. After all, I already took my next step three months ago when I joined Open Humans as the Director of Research. But then I realized that this is a nice opportunity to reflect a bit on my first months and think about what my next goals for Open Humans are.

Where is Open Humans so far?

So far I spent good parts on learning the ropes. First of all, I had to find my way into the technical infrastructure of Open Humans. Learning the code base, the APIs, server setups and so on. And what better way to do this but starting my own projects? I thus integrated two new projects on Open Humans: First I connected my long-standing project openSNP with Open Humans – allowing users of both platforms to re-use their genetic data more easily. Then I started TwArχiv, which not only brings a new data source but also some data-visualization to Open Humans. This integration of Twitter data will hopefully also be a first step towards a more holistic view of personal data that includes non-medical data.

Hand in hand with the technical side of things I also found my way into the community around Open Humans. Learning which projects there are, how to best support them and also how to grow the Open Humans community even more. I not only got to know many of the brilliant individuals inside the Open Humans community, but I also helped them to achieve their goals – be it through bug fixes, relevant connections or finding out how to optimize our website to make it work for their needs. First steps towards a further community growth were also taken: We could announce the first three successful grant applications, all bringing new data sources to Open Humans. And a fourth grant announcement – enhancing existing data sets – will be out soon!

The Open Humans community grows nicely and is becoming more and more engaged. So things are on track. But where should we go from here? And what is the larger vision? Traditional academic research – as well as corporate data silos – put themselves into the center of all data collection. In contrast, Open Humans is very different to this. As Steph laid out in her blog post: Open Humans is a technological platform; a vibrant community; and a paradigm shift to how research is done at the same time. In addition to all these things there is one thing that I always mention when people ask me what Open Humans is: It is empowerment. Putting individuals in control of their own data and of research at large. And to me, this means more than ‘just’ giving people the choice of when and where to share their data.

What should Open Humans be?

Empowerment means giving people the opportunity and chance to explore and understand their own data. Be it on their own – or in collaboration as a community outside the traditional academic research setting. The growth of the independent Open Artificial Pancreas community – which aggregates their own data through Open Humans – is a stellar example for this empowerment. As stewards of the Open Humans ecosystem it is our responsibility to support people to run projects like these. It is up to us to make it easier to create and run projects on Open Humans – empowering more people including those who are not highly programming savvy. Open Humans offers the unique chance to democratize science, enabling people outside academia to do new research that has never existed before. To pull this off we have to become more inclusive in our approach. This means getting everybody on board who has great ideas for research.

First steps towards this direction have been made already: We now have a first data uploader template that allows everyone to create their own, data-collecting Open Humans project while requiring zero programming knowledge. Instead a web browser is enough to do the complete setup. A similar idea for the administration of projects should become a reality in the near future. Furthermore, we are on the way to create shareable analyses notebooks. These can be written and run by everyone – facilitating community-driven data analysis. By increasing our inclusivity more we will not only see more projects on Open Humans, we will also see a much wider diversity in how these projects will use data. I can’t wait to interact with all of them.

I see this diversity reflected in the kinds of data that will be on Open Humans and the kinds of research that will be done with it. Traditionally many of the projects on Open Humans have and had a focus on health. But I don’t see why this should be the sole kind of research that profits by being run with and by highly involved participants. After all, while much of the Quantified Self revolves around health, it is far from the only topic: People are interested in their personal finance data, phone usage, emails and more. And so are social scientists, economists and other academic disciplines. My goal is to get these people on board for Open Humans too, showing them the huge benefit that an engaged study population offers.

Let’s just think of a simple example: Everyone can pay Twitter to get access to their firehose of data or just scrape tweets for keywords from the web. But who but Open Humans can offer potential access to 200 or more full Twitter archives that are available right now? And more importantly, who offers the possibility to get in touch with these people and as such a way to get additional metadata and consent them? The same is true for virtually all kinds of social media data and many other data types. Humans are more than their bodies, and Open Humans should reflect this.

So this is what’s next for Open Humans: Creating an ecosystem that enables the largest possible number of people to do research; that collects and enables the re-use of the most diverse set of data; and that brings together participants and researchers from all disciplines and walks of life – informing each other and creating the most interesting research.

Today we’re interviewing Anh Nguyet Vu. She is the recipient of one of our Project Grants. With MyFitnessPal Miner she not only brings a new data source to Open Humans, she is also working on visualizing these data and connecting them to genetic data. 

Hey Anh Nguyet, please give our blog readers a quick introduction about who you are! How did you come up with the idea behind MyFitnessPal Miner?

Generally I wouldn’t want to introduce myself by talking about my problems, but in this case it does give you the story behind the project. So when I was a freshman in undergrad, I faced a problem that would eventually lead to the development of MyFitnessPal Miner. This problem, no doubt a familiar one for many others, was weight gain. Since I was (and am) the kind of person who believes that “what cannot be measured cannot be managed”, I started tracking dietary intake. Because I was already tracking what I was eating, I became interested in the quantified self movement, and it wasn’t long before I was convinced that collecting other types of data would be valuable. I experimented with many food logging tools, including MyFitnessPal (which was never my primary app, but it happens to be the most popular one today). I also tried a variety of activity and exercise trackers before Fitbit hit mainstream. Probably my most earnest project was tracking how much time was spent on different activities, down to a minute’s resolution, over a span of three months.

It was inevitable that I would want to incorporate genetic data. To gather all the other kinds of data without considering your personal genetics is to miss out on a crucial part — especially if you wanted to optimize health, as I was a little obsessive about. I had a self-defined area of concentration called “personalized medicine” (also known as “precision medicine”) for my undergraduate major at Stanford. I think more people understand personalized medicine as tailoring drug treatments for an individual’s genetic makeup, but if you believe in “food as medicine”, then it should encompass nutrition as well.

Your project MyFitnessPal Miner was awarded one of the Open Humans project grants. Can you explain us what the project is about?

MyFitnessPal Miner exists with three goals. The first is about making the data more accessible, allowing you to get your own data in a format useful for other projects, including ones on Open Humans. The app ports your data to standard .csv files and does some additional parsing to create potentially useful tags. For example, it tries to recognize instances of fast food by matching records containing restaurant chain names.

The second goal is integrating that data with current genetic resources. There are some really interesting studies on how your genetics influences and interacts with your diet, such as your preferences for salty/sweet/bitter foods, risk for specific food intolerances, and how you’d react to a low-carb versus a high-carb diet. When curating these kinds of studies, I think that many people must also be curious about how the findings apply to them. So if you have 23andMe data and MyFitnessPal data, the app gives you a kind of integrated dashboard of genetics and nutritional behavior. You might, for instance, be able to see that your fast food consumption is greater than average, and that this seems congruent with what a published study has found given your genetic variants. Or next to the summary of your actual sodium intake, you might notice the relevant finding that your genetics predict that your blood pressure is fairly sensitive to how much salt you’re eating. However, because MyFitnessPal doesn’t contain explicit data for vitamins and minerals, not every related published finding can be connected with your real-life dietary data, unless the app can be made to intelligently infer vitamin and mineral intake from the food records.

Beyond comparing existing information, through the app it should be possible to use your real-life dietary data along with your genetic data to suggest something new. This third goal is kind of a reach goal given the limited time frame I have, but it’s the essence of an Open Humans project. I’d still have to think about the questions that are feasible and the methodology for them. Hopefully it won’t be just me, and there will be people in the Open Humans community who’d want to build upon MyFitnessPal Miner.

I do also hope that there will be interest outside of Open Humans. You can recall that all of this started not with my interest in genetics, but with food tracking. Well, it’s the start of a new year, and there will be a lot of people doing that as they pursue a healthier lifestyle. Some will seek understanding of their calorie and macronutrient patterns and then be hungry for additional value from their collected data. Being shown where the genetics tie in to create that additional value can perhaps entice people to bring their genetic data to the project, and therefore to Open Humans.  

When and how did you come to Open Humans?

I consider myself a relatively new member of Open Humans, since I joined in the fall of 2017. Around that time I was doing research for a start-up, where a colleague mentioned Open Humans and said making his genetic data public was something he wouldn’t do. I, on the other hand, was someone who was already quite open, having been to quantified self meetings to hear others share their data and insights and to share mine.

Have you been involved in any projects on Open Humans so far, either as a participant or even running your own?

When I joined Open Humans, I made my data accessible to all studies. I would think more about running my own study after finishing the development of MyFitnessPal Miner.

This is a cross-post from my personal blog, where I also introduced the new Open Humans project I created.

Would you like to analyze your Twitter history? Exactly 5 years ago Twitter started offering the option for users to download their full archive of personal tweets. The archive gives you a change to quickly browse through your personal history and find those funny cat pictures you once posted. But there is additional value in the archive, transcending the trips down to memory lane. For example, by looking into a full Twitter archive one can investigate longitudinal trends in interaction behaviour or geotag-based movement patterns. While Twitter archives come with their own user interface, they are not really designed for such deeper dives into the data. Which is why I have been working on a small tool called TwArχiv that tries to allow for such insights.

The plot above is a static example of what TwArχiv does. It gives you an idea of how my personal reply behaviour has changed over time. Using a gender-guessing Python library it classifies the users I replied to based on their first names. You can quickly see that I did have an extreme male-bias starting in early 2009 that becomes less pronounced in more recent years. What happened in 2009? I became an active member of the overwhelmingly male German Pirate Party. Go figure.

The plots that the TwArχiv generates include further interesting data. You can see a full, interactive demo done on my personal Twitter archive. The TwArχiv also looks into how your tweet behaviour changes with respect to whether you use Twitter for replying, retweeting or making original posts on your own; at which times of the day you tweet; and even how you move across the globe while tweeting. The gif below gives you a small glimpse into parts of my 2016 movements based on tweets.

If you want to analyse your own Twitter archive please give TwArχiv a try. As it uses Open Humans for the archive storage you can optionally also choose to make that data publicly available. If you have an unprotected Twitter account this data is already public but harder to systematically access (at least for anyone but Twitter itself). So why not help out social media researchers everywhere by giving your data?

If you have ideas for some data analyses that are missing so far: The code of TwArχiv is open source and I’d love to get your contributions and suggestions. And if you have a larger feature/analysis idea that’s not in TwArχiv yet, Open Humans is giving out grants of up to $5,000 for projects that help to grow their eco-system. Improving on TwArχiv is a perfect match for this.