This week I would like to introduce you to Richard Smith, founder and software developer of Nowomics. He kindly agreed to answer some questions for our post blog series and here it is – first hand information on Nowomics. Keep reading to find out more about this company.

Richard Smith, founder and software developer of Nowomics

1. Could you please give us a short introduction to Nowomics (goals, interests, mission)?

Nowomics is a free website to help life scientists keep up with the latest papers and data relevant to their research. It lets researchers ‘follow’ genes and keywords to build their own news feed of what’s new and popular in their field. The aim is to help scientists discover the most useful information and avoid missing important journal articles, but without spending a lot of their time searching websites.

2. What makes Nowomics unique?

Nowomics tracks new papers, but also other sources of curated biological annotation and experimental data. It can tell you if a gene you work on has new annotation added or has been linked to a disease in a recent study. The aim is to build knowledge of these biological relationships into the software to help scientists navigate and discover information, rather than recommending papers simply by text similarity.

3. When did you realise that a tool such as Nowomics would be of a great help to the genomic research community?

I’ve been building websites and databases for biologists for a long time and have heard from many scientists how hard it is to keep up with the flood of new information. There are around 20,000 biomedical journal articles published every week and hundreds of sources of data online, receiving lots of emails with lists of paper titles isn’t a great solution. In social media interactive news feeds that adapt to an individual are now commonly used as an excellent way to consume large amounts of new information, I wanted to apply these principles to tracking biology research.

4. Which part of developing the tool you found most challenging?

As with a lot of software, making sure Nowomics is as useful as possible to users has been the hardest part. It’s quite straightforward to identify a problem and build some software, but making sure the two are correctly aligned to provide maximum value to users has been the difficult part. It has meant trying many things, demonstrating ideas and listening to a lot of feedback. Handling large amounts of data and writing text mining software to identify thousands of biological terms is simple by comparison!

5. What are your plans for the future of Nowomics? Are you working on adding new features/apps?

There are lots of new features planned. Currently Nowomics focuses on genes/proteins and selected organisms. We’ll soon make this much broader, so scientists will be able to follow diseases, pathways, species, processes and many other keywords. We’re working on how these terms can be combined together for fine grained control of what appears in news feeds. It’s also important to make sharing with colleagues and recommending research extremely simple.

6. Can you think of examples of how Nowomics supports data access and knowledge dissemination within the genomics community?

The first step to sharing data sets and accessing research is for the right people to know they exist. This is exactly what Nowomics was set up to achieve, to benefit both scientists who need to be alerted to useful information and for those generating or funding research to reach the best possible audience. Hopefully Nowomics will also alert people to relevant shared genomics data in future.

7. What does ethical data sharing mean to you?

For data that can advance scientific and medical research the most ethical thing to do is to share it with other researchers to help make progress. This is especially true for data resulting from publicly funded research. However, with medical and genomics data the issues of confidentiality and privacy must take priority, and individuals must be aware what their information may be used for.

8. What are the most important things that you think should be done in the field of genetic data sharing?

The challenge is to find a way to unlock the huge potential of sharing genomics data for analysis while respecting the very real privacy concerns. A platform that enables sharing in a secure, controlled manner which preserves privacy and anonymity seems essential, I’m very interested in what DNADigest are doing in this regard.

DNAdigest continues with the series of interviews. Here we would like to introduce you to Mr Mark Kaganovich, CEO of SolveBio, who agreed on an interview with us. He shared a lot about what SolveBio does and discussed with us the importance of genomic data sharing.

Mark Kaganovich, CEO of SolveBio

1) Could you describe what SolveBio does?

SolveBio delivers the critical reference data used by hospitals and companies to run genomic applications. These applications use SolveBio’s data to predict the effects of slight DNA variants on a person’s health. SolveBio has designed a secure platform for the robust delivery of complex reference datasets. We make the data easy to access so that our customers can focus on building clinical grade molecular diagnostics applications, faster.

2) How did you come up with the idea of building a system that integrates genomic reference data into diagnostic and research applications? And what was the crucial moment when you realised the importance of creating it?

As a graduate student I spent a lot of time parsing, re-formatting, and integrating data just to answer some basic questions in genomics. At the same time (this was about two years ago) it was becoming clear that genomics was going to be an important industry with a yet unsolved IT component. David Caplan (SolveBio’s CTO) and I started hacking away at ways to simplify genome analysis in the anticipation that interpreting DNA would be a significant problem in both research and the clinic. One thing we noticed was that there were no companies or services out there to help out guys like us – people that were programming with genomic data. There were a few attempts at kludgy interfaces for bioinformatics and a number of people were trying to solve the read mapping computing infrastructure problem, but there were no “developer tools” for integrating genomic data. In part, that was because a couple years ago there wasn’t that much data out there, so parsing, formatting, cleaning, indexing, updating, and integrating data wasn’t as big of a problem as it is now (or will be in a few years). We set out to build an API to the world’s genomic data so that other programmers could build amazing applications with the data without having to repeat painful meaningless tasks.

As we started talking to people about our API we realized how valuable a genomic data service is for the clinic. Genomics is no longer solely an academic problem. When we started talking to hospitals and commercial diagnostic labs, that’s when we realized that this is a crucial problem. That’s also when we realized that an API to public data is just the tip of the iceberg. Access to clinical genomic information that can be used as reference data is the key to interpreting DNA as a clinical metric.

3) After the molecular technology revolution made it possible for us to collect large amounts of precise medical data at low cost, another problem appeared to take over. How do you see the solution of the problem that the data are not in a language doctors can understand?

The molecular technology revolution will make it possible to move from “Intuitive Medicine” to “Precision Medicine”, in the language of Clay Christensen and colleagues in “Innovator’s Prescription”. Molecular markers are much closer to being unique fingerprints of the individual than whatever can be expressed by the English language in a doctor’s note. If these markers can be conclusively associated with diagnosis and treatment, medicine will be an order of magnitude better, faster, cheaper than it is now. Doctors can’t possibly be expected to read the three billion base pairs or so that make up the genome of every patient and recall which diagnosis and treatment is the best fit in light of the genetic information. This is where the digital revolution – i.e. computing – comes in. Aggregating silo’ed data while maintaining the privacy of the patients using bleeding edge software will allow doctors to use clinical genomic data to better medicine.

4) What are your plans for the future of SolveBio? Are you working on developing more tools/apps?

Our goal is to be the data delivery system for genomic medicine. We’ve built the tools necessary to integrate data into a genomic medical application, such as a diagnostic tool or variant annotator. We are now building some of these applications to make life easier for people running genetic tests.

5) Do you recognise the problem of limited sharing of genomics data for research and diagnosis? Can you think of an example of how the work of SolveBio supports data access and knowledge sharing within the genomics community?

The information we can glean from DNA sequence is only as good as the reference data that is used for research and diagnostic applications. We are particularly interested in genomics data from the perspective of how linking data from different sources creates the best possible reference for clinical genomics. This is, in a way, a data sharing problem.

I would add though that a huge disincentive to distributing data is the privacy, security, liability, and branding concern that clinical and commercial outfits are right to take into account. As a result, we are especially tailoring our platform to address those concerns.

However, even the data that is currently being “shared” openly, largely as a product of the taxpayer funded academic community, is very difficult and costly to access. Open data isn’t free. It involves building and maintaining substantial infrastructure to make sure the data is up-to-date and to verify quality. SolveBio solves that problem. Developers building DNA interpretation tools no longer have to worry about setting up their data infrastructure. They can integrate data with a few lines of code through SolveBio.

6) Which is the most important thing that should be done in the field of genetic data sharing and what does ethical data sharing mean to you?

Ethical data sharing means keeping patient data private and secure. If data is used for research or diagnostic purposes and needs to be transferred among doctors, scientists, or engineers then privacy and security is a key concern. Without privacy and security controls genomic data will never benefit from the aggregate knowledge of programmers and clinicians because patients will be rightly opposed to measuring, let alone distributing, their genomic information. Patient data belongs to the patient. Sometimes clinicians and researchers forget that. I definitely think the single most important thing to get right is the data privacy and security standard. The entire field depends upon it.