Congratulations to Our Newest iPad Winners!

We’re delighted to see that more and more people are signing up to keep updated on the status of the upcoming launch of our new platform. As promised, each month we have drawn a name from that growing list to be the lucky winner of a new iPad.

We have two new winners to announce:

For July, the lucky person was Marie Fahey, a bioinformatics analyst with Asuragen. And for June, the winner was Ravi Madduri, a fellow in the Computation Institute at the University of Chicago. Congratulations to both!

If you haven’t already done so, don’t forget to sign up on dnanexusx.com to be the first to know when we unveil the new platform.

First Two iPad Winners Announced, and Another Chance to Win!

Many thanks to everyone who has signed up so far at our DNAnexusX landing
page to find out about the new platform we’ll be launching later this year.
Our developers are working furiously on the final touches, and we can’t
wait to give you all a peek at the new product.

In addition to the updates we’ll be sending about the new platform as we get
closer to launch, people who sign up on the landing page are also entered
into a monthly drawing for a free iPad.  We’re delighted to announce that
our winner for April was Mike Warfe, solutions architect at Case Western
Reserve University, and our winner for May is Bingbing Yuan in
bioinformatics at the Whitehead Institute.
Congratulations to both of them!

We’ve more iPad giveaways planned for June and July, so it’s not too
late to sign up at the landing page to be automatically entered to win.
Simply go to dnanexusx.com and enter your e-mail address.

On Being Platform Agnostic

One inevitable outcome of the ever-expanding number of DNA sequencing platforms is the lock-step addition of new data types. The technologies developed by Complete Genomics, Illumina, Life Tech/ABI/Ion Torrent and Pacific Biosciences produce the lion’s share of genomic data today. But Genia, GnuBio, NABsys, Oxford Nanopore and others are in the wings, poised to pile significantly more on.

Every sequencing platform relies on a different technology to read the As, Ts, Cs, and Gs of a genome. This presents a number of major challenges in assembly and sequence accuracy across platforms due to varying read lengths, method-specific data generation, sequencing errors, and so forth. However, while all have their nuances, they all have potential value to the progress of life science and medical research.

A complete solution to this problem would involve models for each platform, accounting for the generation and characteristics of libraries, data collection, transcript distributions, read lengths, error rates, and so on. The fact that a standard solution for integrating all these data types doesn’t currently exist is a testament to the difficulty of this task, which shouldn’t be underestimated.

The solutions most commonly used today for managing this diversity of data are the products of enterprising bioinformaticians who have developed “home-brewed” applications capable of taking primary data created by the instrument and, among other tricks, performing alignments to a reference genome and/or completing assemblies. While these workarounds provide a band-aid, they are not available for all platforms, rarely scalable and take highly experienced technical users to manage.

As genomic data continues its march beyond core facilities and into a broader range of research labs, healthcare organizations and, eventually, point-of-care providers, the need becomes even more acute for technologies that can — as far as the user is concerned — effortlessly perform the challenging tasks of integrating data from multiple sources for annotation and interpretation and combining them with the analysis and collaboration tools needed to glean insights.

As an industry, we need to start taking a more platform-agnostic approach towards the analysis and visualization of sequencing data. This is particularly critical as new platforms enter the market, collaborations across institutions, labs and borders expand and “legacy” data is incorporated into new repositories.

At DNAnexus, we are committed to removing the complexities inherent in working with diverse datasets so that scientists and clinicians can focus on the more impactful areas of data analysis and knowledge extraction. We are also committed to providing a secure and user-friendly online workspace where collaboration and data sharing can flourish.

Stay tuned for much more on this topic and let us know about the challenges you face when working with multiple data types and what kind of datasets you’d like to see more easily integrated into your work.