With data intensive applications becoming more common in the life sciences, I'm often asked for advice on how to prepare for a career in bioinformatics or transition an existing career to focus on life science applications. A few years back, I responded to a post on Slashdot with some thoughts on bioinformatics career[...]

In a previous post, we talked about sequencing as a big data problem, with our focus on the scale of data generated by sequencing instruments. In this post, we'll look at the problem from the context of biology to help understand the fundamental reasons why sequencing will always be a big data problem.

In the early days of next-generation sequencing, I was approached by a local R&D group about designing a cluster for their two new SOLiD instruments.  The budget was modest - tens of thousands of dollars - and the expectations were high - a run every ten days for each instrument, or one run a week for the lab.[...]

(First of all, let me welcome you to the Lab7 Systems blog, which will be full of our thoughts, insights, and musings for the scientific community and our users.  Enjoy the ride with us! – Varshal)

At Lab7, we develop code to manage the flow of data between tasks spread out across a cluster. Of course, having access to a cluster is a requirement for development and test, but as a young and mobile company we don’t necessarily want to be tied to an entrenched cluster, we are young and mobile after all.

First off, welcome to the Lab7 blog!