Science of the Yesteryear

Are you old enough to remember The Magic Roundabout, Hong-Kong Phooey and Mr Ben?  Did you finish your degree barely touching a computer?  When you graduated was 'genomics' a mere glint in Fred Sanger's eye?  If you answered 'yes' to any of these questions then you, like me, may feel befuddled by the dizzying speed of technological advances.

Don't despair, even when you say you went to 'Glastonbury' in 1990 and realise the app-savvy, linked-in, 'omics'-brains you are talking to weren't even born.  If you have spent years in fusty, ill-funded labs only to stumble, blinded, into the light of modern science, here are my rules for survival:

1.  Don't cry

2.  Even if you don't know what the piece of data being flashed on the screen is telling you, you are still a good person

3.  You are still making a contribution, however small

4.  Don't waste money on expensive running shoes; you'll never be able to catch the latest advances

5.  Accept your limits.  You have fewer brain cells than you had when you were 20

6.  It is inevitable that one day you will be replaced by a robot, but as the great Loudon      Wainwright III said (young folk may be more familiar with his famous son, Rufus), 'at least you've been a has-been and not just a never-was.'

7.  It's not your fault; you were born too soon.

qPCR quantification using our new Agilent Bravo robot

We have an exciting new instrument in the Genomics Core which will enable us to automate several of our protocols which until now have been quite labour intensive. This is the Bravo Robot by Agilent. After some previous experience with automation, I think that the results we have seen up until now are really quite promising with the aim of saving hands on time and providing consistency and accuracy within protocols.

qPCR test run - One of the first tests we ran upon installation of the Bravo was qPCR quantification. We quantify all libraries which are submitted to our sequencing service so we can aim to generate cluster densities on the flowcell which will yield large amounts of high quality data.

For this test, a single RNA-seq library was quantified using our standard method with the Illumina quantification kit by Kappa Biosystems. To test the reproducibility of the robot, we performed qPCR on this one sample 24 times as this is the maximum number of tubes which can be loaded per run.

In addition to this, each of the same 24 aliquots of this one library was set up in the same way but manually. This test was useful to determine how good the liquid handling on the Bravo is by looking at the reproducibility of the 24 replicates but additionally for comparing that between manual versus automated set up. The library had also been quantified previously so we expected the concentration to be 50nM.

Agilent Bravo Robot

The results show that there is higher variation in concentrations achieved manually although both methods slightly overestimated the expected concentration of 50nM. We saw an average concentration of 55.6nM manually (an 11% increase from 50nM) in comparison to a concentration of 52.7nM (a 5.5% increase from 50nM) on the Bravo.

Despite there being an about 1.8% difference between the average concentrations seen on the manual set-up in comparison to that of the automated, we can see that the Bravo has yielded more consistant results which is what we would expect and also good news. Since this test, we are now quantifying all SLX library submissions using the Bravo.

Although the robot will not be for general use and we will be unable to run individuals qPCR, we will be using it for all qPCR quantification for libraries submitted to us for our NGS service and for generating RNA-seq libraries. Once these protocols become robust within our lab, we will explore the option of utilising the robot in other protocols including Exome libary prep. It is additionally going to be used for automation of ChIP by the Odom group.