In a post earlier this week I talked about the hacking of a MiSeq run by MadsAlbertsen, one comment on the post drew my attention to another paper I'd missed where the authors hacked their MiSeq to perform 600bp reads (PE300). Considering this was a year before Illumina sold us kits I'd say that's quite an achievement!
The Genome Sciences Centre, British Columbia Cancer Agency in Vancouver, did the sequencing for the Spruce genome paper (1). One of the authors is Robin Coope (Group Leader, Instrumentation BCCA Genome Sciences Centre) and he has been behind some pretty cool engineering in the genome sciences. In the Spruce paper his group demonstrated how to crack open a MiSeq cartridge and replace the insides with a larger reagent reservoir so kits can be mixed allowing much longer runs than Illumina intended (at the time of publishing).
The image below is from their supplementary data, I don't recommend you do this at home!
I met Robin when he was speaking at European Lab Automation 2013 in Hamburg last June. He gave an excellent talk on Automation Challenges in Next Generation Sequencing; we also had excellent weiner-schnitzel and dark bier once the conference finished. He spoke about the problems of quantifying NGS libraries on Bioanalyser and qPCR; we want molarity but get DNA concentration and these are not the same thing! Current methods allow you to use a simple calculation to convert between the two but this is heavily reliant on library size estimation. It is pretty much impossible to get the size right in the first place without measuring it and most people use the BioAnalyser. This is where Robin's talk really got interesting for me...
Unfortunately I can't share the slides (it was a commercial conference) but you could email Robin and ask him for a copy (or to hurry up and publish). Basically he described the deficiencies of the Bioanalyser software and introduced the concept of intelligent Fluorescent Units (iFU) to change the way the Bioanalyser does its analysis.
The Bioanalyser does a reasonable job of calculating size and molarity that works well on “tight” libraries, equally a visual estimation of mean insert size gives good results and cluster errors are more likely to be from mass quantification errors than insert size estimation errors. However for wider library distributions like Nextera or amplicons, iFU improves cluster density prediction and reduces cluster density error by 60% in the set (n=28) of amplicons he presented.
Of course none of this would be needed if we were using probe-based assays or digital PCR to count library molecules, but that is a whole other post!
Finally Robin went on to describe his groups work on the Barracuda a robot for 96 samples gel size selection.