{"id":1,"date":"2016-03-04T21:29:40","date_gmt":"2016-03-04T21:29:40","guid":{"rendered":"https:\/\/blogs.ubc.ca\/gardylab\/?p=1"},"modified":"2016-03-04T14:38:46","modified_gmt":"2016-03-04T21:38:46","slug":"hello-world","status":"publish","type":"post","link":"https:\/\/blogs.ubc.ca\/gardylab\/2016\/03\/04\/hello-world\/","title":{"rendered":"Gardylab goes live"},"content":{"rendered":"

Here begins the era of the open Gardylab notebook.<\/p>\n

This week, we received the fastq data from 276 M. tuberculosis genomes we had sent down to the BCGSC for sequencing. A few years back, we started a project to resurrect all the TB isolates we had in our freezers (~4500, going back to 1999), MIRU-VNTR genotype a bunch of them (we were able to do the complete decade from 01\/2005 through 12\/2014, ~2500 bugs in all), and then sequence all those isolates with a genotype in common with at least one other isolate – these represent potential cases of recent transmission, though only genomic data can provide high enough resolution to confirm\/refute said recent transmission.<\/p>\n

The extracted DNA varied rather wildly in concentration – our Nanodrop was giving readings that were sometimes orders of magnitude higher than what the GSC saw with Quant-iT. On average, the Nanodrop reported concentrations (ng\/uL) that were 13x higher than what Quant-iT showed. A table comparing the quants from both systems on seven plates’ worth of isolates is up at Figshare<\/a>.<\/p>\n

We looked through the 644 isolates that had initial QC data and identified three plates’ worth of isolates that we wanted to test out on the HiSeq before going any further with the sequencing – we made up one plate of high-concentration samples, one of medium, and one of low. The TechDev team at the BCGSC tried out a new library prep method for low-input samples and the samples went into the queue a few months ago.<\/p>\n

The fastq data arrived in our inboxes this week, and we were curious to see how well the low-concentration samples fared. GOOD NEWS. Everything worked brilliantly. When we assembled against the H37Rv reference, the minimum % of reference covered was 98.89% and the max 99.99%. All but two samples had average depth of coverage >100x, and the other two were 27x and 35x – enough for good variant calling.<\/p>\n

This is only a subset of our complete dataset, which we’ll be exploring to look at transmission dynamics of TB within BC, but over the next weeks\/months as we wait for the rest of the genotypically-clustered genomes to come in, we will be rooting around in these a bit to see what tumbles out.<\/p>\n

Other things to note this week:<\/strong><\/p>\n