1. Analyzing mitochondiral heteroplasmy of C. neoformans

    Strains were aligned and analyzed as previously described. The VCF files contain a wealth of information, some of which I'm not entirely sure what it means. Therefore I'll limit my analysis to 2 basic pieces of information: where the SNP is (record.POS) and the number of "alternate" (not exactly sure what alternate means in this context) alleles called (record.INFO['AC']). I could change it to record.INFO['AF'] because it's a float of the frequency, but having it in the range of 1-100 works pretty well for visualization. These python blurbs utilize PyVCF.

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  2. Using FreeBayes to measure mitochondrial heteroplasmy


    I have genomic data collected on 10 strains of Cryptococcus neoformans, a human fungal pathogen. It was collected a few years ago, and is, in a word, terrible. It is SOLiD3 or SOLid4 50bp single end data, 20M-30M reads per sample, but a best half survive quality trimming. In an attempt to salvage something out of the data, I noticed that while coverage of the genome was pretty awful, coverage of the mitochondria wasn't bad. Even better, while mapping is very sensitive to using the right variant (var neoformans, common name JEC21 vs var grubii, common name H99 ...




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