We analyzed SMRT sequence data using SMRTLink, a web-based end-to-end workflow manager for PacBio Sequel Systems, and SMRT Tools, a suite of command-line tools included with SMRTLink. We chose only those circular consensus sequence (CCS) reads that resulted from three complete passes of DNA, and that had a predicted accuracy of 99 percent. We also discarded any CCS reads that were shorter than 850 base pairs to exclude partial GFP sequences. We then de-multiplexed the CCS reads with a barcode score of 80 or higher. To this end, we used the 384 Sequel barcodes provided by PacBio and searched for all possible barcode combinations in our sequence data. We could accurately identify the 136 barcode combinations that we had used, out of more than seventy thousand possible combinations. We mapped the de-multiplexed reads using the SMRT tool ‘pbalign’, which uses the ‘blasr’ algorithm60 (link). We restricted the minimum mapping length to at least 900 base pairs, the maximum possible divergence from the ancestral sequence to 75%, and the minimum mapping accuracy to 90%. Through this procedure, we recovered 819 to 4241 reads for each population.
We then used SAMtools to convert the mapped sam files to bam format61 (link). We used custom Python scripts and MEGA software (Molecular Evolutionary Genetics Analysis, v10.0.5)62 (link) for all further data analysis.
We then used SAMtools to convert the mapped sam files to bam format61 (link). We used custom Python scripts and MEGA software (Molecular Evolutionary Genetics Analysis, v10.0.5)62 (link) for all further data analysis.