The next step in the pipeline after the alignments are conducted and adjusted is to call the SNPs and genotypes. There are many programs that will do this such as the ML script that many people in the Rieseberg lab use, freeBayes, mpileup and bcftools, as well as the GATK UnifiedGenotyper. To bench mark the alignment steps in these SNP calling blog posts I used mpileup and bcftools. For example:
The best practices protocol from Broad highly recommends base quality recalibration (http://gatkforums.broadinstitute.org/discussion/44/base-quality-score-recalibration-bqsr). Apparently, the base quality scores off the machine are not very accurate and can provide false confidence in the base calls and consequently influence SNP calling. This tool will adjust average quality scores and also adjust the scores depending on the machine cycle and sequence context (i.e. start versus end of read, type of dinucleotide repeat). After recalibration, the quality score is closer to its actual probability of mismatching the reference genome.
To run this you need a database of known SNPs so the tool can assess the error rate at other sites based on the actual sequence data. Unlike humans, which this tool was designed for, most species do not have comprehensive SNP databases. However, such a table can be created by identifying SNPs in a preliminary run without the base quality score recalibration. I included any SNP that had a Qual score of 20 in my SNP vcf file.
You can see below that it had a large effect on the number of SNPs that I called. With the BAQ filter off 263,360 SNPS overlapped between the methods. 14,591 were unique to the recalibrated alignments and 131,354 were unique to the alignments without base quality recalibration.