Genotyping of evolving prokaryotic populations

1 2 Genomic heterogeneity of bacterial species is observed and studied in experimental evolution 3 experiments, clinical diagnostics and occurs as micro-diversity of natural habitats. The challenge for 4 genome research is to accurately capture this heterogeneity with the currently used short sequencing 5 reads. Recent advances in NGS technologies improved the speed and coverage and thus allowed for 6 deep sequencing of bacterial populations. This facilitates the quantitative assessment of genomic 7 heterogeneity, including low frequent alleles or haplotypes. However, false positive variant predictions 8 due to sequencing errors and mapping artifacts of short reads need to be prevented. 9 We therefore created VarCap, a workflow for the reliable prediction of different types of variants even 10 at low frequencies. In order to predict SNPs, indels and structural variations, we evaluated the 11 sensitivity and accuracy of different software tools using synthetic read data. The results suggested 12 that the best sensitivity could be reached by a combination of different tools. We identified possible 13 reasons for false predictions and used this knowledge to improve the accuracy by post-filtering the 14 predicted variants according to properties such as frequency, coverage, genomic 15 environment/localization and co-localization with other variants. This resulted in the reliable prediction 16 of variants above a minimum relative abundance of 2%. 17 VarCap is designed for being routinely used within experimental evolution experiments or for clinical 18 diagnostics. The detected variants are reported as frequencies within a vcf file and as a graphical 19 overview of the distribution of the different variant/allele/haplotype frequencies. The source code of 20 VarCap is available at https://github.com/ma2o/VarCap. In order to provide this workflow to a broad 21 community, we implemeted VarCap on a Galaxy webserver (Afgan et al. 2016), which is accessible at 22 http://galaxy.csb.univie.ac.at. 23 PeerJ Preprints | https://doi.org/10.7287/peerj.preprints.2449v1 | CC BY 4.0 Open Access | rec: 14 Sep 2016, publ: 14 Sep 2016