Percent of reads mapping to <i>Prevotella</i> and Human (background) sequences (15 Mbp sequenced).

Percent of reads mapping to Prevotella and Human (background) sequences (15 Mbp sequenced).</p

Differences between swga1.0 and swga2.0.

Addressing many of the major outstanding questions in the fields of microbial evolution and pathogenesis will require analyses of populations of microbial genomes. Although population genomic studies provide the analytical resolution to investigate evolutionary and mechanistic processes at fine spatial and temporal scales—precisely the scales at which these processes occur—microbial population genomic research is currently hindered by the practicalities of obtaining sufficient quantities of the relatively pure microbial genomic DNA necessary for next-generation sequencing. Here we present swga2.0, an optimized and parallelized pipeline to design selective whole genome amplification (SWGA) primer sets. Unlike previous methods, swga2.0 incorporates active and machine learning methods to evaluate the amplification efficacy of individual primers and primer sets. Additionally, swga2.0 optimizes primer set search and evaluation strategies, including parallelization at each stage of the pipeline, to dramatically decrease program runtime. Here we describe the swga2.0 pipeline, including the empirical data used to identify primer and primer set characteristics, that improve amplification performance. Additionally, we evaluate the novel swga2.0 pipeline by designing primers sets that successfully amplify Prevotella melaninogenica, an important component of the lung microbiome in cystic fibrosis patients, from samples dominated by human DNA.</div

Poor performing primers are filtered out in Stage 3 of swga2.0.

Higher threshold values in the random forest regression model filter greater proportions of lower-amplification primers but few moderate or efficient primers.</p

The accuracy of amplification efficacy predictions increased after three iterations of the active learning approach designed to identify primer characteristics associated with effective priming.

Amplification of the target plasmid was weak in the majority of the randomly selected primers experimentally investigated in Round 1 (blue points represent the 204 primers evaluated). Target plasmid amplification was equally poor for the primers selected for Round 2 experimentation (96 orange points) by the random forest regressor model trained on the data from Round 1. The majority of primers selected for Round 3 experimentation (96 green points) by the updated random forest regressor model trained on the data from Rounds 1 and 2 resulted in moderate and high amplification of the target plasmid. Points are adjusted along the x-axis so that they do not overlap.</p

Overview of the swga2.0 pipeline.

The process is broken into four stages: 1) preprocessing of locations in the target and off-target genomes, 2) filtering motifs in the target genome based on individual primer properties and frequencies in the genomes, 3) scoring the remaining primers for amplification efficacy using a machine learning model, and 4) searching and evaluating aggregations of primers as candidate primer sets.</p

Ridge regression variable descriptions and coefficient values for primer set evaluation.

Ridge regression variable descriptions and coefficient values for primer set evaluation.</p

Feature importances based on the random forest regressor model.

Feature importances based on the random forest regressor model.</p

Summary schematic of Stage 4 (Primer set search and evaluation) of the swga2.0 pipeline.

Stage 4 begins with one randomly selected primer for each primer set. Each primer set is built in parallel until the improvements in evaluation score no longer exceed a user-defined parameter (ϵ) or until the maximum number of iterations is reached. A drop-out iteration forces each of the highest-scoring primer sets of size n to reduce to the subset of size n − 1 with the highest computed score.</p

Deeper sequencing of the three successful primer sets—Prev03, Prev06, and Prev04—confirms the efficient and even selective amplification of <i>P. melaninogenica</i>.

The solid colored lines indicate individual replicates and the green dashed line represents the pooled total. Each of the three primer sets yield dramatic increases in sequencing depth compared to the unamplified samples (black dashed line). Each primer set reached 10× coverage across 23–74% of the target genome, while the unamplified samples reached 10× coverage at <1% of the target genome, with 500 Mbp of sequencing effort.</p

Primer set statistics and sequences.

Addressing many of the major outstanding questions in the fields of microbial evolution and pathogenesis will require analyses of populations of microbial genomes. Although population genomic studies provide the analytical resolution to investigate evolutionary and mechanistic processes at fine spatial and temporal scales—precisely the scales at which these processes occur—microbial population genomic research is currently hindered by the practicalities of obtaining sufficient quantities of the relatively pure microbial genomic DNA necessary for next-generation sequencing. Here we present swga2.0, an optimized and parallelized pipeline to design selective whole genome amplification (SWGA) primer sets. Unlike previous methods, swga2.0 incorporates active and machine learning methods to evaluate the amplification efficacy of individual primers and primer sets. Additionally, swga2.0 optimizes primer set search and evaluation strategies, including parallelization at each stage of the pipeline, to dramatically decrease program runtime. Here we describe the swga2.0 pipeline, including the empirical data used to identify primer and primer set characteristics, that improve amplification performance. Additionally, we evaluate the novel swga2.0 pipeline by designing primers sets that successfully amplify Prevotella melaninogenica, an important component of the lung microbiome in cystic fibrosis patients, from samples dominated by human DNA.</div