Brian Bushnell

Merging paired-end shotgun reads generated on high-throughput sequencing platforms can substantially improve various subsequent bioinformatics processes, including genome assembly, binning, mapping, annotation, and clustering for taxonomic analysis. With the inexorable growth of sequence data volume and CPU core counts, the speed and scalability of read-processing tools becomes ever-more important. The accuracy of shotgun read merging is crucial as well, as errors introduced by incorrect merging percolate through to reduce the quality of downstream analysis. Thus, we designed a new tool to maximize accuracy and minimize processing time, allowing the use of read merging on larger datasets, and in analyses highly sensitive to errors. We present BBMerge, a new merging tool for paired-end shotgun sequence data. We benchmark BBMerge by comparison with eight other widely used merging tools, assessing speed, accuracy and scalability. Evaluations of both synthetic and real-world datasets demonstrate that BBMerge produces merged shotgun reads with greater accuracy and at higher speed than any existing merging tool examined. BBMerge also provides the ability to merge non-overlapping shotgun read pairs by using k-mer frequency information to assemble the unsequenced gap between reads, achieving a significantly higher merge rate while maintaining or increasing accuracy.

Alignment of reads is one of the primary computational tasks in bioinformatics. Of paramount importance to resequencing, alignment is also crucial to other areas - quality control, scaffolding, string-graph assembly, homology detection, assembly evaluation, error-correction, expression quantification, and even as a tool to evaluate other tools. An optimal aligner would greatly improve virtually any sequencing process, but optimal alignment is prohibitively expensive for gigabases of data. Here, we will present BBMap [1], a fast splice-aware aligner for short and long reads. We will demonstrate that BBMap has superior speed, sensitivity, and specificity to alternative high-throughput aligners bowtie2 [2], bwa [3], smalt, [4] GSNAP [5], and BLASR [6].

Over the past decade, high-throughput short-read 16S rRNA gene amplicon sequencing has eclipsed clone-dependent long-read Sanger sequencing for microbial community profiling. The transition to new technologies has provided more quantitative information at the expense of taxonomic resolution with implications for inferring metabolic traits in various ecosystems. We applied single-molecule real-time sequencing for microbial community profiling, generating full-length 16S rRNA gene sequences at high throughput, which we propose to name PhyloTags. We benchmarked and validated this approach using a defined microbial community. When further applied to samples from the water column of meromictic Sakinaw Lake, we show that while community structures at the phylum level are comparable between PhyloTags and Illumina V4 16S rRNA gene sequences (iTags), variance increases with community complexity at greater water depths. PhyloTags moreover allowed less ambiguous classification. Last, a platform-independent comparison of PhyloTags and in silico generated partial 16S rRNA gene sequences demonstrated significant differences in community structure and phylogenetic resolution across multiple taxonomic levels, including a severe underestimation in the abundance of specific microbial genera involved in nitrogen and methane cycling across the Lake's water column. Thus, PhyloTags provide a reliable adjunct or alternative to cost-effective iTags, enabling more accurate phylogenetic resolution of microbial communities and predictions on their metabolic potential.

The coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread globally, with >365,000 cases in California as of 17 July 2020. We investigated the genomic epidemiology of SARS-CoV-2 in Northern California from late January to mid-March 2020, using samples from 36 patients spanning nine counties and the Grand Princess cruise ship. Phylogenetic analyses revealed the cryptic introduction of at least seven different SARS-CoV-2 lineages into California, including epidemic WA1 strains associated with Washington state, with lack of a predominant lineage and limited transmission among communities. Lineages associated with outbreak clusters in two counties were defined by a single base substitution in the viral genome. These findings support contact tracing, social distancing, and travel restrictions to contain the spread of SARS-CoV-2 in California and other states.

Multiple models describe the formation and evolution of distinct microbial phylogenetic groups. These evolutionary models make different predictions regarding how adaptive alleles spread through populations and how genetic diversity is maintained. Processes predicted by competing evolutionary models, for example, genome-wide selective sweeps vs gene-specific sweeps, could be captured in natural populations using time-series metagenomics if the approach were applied over a sufficiently long time frame. Direct observations of either process would help resolve how distinct microbial groups evolve. Here, from a 9-year metagenomic study of a freshwater lake (2005-2013), we explore changes in single-nucleotide polymorphism (SNP) frequencies and patterns of gene gain and loss in 30 bacterial populations. SNP analyses revealed substantial genetic heterogeneity within these populations, although the degree of heterogeneity varied by >1000-fold among populations. SNP allele frequencies also changed dramatically over time within some populations. Interestingly, nearly all SNP variants were slowly purged over several years from one population of green sulfur bacteria, while at the same time multiple genes either swept through or were lost from this population. These patterns were consistent with a genome-wide selective sweep in progress, a process predicted by the 'ecotype model' of speciation but not previously observed in nature. In contrast, other populations contained large, SNP-free genomic regions that appear to have swept independently through the populations prior to the study without purging diversity elsewhere in the genome. Evidence for both genome-wide and gene-specific sweeps suggests that different models of bacterial speciation may apply to different populations coexisting in the same environment.