Background: Phylogenomic analysis has become an inseparable part of studies of bacterial diversity and evolution, and many different bacterial core genes have been collated and used for phylogenomic tree reconstruction. However, these genes have been selected based on their presence and single-copy ratio in all bacterial genomes, leaving out the gene's 'phylogenetic fidelity' unexamined. Results: From 30,522 complete genomes covering 11,262 species, we examined 148 bacterial core genes that have been previously used for phylogenomic analysis. In addition to the gene presence and single-copy rations, we evaluated the gene's phylogenetic fidelity by comparing each gene's phylogeny with its corresponding 16S rRNA gene tree. Out of the 148 bacterial genes, 20 validated bacterial core genes (VBCG) were selected as the core gene set with the highest bacterial phylogenetic fidelity. Compared to the larger gene set, the 20-gene core set resulted in more species having all genes present and fewer species with missing data, thereby enhancing the accuracy of phylogenomic analysis. Using Escherichia coli strains as examples of prominent bacterial foodborne pathogens, we demonstrated that the 20 VBCG produced phylogenies with higher fidelity and resolution at species and strain levels while 16S rRNA gene tree alone could not. Conclusion: The 20 validated core gene set improves the fidelity and speed of phylogenomic analysis. Among other uses, this tool improves our ability to explore the evolution, typing and tracking of bacterial strains, such as human pathogens. We have developed a Python pipeline and a desktop graphic app (available on GitHub) for users to perform phylogenomic analysis with high fidelity and resolution.

Plasmids are extrachromosomal DNA found in microorganisms. They often carry beneficial genes that help bacteria adapt to harsh conditions. Plasmids are also important tools in genetic engineering, gene therapy, and drug production. However, it can be difficult to identify plasmid sequences from chromosomal sequences in genomic and metagenomic data. Here, we have developed a new tool called PlasmidHunter, which uses machine learning to predict plasmid sequences based on gene content profile. PlasmidHunter can achieve high accuracies (up to 97.6%) and high speeds in benchmark tests including both simulated contigs and real metagenomic plasmidome data, outperforming other existing tools.

The development and application of modern sequencing technologies have led to many new improvements in food safety and public health. With unprecedented resolution and big data, high-throughput sequencing (HTS) has enabled food safety specialists to sequence marker genes, whole genomes, and transcriptomes of microorganisms almost in real-time. These data reveal not only the identity of a pathogen or an organism of interest in the food supply but its virulence potential and functional characteristics. HTS of amplicons, allow better characterization of the microbial communities associated with food and the environment. New and powerful bioinformatics tools, algorithms, and machine learning allow for development of new models to predict and tackle important events such as foodborne disease outbreaks. Despite its potential, the integration of HTS into current food safety systems is far from complete. Government agencies have embraced this new technology, and use it for disease diagnostics, food safety inspections, and outbreak investigations. However, adoption and application of HTS by the food industry have been comparatively slow, sporadic, and fragmented. Incorporation of HTS by food manufacturers in their food safety programs could reinforce the design and verification of effectiveness of control measures by providing greater insight into the characteristics, origin, relatedness, and evolution of microorganisms in our foods and environment. Here, we discuss this new technology, its power, and potential. A brief history of implementation by public health agencies is presented, as are the benefits and challenges for the food industry, and its future in the context of food safety.

At least 40 toxin subtypes of botulinum neurotoxins (BoNTs), a heterogenous group of bacterial proteins, are produced by seven different clostridial species. A key factor that drives the diversity of neurotoxigenic clostridia is the association of bont gene clusters with various genomic locations including plasmids, phages and the chromosome. Analysis of Clostridium sporogenes BoNT/B1 strain CDC 1632, C. argentinense BoNT/G strain CDC 2741, and Clostridium parabotulinum BoNT/B1 strain DFPST0006 genomes revealed bont gene clusters within plasmid-like sequences within the chromosome or nested in large contigs, with no evidence of extrachromosomal elements. A nucleotide sequence (255,474 bp) identified in CDC 1632 shared 99.5% identity (88% coverage) with bont/B1-containing plasmid pNPD7 of C. sporogenes CDC 67071; CDC 2741 contig AYSO01000020 (1.1 MB) contained a ~140 kb region which shared 99.99% identity (100% coverage) with plasmid pRSJ17_1 of C. argentinense BoNT/G strain 89G; and DFPST0006 contig JACBDK0100002 (573 kb) contained a region that shared 100% identity (99%) coverage with the bont/B1-containing plasmid pCLD of C. parabotulinum Okra. This is the first report of full-length plasmid DNA-carrying complete neurotoxin gene clusters integrated in three distinct neurotoxigenic species: C. parabotulinum, C. sporogenes and C. argentinense.

Background: Amplicon sequencing of marker genes such as 16S rDNA have been widely used to survey and characterize microbial community. However, the complex data analyses have required many interfering manual steps often leading to inconsistencies in results. Results: Here, we have developed a pipeline, amplicon sequence analysis pipeline 2 (ASAP 2), to automate and glide through the processes without the usual manual inspections and user’s interference, for instance, in the detection of barcode orientation, selection of high-quality region of reads, and determination of resampling depth and many more. The pipeline integrates all the analytical processes such as importing data, demultiplexing, summarizing read profiles, trimming quality, denoising, removing chimeric sequences and making the feature table among others. The pipeline accepts multiple file formats as input including multiplexed or demultiplexed, paired-end or single-end, barcode inside or outside and raw or intermediate data (e.g. feature table). The outputs include taxonomic classification, alpha/beta diversity, community composition, ordination analysis and statistical tests. ASAP 2 supports merging multiple sequencing runs which helps integrate and compare data from different sources (public databases and collaborators). Conclusions: Our pipeline minimizes hands-on interference and runs amplicon sequence variant (ASV)-based amplicon sequencing analysis automatically and consistently. Our web server assists researchers that have no access to high performance computer (HPC) or have limited bioinformatics skills. The pipeline and web server can be accessed at https://github.com/tianrenmaogithub/asap2 and https://hts.iit.edu/asap2, respectively.

Plasmids are extrachromosomal DNA found in microorganisms. They often carry beneficial genes that help bacteria adapt to harsh conditions, but they can also carry genes that make bacteria harmful to humans. Plasmids are also important tools in genetic engineering, gene therapy, and drug production. However, it can be difficult to identify plasmid sequences from chromosomal sequences in genomic and metagenomic data. Here, we have developed a new tool called PlasmidHunter, which uses machine learning to predict plasmid sequences based on gene content profile. PlasmidHunter achieved high accuracies (up to 96.7%) and fast speeds in benchmark tests, outperforming other existing tools.