The last decade has witnessed a remarkable increase in our ability to measure genetic information. Advancements of sequencing technologies are challenging the existing methods of data storage and analysis. While methods to cope with the data deluge are progressing, many biologists have lagged behind due to the fast pace of computational advancements and tools available to address their scientific questions. Future generations of biologists must be more computationally aware and capable. This means they should be trained to give them the computational skills to keep pace with technological developments. Here, we propose a model that bridges experimental and bioinformatics concepts using the Oxford Nanopore Technologies (ONT) sequencing platform. We provide both a guide to begin to empower the new generation of educators, scientists, and students in performing long-read assembly of bacterial and bacteriophage genomes and a standalone virtual machine containing all the required software and learning materials for the course.

Motivation: A long-standing limitation in comparative genomic studies is the dependency on a reference genome, which hinders the spectrum of genetic diversity that can be identified across a population of organisms. This is especially true in the microbial world where genome architectures can significantly vary. There is therefore a need for computational methods that can simultaneously analyze the architectures of multiple genomes without introducing bias from a reference. Results: In this article, we present Ptolemy: a novel method for studying the diversity of genome architectures-such as structural variation and pan-genomes-across a collection of microbial assemblies without the need of a reference. Ptolemy is a 'top-down' approach to compare whole genome assemblies. Genomes are represented as labeled multi-directed graphs-known as quivers-which are then merged into a single, canonical quiver by identifying 'gene anchors' via synteny analysis. The canonical quiver represents an approximate, structural alignment of all genomes in a given collection encoding structural variation across (sub-) populations within the collection. We highlight various applications of Ptolemy by analyzing structural variation and the pan-genomes of different datasets composing of Mycobacterium, Saccharomyces, Escherichia and Shigella species. Our results show that Ptolemy is flexible and can handle both conserved and highly dynamic genome architectures. Ptolemy is user-friendly-requires only FASTA-formatted assembly along with a corresponding GFF-formatted file-and resource-friendly-can align 24 genomes in ∼10 mins with four CPUs and <2 GB of RAM. Availability and implementation: Github: https://github.com/AbeelLab/ptolemy. Supplementary information: Supplementary data are available at Bioinformatics online.

To evaluate Emu's ability to resolve alternate representations of LSVs, we introduced 179 simulated LSVs into the H37Rv genome--a carefully curated and finished reference genome for Mycobacterium tuberculosis (Mtb). We then used Pilon to identify variants in a set of 146 clinical samples of Mtb that were collected in China using the modified H37Rv genome as a reference [ 4 ]. We identified a total of 10,001 unique variant representations. The average number of non-identical representations of each simulated LSV was 56 (in the range of 1 to 145). We then applied Emu to identify the non-identical representations across the genomes of the 146 clinical samples and canonicalize them to a single form. Emu reduced the total number of non-identical representations to 676 LSVs bringing the average number of non-identical representations at each LSV to 4, with 15 LSVs reduced to a single representation and no LSV having more than 25 representations. We then investigated how Emu's ability to resolve alternate representations might impact association analyses, e.g., associating LSVs with population structure. We ran Pilon again on the set of 161 clinical samples from China, but used the unmodified H37Rv genome. Pilon identified a total of 20,512 distinct LSVs when compared to the unmodified H37Rv genome. By applying Emu, the number of distinct LSVs decreased by almost 50% to 10,936 LSVs. Emu also increased the power of association tests on the LSVs. While we initially identified a total number of 69 LSVs that were significantly associated (p < 0.01) with membership to a specific clade, after processing with Emu that number increased to 94. Emu enables comprehensive analysis of LSVs in bacterial genomes by reducing the cross-sample noise that results from per-sample variant calls. By normalizing our variant calls with Emu, we increased our power to utilize LSVs association tests. Pilon and Emu are open source tools that can also be applied to identify and normalize variants in other organisms.