A pangenome represents the entire sequence content and variation of a population. As collections of complete reference quality genomes become more common, so does the prevalence of pangenomes, necessitating the need for scalable computational methods for their analysis. Previously, we developed FindFRs for identifying Frequented Regions in pangenome graphs, where a Frequented Region is a subgraph that is frequently traversed by multiple sequences. In this work, we propose FindFRs3, which is an updated version of FindFRs capable of identifying Frequented Regions with improved runtime and memory efficiency, enabling the analysis of much larger pangenome graphs. In addition, FindFRs3 identifies Frequented Region Variants (the unique subpaths through each region). We demonstrate the utility of these variants by using them as input features for machine learning models that can predict genotype-to-phenotype associations in a large yeast pangenome. Biological insights gained from these variants show that this novel technique allows for a more nuanced and detailed analysis of larger pangenomes.

It is known that a context-free grammar (CFG) that produces a single string can be derived from the compact directed acyclic word graph (CDAWG) for the same string. In this work, we show that the CFG derived from a CDAWG is deeply connected to the maximal repeat content of the string it produces and thus has O(m) rules, where m is the number of maximal repeats in the string. We then provide a generic algorithm based on this insight for constructing the CFG from the LCP-intervals of a string in O(n) time, where n is the length of the string. This includes a novel data-structure to support stabbing queries on LCPintervals in O(1+k) time after O(n) preprocessing time, where k is the number of intervals stabbed. These results connect the CFG to properties of the string it produces and relates it to other string data-structures, allowing it to be studied independently of the CDAWG and providing opportunity for innovation of grammar-based compression algorithms.

Visualization is key for expanding and communicating knowledge to both specialized and broad audiences-after all, "a picture is worth a thousand words," right?That much has become clear during the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) outbreak when "Flatten the curve!" [1] turned into a catchier watchword than the usual "Wash your hands!," by referring to the related graphics rather than the health discourses themselves.Coronavirus Disease 2019 (COVID-19) visualizations, good and bad [2], became omnipresent in the public debates, with interactive and dynamic visualization platforms like Nextstrain [3] gaining a lot of popularity.We are now used to seeing graphs and charts in our everyday lives and creating them for scientific papers as part of research.Unfortunately, most of the time, classic visual representations are insufficient to effectively communicate data complexity, and as O'Donoghue puts it, "often dedicated communication approaches need to be developed to address specific data challenges, especially when conveying complex or unfamiliar ideas" [4].Given the gap between creating static figures and building a visualization tool from the ground up, it can be easy to get lost in this nontrivial journey.Our following 10 simple rules are dedicated to biologists and bioinformaticians who, while already being at the crossroads of many fields, want to venture further into the land of Data Visualization ("datavis" or "dataviz" for short).They combine tips and advice that we would have wanted when we first started our own journeys, gathered from our experiences in building genomic and/or datavis tools, and the time spent with related communities.Additionally, they address current challenges in computational biology and the needs of the community.We aim these rules at bioinfo-to-datavis novices looking for guidelines, particularly regarding genomics visualization tools, but experienced practitioners may find it useful to see them gathered in one place too.For better reading comfort, we organized the rules chronologically depending on when they would matter the most during a visualization tool's life cycle, starting with the design (Rules 1 to 5) then development (Rules 5 to 8-ish) phases until it is shared with the rest of the world.Please note, however, that they are still relevant during the whole gestation and that creating a visualization tool is rarely a fully linear process-it does not even end after the initial release, as continued development and support is a cyclic process to be sustained over the years.

We consider the problem of identifying regions within a pan-genome De Bruijn graph that are traversed by many sequence paths. We define such regions and the subpaths that traverse them as frequented regions (FRs). In this work, we formalize the FR problem and describe an efficient algorithm for finding FRs. Subsequently, we propose some applications of FRs based on machine-learning and pan-genome graph simplification. We demonstrate the effectiveness of these applications using data sets for the organisms Staphylococcus aureus (bacterium) and Saccharomyces cerevisiae (yeast). We corroborate the biological relevance of FRs such as identifying introgressions in yeast that aid in alcohol tolerance, and show that FRs are useful for classification of yeast strains by industrial use and visualizing pan-genomic space.

Summary: The Genome Context Viewer is a visual data-mining tool that allows users to search across multiple providers of genome data for regions with similarly annotated content that may be aligned and visualized at the level of their shared functional elements. By handling ordered sequences of gene family memberships as a unit of search and comparison, the user interface enables quick and intuitive assessment of the degree of gene content divergence and the presence of various types of structural events within syntenic contexts. Insights into functionally significant differences seen at this level of abstraction can then serve to direct the user to more detailed explorations of the underlying data in other interconnected, provider-specific tools. Availability and implementation: GCV is provided under the GNU General Public License version 3 (GPL-3.0). Source code is available at https://github.com/legumeinfo/lis_context_viewer. Contact: [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online.

In this paper we present our experiences using a novel programming style, reactive programming, to deliver a summer camp for students in grades 8 through 12. This software uses a declarative programming approach to allow students without a background in computing to explore a wide variety of subject material within a 3D virtual environment, including computer science, mathematics, physics, and art. This work is based on PyFRP, a reactive programming library written in Python. We describe our camp experience and provide examples of how this style of programming supports a wide variety of educational activities.