AnnoView enables large-scale analysis, comparison, and visualization of microbial gene neighborhoods.

The analysis and comparison of gene neighborhoods is a powerful approach for exploring microbial genome structure, function, and evolution. Although numerous tools exist for genome visualization and comparison, genome exploration across large genomic databases or user-generated datasets remains a challenge. Here, we introduce AnnoView, a web server designed for interactive exploration of gene neighborhoods across the bacterial and archaeal tree of life. Our server offers users the ability to identify, compare, and visualize gene neighborhoods of interest from 30 238 bacterial genomes and 1672 archaeal genomes, through integration with the comprehensive Genome Taxonomy Database and AnnoTree databases. Identified gene neighborhoods can be visualized using pre-computed functional annotations from different sources such as KEGG, Pfam and TIGRFAM, or clustered based on similarity. Alternatively, users can upload and explore their own custom genomic datasets in GBK, GFF or CSV format, or use AnnoView as a genome browser for relatively small genomes (e.g. viruses and plasmids). Ultimately, we anticipate that AnnoView will catalyze biological discovery by enabling user-friendly search, comparison, and visualization of genomic data. AnnoView is available at http://annoview.uwaterloo.ca.

Pan-genome de Bruijn graph using the bidirectional FM-index.

BACKGROUND: Pan-genome graphs are gaining importance in the field of bioinformatics as data structures to represent and jointly analyze multiple genomes. Compacted de Bruijn graphs are inherently suited for this purpose, as their graph topology naturally reveals similarity and divergence within the pan-genome. Most state-of-the-art pan-genome graphs are represented explicitly in terms of nodes and edges. Recently, an alternative, implicit graph representation was proposed that builds directly upon the unidirectional FM-index. As such, a memory-efficient graph data structure is obtained that inherits the FM-index' backward search functionality. However, this representation suffers from a number of shortcomings in terms of functionality and algorithmic performance. RESULTS: We present a data structure for a pan-genome, compacted de Bruijn graph that aims to address these shortcomings. It is built on the bidirectional FM-index, extending the ability of its unidirectional counterpart to navigate and search the graph in both directions. All basic graph navigation steps can be performed in constant time. Based on these features, we implement subgraph visualization as well as lossless approximate pattern matching to the graph using search schemes. We demonstrate that we can retrieve all occurrences corresponding to a read within a certain edit distance in a very efficient manner. Through a case study, we show the potential of exploiting the information embedded in the graph's topology through visualization and sequence alignment. CONCLUSIONS: We propose a memory-efficient representation of the pan-genome graph that supports subgraph visualization and lossless approximate pattern matching of reads against the graph using search schemes. The C++ source code of our software, called Nexus, is available at https://github.com/biointec/nexus under AGPL-3.0 license.

ChromoMap: an R package for interactive visualization of multi-omics data and annotation of chromosomes.

BACKGROUND: The recent advancements in high-throughput sequencing have resulted in the availability of annotated genomes, as well as of multi-omics data for many living organisms. This has increased the need for graphic tools that allow the concurrent visualization of genomes and feature-associated multi-omics data on single publication-ready plots. RESULTS: We present chromoMap, an R package, developed for the construction of interactive visualizations of chromosomes/chromosomal regions, mapping of any chromosomal feature with known coordinates (i.e., protein coding genes, transposable elements, non-coding RNAs, microsatellites, etc.), and chromosomal regional characteristics (i.e. genomic feature density, gene expression, DNA methylation, chromatin modifications, etc.) of organisms with a genome assembly. ChromoMap can also integrate multi-omics data (genomics, transcriptomics and epigenomics) in relation to their occurrence across chromosomes. ChromoMap takes tab-delimited files (BED like) or alternatively R objects to specify the genomic co-ordinates of the chromosomes and elements to annotate. Rendered chromosomes are composed of continuous windows of a given range, which, on hover, display detailed information about the elements annotated within that range. By adjusting parameters of a single function, users can generate a variety of plots that can either be saved as static image or as HTML documents. CONCLUSIONS: ChromoMap's flexibility allows for concurrent visualization of genomic data in each strand of a given chromosome, or of more than one homologous chromosome; allowing the comparison of multi-omic data between genotypes (e.g. species, varieties, etc.) or between homologous chromosomes of phased diploid/polyploid genomes. chromoMap is an extensive tool that can be potentially used in various bioinformatics analysis pipelines for genomic visualization of multi-omics data.

Visualizing and comparing circular genomes using the CGView family of tools.

Graphical genome maps are widely used to assess genome features and sequence characteristics. The CGView (Circular Genome Viewer) software family is a popular collection of tools for generating genome maps for bacteria, organelles and viruses. In this review, we describe the capabilities of the original CGView program along with those of subsequent companion applications, including the CGView Server and the CGView Comparison Tool. We also discuss GView, a graphical user interface-enabled rewrite of CGView, and the GView Server, which offers several integrated analyses for identifying shared or unique genome regions relative to a collection of comparison genomes. We conclude with some remarks about our current development efforts related to CGView aimed at adding new functionality while increasing ease of use.

PGAP-X: extension on pan-genome analysis pipeline.

BACKGROUND: Since PGAP (pan-genome analysis pipeline) was published in 2012, it has been widely employed in bacterial genomics research. Though PGAP has integrated several modules for pan-genomics analysis, how to properly and effectively interpret and visualize the results data is still a challenge. RESULT: To well present bacterial genomic characteristics, a novel cross-platform software was developed, named PGAP-X. Four kinds of data analysis modules were developed and integrated: whole genome sequences alignment, orthologous genes clustering, pan-genome profile analysis, and genetic variants analysis. The results from these analyses can be directly visualized in PGAP-X. The modules for data visualization in PGAP-X include: comparison of genome structure, gene distribution by conservation, pan-genome profile curve and variation on genic and genomic region. Meanwhile, result data produced by other programs with similar function can be imported to be further analyzed and visualized in PGAP-X. To test the performance of PGAP-X, we comprehensively analyzed 14 Streptococcus pneumonia strains and 14 Chlamydia trachomatis. The results show that, S. pneumonia strains have higher diversity on genome structure and gene contents than C. trachomatis strains. In addition, S. pneumonia strains might have suffered many evolutionary events, such genomic rearrangements, frequent horizontal gene transfer, homologous recombination, and other evolutionary process. CONCLUSION: Briefly, PGAP-X directly presents the characteristics of bacterial genomic diversity with different visualization methods, which could help us to intuitively understand dynamics and evolution in bacterial genomes. The source code and the pre-complied executable programs are freely available from http://pgapx.ybzhao.com .

Repetitive element signature-based visualization, distance computation, and classification of 1766 microbial genomes.

The genomes of living organisms are populated with pleomorphic repetitive elements (REs) of varying densities. Our hypothesis that genomic RE landscapes are species/strain/individual-specific was implemented into the Genome Signature Imaging system to visualize and compute the RE-based signatures of any genome. Following the occurrence profiling of 5-nucleotide REs/words, the information from top-50 frequency words was transformed into a genome-specific signature and visualized as Genome Signature Images (GSIs), using a CMYK scheme. An algorithm for computing distances among GSIs was formulated using the GSIs' variables (word identity, frequency, and frequency order). The utility of the GSI-distance computation system was demonstrated with control genomes. GSI-based computation of genome-relatedness among 1766 microbes (117 archaea and 1649 bacteria) identified their clustering patterns; although the majority paralleled the established classification, some did not. The Genome Signature Imaging system, with its visualization and distance computation functions, enables genome-scale evolutionary studies involving numerous genomes with varying sizes.

zDB: bacterial comparative genomics made easy.

The analysis and comparison of genomes rely on different tools for tasks such as annotation, orthology prediction, and phylogenetic inference. Most tools are specialized for a single task, and additional efforts are necessary to integrate and visualize the results. To fill this gap, we developed zDB, an application integrating a Nextflow analysis pipeline and a Python visualization platform built on the Django framework. The application is available on GitHub (https://github.com/metagenlab/zDB) and from the bioconda channel. Starting from annotated Genbank files, zDB identifies orthologs and infers a phylogeny for each orthogroup. A species phylogeny is also constructed from shared single-copy orthologs. The results can be enriched with Pfam protein domain prediction, Cluster of Orthologs Genes and Kyoto Encyclopedia of Genes and Genomes annotations, and Swissprot homologs. The web application allows searching for specific genes or annotations, running Blast queries, and comparing genomic regions and whole genomes. The metabolic capacities of organisms can be compared at either the module or pathway levels. Finally, users can run queries to examine the conservation of specific genes or annotations across a chosen subset of genomes and display the results as a list of genes, Venn diagram, or heatmaps. Those features make zDB useful for both bioinformaticians and researchers more accustomed to laboratory research.IMPORTANCEGenome comparison and analysis rely on many independent tools, leaving to scientists the burden to integrate and visualize their results for interpretation. To alleviate this burden, we have built zDB, a comparative genomics tool that includes both an analysis pipeline and a visualization platform. The analysis pipeline automates gene annotation, orthology prediction, and phylogenetic inference, while the visualization platform allows scientists to easily explore the results in a web browser. Among other features, the interface allows users to visually compare whole genomes and targeted regions, assess the conservation of genes or metabolic pathways, perform Blast searches, or look for specific annotations. Altogether, this tool will be useful for a broad range of applications in comparative studies between two and hundred genomes. Furthermore, it is designed to allow sharing of data sets easily at a local or international scale, thereby supporting exploratory analyses for non-bioinformaticians on the genome of their favorite organisms.

Online Resources Useful for Plant Cytogenetics and Cytogenomics Research.

The advancements in research in the field of plant cytogenetics and genomics in recent decades have led to a significant increase in publications. To simplify access to the widely dispersed data, there has been a rise in the number of online databases, repositories, and analytical tools. This chapter presents a comprehensive overview of these resources, which can be beneficial to researchers in these areas. It includes, among others, databases on chromosome numbers, special chromosomes (such as B chromosomes or sex chromosomes), some of which are taxon-specific; genome sizes, cytogenetics; and online applications and tools for genomic analysis and visualization.

Visualization Tools for Genomic Conservation.

SynVisio and Accusyn ( genomevis.usask.ca ) are freely available web-based tools for visualizing genomic conservation that provide easy-to-access visualizations for researchers to interact with their datasets and change parameters in real time to carry out synteny exploration and analysis through multiple coordinated visual representations. The tools use standard file formats and outputs from existing synteny detection systems such as MCScanX or DAGChainer, and provide several features that are valuable for large-scale genomic analysis: a range of visualization scales from full genomes down to single collinearity blocks; single-level and multiple-level plots that enable the analysis of more than two genomic regions; annotation tracks that can be loaded using standard BedGraph files; several techniques for reducing visual clutter in visualizations; the ability to download high-quality images of the visualizations; and a snapshot panel for storing configurations of the interface for later revisitation.

Visualization of Marek's Disease Virus Genomes in Living Cells during Lytic Replication and Latency.

Visualization of the herpesvirus genomes during lytic replication and latency is mainly achieved by fluorescence in situ hybridization (FISH). Unfortunately, this technique cannot be used for the real-time detection of viral genome in living cells. To facilitate the visualization of the Marek's disease virus (MDV) genome during all stages of the virus lifecycle, we took advantage of the well-established tetracycline operator/repressor (TetO/TetR) system. This system consists of a fluorescently labeled TetR (TetR-GFP) that specifically binds to an array of tetO sequences. This tetO repeat array was first inserted into the MDV genome (vTetO). Subsequently, we fused TetR-GFP via a P2a self-cleaving peptide to the C-terminus of the viral interleukin 8 (vIL8), which is expressed during lytic replication and latency. Upon reconstitution of this vTetO-TetR virus, fluorescently labeled replication compartments were detected in the nucleus during lytic replication. After validating the specificity of the observed signal, we used the system to visualize the genesis and mobility of the viral replication compartments. In addition, we assessed the infection of nuclei in syncytia as well as lytic replication and latency in T cells. Taken together, we established a system allowing us to track the MDV genome in living cells that can be applied to many other DNA viruses.

Exploring the Genomic Landscape of Cancer Patient Cohorts with GenVisR.

The creation of visualizations to interpret genomics data remains an important aspect of data science within computational biology. The GenVisR Bioconductor package was created to lower the entry point for publication-quality graphics and has remained a popular suite of tools within this domain. GenVisR supports visualizations covering a breadth of topics including functions to produce visual summaries of copy-number alterations, somatic variants, sequence quality metrics, and more. Recently, the GenVisR package has undergone significant updates to increase performance and functionality. To demonstrate the utility of GenVisR, we present protocols for use of the updated Waterfall() function to create a customizable Oncoprint-style plot of the mutational landscape of a tumor cohort. We explain the basics of installation, data import, configuration, plotting, clinical annotation, and customization. A companion online workshop describing the GenVisR library, Waterfall() function, and other genomic visualization tools is available at genviz.org. © 2021 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Generating a Waterfall() plot from original mutation data Basic Protocol 2: Adding clinical data to a Waterfall() plot Basic Protocol 3: Customizing mutation burden in Waterfall() plots Basic Protocol 4: Brief exploration of customizable options Support Protocol 1: Installing GenVisR.

Mango: Exploratory Data Analysis for Large-Scale Sequencing Datasets.

The decreasing cost of DNA sequencing over the past decade has led to an explosion of sequencing datasets, leaving us with petabytes of data to analyze. However, current sequencing visualization tools are designed to run on single machines, which limits their scalability and interactivity on modern genomic datasets. Here, we leverage the scalability of Apache Spark to provide Mango, consisting of a Jupyter notebook and genome browser, which removes scalability and interactivity constraints by leveraging multi-node compute clusters to allow interactive analysis over terabytes of sequencing data. We demonstrate scalability of the Mango tools by performing quality control analyses on 10 terabytes of 100 high-coverage sequencing samples from the Simons Genome Diversity Project, enabling capability for interactive genomic exploration of multi-sample datasets that surpass the computational limitations of single-node visualization tools. Mango is freely available for download with full documentation at https://bdg-mango.readthedocs.io/en/latest/.

GAMOLA2, a Comprehensive Software Package for the Annotation and Curation of Draft and Complete Microbial Genomes.

Expert curated annotation remains one of the critical steps in achieving a reliable biological relevant annotation. Here we announce the release of GAMOLA2, a user friendly and comprehensive software package to process, annotate and curate draft and complete bacterial, archaeal, and viral genomes. GAMOLA2 represents a wrapping tool to combine gene model determination, functional Blast, COG, Pfam, and TIGRfam analyses with structural predictions including detection of tRNAs, rRNA genes, non-coding RNAs, signal protein cleavage sites, transmembrane helices, CRISPR repeats and vector sequence contaminations. GAMOLA2 has already been validated in a wide range of bacterial and archaeal genomes, and its modular concept allows easy addition of further functionality in future releases. A modified and adapted version of the Artemis Genome Viewer (Sanger Institute) has been developed to leverage the additional features and underlying information provided by the GAMOLA2 analysis, and is part of the software distribution. In addition to genome annotations, GAMOLA2 features, among others, supplemental modules that assist in the creation of custom Blast databases, annotation transfers between genome versions, and the preparation of Genbank files for submission via the NCBI Sequin tool. GAMOLA2 is intended to be run under a Linux environment, whereas the subsequent visualization and manual curation in Artemis is mobile and platform independent. The development of GAMOLA2 is ongoing and community driven. New functionality can easily be added upon user requests, ensuring that GAMOLA2 provides information relevant to microbiologists. The software is available free of charge for academic use.