Abstract Motivation In the era of multiple genome references, researchers often align sequencing reads against distinct assemblies or even multiple references simultaneously. This enables applications such as the detection of introgressed segments or highly variable genomic regions, which are especially prevalent in large-genome crop species such as lettuce or wheat. However, these applications come at the cost of increased computational burden, inconsistencies in mapping methods, and reduced reproducibility across studies. To address these limitations, we developed KCFtools, a Java-based toolkit that identifies the presence and absence of k-mers in nonoverlapping genomic or transcriptomic windows by comparing query and reference genomes. This alignment-free approach enables the efficient computation of an identity score for each window, thereby facilitating robust detection of introgressed or variable regions across genomes. Results We systematically evaluated the performance and accuracy of the k-mer-based method implemented in KCFtools, benchmarking it against conventional single nucleotide variation-based introgression detection pipelines. Our results demonstrate that KCFtools effectively captures introgressed segments and structurally diverse regions, even in species with fragmented or highly divergent reference genomes. In addition, we extended KCFtools to generate genotype matrices from k-mer variation tables. These matrices are compatible with genome-wide association studies software and allow the identification of loci associated with phenotypic traits. We showcase the utility of this approach by detecting known and novel associations for downy mildew resistance in lettuce, underscoring the pipeline’s potential for high-resolution, reference-agnostic population genetic analysis. Availability and implementation https://github.com/sivasubramanics/kcftools

Abstract Comparing gene organization across genomic sequences reveals insights into evolutionary and functional diversity among different organisms and varieties. Performing this task across many sequences, such as from a pangenome, is challenging because of the scale, the density of information, and the inherent variation. Often, analyses are centered on a genomic region of interest—a locus that might be associated with a trait or contain genes within the same family or biological pathway. Within these regions, researchers examine the conservation of gene order and orientation across organisms and assess sequence similarity, along with other gene content features such as gene size, to find biological variations or potential errors in the data. Automated methods in comparative genomics struggle to identify meaningful patterns due to varying and often unknown features of interest, leaving manual, time‐intensive, and scalability‐challenged visualization as the primary alternative. To address these challenges, we present a multiscale design for studying gene organization within pangenomes, developed in close collaboration with domain experts. Our tool, Multipla, enables users to explore organization at multiple levels of detail in a decluttered manner through layout abstractions, semantic zooming, and layouts with flexible distance definitions and feature selections, combining the advantages of manual and automated methods used in practice. We evaluate the design of Multipla through two pangenomic use cases and conclude with lessons learned from designing multiscale views for pangenomic locus analysis.

Genomics researchers increasingly use multiple reference genomes to comprehensively explore genetic variants underlying differences in detectable characteristics between organisms. Pangenomes allow for an efficient data representation of multiple related genomes and their associated metadata. However, current visual analysis approaches for exploring these complex genotype-phenotype relationships are often based on single reference approaches or lack adequate support for interpreting the variants in the genomic context with heterogeneous (meta)data. This design study introduces PanVA, a visual analytics design for pangenomic variant analysis developed with the active participation of genomics researchers. The design uniquely combines tailored visual representations with interactions such as sorting, grouping, and aggregation, allowing users to navigate and explore different perspectives on complex genotype-phenotype relations. Through evaluation in the context of plants and pathogen research, we show that PanVA helps researchers explore variants in genes and generate hypotheses about their role in phenotypic variation.

SUMMARY: The ever-increasing number of sequenced genomes necessitates the development of pangenomic approaches for comparative genomics. Introduced in 2016, PanTools is a platform that allows pangenome construction, homology grouping and pangenomic read mapping. The use of graph database technology makes PanTools versatile, applicable from small viral genomes like SARS-CoV-2 up to large plant or animal genomes like tomato or human. Here, we present our third major update to PanTools that enables the integration of functional annotations and provides both gene-level analyses and phylogenetics. AVAILABILITY AND IMPLEMENTATION: PanTools is implemented in Java 8 and released under the GNU GPLv3 license. Software and documentation are available at https://git.wur.nl/bioinformatics/pantools. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SUMMARY: Analysis and comparison of genomic and transcriptomic datasets have become standard procedures in biological research. However, for non-model organisms no efficient tools exist to visually work with multiple genomes and their metadata, and to annotate such data in a collaborative way. Here we present GeneNoteBook: a web based collaborative notebook for comparative genomics. GeneNoteBook allows experimental and computational researchers to query, browse, visualize and curate bioinformatic analysis results for multiple genomes. GeneNoteBook is particularly suitable for the analysis of non-model organisms, as it allows for comparing newly sequenced genomes to those of model organisms. AVAILABILITY AND IMPLEMENTATION: GeneNoteBook is implemented as a node.js web application and depends on MongoDB and NCBI BLAST. Source code is available at https://github.com/genenotebook/genenotebook. Additionally, GeneNoteBook can be installed through Bioconda and as a Docker image. Full installation instructions and online documentation are available at https://genenotebook.github.io. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: Identification of homologous genes is fundamental to comparative genomics, functional genomics and phylogenomics. Extensive public homology databases are of great value for investigating homology but need to be continually updated to incorporate new sequences. As new sequences are rapidly being generated, there is a need for efficient standalone tools to detect homologs in novel data. RESULTS: To address this, we present a fast method for detecting homology groups across a large number of individuals and/or species. We adopted a k-mer based approach which considerably reduces the number of pairwise protein alignments without sacrificing sensitivity. We demonstrate accuracy, scalability, efficiency and applicability of the presented method for detecting homology in large proteomes of bacteria, fungi, plants and Metazoa. CONCLUSIONS: We clearly observed the trade-off between recall and precision in our homology inference. Favoring recall or precision strongly depends on the application. The clustering behavior of our program can be optimized for particular applications by altering a few key parameters. The program is available for public use at https://github.com/sheikhizadeh/pantools as an extension to our pan-genomic analysis tool, PanTools.

Genome sequences nowadays play a central role in molecular biology and bioinformatics. These sequences are shared with the scientific community through sequence databases. The sequence repositories of the International Nucleotide Sequence Database Collaboration (INSDC, comprising GenBank, ENA and DDBJ) are the largest in the world. Preparing an annotated sequence in such a way that it will be accepted by the database is challenging because many validation criteria apply. In our opinion, it is an undesirable situation that researchers who want to submit their sequence need either a lot of experience or help from partners to get the job done. To save valuable time and money, we list a number of recommendations for people who want to submit an annotated genome to a sequence database, as well as for tool developers, who could help to ease the process.

MOTIVATION: Next-generation sequencing technology is generating a wealth of highly similar genome sequences for many species, paving the way for a transition from single-genome to pan-genome analyses. Accordingly, genomics research is going to switch from reference-centric to pan-genomic approaches. We define the pan-genome as a comprehensive representation of multiple annotated genomes, facilitating analyses on the similarity and divergence of the constituent genomes at the nucleotide, gene and genome structure level. Current pan-genomic approaches do not thoroughly address scalability, functionality and usability. RESULTS: We introduce a generalized De Bruijn graph as a pan-genome representation, as well as an online algorithm to construct it. This representation is stored in a Neo4j graph database, which makes our approach scalable to large eukaryotic genomes. Besides the construction algorithm, our software package, called PanTools, currently provides functionality for annotating pan-genomes, adding sequences, grouping genes, retrieving gene sequences or genomic regions, reconstructing genomes and comparing and querying pan-genomes. We demonstrate the performance of the tool using datasets of 62 E. coli genomes, 93 yeast genomes and 19 Arabidopsis thaliana genomes. AVAILABILITY AND IMPLEMENTATION: The Java implementation of PanTools is publicly available at http://www.bif.wur.nl CONTACT: [email protected].

From prokaryotes to eukaryotes, phenotypic variation, adaptation and speciation has been associated with structural variation between genomes of individuals within the same species. Many computer algorithms detecting such variations (callers) have recently been developed, spurred by the advent of the next-generation sequencing technology. Such callers mainly exploit split-read mapping or paired-end read mapping. However, as different callers are geared towards different types of structural variation, there is still no single caller that can be considered a community standard; instead, increasingly the various callers are combined in integrated pipelines. In this article, we review a wide range of callers, discuss challenges in the integration step and present a survey of pipelines used in population genomics studies. Based on our findings, we provide general recommendations on how to set-up such pipelines. Finally, we present an outlook on future challenges in structural variation detection.