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

SUMMARY: Interpreting and visualizing synteny relationships across several genomes is a challenging task. We previously proposed a network-based approach for better visualization and interpretation of large-scale microsynteny analyses. Here, we present syntenet, an R package to infer and analyze synteny networks from whole-genome protein sequence data. The package offers a simple and complete framework, including data preprocessing, synteny detection and network inference, network clustering and phylogenomic profiling, and microsynteny-based phylogeny inference. Graphical functions are also available to create publication-ready plots. Synteny networks inferred with syntenet can highlight taxon-specific gene clusters that likely contributed to the evolution of important traits, and microsynteny-based phylogenies can help resolve phylogenetic relationships under debate. AVAILABILITY AND IMPLEMENTATION: syntenet is available on Bioconductor (https://bioconductor.org/packages/syntenet), and the source code is available on a GitHub repository (https://github.com/almeidasilvaf/syntenet). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Fibrillarin (FIB), a methyltransferase essential for life in the vast majority of eukaryotes, is involved in methylation of rRNA required for proper ribosome assembly, as well as methylation of histone H2A of promoter regions of rRNA genes. RNA viral progression that affects both plants and animals requires FIB proteins. Despite the importance and high conservation of fibrillarins, there little is known about the evolutionary dynamics of this small gene family. We applied a phylogenomic microsynteny-network approach to elucidate the evolutionary history of FIB proteins across the Tree of Life. We identified 1063 non-redundant FIB sequences across 1049 completely sequenced genomes from Viruses, Bacteria, Archaea, and Eukarya. FIB is a highly conserved single-copy gene through Archaea and Eukarya lineages, except for plants, which have a gene family expansion due to paleopolyploidy and tandem duplications. We found a high conservation of the FIB genomic context during plant evolution. Surprisingly, FIB in mammals duplicated after the Eutheria split (e.g., ruminants, felines, primates) from therian mammals (e.g., marsupials) to form two main groups of sequences, the FIB and FIB-like groups. The FIB-like group transposed to another genomic context and remained syntenic in all the eutherian mammals. This transposition correlates with differences in the expression patterns of FIB-like proteins and with elevated Ks values potentially due to reduced evolutionary constraints of the duplicated copy. Our results point to a unique evolutionary event in mammals, between FIB and FIB-like genes, that led to non-redundant roles of the vital processes in which this protein is involved.

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.

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].