Gene duplication is an important evolutionary mechanism capable of providing new genetic material for adaptive and nonadaptive evolution. However, bioinformatics tools for identifying duplicate genes are often limited to the detection of paralogs in multiple species or to specific types of gene duplicates, such as retrocopies. Here, we present a user-friendly, BLAST-based web tool, called HSDFinder, which can identify, annotate, categorize, and visualize highly similar duplicate genes (HSDs) in eukaryotic nuclear genomes. HSDFinder includes an online heatmap plotting option, allowing users to compare HSDs among different species and visualize the results in different Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway functional categories. The external software requirements are BLAST, InterProScan, and KEGG. The utility of HSDFinder was tested on various model eukaryotic species, including Chlamydomonas reinhardtii, Arabidopsis thaliana, Oryza sativa, and Zea mays as well as the psychrophilic green alga Chlamydomonas sp. UWO241, and was proven to be a practical and accurate tool for gene duplication analyses. The web tool is free to use at http://hsdfinder.com. Documentation and tutorials can be found via the GitHub: https://github.com/zx0223winner/HSDFinder.

Although gene duplications have been documented in many species, the precise numbers of highly similar duplicated genes (HSDs) in eukaryotic nuclear genomes remain largely unknown and can be time-consuming to explore. We developed HSDFinder to identify, categorize, and visualize HSDs in eukaryotic nuclear genomes using protein family domains and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. In contrast to existing tools, HSDFinder allows users to compare HSDs among different species and visualize results in different KEGG pathway functional categories via heatmap plotting.

Single-nucleotide polymorphisms (SNPs) are useful biomarkers for linking genotype to phenotype. Machine learning is powerful for predicting antimicrobial resistance (AMR) from bacterial genome sequence data. Here, we present AMRLearn, a machine learning pipeline to assist users in the prediction and visualization of AMR phenotypes associated with SNP genotypes. We describe the steps needed for input data preparation, prediction model selection, and result visualization. AMRLearn is useful for researchers wanting to extract information relevant to AMR from whole-genome sequence data.

Gene duplication plays an important role in evolutionary mechanism, which can act as a new source of genetic material in genome evolution. However, detecting duplicate genes from genomic data can be challenging. Various bioinformatics resources have been developed to identify duplicate genes from single and/or multiple species. Here, we summarize the metrics used to measure sequence identity among gene duplicates within species, compare several computational approaches that have been used to predict gene duplicates, and review recent advancements of a Basic Local Alignment Search Tool (BLAST)-based web tool and database, allowing future researchers to easily identify intra-species gene duplications. This article is a quick reference guide for research tools used for detecting gene duplicates.

Many tools have been developed to measure the degree of similarity between gene duplicates within and between species. Here, we present HSDecipher, a bioinformatics pipeline to assist users in the analysis and visualization of highly similar duplicate genes (HSDs). We describe the steps for analysis of HSDs statistics, expanding HSD gene sets, and visualizing the results of comparative genomic analyses. HSDecipher represents a useful tool for researchers exploring the evolution of duplicate genes in select eukaryotic species. For complete details on the use and execution of this protocol, please refer to Zhang et al. (2021)¹ and Zhang et al. (2022).².