When a data set is repeatedly clustered using unsupervised techniques, the resulting clusterings, even if highly similar, may list their clusters in different orders. This so-called 'label-switching' phenomenon obscures meaningful differences between clusterings, complicating their comparison and summary. The problem often arises in the context of population structure analysis based on multilocus genotype data. In this field, a variety of popular tools apply model-based clustering, assigning individuals to a prespecified number of ancestral populations. Since such methods often involve stochastic components, it is a common practice to perform multiple replicate analyses based on the same input data and parameter settings. Available postprocessing tools allow to mitigate label switching, but leave room for improvements, in particular, regarding large input data sets. In this work, I present Crimp, a lightweight command-line tool, which offers a relatively fast and scalable heuristic to align clusters across replicate clusterings consisting of the same number of clusters. For small problem sizes, an exact algorithm can be used as an alternative. Additional features include row-specific weights, input and output files similar to those of CLUMPP (Jakobsson & Rosenberg, 2007) and the evaluation of a given solution in terms of CLUMPP as well as its own objective functions. Benchmark analyses show that Crimp, especially when applied to larger data sets, tends to outperform alternative tools considering runtime requirements and various quality measures. While primarily targeting population structure analysis, Crimp can be used as a generic tool to correct multiple clusterings for label switching. This facilitates their comparison and allows to generate an averaged clustering. Crimp's computational efficiency makes it even applicable to relatively large data sets while offering competitive solution quality.

Despite the increasing accessibility of high-throughput sequencing, obtaining high-quality genomic data on non-model organisms without proximate well-assembled and annotated genomes remains challenging. Here, we describe a workflow that takes advantage of distant genomic resources and ingroup transcriptomes to select and jointly enrich long open reading frames (ORFs) and ultraconserved elements (UCEs) from genomic samples for integrative studies of microevolutionary and macroevolutionary dynamics. This workflow is applied to samples of the African unionid bivalve tribe Coelaturini (Parreysiinae) at basin and continent-wide scales. Our results indicate that ORFs are efficiently captured without prior identification of intron-exon boundaries. The enrichment of UCEs was less successful, but nevertheless produced substantial data sets. Exploratory continent-wide phylogenetic analyses with ORF supercontigs (>515,000 parsimony informative sites) resulted in a fully resolved phylogeny, the backbone of which was also retrieved with UCEs (>11,000 informative sites). Variant calling on ORFs and UCEs of Coelaturini from the Malawi Basin produced ~2000 SNPs per population pair. Estimates of nucleotide diversity and population differentiation were similar for ORFs and UCEs. They were low compared to previous estimates in molluscs, but comparable to those in recently diversifying Malawi cichlids and other taxa at an early stage of speciation. Skimming off-target sequence data from the same enriched libraries of Coelaturini from the Malawi Basin, we reconstructed the maternally-inherited mitogenome, which displays the gene order inferred for the most recent common ancestor of Unionidae. Overall, our workflow and results provide exciting perspectives for integrative genomic studies of microevolutionary and macroevolutionary dynamics in non-model organisms.

Despite recent advances in high-throughput DNA sequencing technologies, a lack of locally relevant DNA reference databases limits the potential for DNA-based monitoring of biodiversity for conservation and biosecurity applications. Museums and national collections represent a compelling source of authoritatively identified genetic material for DNA database development, yet obtaining DNA barcodes from long-stored specimens may be difficult due to sample degradation. Here we demonstrate a sensitive and efficient laboratory and bioinformatic process for generating DNA barcodes from hundreds of invertebrate specimens simultaneously via the Illumina MiSeq system. Using this process, we recovered full-length (334) or partial (105) COI barcodes from 439 of 450 (98%) national collection-held invertebrate specimens. This included full-length barcodes from 146 specimens which produced low-yield DNA and no visible PCR bands, and which produced as little as a single sequence per specimen, demonstrating high sensitivity of the process. In many cases, the identity of the most abundant sequences per specimen were not the correct barcodes, necessitating the development of a taxonomy-informed process for identifying correct sequences among the sequencing output. The recovery of only partial barcodes for some taxa indicates a need to refine certain PCR primers. Nonetheless, our approach represents a highly sensitive, accurate and efficient method for targeted reference database generation, providing a foundation for DNA-based assessments and monitoring of biodiversity.

Microbiome data are characterized by several aspects that make them challenging to analyse statistically: they are compositional, high dimensional and rich in zeros. A large array of statistical methods exist to analyse these data. Some are borrowed from other fields, such as ecology or RNA-sequencing, while others are custom-made for microbiome data. The large range of available methods, and which is continuously expanding, means that researchers have to invest considerable effort in choosing what method(s) to apply. In this paper we list 14 statistical methods or approaches that we think should be generally avoided. In several cases this is because we believe the assumptions behind the method are unlikely to be met for microbiome data. In other cases we see methods that are used in ways they are not intended to be used. We believe researchers would be helped by more critical evaluations of existing methods, as not all methods in use are suitable or have been sufficiently reviewed. We hope this paper contributes to a critical discussion on what methods are appropriate to use in the analysis of microbiome data.

The evolutionary direction of gonochorism and hermaphroditism is an intriguing mystery to be solved. The special transient hermaphroditic stage makes the little yellow croaker (Larimichthys polyactis) an appealing model for studying hermaphrodite formation. However, the origin and evolutionary relationship between of L. polyactis and Larimichthys crocea, the most famous commercial fish species in East Asia, remain unclear. Here, we report the sequence of the L. polyactis genome, which we found is ~706 Mb long (contig N50 = 1.21 Mb and scaffold N50 = 4.52 Mb) and contains 25,233 protein-coding genes. Phylogenomic analysis suggested that L. polyactis diverged from the common ancestor, L. crocea, approximately 25.4 million years ago. Our high-quality genome assembly enabled comparative genomic analysis, which revealed several within-chromosome rearrangements and translocations, without major chromosome fission or fusion events between the two species. The dmrt1 gene was identified as the male-specific gene in L. polyactis. Transcriptome analysis showed that the expression of dmrt1 and its upstream regulatory gene (rnf183) were both sexually dimorphic. Rnf183, unlike its two paralogues rnf223 and rnf225, is only present in Larimichthys and Lates but not in other teleost species, suggesting that it originated from lineage-specific duplication or was lost in other teleosts. Phylogenetic analysis shows that the hermaphrodite stage in male L. polyactis may be explained by the sequence evolution of dmrt1. Decoding the L. polyactis genome not only provides insight into the genetic underpinnings of hermaphrodite evolution, but also provides valuable information for enhancing fish aquaculture.

The measurement of biodiversity is an integral aspect of life science research. With the establishment of second- and third-generation sequencing technologies, an increasing amount of metabarcoding data is being generated as we seek to describe the extent and patterns of biodiversity in multiple contexts. The reliability and accuracy of taxonomically assigning metabarcoding sequencing data have been shown to be critically influenced by the quality and completeness of reference databases. Custom, curated, eukaryotic reference databases, however, are scarce, as are the software programs for generating them. Here, we present crabs (Creating Reference databases for Amplicon-Based Sequencing), a software package to create custom reference databases for metabarcoding studies. crabs includes tools to download sequences from multiple online repositories (i.e., NCBI, BOLD, EMBL, MitoFish), retrieve amplicon regions through in silico PCR analysis and pairwise global alignments, curate the database through multiple filtering parameters (e.g., dereplication, sequence length, sequence quality, unresolved taxonomy, inclusion/exclusion filter), export the reference database in multiple formats for immediate use in taxonomy assignment software, and investigate the reference database through implemented visualizations for diversity, primer efficiency, reference sequence length, database completeness and taxonomic resolution. crabs is a versatile tool for generating curated reference databases of user-specified genetic markers to aid taxonomy assignment from metabarcoding sequencing data. crabs can be installed via docker and is available for download as a conda package and via GitHub (https://github.com/gjeunen/reference_database_creator).

Rust fungi are characterized by large genomes with high repeat content and have two haploid nuclei in most life stages, which makes achieving high-quality genome assemblies challenging. Here, we described a pipeline using HiFi reads and Hi-C data to assemble a gigabase-sized fungal pathogen, Puccinia polysora f.sp. zeae, to haplotype-phased and chromosome-scale. The final assembled genome is 1.71 Gbp, with ~850 Mbp and 18 chromosomes in each haplotype, being currently one of the two giga-scale fungi assembled to chromosome level. Transcript-based annotation identified 47,512 genes for the dikaryotic genome with a similar number for each haplotype. A high level of interhaplotype variation was found with 10% haplotype-specific BUSCO genes, 5.8 SNPs/kbp, and structural variation accounting for 3% of the genome size. The P. polysora genome displayed over 85% repeat contents, with genome-size expansion and copy number increasing of species-specific orthogroups. Interestingly, these features did not affect overall synteny with other Puccinia species having smaller genomes. Fine-time-point transcriptomics revealed seven clusters of coexpressed secreted proteins that are conserved between two haplotypes. The fact that candidate effectors interspersed with all genes indicated the absence of a "two-speed genome" evolution in P. polysora. Genome resequencing of 79 additional isolates revealed a clonal population structure of P. polysora in China with low geographic differentiation. Nevertheless, a minor population differentiated from the major population by having mutations on secreted proteins including AvrRppC, indicating the ongoing virulence to evade recognition by RppC, a major resistance gene in Chinese corn cultivars. The high-quality assembly provides valuable genomic resources for future studies on disease management and the evolution of P. polysora.

Whole-genome alignment allows researchers to understand the genomic structure and variation among genomes. Approaches based on direct pairwise comparisons of DNA sequences require large computational capacities. As a consequence, pipelines combining tools for orthologous gene identification and synteny have been developed. In this manuscript, we present the latest functionalities implemented in NGSEP 4, to identify orthogroups and perform whole genome alignments. NGSEP implements functionalities for identification of clusters of homologus genes, synteny analysis and whole genome alignment. Our results showed that the NGSEP algorithm for orthogroups identification has competitive accuracy and efficiency in comparison to commonly used tools. The implementation also includes a visualization of the whole genome alignment based on synteny of the orthogroups that were identified, and a reconstruction of the pangenome based on frequencies of the orthogroups among the genomes. NGSEP 4 also includes a new graphical user interface based on the JavaFX technology. We expect that these new developments will be very useful for several studies in evolutionary biology and population genomics.

Environmental DNA (eDNA)-based methods of species detection are enabling various applications in ecology and conservation including large-scale biomonitoring efforts. qPCR is widely used as the standard approach for species-specific detection, often targeting a fish species of interest from aquatic eDNA. However, DNA metabarcoding has the potential to displace qPCR in certain eDNA applications. In this study, we compare the sensitivity of the latest Illumina NovaSeq 6000 NGS platform to qPCR TaqMan assays by measuring limits of detection and by analysing eDNA from water samples collected from Churchill River and Lake Melville, NL, Canada. Species-specific, targeted next generation sequencing (NGS) assays had significantly higher sensitivity than qPCR, with limits of detection 14- to 29-fold lower. For example, when analysing eDNA, qPCR detected Gadus ogac (Greenland cod) in 21% of samples, but targeted NGS detected this species in 29% of samples. General NGS assays were as sensitive as qPCR, while simultaneously detecting 15 fish species from eDNA samples. With over 34,000 fish species on the planet, parallel and sensitive methods such as NGS will be required to support effective biomonitoring at both regional and global scales.

Clonal reproduction, the formation of nearly identical individuals via mitosis in the absence of genetic recombination, is a very common reproductive mode across plants, fungi and animals. To detect clonal genetic structure, genetic similarity indices based on shared alleles are widely used, such as the Jaccard index, or identity by state. Here we propose a new pairwise genetic similarity index, the SH index, based on segregating genetic marker loci (typically single nucleotide polymorphisms) that are identically heterozygous for pairs of samples (NSH ). To test our method, we analyse two old seagrass clones (Posidonia australis, estimated to be around 8500 years old; Zostera marina, >750 years old) along with two young Z. marina clones of known age (17 years old). We show that focusing on shared heterozygosity amplifies the power to distinguish sample pairs belonging to different clones compared to methods focusing on all shared alleles. Our proposed workflow can successfully detect clonemates at a location dominated by a single clone. When the collected samples involve two or more clones, the SH index shows a clear gap between clonemate pairs and interclone sample pairs. Ideally NSH should be on the order of approximately ≥3000, a number easily achievable via restriction-site associated DNA (RAD) sequencing or whole-genome resequencing. Another potential application of the SH index is to detect possible parent-descendant pairs under selfing. Our proposed workflow takes advantage of the availability of the larger number of genetic markers in the genomic era, and improves the ability to distinguish clonemates from nonclonemates in multicellular diploid clonal species.