Admixture is a fundamental evolutionary process that has influenced genetic patterns in numerous species. Maximum-likelihood approaches based on allele frequencies and linkage-disequilibrium have been extensively used to infer admixture processes from genome-wide datasets, mostly in human populations. Nevertheless, complex admixture histories, beyond one or two pulses of admixture, remain methodologically challenging to reconstruct. We develop an Approximate Bayesian Computation (ABC) framework to reconstruct highly complex admixture histories from independent genetic markers. We built the software package MetHis to simulate independent SNPs or microsatellites in a two-way admixed population for scenarios with multiple admixture pulses, monotonically decreasing or increasing recurring admixture, or combinations of these scenarios; and draw model-parameter values from prior distributions set by the user. For each simulation, MetHis calculates 24 summary-statistics describing genetic diversity and moments of individual admixture fractions. We coupled MetHis with existing machine-learning ABC algorithms and investigate the admixture history of hybrid populations. Results show that Random-Forest ABC scenario-choice can accurately distinguish most complex admixture scenarios and errors are mainly found in regions of the parameter space where scenarios are highly nested, and, thus, biologically similar. We focus on African American and Barbadian populations as case studies. We find that Neural-Network ABC posterior parameter estimation is accurate and reasonably conservative under complex admixture scenarios. For both admixed populations, we find that monotonically decreasing contributions over time, from Europe and Africa, explain the observed data more accurately than multiple admixture pulses. This approach will allow for reconstructing detailed admixture histories when maximum-likelihood methods are intractable.

DNA metabarcoding has become a powerful approach for analyzing complex communities from environmental samples, but there are still methodological challenges limiting its full potential. While conserved DNA markers, like 16S and 18S, often are not able to discriminate among closely related species, other more variable markers – like the fungal ITS region, may include considerable intraspecific variation, which can lead to over-splitting of species during DNA metabarcoding analyses. Here we assess the effects of intraspecific sequence variation in DNA metabarcoding, by analyzing local populations of eleven fungal species. We investigated the allelic diversity of ITS2 haplotypes using both Sanger sequencing and high throughput sequencing (HTS), coupled with error correction with the software DADA2. All focal species, except one, included some level of intraspecific variation in the ITS2 region. Overall, we observed a high correspondence between haplotypes generated by Sanger sequencing and HTS, with the exception of a few additional haplotypes detected using either approach. These extra haplotypes, often occurring in low frequencies, were likely due to PCR and sequencing errors or intragenomic variation in the rDNA region. The presence of intraspecific (and possibly intragenomic) variation in ITS2 suggest that haplotypes (or ASVs) should not be used as basic units in ITS-based fungal community analyses, but an extra clustering step is needed to approach species-level resolution.

Technological advances in DNA sequencing over the last decade now permit the production and curation of large genomic datasets in an increasing number of non-model species. Additionally, this new data provides the opportunity for combining datasets, resulting in larger studies with a broader taxonomic range. Whilst the benefits of new sequencing platforms are obvious, shifts in sequencing technology can also pose challenges for those wishing to combine new sequencing data with data sequenced on older platforms. Here, we outline the types of studies where the use of curated data might be beneficial, and highlight potential biases that might be introduced by combining data from different sequencing platforms. As an example of the challenges associated with combining data across sequencing platforms, we focus on the impact of the shift in Illumina's base calling technology from a four-channel to a two-channel system. We caution that when data is combined from these two systems, erroneous guanine base calls that result from the two-channel chemistry can make their way through a bioinformatic pipeline, eventually leading to inaccurate and potentially misleading conclusions. We also suggest solutions for dealing with such potential artifacts, which make samples sequenced on different sequencing platforms appear more differentiated from one another than they really are. Finally, we stress the importance of archiving tissue samples and the associated sequences for the continued reproducibility and reusability of sequencing data in the face of ever-changing sequencing platform technology.

Version of record in Molecular Ecology Resources:  https://doi.org/10.1111/1755-0998.13402 The Maroni is one of the most speciose basins of the Guianas and hosts a megadiverse freshwater fish community. Although taxonomical references exist for both the Surinamese and Guyanese parts of the basin, these lists were mainly based on morphological identification and there are still taxonomical uncertainties concerning the status of several fish species. Here we present a barcode dataset of 1,284 COI sequences from 199 freshwater fish species (68.86% of the total number of strictly freshwater fishes from the basin) from 124 genera, 36 families, and 8 orders. DNA barcoding allowed for fast and efficient identification of all specimens studied as well as unveiling a consequent cryptic diversity, with the detection of 20 putative cryptic species and 5 species flagged for re-identification. In order to explore global genetic patterns across the basin, genetic divergence landscapes were computed for 128 species, showing a global trend of high genetic divergence between the Surinamese south-west (Tapanahony and Paloemeu), the Guianese south-east (Marouini, Litany, Tampok, Lawa…), and the river mouth in the north. This could be explained either by lower levels of connectivity between these three main parts or by the exchange of individuals with the surrounding basins. A new method of ordination of genetic landscapes successfully assigned species into cluster groups based on their respective pattern of genetic divergence across the Maroni Basin: genetically homogenous species across the basin were effectively discriminated from species showing high spatial genetic fragmentation and possible lower capacity for dispersal.

A decade ago, de novo transcriptome assembly evolved as a versatile and powerful approach to make evolutionary assumptions, analyze gene expression, and annotate novel transcripts, in particular, for non-model organisms lacking an appropriate reference genome. Various tools have been developed to generate a transcriptome assembly, and even more computational methods depend on the results of these tools for further downstream analyses. In this issue of Molecular Ecology Resources, Freedman et al. (2020) present a comprehensive analysis of errors in de novo transcriptome assemblies across public data sets and different assembly methods. They focus on two implicit assumptions that are often violated: First, the assembly presents an unbiased view of the transcriptome. Second, the expression estimates derived from the assembly are reasonable, albeit noisy, approximations of the relative frequency of expressed transcripts. They show that appropriate filtering can reduce this bias but can also lead to the loss of a reasonable number of highly expressed transcripts. Thus, to partly alleviate the noise in expression estimates, they propose a new normalization method called length-rescaled CPM. Remarkably, the authors found considerable distortions at the nucleotide level, which leads to an underestimation of diversity in transcriptome assemblies. The study by Freedman et al. clearly shows that we have not yet reached “high-quality” in the field of transcriptome assembly. Above all, it helps researchers be aware of these problems and filter and interpret their transcriptome assembly data appropriately and with caution.

Genotyping novel complex multigene families is particularly challenging in non-model organisms. Target primers frequently amplify simultaneously multiple loci leading to high PCR and sequencing artefacts such as chimeras and allele amplification bias. Most genotyping pipelines have been validated in non-model systems whereby the real genotype is unknown and the generation of artefacts may be highly repeatable. Further hindering accurate genotyping, the relationship between artefacts and genotype complexity (i.e. number of alleles per genotype) within a PCR remains poorly described. Here we investigated the latter by experimentally combining multiple known major histocompatibility complex (MHC) haplotypes of a model organism (chicken, \textit{Gallus gallus}, 43 artificial genotypes with 2-13 alleles per amplicon). In addition to well defined “optimal” primers, we simulated a non-model species situation by designing “cross-species” primers, with sequence data from closely related Galliforme species. We applied a novel open-source genotyping pipeline (ACACIA; \url{https://gitlab.com/psc_santos/ACACIA}), and compared its performance with another, previously published pipeline (AmpliSAS). Allele calling accuracy was higher when using ACACIA (98.5\% vs 97\% and 77.8\% vs 75.2\% for the “optimal” and “cross-species” datasets respectively). Systematic allele dropout of three alleles owing to primer mismatch in the “cross-species” dataset explained high allele calling repeatability (100\% when using ACACIA) despite low accuracy, demonstrating that repeatability can be misleading when evaluating genotyping workflows. Genotype complexity was positively associated with non-chimeric artefacts, chimeric artefacts (nonlinearly by leveling when amplifying more than 4-6 alleles) and allele amplification bias. Our study exemplifies and demonstrates pitfalls researchers should avoid to reliably genotype complex multigene families.

Gradient Forests is a machine learning algorithm that is gaining in popularity for studying the environmental drivers of genomic variation and for incorporating genomic information into climate change impact assessments. Here we provide the first experimental evaluation of the ability of ‘genomic offsets’ - a metric of climate maladaptation derived from Gradient Forests - to predict organismal responses to environmental change. We used high-throughput sequencing, genome scans, and several methods (including Gradient Forests) to identify candidate loci associated with climate adaptation in balsam poplar (Populus balsamifera L.). Individuals collected throughout balsam poplar’s range also were planted in two common garden experiments. We used Gradient Forests to relate candidate loci to environmental gradients and to predict the expected magnitude of response (i.e., the genetic offset) of populations when transplanted from their “home” environment to the new environments in the common gardens. We then compared the predicted genetic offsets to measurements of population performance in the common gardens. We found the expected inverse relationship between genetic offset and performance in the common gardens: populations with larger predicted genetic offsets performed worse in the common gardens than populations with smaller offsets. Also, genetic offset better predicted performance in the common gardens than did ‘naive’ climate distances. Our results provide preliminary evidence that genomic offsets may provide a first order estimate of the degree of expected maladaptation of populations exposed to rapid environmental change.

The peach fruit moth (PFM), Carposina sasakii Matsumura, is a major phytophagous orchard pest widely distributed across Northeast Asia. Here, we report the chromosome-level genome for the PFM, representing the first genome for the family Carposinidae, from the lepidopteran superfamily Copromorphoidea. The genome was assembled into 404.83 Mb sequences using PacBio long-read and Illumina short-read sequences, including 275 contigs, with a contig N50 length of 2.62 Mb. All contigs were assembled into 32 linkage groups assisted by the Hi-C technique, including 30 autosomes, a female-specific W chromosome and a Z chromosome. BUSCO analysis showed that 98.2% genes were complete and 0.4% of genes were fragmented, while 1.4% of genes were missing in the assembled genome. In total, 23,218 protein-coding genes were predicted, of which 82.72% were functionally annotated. Because of the importance of diapause triggered by photoperiod in PFM, five circadian genes in the PFM as well as in the other related species were annotated, and potential genes related to diapause and photoperiodic reaction were also identified from transcriptome sequencing. In addition, manual annotation of detoxification gene families was undertaken and showed a higher number of ABC and GST genes in PFM than in most other lepidopterans, in contrast to a lower number of UGT genes, suggesting different detoxication pathways in this moth. The high-quality genome provides a resource for comparative evolutionary studies of this moth and its relatives within the context of radiations across Lepidoptera.

Understanding the processes of environmental DNA (eDNA) persistence and degradation is essential to determine the spatiotemporal scale of eDNA signals and accurately estimate species distribution. The effects of environmental factors on eDNA persistence have previously been examined; however, the influence of the physiochemical and molecular states of eDNA on its persistence is not completely understood. Here, we performed meta-analyses including 26 previously published papers on the estimation of first-order eDNA decay rate constants, and assessed the effects of filter pore size, DNA fragment size, target gene, and environmental conditions on eDNA decay rates. Almost all supported models included the interactions between the filter pore size and water temperature, between the target gene and water temperature, and between the target gene and water source, implying the influence of complex interactions between the eDNA state and environmental conditions on eDNA persistence. These findings were generally consistent with the results of a re-analysis of a previous tank experiment which measured the time-series changes in marine fish eDNA concentrations in multiple size fractions after fish removal. Our results suggest that the mechanism of eDNA persistence and degradation cannot be fully understood without knowing not only environmental factors but also cellular and molecular states of eDNA in water. Further verification of the relationship between eDNA state and persistence is required by obtaining more information on eDNA persistence in various experimental and environmental conditions, which will enhance our knowledge on eDNA persistence and support our findings.

Non-random mating among individuals can lead to spatial clustering of genetically similar individuals and population stratification. This deviation from panmixia is commonly observed in natural populations. Consequently, individuals can have parentage in single populations or involving hybridization between differentiated populations. Accounting for this mixture and structure is important when mapping the genetics of traits and learning about the formative evolutionary processes that shape genetic variation among individuals and populations. Stratified genetic relatedness among individuals is commonly quantified using estimates of ancestry that are derived from a statistical model. Development of these models for polyploid and mixed-ploidy individuals and populations has lagged behind those for diploids. Here, we extend and test a hierarchical Bayesian model, called entropy, which can utilize low-depth sequence data to estimate genotype and ancestry parameters in autopolyploid and mixed-ploidy individuals (including sex chromosomes and autosomes within individuals). Our analysis of simulated data illustrated the trade-off between sequencing depth and genome coverage and found lower error associated with low depth sequencing across a larger fraction of the genome than with high depth sequencing across a smaller fraction of the genome. The model has high accuracy and sensitivity as verified with simulated data and through analysis of admixture among populations of diploid and tetraploid Arabidopsis arenosa.

Functional annotation aims to assess the biochemical and biological functions of sets of genomic or transcriptomic sequences yielded after next-generation sequencing experiments. One common way to perform functional annotation of a set of sequences obtained from a next-generation sequencing experiment, is by searching for homologous sequences and accessing to the related functional information deposited in genomic databases. Functional annotation is especially challenging in de novo assemblies of transcriptomes of non-model organisms, like many plant species. In such cases, existing commercial and open access general purpose applications may not offer complete and accurate results. We present TOA (Taxonomy-oriented annotation), a user-friendly open-access application designed to establish functional annotation pipelines geared towards non-model plant species. TOA performs homology searches against proteins stored in the PLAZA platform databases, NCBI RefSeq Plant, Nucleotide Database and Non-Redundant Protein Sequence Database, and retrieves functional information for several gene ontology systems. The software performance was validated by comparing the runtimes, total number of annotated sequences and accuracy of the functional information obtained for several plant benchmark datasets with TOA and other open-access functional annotation solutions. TOA outperformed the other software in terms of number of annotated sequences and accuracy of the annotation, and constitutes a good alternative to improve functional annotation in plants. TOA is especially recommended for gymnosperms or for low quality sequence datasets of non-model plants.

Karyotypic changes in chromosome number and structure are drivers in the divergent evolution of diverse plant species and lineages. This study aimed to reveal the origins of the unique karyotype (2n = 12) and phylogenetic relationships of the genus Megadenia (Brassicaceae). A high-quality chromosome-scale genome was assembled for Megadenia pygmaea using Nanopore long reads and high-throughput chromosome conformation capture (Hi-C). The assembled genome is 215.2-Mb and is anchored on six pseudo-chromosomes. We annotated a total of 25,607 high-confidence protein-coding genes and corroborated the phylogenetic affinity of Megadenia with the expanded Lineage II, which contains numerous agricultural crops. We dated the divergence of Megadenia from its closest relatives to 27.04 (19.11-36.60) million years ago. A reconstruction of the chromosomal composition of the species was performed based on the de novo assembled genome and comparative chromosome painting analysis. The karyotype structure of M. pygmaea is very similar to the previously inferred Proto-Calepineae Karyotype (PCK; n = 7) of the Brassicaceae Lineage II. However, an end-to-end translocation between two ancestral chromosomes reduced the chromosome number from n = 7 to n = 6, comparable to Megadenia. Our reference genome provides fundamental information for use in horticulture, plant breeding and evolutionary study of this genus.

Plant interactions are as important belowground as aboveground. Belowground plant interactions are however inherently difficult to quantify, as roots of different species are difficult to disentangle. Although for a couple of decades molecular techniques have been successfully applied to quantify root abundance, root identification and quantification in multi-species plant communities remains particularly challenging. Here we present a novel methodology, multi-species Genotyping By Sequencing (msGBS), as a next step to tackle this challenge. First, a multi-species meta-reference database containing thousands of gDNA clusters per species is created from GBS derived High Throughput Sequencing (HTS) reads. Second, GBS derived HTS reads from multi-species root samples are mapped to this meta-reference which, after a filter procedure to increase the taxonomic resolution, allows the parallel quantification of multiple species. The msGBS signal of 111 mock-mixture root samples, with up to 8 plant species per sample, was used to calculate the within-species abundance. Optional subsequent calibration yielded the across-species abundance. The within- and across-species abundances highly correlated (R2 range 0.72-0.94 and 0.85-0.98, respectively) to the biomass-based species abundance. Compared to a qPCR based method which was previously used to analyze the same set of samples, msGBS provided similar results. Additional data on 11 congener species groups within 105 natural field root samples showed high taxonomic resolution of the method. msGBS is highly scalable in terms of sensitivity and species numbers within samples, which is a major advantage compared to the qPCR method and advances our tools to reveal the hidden belowground interactions.

Biodiversity studies greatly benefit from molecular tools, such as DNA metabarcoding, which provides an effective identification tool in biomonitoring and conservation programmes. The accuracy of species-level assignment, and consequent taxonomic coverage, relies on comprehensive DNA barcode reference libraries. The role of these libraries is to support species identification, but accidental errors in the generation of the barcodes may compromise their accuracy. Here we present an R-based application, BAGS (Barcode, Audit & Grade System), that performs automated auditing and annotation of cytochrome c oxidase subunit I (COI) sequences libraries, for a given taxonomic group of animals, available in the Barcode of Life Data System (BOLD). This is followed by implementing a qualitative ranking system that assigns one of five grades (A to E) to each species in the reference library, according to the attributes of the data and congruency of species names with sequences clustered in Barcode Index Numbers (BINs). Our ultimate goal is to allow researchers to obtain the most useful and reliable data, highlighting and segregating records according to their congruency. Different tests were performed to perceive its usefulness and limitations. BAGS fulfils a significant gap in the current landscape of DNA barcoding research tools by quickly screening reference libraries to gauge the congruence status of data and facilitate the triage of ambiguous data for posterior review. Thereby, BAGS have the potential to become a valuable addition in forthcoming DNA metabarcoding studies, in the long term contributing to globally improve the quality and reliability of the public reference libraries.

Abstract: Interrogation of chromatin modifications, such as DNA methylation, has potential to improve forecasting and conservation of marine ecosystems. The standard method for assaying DNA methylation (Whole Genome Bisulfite Sequencing), however, is too costly to apply at the scales required for ecological research. Here we evaluate different methods for measuring DNA methylation for ecological epigenetics. We compare Whole Genome Bisulfite Sequencing (WGBS) with Methylated CpG Binding Domain Sequencing (MBD-seq), and a modified version of MethylRAD we term methylation-dependent Restriction site-Associated DNA sequencing (mdRAD). We evaluate these three assays in measuring variation in methylation across the genome, between genotypes, and between polyp types in the reef-building coral Acropora millepora. We find that all three assays measure absolute methylation levels similarly, with tight correlations for methylation of gene bodies (gbM), as well as exons and 1Kb windows. Correlations for differential gbM between genotypes were weaker, but still concurrent across assays. We detected little to no reproducible differences in gbM between polyp types. We conclude that MBD-seq and mdRAD are reliable cost-effective alternatives to WGBS. Moreover, the considerably lower sequencing effort required for mdRAD to produce comparable methylation estimates makes it particularly useful for ecological epigenetics.

Microsporidia are obligate intracellular eukaryotic parasites that infect nearly all animal groups, including humans. The most common molecular methods for Microsporidia detection rely on species-targeting qPCR or end-point PCR using group-specific primers. However, these methods could be not specific enough or fail in case of mixed infections. We developed a method for parallel detection of both microsporidian infection and the host species. We designed new primer sets: one specific for the classical Microsporidia (targeting hypervariable V5 region of ssu rDNA), and a second one targeting a shortened fragment of the COI gene (standard metazoan DNA-barcode); both markers are well suited for a NGS approach. The analysis of ssu rDNA dataset representing 607 microsporidian species (120 genera) indicated that the V5 region enables identification of >98% species in the dataset (596/607). To test the method, we used microsporidians that infect mosquitoes in natural populations. Using mini-COI data, all field-collected mosquitoes were unambiguously assigned to seven species; among them almost 60% of specimens (127/212) were positive for at least 11 different microsporidian species, including a new microsporidian ssu rDNA sequence (Microsporidium sp. PL01). Phylogenetic analysis of Microsporidium sp. PL01 ssu rDNA showed that this species belongs to one of the two main clades in the Terresporidia. In addition, the level of microsporidian mixed infections was relatively high (9.4%). The numbers of sequence reads for the OTUs suggest that the occurrence of Nosema spp. in co-infections could benefit them; however, this observation should be re-tested using more intensive host sampling. The proposed method for detection of Microsporidia can be applied to all types of DNA extracts, including medical and environmental samples.

Partial clonality is widespread across the tree of life, but most population genetics models are designed for exclusively clonal or sexual organisms. This gap hampers our understanding of the influence of clonality on evolutionary trajectories and the interpretation of population genetics data. We performed forward simulations of diploid populations at increasing rates of clonality (c), analysed their relationships with genotypic (clonal richness, R, and distribution of clonal sizes, Pareto β) and genetic (FIS and linkage disequilibrium) indices, and tested predictions of c from population genetics data through supervised machine learning. Two complementary behaviours emerged from the probability distributions of genotypic and genetic indices with increasing c. While the impact of c on R and Pareto β was easily described by simple mathematical equations, its effects on genetic indices were noticeable only at the highest levels (c>0.95). Consequently, genotypic indices allowed reliable estimates of c, while genetic descriptors led to poorer performances when c<0.95. These results provide clear baseline expectations for genotypic and genetic diversity and dynamics under partial clonality. Worryingly, however, the use of realistic sample sizes to acquire empirical data systematically led to gross underestimates (often of one to two orders of magnitude) of c, suggesting that many interpretations hitherto proposed in the literature, mostly based on genotypic richness, should be reappraised. We propose future avenues to derive realistic confidence intervals for c and show that, although still approximate, a supervised learning method would greatly improve the estimation of c from population genetics data.

Samia ricini, a gigantic saturniid moth, has the potential to be a novel lepidopteran model species. Since S. ricini is much more tough and resistant to diseases than the current model species Bombyx mori, the former can be easily reared compared to the latter. In addition, genetic resources available for S. ricini rival or even exceed those for B. mori: at least 26 eco-races of S. ricini are reported and S. ricini can hybridise with wild Samia species, which are distributed throughout Asian countries, and produce fertile progenies. Physiological traits such as food preference, integument colour, larval spot pattern, etc. are different between S. ricini and wild Samia species so that those traits can be the target for forward genetic analysis. In order to facilitate genetic research in S. ricini, we determined the whole genome sequence of S. ricini. The assembled genome of S. ricini was 458 Mb with 155 scaffolds, and the N50 length of the assembly was approximately 21 Mb. 16,702 protein coding genes were predicted in the assembly. Although the gene repertoire of S. ricini was not so different from that of B. mori, some genes, such as chorion genes and fibroin genes, seemed to have specifically evolved in S. ricini.