The hyper-diverse order Coleoptera comprises a staggering ~25% of known species on Earth. Despite recent breakthroughs in next generation sequencing, there remains a limited representation of beetle diversity in assembled genomes. Most notably, the ground beetle family Carabidae, comprising more than 40,000 described species, has not been studied in a comparative genomics framework using whole genome data. Here we generate a high-quality genome assembly for Nebria riversi, to examine sources of novelty in the genome evolution of beetles, as well as genetic changes associated with specialization to high elevation alpine habitats. In particular, this genome resource provides a foundation for expanding comparative molecular research into mechanisms of insect cold adaptation. Comparison to other beetles shows a strong signature of genome compaction, with N. riversi possessing a relatively small genome (~147 Mb) compared to other beetles, with associated reductions in repeat element content and intron length. Small genome size is not, however, associated with fewer protein-coding genes, and an analysis of gene family diversity shows significant expansions of genes associated with cellular membranes and membrane transport, as well as protein phosphorylation and muscle filament structure. Finally, our genomic analyses show that these high elevation beetles have endosymbiotic Spiroplasma, with several metabolic pathways (e.g. propanoate biosynthesis) that might complement N. riversi, although its role as a beneficial symbiont or as a reproductive parasite remains equivocal.

We used long read sequencing data generated from Knightia excelsaI R.Br, a nectar producing Proteaceae tree endemic to Aotearoa New Zealand, to explore how sequencing data type, volume and workflows can impact final assembly accuracy and chromosome construction. Establishing a high-quality genome for this species has specific cultural importance to Māori, the indigenous people, as well as commercial importance to honey producers in Aotearoa New Zealand. Assemblies were produced by five long read assemblers using data subsampled based on read lengths, two polishing strategies, and two Hi-C mapping methods. Our results from subsampling the data by read length showed that each assembler tested performed differently depending on the coverage and the read length of the data. Assemblies that used longer read lengths (>30 kb) and lower coverage were the most contiguous, kmer and gene complete. The final genome assembly was constructed into pseudo-chromosomes using all available data assembled with FLYE, polished using Racon/Medaka/Pilon combined, scaffolded using SALSA2 and AllHiC, curated using Juicebox, and validated by synteny with Macadamia. We highlighted the importance of developing assembly workflows based on the volume and type of sequencing data and establishing a set of robust quality metrics for generating high quality assemblies. Scaffolding analyses highlighted that problems found in the initial assemblies could not be resolved accurately by utilizing Hi-C data and that scaffolded assemblies were more accurate when the underlying contig assembly was of higher accuracy. These findings provide insight into what is required for future high-quality de-novo assemblies of non-model organisms.

The bean bug (Riptortus pedestris) causes great economic losses of soybeans by piercing and sucking pods and seeds. Although R. pedestris has become the focus of numerous studies associated with insect–microbe interactions, plant–insect interactions, and pesticide resistance, a lack of genomic resources has limited deeper insights. In this study, we report the first R. pedestris genome at the chromosomal level using PacBio, Illumina, and Hi-C technologies. The assembled genome was 1.193 Gb in size with a contig N50 of 13.97 Mb. More than 95.7% of the total genome bases were successfully anchored to 6 unique chromosomes, with the scaffold N50 reaching 181.34 Mb. Genome resequencing of male and female individuals and chromosomic staining demonstrated that the sex chromosome system of R. pedestris is XO, and the shortest chromosome is the X chromosome. In total, 21,562 protein-coding genes were predicted, 21,320 of which were validated as being expressed in different tissues or different developmental stages. Evolutionary analysis demonstrated that R. pedestris and Oncopeltus fasciatus formed a sister group and split ∼35 million years ago. Additionally, a 5.04 Mb complete genome of symbiotic Serratia marcescens Rip1 was assembled, and the virulence factors that account for successful colonization in the host midgut were identified. The high-quality R. pedestris genome provides a valuable resource for further research, as well as for the pest management of bug pests.

Microbiome composition data collected through amplicon sequencing are count data on taxa in which the total count per sample (the library size) is an artifact of the sequencing platform and as a result such data are compositional. To avoid library size dependency, one common way of analyzing multivariate compositional data is to perform a principal component analysis (PCA) on data transformed with the centered log-ratio, hereafter called a log-ratio PCA. Two aspects typical of amplicon sequencing data are the large differences in library size and the large number of zeroes. In this paper we show on real data and by simulation that, applied to data that combines these two aspects, log-ratio PCA is nevertheless heavily dependent on the library size. This leads to a reduction in power when testing against any explanatory variable in log-ratio redundancy analysis. If there is additionally a correlation between the library size and the explanatory variable, then the type 1 error becomes inflated. We explore putative solutions to this problem.

Sea Lettuce (Ulva spp.; Ulvophyceae, Ulvales, Ulvaceae) is an important ecological and economical entity, with a worldwide distribution and is a well-known source of near-shore blooms blighting many coastlines. Species of Ulva are frequently misidentified in public repositories, including herbaria and gene banks, making species identification based on traditional barcoding hazardous. We investigated the species distribution of 295 individual distromatic foliose strains from the North East Atlantic by traditional barcoding or next generation sequencing. We found seven distinct species, and compared our results with all worldwide Ulva spp sequences present in the NCBI database for the three barcodes rbcL, tufA and the ITS1. Our results demonstrate a large degree of species misidentification in the NCBI database. We estimate that 21% of the entries pertaining to foliose species are misannotated. In the extreme case of U. lactuca, 65% of the entries are erroneously labelled specimens of another Ulva species, typically U. fenestrata. In addition, 30% of U. rigida entries are misannotated, U. rigida being relatively rare and often misannotated U. laetevirens. Furthermore, U. armoricana and U. scandinavica present as being synonymous to U. laetevirens. An analysis of the global distribution of registered samples from foliose species also indicates possible geographical isolation for some species, and the absence of U. lactuca from Northern Europe. Altogether, exhaustive taxonomic clarification by aggregation of a library of barcode sequences highlights misannotations, and delivers an improved representation of Ulva species diversity and distribution. This approach could be easily adapted to other taxa.

Interactions of organisms with their environment are complex and environmental regulation at different levels of biological organization is often non-linear. Therefore, the genotype to phenotype continuum requires study at multiple levels of organization. While studies of transcriptome regulation are now common for many species, quantitative studies of environmental effects on proteomes are needed. Here we report the generation of a data-independent acquisition (DIA) assay library that enables simultaneous targeted proteomics of thousands of Oreochromis niloticus kidney proteins using a label- and gel-free workflow that is well suited for ecologically relevant field samples. We demonstrate the usefulness of this DIA assay library by discerning environmental effects on the kidney proteome of O. niloticus. Moreover, we demonstrate that the DIA assay library approach generates data that are complimentary rather than redundant to transcriptomics data. Transcript and protein abundance differences in kidneys of tilapia acclimated to freshwater and brackish water (25 g/kg) were correlated for 2114 unique genes. A high degree of non-linearity in salinity-dependent regulation of transcriptomes and proteomes was revealed suggesting that the regulation of O. niloticus renal function by environmental salinity relies heavily on post-transcriptional mechanisms. The application of functional enrichment analyses using STRING and KEGG to DIA assay datasets is demonstrated by identifying myo-inositol metabolism, antioxidant and xenobiotic functions, and signaling mechanisms as key elements controlled by salinity in tilapia kidneys. The DIA assay library resource presented here can be adopted for other tissues and other organisms to study proteome dynamics during changing ecological contexts.

The diploid Poropuntius huangchuchieni in the cyprinid family, which is widely distributed in the Mekong and Red River basins, is one of the most closely related diploid progenitor-like species of allotetraploid common carp, which was generated by merging of two diploid genomes during evolution. Therefore, the P. huangchuchieni genome is essential for polyploidy evolution studies in Cyprinidae. Here, we report a high-quality chromosome-level genome assembly of P. huangchuchieni by integrating Oxford Nanopore and Hi-C technology. The assembled genome size was 1021.38 Mb, 895.66 Mb of which was anchored onto 25 chromosomes with a N50 of 32.93 Mb. The genome contained 486.28 Mb repetitive elements and 24,099 protein-coding genes. Approximately 95.9% of the complete BUSCOs were detected, suggesting a high completeness of the genome. Evolutionary analysis revealed that P. huangchuchieni diverged from Cyprinus carpio at approximately 12 Mya. Genome comparison between P. huangchuchieni and the B subgenome of C. carpio provided insights into chromosomal rearrangements during the allotetraploid speciation. With the complete gene set, 17,474 orthologous genes were identified between P. huangchuchieni and C. carpio, providing a broad view of the gene component in the allotetraploid genome, which is critical for future genetic analyses. The high-quality genomic dataset created for P. huangchuchieni provides a diploid progenitor-like reference for the evolution and adaptation of allotetraploid carps.

Fungi form diverse communities and play essential roles in many terrestrial ecosystems, yet there are methodological challenges in taxonomic and phylogenetic placement of fungi from environmental sequences. To address such challenges we investigated spatio-temporal structure of a fungal community using soil metabarcoding with four different sequencing strategies: short amplicon sequencing of the ITS2 region (300–400\ bp) with Illumina MiSeq, Ion Torrent Ion S5, and PacBio RS II, all from the same PCR library, as well as long amplicon sequencing of the full ITS and partial LSU regions (1200–1600\ bp) with PacBio RS II. Resulting community structure and diversity depended more on statistical method than sequencing technology. The use of long-amplicon sequencing enables construction of a phylogenetic tree from metabarcoding reads, which facilitates taxonomic identification of sequences. However, long reads present issues for denoising algorithms in diverse communities. We present a solution that splits the reads into shorter homologous regions prior to denoising, and then reconstructs the full denoised reads. In the choice between short and long amplicons, we suggest a hybrid approach using short amplicons for sampling breadth and depth, and long amplicons to characterize the local species pool for improved identification and phylogenetic analyses.

Characterization of microbial assemblages via environmental DNA metabarcoding is increasingly being used in routine monitoring programs due to its sensitivity and cost-effectiveness. Several programs have been developed recently which infer functional profiles from 16S rRNA gene data using hidden-state prediction (HSP) algorithms. These might offer an economic and scalable alter-native to shotgun metagenomics. To date, HSP-based methods have seen limited use for benthic marine surveys and their performance in these environments remains unevaluated. In this study, 16S rRNA metabarcoding was applied to sediment samples collected at 0 and ≥ 1200 m from Norwegian salmon farms, and three metabolic inference approaches (PAPRICA, PICRUSt2 and TAX4FUN2) evaluated against metagenomics and environmental data. While metabarcoding and metagenomics recovered a comparable functional diversity, the taxonomic composition differed be-tween approaches, with genera richness up to 20× higher for metabarcoding. Comparisons between the sensitivity (highest true positive rates) and specificity (lowest true negative rates) of HSP-based programs in detecting functions found in metagenomics data ranged, respectively, from 0.52 and 0.60 to 0.76 and 0.79. However, little correlation was observed between the relative abundance of their specific functions. Functional beta-diversity of HSP-based data was strongly associated with that of metagenomics (r ≥ 0.86 for PAPRICA and TAX4FUN2) and responded similarly to the impact of fish farm activities. Our results demonstrate that although HSP-based metabarcoding approaches provide a slightly different functional profile than metagenomics, partly due to recovering a distinct community, they represent a cost-effective and valuable tool for characterizing and assessing the effects of fish farming on benthic ecosystems.

The burbot (Lota lota) is the only member of the cod family (Gadidae) that is adapted solely to freshwater. This species shows the widest longitudinal range of freshwater fish in the world. The burbot is a good model for studies on adaptive genome evolution from marine to freshwater environment. However, no high-quality reference genome has been released. Here, the first chromosome-level genome of the burbot was constructed using PacBio long sequencing and Hi-C technology. A total of 95.24 Gb polished PacBio sequences were generated, and the preliminary genome assembly was 575.83 Mb in size with a contig N50 size of 2.15 Mb. The assembled sequences were anchored to 22 pseudo-chromosomes by using the Hi-C data. The final assembled genome after Hi-C correction was 575.92 Mb, with a contig N50 of 2.01 Mb and a scaffold N50 of 22.10 Mb. A total of 22,067 protein-coding genes were predicted, 94.82% of which were functionally annotated. Phylogenetic analyses indicated that burbot diverged with the Atlantic cod about 44.4 million years ago. In addition, 377 putative genes that appear to be under positive selection in burbot were identified. These positively selected genes might adapt to the freshwater environment. These genome data provide an invaluable resource for the ecological and evolutionary study of the order Gadiformes.

The Ocean Barcode Atlas (OBA) is a user friendly web service designed for biologists who wish to explore the biodiversity and biogeography of marine organisms locked in otherwise difficult to mine planetary scale DNA metabarcode datasets. Using just a web browser, a comprehensive picture of the diversity of a taxon or a barcode sequence is visualized graphically on world maps and interactive charts. Interactive results panels allow dynamic threshold adjustments and the display of diversity results in their environmental context measured at the time of sampling (temperature, oxygen, latitude, etc.). Ecological analyses such as alpha and beta-diversity plots are produced via publication quality vector graphics representations. Currently, the Ocean Barcode Altas is deployed online with the i) Tara Oceans eukaryotic 18S-V9 rDNA metabarcodes, ii) Tara Oceans 16S/18S rRNA miTags, and iii) 16S-V4V5 metabarcodes collected during the Malaspina-2010 expedition. Additional prokaryotic or eukaryotic plankton barcode datasets will be added upon availability, given they provide the required complement of barcodes (including raw reads to compute barcode abundance) associated with their contextual environmental variables. Ocean Barcode Atlas is a freely-available web service at: http://oba.mio.osupytheas.fr/ocean-atlas/.

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.