Molecular Ecology Resources

Li, Ming; Xu, Xiwen; Liu, Shanshan; Fan, Guangyi; Zhou, Qian; Chen, Songlin

doi: 10.1111/1755-0998.13648pmid: 35593537

Fishes of the genus Seriola are widely farmed and highly valued in global aquaculture production. To further understand their economically important traits and help improve aquaculture product quality and sustainability, we performed a chromosome‐level genome construction for Seriola aureovittata. Combining two technologies, PacBio and BGISEQ‐500, we assembled 649.86 Mb S. aureovittata genome sequences with a contig N50 of 22.21 Mb, and 98% of BUSCO genes were detected in total. The initial assembly was then further scaffolded into 24 pseudochromosomes using Hi‐C data, indicating the high quality of the genome. Genome evolution analysis showed that many genes related to fatty acid metabolism and oxygen binding, or transport were expanded, which provided insights into the metabolic characteristics of fatty acids and efficient oxygen transport. Based on the genome data, we confirmed the evolutionary relationship of S. aureovittata, S. dorsalis and S. lalandi and identified chr12 as the putative sex chromosome of S. aureovittata. Our chromosome‐level genome assembly provides a genetic foundation for the phylogenetic and taxonomic investigation of different Seriola species. Moreover, the genome will provide an important genomic resource for further biological and aquaculture studies of S. aureovittata.

Pearman, William S.; Urban, Lara; Alexander, Alana

doi: 10.1111/1755-0998.13646pmid: 35593534

Reduced representation sequencing (RRS) is a widely used method to assay the diversity of genetic loci across the genome of an organism. The dominant class of RRS approaches assay loci associated with restriction sites within the genome (restriction site associated DNA sequencing, or RADseq). RADseq is frequently applied to non‐model organisms since it enables population genetic studies without relying on well‐characterized reference genomes. However, RADseq requires the use of many bioinformatic filters to ensure the quality of genotyping calls. These filters can have direct impacts on population genetic inference, and therefore require careful consideration. One widely used filtering approach is the removal of loci that do not conform to expectations of Hardy–Weinberg equilibrium (HWE). Despite being widely used, we show that this filtering approach is rarely described in sufficient detail to enable replication. Furthermore, through analyses of in silico and empirical data sets we show that some of the most widely used HWE filtering approaches dramatically impact inference of population structure. In particular, the removal of loci exhibiting departures from HWE after pooling across samples significantly reduces the degree of inferred population structure within a data set (despite this approach being widely used). Based on these results, we provide recommendations for best practice regarding the implementation of HWE filtering for RADseq data sets.

Vu, Duong; Nilsson, R. Henrik; Verkley, Gerard J. M.

doi: 10.1111/1755-0998.13651pmid: 35621380

The accuracy and precision of fungal molecular identification and classification are challenging, particularly in environmental metabarcoding approaches as these often trade accuracy for efficiency given the large data volumes at hand. In most ecological studies, only a single similarity cutoff value is used for sequence identification. This is not sufficient since the most commonly used DNA markers are known to vary widely in terms of inter‐ and intraspecific variability. We address this problem by presenting a new tool, dnabarcoder, to predict local similarity cutoffs and measure the resolving powers of a biomarker for sequence identification for different clades of fungi. It was shown that the predicted similarity cutoffs varied significantly between the clades of a recently released ITS DNA barcode data set from the CBS culture collection of the Westerdijk Fungal Biodiversity Institute. When classifying a large public fungal ITS data set—the UNITE database—against the barcode data set, the local similarity cutoffs assigned fewer sequences than the traditional cutoffs used in metabarcoding studies. However, the obtained accuracy and precision were significantly improved. Our study showed that it might be better to extract the ITS region from the ITS barcodes to optimize taxonomic assignment accuracy. Furthermore, 15.3, 25.6, and 26.3% of the fungal species of the barcode data set were indistinguishable by full‐length ITS, ITS1, and ITS2, respectively. Except for these indistinguishable species, the resolving powers of full‐length ITS, ITS1, and ITS2 sequences were similar at the species level. Nevertheless, the complete ITS region had a better resolving power at higher taxonomic levels.

Neidel, Veronika; Sint, Daniela; Wallinger, Corinna; Traugott, Michael

doi: 10.1111/1755-0998.13659pmid: 35668675

Facultative scavenging by predatory carnivores is a prevalent but frequently underestimated feeding strategy. DNA‐based methods for diet analysis, however, do not allow to distinguish between scavenging and predation, thus, the significance of scavenging on population dynamics and resource partitioning is widely unknown. Here, we present a methodological innovation to differentiate between scavenging and fresh prey consumption using prey RNA as a target molecule. We hypothesized that the rapid post‐mortem breakdown of RNA in prey tissue should lead to a significantly lower detection probability of prey RNA than DNA when carrion rather than fresh prey is consumed. To test this hypothesis, ground beetles (Pseudoophonus rufipes [De Geer]) were offered either fresh or 1‐day‐old dead Drosophila melanogaster fruit flies (carrion). The detectability of prey RNA and DNA in the beetles' regurgitates was assessed with diagnostic Drosophila‐specific RT‐PCR and PCR assays at 0, 6, 12, 24 and 48 h post‐feeding. After fresh fly consumption, prey RNA and DNA were detectable equally well at all times. When carrion prey was consumed, the detection strength of prey RNA immediately after feeding was significantly lower than that of prey DNA and reached zero in most samples within 6 h of digestion. Our findings provide evidence that prey RNA allows distinguishing between the consumption of fresh and scavenged prey, thereby overcoming a long‐known weakness of molecular diet analysis. The assessment of prey RNA offers a generally applicable approach for examining the importance of scavenging in food webs to unravel its functional consequences for populations, communities, and ecosystems.

Farrell, Jessica A.; Whitmore, Liam; Mashkour, Narges; Rollinson Ramia, Devon R.; Thomas, Rachel S.; Eastman, Catherine B.; Burkhalter, Brooke; Yetsko, Kelsey; Mott, Cody; Wood, Larry; Zirkelbach, Bette; Meers, Lucas; Kleinsasser, Pat; Stock, Sharon; Libert, Elizabeth;

Stapleton, Tess E.; Weinstein, Sara B.; Greenhalgh, Robert; Dearing, M. Denise

doi: 10.1111/1755-0998.13643pmid: 35579046

DNA metabarcoding is widely used to determine wild animal diets, but whether this technique provides accurate, quantitative measurements is still under debate. To test our ability to accurately estimate the abundance of dietary items using metabarcoding, we fed wild‐caught desert woodrats (Neotoma lepida) diets consisting of constant amounts of juniper (Juniperus osteosperma, 15%) and varying amounts of creosote (Larrea tridentata, 1%–60%), cactus (Opuntia sp., 0%–100%) and commercial chow (0%–85%). Using metabarcoding, we compared the representation of items in the original diet samples to that in the faecal samples to test the sensitivity and accuracy of diet metabarcoding, the performance of different bioinformatic pipelines and our ability to correct sequence counts. Metabarcoding, using standard trnL primers, detected creosote, juniper and chow. Different pipelines for assigning taxonomy performed similarly. While creosote was detectable at dietary proportions as low as 1%, we failed to detect cactus in most samples, probably due to a primer mismatch. Creosote read counts increased as its proportion in the diet increased, and we could differentiate when creosote was a minor and major component of the diet. However, we found that estimates of juniper and creosote varied. Using previously suggested methods to correct these errors did not improve accuracy estimates of creosote, but did reduce error for juniper and chow. Our results indicate that metabarcoding can provide quantitative information on dietary composition, but may be limited. We suggest that researchers use caution when quantitatively interpreting diet metabarcoding results unless they first experimentally determine the extent of possible biases.

Irestedt, Martin; Thörn, Filip; Müller, Ingo A.; Jønsson, Knud A.; Ericson, Per G. P.; Blom, Mozes P. K.

doi: 10.1111/1755-0998.13660pmid: 35661418

Biological specimens in natural history collections constitute a massive repository of genetic information. Many specimens have been collected in areas in which they no longer exist or in areas where present‐day collecting is not possible. There are also specimens in collections representing populations or species that have gone extinct. Furthermore, species or populations may have been sampled throughout an extensive time period, which is particularly valuable for studies of genetic change through time. With the advent of high‐throughput sequencing, natural history museum resources have become accessible for genomic research. Consequently, these unique resources are increasingly being used across many fields of natural history. In this paper, we summarize our experiences of resequencing hundreds of genomes from historical avian museum specimens. We publish the protocols we have used and discuss the entire workflow from sampling and laboratory procedures, to the bioinformatic processing of historical specimen data.

Roved, Jacob; Hansson, Bengt; Stervander, Martin; Hasselquist, Dennis; Westerdahl, Helena

doi: 10.1111/1755-0998.13645pmid: 35587892

The major histocompatibility complex (MHC) plays a central role in the vertebrate adaptive immune system and has been of long‐term interest in evolutionary biology. While several protocols have been developed for MHC genotyping, there is a lack of transparent and standardized tools for downstream analysis of MHC data. Here, we present the r package mhctools and demonstrate the use of its functions to (i) assist accurate MHC genotyping from high‐throughput amplicon‐sequencing data, (ii) infer functional MHC supertypes using bootstrapped clustering analysis, (iii) identify segregating MHC haplotypes from family data, and (iv) analyse functional and genetic distances between MHC sequences. We employed mhctools to analyse MHC class I (MHC‐I) amplicon data of 559 great reed warblers (Acrocephalus arundinaceus). We identified 390 MHC‐I alleles which clustered into 14 functional supertypes. A phylogenetic analysis and analyses of positive selection suggested that the MHC‐I alleles belong to several distinct functional groups. We furthermore identified 107 segregating haplotypes among 116 families, and found substantial variation in diversity with 4–21 MHC‐I alleles and 3–13 MHC‐I supertypes per haplotype. Finally, we show that the great reed warbler haplotypes harboured combinations of MHC‐I supertypes with greater functional divergence than observed in simulated populations of possible haplotypes, a result that is in accordance with the divergent allele advantage hypothesis. Our study demonstrates the power of mhctools to support genotyping and analysis of MHC in non‐model species, which we hope will encourage broad implementation among researchers in MHC genetics and evolution.

Wang, Danrui; Wang, Shang; Du, Xiongfeng; He, Qing; Liu, Yue; Wang, Zhujun; Feng, Kai; Li, Yan; Deng, Ye

doi: 10.1111/1755-0998.13644pmid: 35587727

Quantitative real‐time PCR (qPCR) has been widely used in quantifying bacterial and fungal populations in various ecosystems, as well as the fungi to bacteria ratio (F:B ratio). Recently, researchers have begun to apply droplet digital PCR (ddPCR) to this area; however, no study has systematically compared qPCR and ddPCR for quantitating both bacteria and fungi in environmental samples at the same time. Here, we designed probe‐primer pair combinations targeting the 16S rRNA gene and internal transcribed spacer (ITS) for the detection of bacteria and fungi, respectively, and tested both SYBR Green and TaqMan approaches in qPCR and ddPCR methods for mock communities and in real environmental samples. In mock communities, the quantification results of ddPCR were significantly closer to expected values (p < .05), and had smaller coefficients of variations (p < .05) than qPCR, suggesting ddPCR was more accurate and repeatable. In environmental samples, ddPCR consistently quantified ITS and 16S rRNA gene concentrations in all four habitats without abnormal overestimation or underestimation, and the F:B ratio obtained by ddPCR was consistent with phospholipid fatty acid analysis. Our results indicated that ddPCR had better precision, repeatability, sensitivity, and stability in bacterial and fungal quantitation than qPCR. Although ddPCR has high cost, complicated processes and restricted detection range, it shows insensitivity to PCR inhibitors and the potential of quantifying long target fragments. We expect that ddPCR, which is complementary to qPCR, will contribute to microbial quantification in environmental monitoring and evaluation.

Wilcox, Taylor Matthew; Jensen, Mads Reinholdt

doi: 10.1111/1755-0998.13686pmid: 35837874

Environmental DNA (eDNA) sampling uses genetic material in the environment to infer species presence sight‐unseen. The method has rapidly become a powerful tool for monitoring biodiversity. However, biological diversity, as per the Convention on Biological Diversity definition of “diversity within species, between species and of ecosystems” is more inclusive than most eDNA studies cover: The vast majority focus only on between‐species and ecosystem‐level biodiversity. However, a tantalizing prospect, as illustrated by Farrell et al. (2022) in this issue of Molecular Ecology Resources, is that we might also be able to unlock information about individual and population‐level diversity via population genomic analysis of these environmental samples. Farrell et al. (2022) found that targeted samples of beach sand contained genetic material not just informative about sea turtle presence, but also indicated the presence of pathogens and genome‐wide mitochondrial and nuclear sequences that could accurately infer individual turtle source population. Moving from proof‐of‐concept to robust, population genomic inference will require a growth of genomic resources for nonmodel organisms and careful study design considerations, some of which have already been pioneered by related fields.