While high-throughput sequencing methods are revolutionizing fungal ecology, recovering accurate estimates of species richness and abundance has proven elusive. We sought to design internal transcribed spacer (ITS) primers and an Illumina protocol that would maximize coverage of the kingdom Fungi while minimizing nontarget eukaryotes. We inspected alignments of the 5.8S and large subunit (LSU) ribosomal genes and evaluated potential primers using PrimerProspector. We tested the resulting primers using tiered-abundance mock communities and five previously characterized soil samples. We recovered operational taxonomic units (OTUs) belonging to all 8 members in both mock communities, despite DNA abundances spanning 3 orders of magnitude. The expected and observed read counts were strongly correlated (r = 0.94 to 0.97). However, several taxa were consistently over- or underrepresented, likely due to variation in rRNA gene copy numbers. The Illumina data resulted in clustering of soil samples identical to that obtained with Sanger sequence clone library data using different primers. Furthermore, the two methods produced distance matrices with a Mantel correlation of 0.92. Nonfungal sequences comprised less than 0.5% of the soil data set, with most attributable to vascular plants. Our results suggest that high-throughput methods can produce fairly accurate estimates of fungal abundances in complex communities. Further improvements might be achieved through corrections for rRNA copy number and utilization of standardized mock communities. IMPORTANCE Fungi play numerous important roles in the environment. Improvements in sequencing methods are providing revolutionary insights into fungal biodiversity, yet accurate estimates of the number of fungal species (i.e., richness) and their relative abundances in an environmental sample (e.g., soil, roots, water, etc.) remain difficult to obtain. We present improved methods for high-throughput Illumina sequencing of the species-diagnostic fungal ribosomal marker gene that improve the accuracy of richness and abundance estimates. The improvements include new PCR primers and library preparation, validation using a known mock community, and bioinformatic parameter tuning.
Reed canarygrass (Phalaris arundinacea L.) is a cool‐season perennial with a circumglobal distribution in the northern hemisphere, native to Europe, Asia, and North America. Repeated introductions of European germplasm into North America have created confusion over the origins of reed canarygrass germplasm found in wetlands, pastures, and breeding programs. The objectives of this study were to identify sources of DNA marker variation among reed canarygrass cultivars from Europe and North America and between landraces and improved cultivars from North America. Analysis of 205 reed canarygrass plants from 15 cultivars based on 102 amplified fragment length polymorphic (AFLP) DNA markers revealed two groups of cultivars. One group consisted of three closely related but geographically diverse North American landraces that were completely separated from all other plants in only two dimensions of the AFLP incidence matrix. The complete discrimination of these plants from all European plants suggests their possible origin from native North American germplasm. These results were supported by chloroplast DNA sequence analysis, which additionally revealed separation of a potential Scandinavian cytoplasmic race from the continental European cytoplasmic race. This is the strongest evidence to date suggesting that native North American reed canarygrass germplasm has been preserved within cultivars of this species.
We present here the complete genome sequences of nine phages that infect Paenibacillus larvae, the causative agent of American foulbrood disease in honeybees. The phages were isolated from soil, propolis, and infected bees from three U.S. states. This is the largest number of P. larvae phage genomes sequenced in a single publication to date.
Microsatellites have been utilized for decades for genotyping individuals in various types of research. Automated scoring of microsatellite loci has allowed for rapid interpretation of large datasets. Although the use of software produces an automated process to score or genotype samples, several sources of error have to be taken into account to produce accurate genotypes. A variety of problems (from extracting DNA to entering a genotype into a database) which can arise throughout this process might result in erroneous genotype assignment to one or more samples, potentially confounding the conclusions of your study. Correctly assigning a genotype to a sample requires knowledge of the chemistry you use to generate the data as well as the software you use to analyze these results. In this chapter we describe the critical and more common points that researchers experience when scoring microsatellite loci. More importantly we provide insight from an experienced perspective for these challenges.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.