dnabarcoder: an open-source software package for analyzing and predicting DNA sequence similarity cut-offs for fungal sequence identification

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 cut-off 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 intra-specific variability. We address this problem by presenting a new tool, dnabarcoder, to analyze and predict different local similarity cut-offs for sequence identification for different clades of fungi. For each similarity cut-off in a clade, a confidence measure is computed to evaluate the resolving power of the genetic marker in that clade. Experimental results showed that when analyzing a recently released filamentous fungal ITS DNA barcode dataset of CBS strains from the Westerdijk Fungal Biodiversity Institute, the predicted local similarity cut-offs varied immensely between the clades of the dataset. In addition, most of them had a higher confidence measure than the global similarity cut-off predicted for the whole dataset. When classifying a large public fungal ITS dataset – the UNITE database - against the barcode dataset, the local similarity cut-offs assigned fewer sequences than the traditional cut-offs used in metabarcoding studies. However, the obtained accuracy and precision were significantly improved.

A Two-Step PCR Protocol Enabling Flexible Primer Choice and High Sequencing Yield for Illumina MiSeq Meta-Barcoding

High-throughput amplicon sequencing that primarily targets the 16S ribosomal DNA (rDNA) (for bacteria and archaea) and the Internal Transcribed Spacer rDNA (for fungi) have facilitated microbial community discovery across diverse environments. A three-step PCR that utilizes flexible primer choices to construct the library for Illumina amplicon sequencing has been applied to several studies in forest and agricultural systems. The three-step PCR protocol, while producing high-quality reads, often yields a large number (up to 46%) of reads that are unable to be assigned to a specific sample according to its barcode. Here, we improve this technique through an optimized two-step PCR protocol. We tested and compared the improved two-step PCR meta-barcoding protocol against the three-step PCR protocol using four different primer pairs (fungal ITS: ITS1F-ITS2 and ITS1F-ITS4, and bacterial 16S: 515F-806R and 341F-806R). We demonstrate that the sequence quantity and recovery rate were significantly improved with the two-step PCR approach (fourfold more read counts per sample; determined reads ≈90% per run) while retaining high read quality (Q30 > 80%). Given that synthetic barcodes are incorporated independently from any specific primers, this two-step PCR protocol can be broadly adapted to different genomic regions and organisms of scientific interest.

ARDEP, a Rapid Degenerate Primer Design Pipeline Based on k-mers for Amplicon Microbiome Studies

The survey of microbial diversity in various environments has relied upon the widespread use of well-evaluated amplification primers for taxonomic marker genes (e.g., prokaryotic 16S and fungal ITS). However, it is urgent to develop a fast and accurate bioinformatic program to design primers for microbial functional genes to explore more mechanisms in the microbial community. Here, we provide a rapid degenerate primer design pipeline (ARDEP) based on the k-mer algorithm, which can bypass the time-consuming step of sequence alignment to greatly reduce run times while ensuring accuracy. In addition, we developed an open-access platform for the implementation of primer design projects that could also calculate the amplification product length, GC content, Annealing Temperature (Tm), and ΔG of primer self-folding, and identify covered species and functional groups. Using this new platform, we designed primers for several functional genes in the nitrogen cycle, including napA and amoA. Our newly designed primers achieved higher coverage than the commonly used primers for all tested genes. The program and the associated platform that applied the k-mer algorithm could greatly enhance the design and evaluation of primers for environmental microbiome studies.

Root Fungal Endophytes and Microbial Extracellular Enzyme Activities Show Patterned Responses in Tall Fescues under Drought Conditions

Plant response to water stress can be modified by the rhizosphere microbial community, but the range of responses across plant genotypes is unclear. We imposed drought conditions on 116 Festuca arundinacea (tall fescue) accessions using a rainout shelter for 46 days, followed by irrigation, to stimulate drought recovery in 24 days. We hypothesized that prolonged water deficit results in a range of phenotypic diversity (i.e., green color index) across tall fescue genotypes that are associated with distinct microbial taxonomic and functional traits impacting plant drought tolerance. Microbial extracellular enzyme activities of chitinase and phenol oxidase (targeting chitin and lignin) increased in rhizospheres of the 20 most drought tolerant genotypes. Lower rates of fungal (dark septate) endophyte root infection were found in roots of the most drought tolerant genotypes. Bacterial 16S rRNA gene and fungal ITS sequencing showed shifts in microbial communities across water deficit conditions prior to drought, during drought, and at drought recovery, but was not patterned by drought tolerance levels of the plant host. The results suggest that taxonomic information from bacterial 16S rRNA gene and fungal ITS sequences provided little indication of microbial composition impacting drought tolerance of the host plant, but instead, microbial extracellular enzyme activities and root fungal infection results revealed patterned responses from drought.

HiTaC: Hierarchical Taxonomic Classification of Fungal ITS Sequences

MotivationFungi are key elements in several important ecological functions, ranging from organic matter decomposition to symbiotic associations with plants. Moreover, fungi naturally inhabit the human microbiome and can be causative agents of human infections. An accurate and robust method for fungal ITS classification is not only desired for the purpose of better diversity estimation, but it can also help us gain a deeper insight of the dynamics of environmental communities and ultimately comprehend whether the abundance of certain species correlate with health and disease. Although many methods have been proposed for taxonomic classification, to the best of our knowledge, none of them consider the taxonomic tree hierarchy when building their models. This in turn, leads to lower generalization power and higher risk of committing classification errors.ResultsIn this work, we developed a robust, hierarchical machine learning model for accurate ITS classification, which requires a small amount of data for training and is able to handle imbalanced datasets. We show that our hierarchical model, HiTaC, outperforms state-of-the-art methods when trained over noisy data, consistently achieving higher accuracy and sensitivity across different taxonomic ranks.AvailabilityHiTaC is an open-source software, with documentation and source code available at https://gitlab.com/dacs-hpi/[email protected] informationSupplementary data are available at bioRxiv online.

Accuracy of taxonomy prediction for 16S rRNA and fungal ITS sequences

Prediction of taxonomy for marker gene sequences such as 16S ribosomal RNA (rRNA) is a fundamental task in microbiology. Most experimentally observed sequences are diverged from reference sequences of authoritatively named organisms, creating a challenge for prediction methods. I assessed the accuracy of several algorithms using cross-validation by identity, a new benchmark strategy which explicitly models the variation in distances between query sequences and the closest entry in a reference database. When the accuracy of genus predictions was averaged over a representative range of identities with the reference database (100%, 99%, 97%, 95% and 90%), all tested methods had ≤50% accuracy on the currently-popular V4 region of 16S rRNA. Accuracy was found to fall rapidly with identity; for example, better methods were found to have V4 genus prediction accuracy of ∼100% at 100% identity but ∼50% at 97% identity. The relationship between identity and taxonomy was quantified as the probability that a rank is the lowest shared by a pair of sequences with a given pair-wise identity. With the V4 region, 95% identity was found to be a twilight zone where taxonomy is highly ambiguous because the probabilities that the lowest shared rank between pairs of sequences is genus, family, order or class are approximately equal.