Linked machine learning classifiers improve species classification of fungi when using error-prone long-reads on extended metabarcodes

T, Eenjes; Hu, Yiheng; Irinyi, László; Hoang, Minh Thuy Vi; Smith, Leon M.; Linde, Celeste C.; Meyer, Wieland; Stone, Eric A.; Rathjen, John P.; Mashford, Benjamin S.

doi:10.1101/2021.05.01.442223

Cited by 3 publications

(1 citation statement)

References 69 publications

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“…Another pitfall in the use of NGS technology for routine fungal pathogen identification remains the need of improvement in processing the huge amount of NGS data both at infrastructure level (server and memory power) and bioinformatic skills availability (algorithms and expert technicians). To this aim, an ad hoc pipeline using machine learning (ML) classifiers has been developed as an alternative method for assigning individual error-prone sequence-long reads to taxa [ 28 , 179 , 180 ]. In metabarcoding studies, including those concerning human diseases, ML modeling will help in prediction of disease outputs and in deciphering environmental factors shaping the microbial composition also in agriculture and in natural ecosystems [ 29 , 181 , 182 , 183 , 184 ].…”

Section: Discussionmentioning

confidence: 99%

New-Generation Sequencing Technology in Diagnosis of Fungal Plant Pathogens: A Dream Comes True?

Aragona

Haegi

Valente

et al. 2022

JoF

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The fast and continued progress of high-throughput sequencing (HTS) and the drastic reduction of its costs have boosted new and unpredictable developments in the field of plant pathology. The cost of whole-genome sequencing, which, until few years ago, was prohibitive for many projects, is now so affordable that a new branch, phylogenomics, is being developed. Fungal taxonomy is being deeply influenced by genome comparison, too. It is now easier to discover new genes as potential targets for an accurate diagnosis of new or emerging pathogens, notably those of quarantine concern. Similarly, with the development of metabarcoding and metagenomics techniques, it is now possible to unravel complex diseases or answer crucial questions, such as “What’s in my soil?”, to a good approximation, including fungi, bacteria, nematodes, etc. The new technologies allow to redraw the approach for disease control strategies considering the pathogens within their environment and deciphering the complex interactions between microorganisms and the cultivated crops. This kind of analysis usually generates big data that need sophisticated bioinformatic tools (machine learning, artificial intelligence) for their management. Herein, examples of the use of new technologies for research in fungal diversity and diagnosis of some fungal pathogens are reported.

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Section: Discussionmentioning

confidence: 99%

New-Generation Sequencing Technology in Diagnosis of Fungal Plant Pathogens: A Dream Comes True?

Aragona

Haegi

Valente

et al. 2022

JoF

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Cropping sequence affects the structure and diversity of pathogenic and non-pathogenic soil microbial communities

Islam,

Oviedo-Ludena,

Kutcher

et al. 2023

Plant Soil

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Aims Current understanding of how cropping sequence affects pathogen-suppressive microbiomes in soil is limited. We investigated the effects of several cropping sequences from the 2020–2021 growing seasons, including cereals, pulses, and an oilseed, on microbial communities in rhizosphere and bulk soils in two western Canadian field locations. Methods The fungi and bacteria were characterized by Internal Transcribed Spacer (ITS) and 16S rRNA gene sequencing, respectively. The QIIME 2™ bioinformatic pipeline was used to measure the diversity and abundance of microbial species. Additionally, the concentration of the soil mineral chemicals, including macro and micro nutrients, was determined by colorimetric analysis. Results Ascomycota (62.5%) was the most common fungal phylum, followed by Glomeromycota (11.1%), Mucoromycota (8.9%), and Basidiomycota (6.8%). Pseudomonadota (35.0%), Actinomycetota (21.1%), and Bacillota (10.1%) were the three most common bacterial phyla. Fungal OTU richness and phylogenetic diversity were highest in the cereal-pulse cropping sequencing, and bacterial OTU richness was highest in the pulse-oilseed sequences. Fusarium was the fungal genus most commonly associated with cereal-cereal monoculture and least common in the oilseed-pulse cropping sequences. The fungi (Mortierella, Funneliformis, and Diversispora) and bacteria (Rhizobium, Bradyrhizobium, Flavobacterium, and Candidatus) were higher in the cropping sequences involving pulses. The most prevalent bacteria were Streptomyces in cereal-related sequences and Solirubrobacter and Pseudomonas in oilseed-related sequences. Among soil mineral chemicals, nitrate-nitrogen, copper, calcium, potassium, and chlorine were associated with a number of beneficial fungal and bacterial genera but not with pathogenic fungal genera. Conclusions The results highlight the consequences of crop species selection in cropping sequences and the management of agrochemicals in the agricultural production system.

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Long-read genomics reveal extensive nuclear-specific evolution and allele-specific expression in a dikaryotic fungus

Tam,

Möller,

Luo

et al. 2024

Preprint

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Phased telomere to telomere (T2T) genome assemblies are revolutionising our understanding of long hidden genome biology "dark matter" such as centromeres, rDNA repeats, inter-haplotype variation, and allele specific expression (ASE). Yet insights into dikaryotic fungi that separate their haploid genomes into distinct nuclei is limited. Here we explore the impact of dikaryotism on the genome biology of a long-term asexual clone of the wheat pathogenic fungus Puccinia striiformis f. sp. tritici. We use Oxford Nanopore (ONT) duplex sequencing combined with Hi-C to generate a T2T nuclear-phased assembly with >99.999% consensus accuracy. We show that this fungus has large regional centromeres enriched in LTR retrotransposons with a single centromeric dip in methylation that suggests one kinetochore attachment site per chromosomes. The centromeres of chromosomes pairs are most often highly diverse in sequence and kinetochore attachment sites are not always positionally conserved. Each nucleus carries a unique array of rDNAs with >200 copies that harbour nucleus-specific sequence variations. The inter-haplotype diversity between the two nuclear genomes is caused by large-scale structural variations linked to transposable elements. Nanopore long-read cDNA analysis across distinct infection conditions revealed pervasive allele specific expression for nearly 20% of all heterozygous gene pairs. Genes involved in plant infection were significantly enriched in ASE genes which appears to be mediated by elevated CpG gene body methylation of the lower expressed pair. This suggests that epigenetically regulated ASE is likely a previously overlooked mechanism facilitating plant infection. Overall, our study reveals how dikaryotism uniquely shapes key eukaryotic genome features.

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Linked machine learning classifiers improve species classification of fungi when using error-prone long-reads on extended metabarcodes

Cited by 3 publications

References 69 publications

New-Generation Sequencing Technology in Diagnosis of Fungal Plant Pathogens: A Dream Comes True?

New-Generation Sequencing Technology in Diagnosis of Fungal Plant Pathogens: A Dream Comes True?

Cropping sequence affects the structure and diversity of pathogenic and non-pathogenic soil microbial communities

Long-read genomics reveal extensive nuclear-specific evolution and allele-specific expression in a dikaryotic fungus

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