Sequence analysis of Nanopore based fungal and bacterial metabarcode DNA reads v1
Authors: not provided Rita.Tam 
Published: April 2023

This protocols is part of the ANU Biosecurity mini-research project #1 "Plant Pathogen Diagnostics: Visuals, subcultures, and genomics". You will be provided four pots of 3-4 week old wheat plants that have been infected with different wheat pathogens. Each pot has been infected with one major pathogen. You will not know which pot has been infected with which pathogen. However, you will be provided a compendium of 10-15 wheat pathogens that will guide you to identify the infective agent for each treatment group. The fifth treatment group will be uninfected wheat plants which will be clearly identified. You can use treatment group #5 as negative control for your experiments. In total, each group will obtain five pots each: This specific protocol is a general description of how we analysed the bacterial and fungal metabarcode derived Nanopore reads for you and how you can summarise the data. You will be able to use this protocol as a reference in your methods section. The 16S primers target bacteria and long-ITS primers target fungi (or so we thought). Check the protocols "PCR reaction of marker regions (a.k.a metabarcodes) for two kingdoms V.1" and "Sequencing fungal and bacterial metabarcodes with native barcoding and Nanopore" for how the sequences were generated including details on the primers used and specific Nanopore sequencing chemistry. Long-ITS analysis In brief, we mapped the long-ITS reads against two databases using minimap2 with a winner takes all approach only reporting the best hit in the databases for each read. Not all reads might have a hit in the database as we learned that the primers also amplify plant and human DNA. The "in-house" database is part of a larger project the Schwessinger group is involved to improve the identification of fungal spores from air-sampled material. This is a collaboration with DAFF, UC, SARDI, and USyd. You can check out the spore traps here as part of the iMapPests project. The database includes about 80 species with TG1 and TG2 being part of it but not TG3 and TG4. The entries in the database were generated using the same primer pairs as we used in class and hence have they same length as the ITS reads we generated. The UNITE database is a large public database for fungal ITS sequences which mostly focuses on ITS2. It has about 500,000 'specie' entries in the database. You can read up more on the database here (see also the reference section). The database entries are mostly around 600-700 bp long. This database contains pathogens for TG1, TG3, and TG4. The final goal is to achieve the following: Basecall the fast5 files to fastq files. Filter fastq files on quality using NanoFilt. Assess overall read lengths and quality after filtering using NanoPlot. Map ITS reads separately against the 'in-house' long-ITS and the UNITE ITS2 database using minimap2 and a "winner takes all approach". Perform taxonomic assignment of 16S sequences using EMU. Critically evaluate taxonomic assignments and summaries them using Excel (shown in class) or R. Software used for analysis: Guppy basecaller; https://nanoporetech.com/how-it-works/basecalling NanoFilt; https://github.com/wdecoster/nanofilt NanoPlot; https://github.com/wdecoster/NanoPlot seqtk; https://github.com/lh3/seqtk Minimap2; https://github.com/lh3/minimap2 EMU 16S analysis; https://gitlab.com/treangenlab/emu References: Curry, Kristen D., Qi Wang, Michael G. Nute, Alona Tyshaieva, Elizabeth Reeves, Sirena Soriano, Qinglong Wu, et al. “Emu: Species-Level Microbial Community Profiling of Full-Length 16S RRNA Oxford Nanopore Sequencing Data.” Nature Methods 19, no. 7 (July 2022): 845–53. https://doi.org/10.1038/s41592-022-01520-4. De Coster, Wouter, Svenn D’Hert, Darrin T Schultz, Marc Cruts, and Christine Van Broeckhoven. “NanoPack: Visualizing and Processing Long-Read Sequencing Data.” Bioinformatics 34, no. 15 (August 1, 2018): 2666–69. https://doi.org/10.1093/bioinformatics/bty149. Li, Heng. “Minimap2: Pairwise Alignment for Nucleotide Sequences.” Bioinformatics 34, no. 18 (September 15, 2018): 3094–3100. https://doi.org/10.1093/bioinformatics/bty191. Nilsson, Rolf Henrik, Karl-Henrik Larsson, Andy F S Taylor, Johan Bengtsson-Palme, Thomas S Jeppesen, Dmitry Schigel, Peter Kennedy, et al. “The UNITE Database for Molecular Identification of Fungi: Handling Dark Taxa and Parallel Taxonomic Classifications.” Nucleic Acids Research 47, no. D1 (January 8, 2019): D259–64. https://doi.org/10.1093/nar/gky1022.