Computational protocol: Plant Communities Rather than Soil Properties Structure Arbuscular Mycorrhizal Fungal Communities along Primary Succession on a Mine Spoil

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Protocol publication

[…] Sequence reads were demultiplexed according to the samples-specific barcodes, quality filtered (minimum quality score 20, primers kept, maximum 2 primer mismatches), and trimmed to a minimum length of 300 bp, using QIIME () command –split_libraries.py. The complete dataset was checked for chimeric sequences and removed by UCLUST (), and Operational Taxonomic Units (OTUs) were de novo clustered at 97% similarity threshold by USEARCH, using the SEED workbench (). Global singletons were removed from the data set. All reads of each primary cluster were merged to consensus sequences, using MAFFT’s iterative refinement method (L-INS-i). These consensus representative sequences were clustered a second time at 97% as described in and , and we refer to these secondary clusters as ‘OTUs.’ Affiliation to AMF was checked by BLAST against the public databases (DDBJ/EMBL/GeneBank). According to the closest BLAST non-target sequences were excluded from further analyses and the remaining sequences were manually aligned against a backbone database published in . The maximum-likelihood phylogenetic backbone tree was calculated using RAxML () over the Cipres web-portal (GTRGAMMA, 1000 bootstraps), and for assignment to phylogenetic taxa the Evolutionary Placement Algorithm (EPA) for short sequence reads () was used implemented in QIIME (command –insert_seqs_into_tree.py). These, subsequently called ‘AMF taxa,’ were labeled according to their taxonomical clustering in the calculated phylogenetic EPA tree, as it provides an accurate phylogenetic placement of short reads (). […]

Pipeline specifications

Software tools QIIME, UCLUST, USEARCH, MAFFT, RAxML
Databases DDBJ
Application Phylogenetics