Abstract
IntroductionThe fungi ITS sequence length dissimilarity, non-specific amplicons, including chimaera formed during Polymerase Chain Reaction (PCR), added to sequencing errors, create bias during similarity clustering and abundance estimation in the downstream analysis. To overcome these challenges, we present a novel approach, Hierarchical Clustering with Kraken (HCK), to classify ITS1 amplicons and Abundance-Base Alternative Approach (ABAA) pipeline to detect and filter non-specific amplicons in fungi metabarcoding sequencing datasets.Materials and MethodsWe compared the performances of both pipelines against QIIME, KRAKEN, and DADA2 using publicly available fungi ITS mock community datasets and using BLASTn as a reference. We calculated the Precision, Recall, F-score using the True-Positive, False-positive, and False-negative estimation. Alpha diversity (Chao1 and Shannon metrics) was also used to evaluate the diversity estimation of our method.ResultsThe analysis shows that ABAA reduced the number of false-positive with all metabarcoding methods tested, and HCK increases precision and recall. HCK, coupled with ABAA, improves the F-score and bring alpha diversity metric value close to that of the BLASTn alpha diversity values when compared to QIIME, KRAKEN, and DADA2.ConclusionThe developed HCK-ABAA approach allows better identification of the fungi community structures while avoiding use of a reference database for non-specific amplicons filtration. It results in a more robust and stable methodology over time. The software can be downloaded on the following link: https://bitbucket.org/GottySG36/hck/src/master/.
Highlights
The fungi ITS sequence length dissimilarity, non-specific amplicons, including chimaera formed during Polymerase Chain Reaction (PCR), added to sequencing errors, create bias during similarity clustering and abundance estimation in the downstream analysis
This approach consists of two steps: The amplicons AbundanceBase Alternative Approach (ABAA), a de novo method to filter non-specific amplicons from sequence datasets and a Hierarchical Clustering with Kraken (HCK) to classify ITS amplicons
The analysis shows that HCK without non-specific amplicons removal is slightly better than Kraken and HCK decreases by 13.22% the false-positive detection and by 45.36% of false negatives compared to Kraken
Summary
The fungi ITS sequence length dissimilarity, non-specific amplicons, including chimaera formed during Polymerase Chain Reaction (PCR), added to sequencing errors, create bias during similarity clustering and abundance estimation in the downstream analysis. Several pipelines have been developed to classify fungal species using ITS sequencing These include Plutof (Abarenkov et al, 2010b), Clotu (Kumar et al, 2011), PIPITS (Gweon et al, 2015), CloVR-ITS (White et al, 2013), and BioMaS (Fosso et al, 2015) specially designed to analyse fungi ITS datasets, Kraken (Wood and Salzberg, 2014), Mothur (Schloss et al, 2009) Qiime (Caporaso et al, 2010; Bolyen et al, 2018), Vsearch (Rognes et al, 2016), and DADA2 (Callahan et al, 2016) among many others, to examine both bacterial 16S rRNA and fungal ITS amplicons
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