HCK and ABAA: A Newly Designed Pipeline to Improve Fungi Metabarcoding Analysis - Mlaga, Kodjovi D.; Mathieu, Alban; Beauparlant, Charles Joly; Ott, Alban; Khodr, Ahmad; Perin, Olivier; Droit, Arnaud (Frontiers in Microbiology, 2021)
IThe fungi ITS sequence length dissimilarity and chimaera amplicons formed during Polymerase Chain Reaction (PCR), added to sequencing errors, create bias during similarity clustering and consequently affect community abundance estimation in the downstream analysis. To overcome those challenges, we present a novel approach, Hierarchical Clustering with Kraken (HCK), to classify ITS1 amplicons, implemented with Abundance-Base Alternative Approach (ABAA) to filter chimeric sequences in fungi metabarcoding datasets. We assess both pipelines' performance compared to 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) was also used to evaluate the methods’ diversity estimation. The 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 improve the F-score and bring alpha diversity metric value close to that of the BLASTn alpha diversity value, compared to QIIME, KRAKEN, and DADA2. The developed approach allows better identification of the fungi community structure while avoid using a reference database for chimaera filtration. This results in a more robust and stable methodology over time. The programs can be downloaded on the following link: https://bitbucket.org/GottySG36/hck/src/master/.