HCK and ABAA: A Newly Designed Pipeline to Improve Fungi Metabarcoding Analysis
Bibliographic record
Abstract
INTRODUCTION: 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. 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 METHODS: We 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. RESULTS: 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, 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. CONCLUSION: The 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/.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.007 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".