Reproducibility of pyrosequencing data for biodiversity assessment in complex communities
Bibliographic record
Abstract
Summary High‐throughput sequencing is rapidly becoming a popular method to profile complex communities and has generated deep insights into community biodiversity. However, the reproducibility of this method for biodiversity assessment remains largely unexplored. Here we evaluated reproducibility by analysing 454 pyrosequenced biological replicates of two complex plankton communities collected from one freshwater port and one marine port. We also tested whether reproducibility potentially influences biodiversity estimates, notably α‐ and β‐diversity. Our evaluation of reproducibility revealed a complex scenario, having both technical and biological significance. At the Operational Taxonomic Unit (OTU) level, reproducibility was 100% for high‐abundance OTUs (>100 sequences), although it was lower for low‐abundance OTUs, and sometimes <25% for singletons. BLAST searches showed that >88% of irreproducible OTUs had high sequence similarity to existing records, suggesting that some singletons may reflect rare lineages/genotypes in communities. However, spurious amplification of distantly related taxonomic groups generated mainly low‐abundance OTUs that were characterized by low reproducibility. At a broad taxonomic level (i.e. order level), reproducibility decreased as the abundance of OTUs decreased and was particularly low for distantly related taxonomic groups such as algae and protists that were not the targets of our zooplankton biodiversity survey. At a lower taxonomical level (i.e. family‐level), overall reproducibility was high (>80%) for crustaceans, the dominant group in zooplankton samples. Therefore, we suggest that random variation during both sample collection and sequencing processes can be responsible for low reproducibility. Our analyses also suggest that random sampling processes may influence both α‐ and β‐diversity estimates. Our results add to growing evidence that caution needs to be applied when designing and interpreting experiments utilizing high‐throughput sequencing data for biodiversity assessments. Technical replicates are needed to statistically correct intra‐sample variation, while field‐based replicate samples are desirable to substantiate results. An overestimation of species diversity can occur when OTUs are uniquely characterized by spuriously amplified sequences and errors/artifacts. Therefore, careful management of low‐abundance OTUs is required to reveal unique/rare lineages. Our results suggest that further studies are needed to determine the ecological significance of low‐abundance OTUs in complex communities.
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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 | 0.038 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".