Quantifying microbial communities with 454 pyrosequencing: does read abundance count?
Bibliographic record
Abstract
Pyrosequencing technologies have revolutionized how we describe and compare complex microbial communities. In 454 pyrosequencing data sets, the abundance of reads pertaining to taxa or phylotypes is commonly interpreted as a measure of genic or taxon abundance, useful for quantitative comparisons of community similarity. Potentially systematic biases inherent in sample processing, amplification and sequencing, however, may alter read abundance and reduce the utility of quantitative metrics. Here, we examine the relationship between read abundance and biological abundance in a sample of house dust spiked with known quantities and identities of fungi along a dilution gradient. Our results show one order of magnitude differences in read abundance among species. Precision of quantification within species along the dilution gradient varied from R(2) of 0.96-0.54. Read-quality based processing stringency profoundly affected the abundance of one species containing long homopolymers in a read orientation-biased manner. Order-level composition of background environmental fungal communities determined from pyrosequencing data was comparable with that derived from cloning and Sanger sequencing and was not biased by read orientation. We conclude that read abundance is approximately quantitative within species, but between-species comparisons can be biased by innate sequence structure. Our results showed a trade off between sequence quality stringency and quantification. Careful consideration of sequence processing methods and community analyses are warranted when testing hypotheses using read abundance data.
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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 | 0.029 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".