Reconstruction of small subunit ribosomal RNA from high‐throughput sequencing data: A comparative study of metagenomics and total RNA sequencing
Bibliographic record
Abstract
Abstract The small subunit (SSU) ribosomal RNA (rRNA) is the most commonly used marker for the identification of microbial taxa, but its full‐length reconstruction from high‐throughput sequencing (HTS) data remains challenging. Metagenomics and total RNA sequencing (total RNA‐Seq) are target‐PCR‐free HTS methods that are used to characterize microbial communities and simultaneously reconstruct SSU rRNA sequences. However, more testing is required to determine and improve their effectiveness. We processed metagenomics and total RNA‐Seq data retrieved from a commercially available mock microbial community and an aquarium sample using 112 combinations of data processing tools. We determined the SSU rRNA reconstruction completeness of both sequencing methods for both samples and analysed the impact of data processing tools on SSU rRNA completeness. In contrast to metagenomics, total RNA‐Seq allowed for the complete or near‐complete reconstruction of all mock community SSU rRNA sequences and generated up to 438 SSU rRNA sequences with ≥80% completeness from the aquarium sample using only 1/5 of an Illumina MiSeq run. SSU rRNA completeness of metagenomics significantly correlated with the genome size of mock community species. Data processing tools impacted SSU rRNA completeness, in particular the utilized assemblers. These results are promising for the high‐throughput reconstruction of novel full‐length SSU rRNA sequences and could advance the simultaneous application of multiple ‐omics approaches in routine environmental assessments to allow for more holistic assessments of ecosystems.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".