V-REVCOMP: automated high-throughput detection of reverse complementary 16S rRNA gene sequences in large environmental and taxonomic datasets
Bibliographic record
Abstract
Reverse complementary DNA sequences - sequences that are inadvertently given backwards with all purines and pyrimidines transposed - can affect sequence analysis detrimentally unless taken into account. We present an open-source, high-throughput software tool -v-revcomp (http://www.cmde.science.ubc.ca/mohn/software.html) - to detect and reorient reverse complementary entries of the small-subunit rRNA (16S) gene from sequencing datasets, particularly from environmental sources. The software supports sequence lengths ranging from full length down to the short reads that are characteristic of next-generation sequencing technologies. We evaluated the reliability of v-revcomp by screening all 406 781 16S sequences deposited in release 102 of the curated SILVA database and demonstrated that the tool has a detection accuracy of virtually 100%. We subsequently used v-revcomp to analyse 1 171 646 16S sequences deposited in the International Nucleotide Sequence Databases and found that about 1% of these user-submitted sequences were reverse complementary. In addition, a nontrivial proportion of the entries were otherwise anomalous, including reverse complementary chimeras, sequences associated with wrong taxa, nonribosomal genes, sequences of poor quality or otherwise erroneous sequences without a reasonable match to any other entry in the database. Thus, v-revcomp is highly efficient in detecting and reorienting reverse complementary 16S sequences of almost any length and can be used to detect various sequence anomalies.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.006 |
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".