CDSbank: taxonomy-aware extraction, selection, renaming and formatting of protein-coding DNA or amino acid sequences
Bibliographic record
Abstract
BACKGROUND: Protein-coding DNA sequences and their corresponding amino acid sequences are routinely used to study relationships between sequence, structure, function, and evolution. The rapidly growing size of sequence databases increases the power of such comparative analyses but it makes it more challenging to prepare high quality sequence data sets with control over redundancy, quality, completeness, formatting, and labeling. Software tools for some individual steps in this process exist but manual intervention remains a common and time consuming necessity. DESCRIPTION: CDSbank is a database that stores both the protein-coding DNA sequence (CDS) and amino acid sequence for each protein annotated in Genbank. CDSbank also stores Genbank feature annotation, a flag to indicate incomplete 5' and 3' ends, full taxonomic data, and a heuristic to rank the scientific interest of each species. This rich information allows fully automated data set preparation with a level of sophistication that aims to meet or exceed manual processing. Defaults ensure ease of use for typical scenarios while allowing great flexibility when needed. Access is via a free web server at http://hazeslab.med.ualberta.ca/CDSbank/. CONCLUSIONS: CDSbank presents a user-friendly web server to download, filter, format, and name large sequence data sets. Common usage scenarios can be accessed via pre-programmed default choices, while optional sections give full control over the processing pipeline. Particular strengths are: extract protein-coding DNA sequences just as easily as amino acid sequences, full access to taxonomy for labeling and filtering, awareness of incomplete sequences, and the ability to take one protein sequence and extract all synonymous CDS or identical protein sequences in other species. Finally, CDSbank can also create labeled property files to, for instance, annotate or re-label phylogenetic trees.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.043 | 0.073 |
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".