skDER and CiDDER: two scalable approaches for microbial genome dereplication
Bibliographic record
Abstract
An abundance of microbial genomes have been sequenced in the past two decades. For fundamental comparative genomic investigations, where the goal is to determine the major gain and loss events shaping the pangenome of a species or broader taxon, it is often unnecessary and computationally onerous to include all available genomes in studies. In addition, the over-representation of specific lineages due to sampling and sequencing bias can have undesired effects on evolutionary analyses. To assist users with genomic dereplication , we developed skDER and CiDDER ( https://github.com/raufs/skDER ) to select a subset of representative genomes for downstream comparative genomic investigations. skDER is a nucleotide-based genomic dereplication tool that can dereplicate thousands of microbial genomes leveraging recent advances in average nucleotide identity (ANI) inference. CiDDER dereplicates microbial genomes based on saturation assessment of distinct protein-coding genes. To support usability, auxiliary functionalities are incorporated for testing the number of representative genomes resulting from applying various clustering parameters, automated downloading of genomes belonging to a bacterial species or genus, clustering non-representative genomes to their closest representative genomes and filtering plasmids and phages prior to dereplication. From benchmarking against other ANI-based dereplication tools, skDER, when run in the default mode, was efficient and achieved comparable pangenome coverage and strictly adhered to user-defined cutoffs for both ANI and aligned fraction (AF). Further, we showcase that CiDDER is a convenient alternative to ANI-based dereplication that allows users to more directly optimize the selection of representative genomes to cover a large breadth of a taxon’s pangenome.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Simulation or modeling | low |
| gpt | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Other design | high |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".