DFAST_QC: quality assessment and taxonomic identification tool for prokaryotic Genomes
Bibliographic record
Abstract
BACKGROUND: Accurate taxonomic classification in genome databases is essential for reliable biological research and effective data sharing. Mislabeling or inaccuracies in genome annotations can lead to incorrect scientific conclusions and hinder the reproducibility of research findings. Despite advances in genome analysis techniques, challenges persist in ensuring precise and reliable taxonomic assignments. Existing tools for genome verification often involve extensive computational resources or lengthy processing times, which can limit their accessibility and scalability for large-scale projects. There is a need for more efficient, user-friendly solutions that can handle diverse datasets and provide accurate results with minimal computational demands. This work aimed to address these challenges by introducing a novel tool that enhances taxonomic accuracy, offers a user-friendly interface, and supports large-scale analyses. RESULTS: We introduce a novel tool for the quality control and taxonomic classification tool of prokaryotic genomes, called DFAST_QC, which is available as both a command-line tool and a web service. DFAST_QC can quickly identify species based on NCBI and GTDB taxonomies by combining genome-distance calculations using MASH with ANI calculations using Skani. We evaluated DFAST_QC's performance in species identification and found it to be highly consistent with existing taxonomic standards, successfully identifying species across diverse datasets. In several cases, DFAST_QC identified potential mislabeling of species names in public databases and highlighted discrepancies in current classifications, demonstrating its capability to uncover errors and enhance taxonomic accuracy. Additionally, the tool's efficient design allows it to operate smoothly on local machines with minimal computational requirements, making it a practical choice for large-scale genome projects. CONCLUSIONS: DFAST_QC is a reliable and efficient tool for accurate taxonomic identification and genome quality control, well-suited for large-scale genomic studies. Its compatibility with limited-resource environments, combined with its user-friendly design, ensures seamless integration into existing workflows. DFAST_QC's ability to refine species assignments in public databases highlights its value as a complementary tool for maintaining and enhancing the accuracy of taxonomic data in genomic research. The web version is available at https://dfast.ddbj.nig.ac.jp/dqc/submit/ , and the source code for local use can be found at https://github.com/nigyta/dfast_qc .
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How this classification was reachedexpand
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, unvalidatedMachine predicted; a candidate call from one teacher head, 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".