TELLBASE: a novel tool of TELL-seq barcode-assisted scaffold assembler for bacterial genomes
Bibliographic record
Abstract
Transposase enzyme linked long-read sequencing (TELL-seq) technology generates barcode-linked reads, facilitating whole-genome sequencing (WGS), and complete assembly with improved accuracy and reduced costs. Unlike mate-pair sequencing technology, TELL-seq employs a near-full-sequence tagging strategy that allows more efficient capture of comprehensive genomic information. However, assembly algorithms and software capable of fully leveraging the characteristics of TELL-seq technology to effectively assemble genomic sequences at the megabase-scale are lacking, particularly for bacteria and their plasmids. In this study, we present TELL-seq barcode-assisted scaffold assembler (TELLBASE), a de novo genome assembler designed specifically for assembling bacterial genomes using TELL-seq-derived linked reads. In assembly tests involving bacteria such as Acinetobacter baumannii, Klebsiella pneumoniae, Mycobacterium tuberculosis, and Staphylococcus aureus, TELLBASE exhibited exceptional efficacy in producing chromosome-level bacterial genomic sequences and successful identification of plasmids present in the sequenced strains. Comparative analysis revealed that TELLBASE significantly outperforms existing assemblers tailored for TELL-seq-derived linked reads, such as TuringAssembler and Ariadne, in terms of the completeness and accuracy of the assembled genomes. Therefore, TELLBASE shows promising potential for refining draft bacterial genomes and further applications in related fields. The package for TELLBASE is freely available on GitHub (https://github.com/sosie1/TELLBASE).
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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".