Bibliographic record
Abstract
BACKGROUND: Genome assembly is a fundamental problem with multiple applications. Current technological limitations do not allow assembling of entire genomes and many programs have been designed to produce longer and more reliable contigs. Assessing the quality of these assemblies and comparing those produced by different tools is essential in choosing the best ones. The QUAST program has become the current state-of-the-art in quality assessment of genome assemblies. The only drawback of QUAST is high time and memory usage for large genomes, e.g., over 4 days and 120 GB of RAM for a single human genome assembly. RESULTS: We introduce LASER, a new tool for assembly evaluation that improves greatly the speed and memory requirements of QUAST. For a human genome assembly, LASER is 5.6 times faster than QUAST while using only half the memory; one human genome assembly is evaluated in 17 hours instead of 4 days. The code of LASER is based on that of QUAST and therefore inherits all its features. CONCLUSIONS: Genome assembly evaluation is an essential step in assessing the quality of an assembly that is too often done improperly, in part due to significant resource consumption. With the introduction of LASER, proper evaluation can be performed efficiently.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.029 | 0.015 |
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".