A practical assembly guideline for genomes with various levels of heterozygosity
Bibliographic record
Abstract
Although current long-read sequencing technologies have a long-read length that facilitates assembly for genome reconstruction, they have high sequence errors. While various assemblers with different perspectives have been developed, no systematic evaluation of assemblers with long reads for diploid genomes with varying heterozygosity has been performed. Here, we evaluated a series of processes, including the estimation of genome characteristics such as genome size and heterozygosity, de novo assembly, polishing, and removal of allelic contigs, using six genomes with various heterozygosity levels. We evaluated five long-read-only assemblers (Canu, Flye, miniasm, NextDenovo and Redbean) and five hybrid assemblers that combine short and long reads (HASLR, MaSuRCA, Platanus-allee, SPAdes and WENGAN) and proposed a concrete guideline for the construction of haplotype representation according to the degree of heterozygosity, followed by polishing and purging haplotigs, using stable and high-performance assemblers: Redbean, Flye and MaSuRCA.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.008 |
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".