<tt>trio-sga</tt> : facilitating <i>de novo</i> assembly of highly heterozygous genomes with parent-child trios
Bibliographic record
Abstract
Abstract Motivation Most DNA sequence in diploid organisms is found in two copies, one contributed by the mother and the other by the father. The high density of differences between the maternally and paternally contributed sequences (heterozygous sites) in some organisms makes de novo genome assembly very challenging, even for algorithms specifically designed to deal with these cases. Therefore, various approaches, most commonly inbreeding in the laboratory, are used to reduce heterozygosity in genomic data prior to assembly. However, many species are not amenable to these techniques. Results We introduce trio-sga , a set of three algorithms designed to take advantage of mother-father-offspring trio sequencing to facilitate better quality genome assembly in organisms with moderate to high levels of heterozygosity. Two of the algorithms use haplotype phase information present in the trio data to eliminate the majority of heterozygous sites before the assembly commences. The third algorithm is designed to reduce sequencing costs by enabling the use of parents’ reads in the assembly of the genome of the offspring. We test these algorithms on a ‘simulated trio’ from four hap-loid datasets, and further demonstrate their performance by assembling three highly heterozygous Heliconius butterfly genomes. While the implementation of trio-sga is tuned towards Illumina-generated data, we note that the trio approach to reducing heterozygosity is likely to have cross-platform utility for de novo assembly.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".