Development and Application of a Software Tool/Package for Pan-Genomic Analysis
Bibliographic record
Abstract
Continuing scientific progress in genetics allows us to better understand how structural variations in an organism's gene content can lead to diversity within a species. By analyzing the sum of the genes for an entire species, we can construct a pangenome for the species. The pangenome of a species is the set of all genes present in all sub-species of a species. It consists of the core genome, which represents the genes present in all sub-species, and a variable genome, which refers to genes not present in all sub-species. micropan is an R package designed for the study of microbial pan-genomics. The genomes of prokaryotes (microbes) are relatively simple, leading to relatively simple construction of their pangenome. By comparison, plant genomes are highly repetitive and complex in comparison, and there is no general tool/package developed for pangenome construction for plant species. Due to the computational requirements of constructing such a pangenome, the tool/package required needs to be more flexible, efficient and robust than micropan. In this paper, we developed a pangenome construction pipeline that works for both prokaryotes and eukaryotes. The design of this pipeline will allow it to adapt to different selections of gene annotation and gene clustering methods. With a more efficient and robust tool/package constructed, future research can discover how to extend it from draft or finished genomes to sequencing reads.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.033 | 0.019 |
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".