Data from: Phylogenomics from whole genome sequences using aTRAM
Bibliographic record
Abstract
AbstractNovel sequencing technologies are rapidly expanding the size of data sets that can be applied to phylogenetic studies. Currently the most commonly used phylogenomic approaches involve some form of genome reduction. While these approaches make assembling phylogenomic data sets more economical for organisms with large genomes, they reduce the genomic coverage and thereby the long-term utility of the data. Currently, for organisms with moderate to small genomes (<1000 Mbp) it is feasible to sequence the entire genome at modest coverage (10−30×). Computational challenges for handling these large data sets can be alleviated by assembling targeted reads, rather than assembling the entire genome, to produce a phylogenomic data matrix. Here we demonstrate the use of automated Target Restricted Assembly Method (aTRAM) to assemble 1107 single-copy ortholog genes from whole genome sequencing of sucking lice (Anoplura) and out-groups. We developed a pipeline to extract exon sequences from the aTRAM assemblies by annotating them with respect to the original target protein. We aligned these protein sequences with the inferred amino acids and then performed phylogenetic analyses on both the concatenated matrix of genes and on each gene separately in a coalescent analysis. Finally, we tested the limits of successful assembly in aTRAM by assembling 100 genes from close- to distantly related taxa at high to low levels of coverage.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.017 |
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".