Random Addition Concatenation Analysis: A Novel Approach to the Exploration of Phylogenomic Signal Reveals Strong Agreement between Core and Shell Genomic Partitions in the Cyanobacteria
Bibliographic record
Abstract
Recent whole-genome approaches to microbial phylogeny have emphasized partitioning genes into functional classes, often focusing on differences between a stable core of genes and a variable shell. To rigorously address the effects of partitioning and combining genes in genome-level analyses, we developed a novel technique called Random Addition Concatenation Analysis (RADICAL). RADICAL operates by sequentially concatenating randomly chosen gene partitions starting with a single-gene partition and ending with the entire genomic data set. A phylogenetic tree is built for every successive addition, and the entire process is repeated creating multiple random concatenation paths. The result is a library of trees representing a large variety of differently sized random gene partitions. This library can then be mined to identify unique topologies, assess overall agreement, and measure support for different trees. To evaluate RADICAL, we used 682 orthologous genes across 13 cyanobacterial genomes. Despite previous assertions of substantial differences between a core and a shell set of genes for this data set, RADICAL reveals the two partitions contain congruent phylogenetic signal. Substantial disagreement within the data set is limited to a few nodes and genes involved in metabolism, a functional group that is distributed evenly between the core and the shell partitions. We highlight numerous examples where RADICAL reveals aspects of phylogenetic behavior not evident by examining individual gene trees or a "'total evidence" tree. Our method also demonstrates that most emergent phylogenetic signal appears early in the concatenation process. The software is freely available at http://desalle.amnh.org.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".