The Relative Importance of Modeling Site Pattern Heterogeneity Versus Partition-Wise Heterotachy in Phylogenomic Inference
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Large taxa-rich genome-scale data sets are often necessary for resolving ancient phylogenetic relationships. But accurate phylogenetic inference requires that they are analyzed with realistic models that account for the heterogeneity in substitution patterns amongst the sites, genes and lineages. Two kinds of adjustments are frequently used: models that account for heterogeneity in amino acid frequencies at sites in proteins, and partitioned models that accommodate the heterogeneity in rates (branch lengths) among different proteins in different lineages (protein-wise heterotachy). Although partitioned and site-heterogeneous models are both widely used in isolation, their relative importance to the inference of correct phylogenies has not been carefully evaluated. We conducted several empirical analyses and a large set of simulations to compare the relative performances of partitioned models, site-heterogeneous models, and combined partitioned site heterogeneous models. In general, site-homogeneous models (partitioned or not) performed worse than site heterogeneous, except in simulations with extreme protein-wise heterotachy. Furthermore, simulations using empirically-derived realistic parameter settings showed a marked long-branch attraction (LBA) problem for analyses employing protein-wise partitioning even when the generating model included partitioning. This LBA problem results from a small sample bias compounded over many single protein alignments. In some cases, this problem was ameliorated by clustering similarly-evolving proteins together into larger partitions using the PartitionFinder method. Similar results were obtained under simulations with larger numbers of taxa or heterogeneity in simulating topologies over genes. For an empirical Microsporidia test data set, all but one tested site-heterogeneous models (with or without partitioning) obtain the correct Microsporidia+Fungi grouping, whereas site-homogenous models (with or without partitioning) did not. The single exception was the fully partitioned site-heterogeneous analysis that succumbed to the compounded small sample LBA bias. In general unless protein-wise heterotachy effects are extreme, it is more important to model site-heterogeneity than protein-wise heterotachy in phylogenomic analyses. Complete protein-wise partitioning should be avoided as it can lead to a serious LBA bias. In cases of extreme protein-wise heterotachy, approaches that cluster similarly-evolving proteins together and coupled with site-heterogeneous models work well for phylogenetic estimation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it