Choosing representative proteins based on splicing structure similarity improves the accuracy of gene tree reconstruction
Bibliographic record
Abstract
Abstract Constructing accurate gene trees is important, as gene trees play a key role in several biological studies, such as species tree reconstruction, gene functional analysis and gene family evolution studies. The accuracy of these studies is dependent on the accuracy of the input gene trees. Although several methods have been developed for improving the construction and the correction of gene trees by making use of the relationship with a species tree in addition to multiple sequence alignment, there is still a large room for improvement on the accuracy of gene trees and the computing time. In particular, accounting for alternative splicing that allows eukaryote genes to produce multiple transcripts/proteins per gene is a way to improve the quality of multiple sequence alignments used by gene tree reconstruction methods. Current methods for gene tree reconstruction usually make use of a set of transcripts composed of one representative transcript per gene, to generate multiple sequence alignments which are then used to estimate gene trees. Thus, the accuracy of the estimated gene tree depends on the choice of the representative transcripts. In this work, we present an alternative-splicing-aware method called Splicing Homology Transcript (SHT) method to estimate gene trees based on wisely selecting an accurate set of homologous transcripts to represent the genes of a gene family. We introduce a new similarity measure between transcripts for quantifying the level of homology between transcripts by combining a splicing structure-based similarity score with a sequence-based similarity score. We present a new method to cluster transcripts into a set of splicing homology groups based on the new similarity measure. The method is applied to reconstruct gene trees of the Ensembl database gene families, and a comparison with current EnsemblCompara gene trees is performed. The results show that the new approach improves gene tree accuracy thanks to the use of the new similarity measure between transcripts. An implementation of the method as well as the data used and generated in this work are available at https://github.com/UdeS-CoBIUS/SplicingHomologGeneTree/ .
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".