BlastPhyMe: A toolkit for rapid generation and analysis of protein-coding sequence datasets
Bibliographic record
Abstract
A bstract S ummary We present BlastPhyMe (BLAST, Phylogenies, and Molecular Evolution) a new application to facilitate the fast and easy generation and analysis of protein-coding sequence datasets. The application uses a portable database framework to manage and organize sequences along with a graphical user interface (GUI) that makes the application extremely easy to use. BlastPhyMe utilizes several existing services and applications in a unique way that save researchers considerable time when building and analyzing protein-coding datasets. The application consists of two modules that can be used separately or together. The first module enables the assembly of coding sequence datasets. BLAST searches can be used to obtain all related sequences of interest from NCBI. Full GenBank records are saved within the database and coding sequences are automatically extracted. A feature of particular note is that sequences can be sorted based on NCBI taxonomic hierarchy before export for visualization using existing tools, such as fast. The application provides GUIs for automatic alignment of sequences with the popular tools MUSCLE and PRANK, as well as for reconstructing phylogenetic trees using PhyML. The second module incorporates selection analyses using codon-based likelihood methods. The alignments and phylogenetic trees generated with the dataset module, or those generated elsewhere, can be used to run the models implemented in the codeml PAML package. A GUI allows easy selection of models and parameters. Importantly, replicate analyses with different parameter starting values can be automatically performed in order to ensure selection of the best-fitting model. Multiple analyses can be run simultaneously based on the number of processor cores available, while additional analyses will be run iteratively until completed. Results are saved within the database and can be exported to publication-ready Excel tables, which further automatically compute the appropriate likelihood ratio test between models in order to determine statistical significance. Future updates will add additional options for phylogenetic reconstruction (eg, MrBayes) and selection analyses (eg, HYPHY). BlastPhyMe saves researches of all bioinformatics experience levels considerable time by automating the numerous tasks required for the generation and analysis of protein-coding sequence datasets using a straightforward graphical interface. A vailability Installation package and source code available from: https://github.com/ryankschott/BlastPhyMe C ontact schottr@si.edu
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.034 | 0.045 |
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".