GraphDNA: a Java program for graphical display of DNA composition analyses
Bibliographic record
Abstract
BACKGROUND: Under conditions of no strand bias the number of Gs is equal to that of Cs for each DNA strand; similarly, the total number of Ts is equal to that of As. However, within each strand there are considerable local deviations from the A = T and G = C equality. These asymmetries in nucleotide composition have been extensively analyzed in prokaryotic and eukaryotic genomes and related to chromosome organization, transcription orientation and other processes in certain organisms. To carry out analysis of intra-strand nucleotide distribution several graphical methods have been developed. RESULTS: GraphDNA is a new Java application that provides a simple, user-friendly interface for the visualization of DNA nucleotide composition. The program accepts GenBank, EMBL and FASTA files as an input, and it displays multiple DNA nucleotide composition graphs (skews and walks) in a single window to allow direct comparisons between the sequences. We illustrate the use of DNA skews for characterization of poxvirus and coronavirus genomes. CONCLUSION: GraphDNA is a platform-independent, Open Source, tool for the analysis of nucleotide trends in DNA sequences. Multiple sequence formats can be read and multiple sequences may be plotted in a single results window.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.065 | 0.023 |
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".