DNAskew: Statistical Analysis of Base Compositional Asymmetry and Prediction of Replication Boundaries in the Genome Sequences
Bibliographic record
Abstract
Sueoka and Lobry declared respectively that, in the absence of bias between the two DNA strands for mutation and selection, the base composition within each strand should be A=T and C=G (this state is called Parity Rule type 2, PR2). However, the genome sequences of many bacteria, vertebrates and viruses showed asymmetries in base composition and gene direction. To determine the relationship of base composition skews with replication orientation, gene function, codon usage biases and phylogenetic evolution, in this paper a program called DNAskew was developed for the statistical analysis of strand asymmetry and codon composition bias in the DNA sequence. In addition, the program can also be used to predict the replication boundaries of genome sequences. The method builds on the fact that there are compositional asymmetries between the leading and the lagging strand for replication. DNAskew was written in Perl script language and implemented on the LINUX operating system. It works quickly with annotated or unannotated sequences in GBFF (GenBank flatfile) or fasta format. The source code is freely available for academic use at http://www.epizooty.com/pub/stat/DNAskew.
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.
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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".