Multiple whole-genome alignments without a reference organism
Bibliographic record
Abstract
Multiple sequence alignments have become one of the most commonly used resources in genomics research. Most algorithms for multiple alignment of whole genomes rely either on a reference genome, against which all of the other sequences are laid out, or require a one-to-one mapping between the nucleotides of the genomes, preventing the alignment of recently duplicated regions. Both approaches have drawbacks for whole-genome comparisons. In this paper we present a novel symmetric alignment algorithm. The resulting alignments not only represent all of the genomes equally well, but also include all relevant duplications that occurred since the divergence from the last common ancestor. Our algorithm, implemented as a part of the VISTA Genome Pipeline (VGP), was used to align seven vertebrate and six Drosophila genomes. The resulting whole-genome alignments demonstrate a higher sensitivity and specificity than the pairwise alignments previously available through the VGP and have higher exon alignment accuracy than comparable public whole-genome alignments. Of the multiple alignment methods tested, ours performed the best at aligning genes from multigene families-perhaps the most challenging test for whole-genome alignments. Our whole-genome multiple alignments are available through the VISTA Browser at http://genome.lbl.gov/vista/index.shtml.
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How this classification was reachedexpand
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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".