OrthoClusterDB: an online platform for synteny blocks
Bibliographic record
Abstract
BACKGROUND: The recent availability of an expanding collection of genome sequences driven by technological advances has facilitated comparative genomics and in particular the identification of synteny among multiple genomes. However, the development of effective and easy-to-use methods for identifying such conserved gene clusters among multiple genomes-synteny blocks-as well as databases, which host synteny blocks from various groups of species (especially eukaryotes) and also allow users to run synteny-identification programs, lags behind. DESCRIPTIONS: OrthoClusterDB is a new online platform for the identification and visualization of synteny blocks. OrthoClusterDB consists of two key web pages: Run OrthoCluster and View Synteny. The Run OrthoCluster page serves as web front-end to OrthoCluster, a recently developed program for synteny block detection. Run OrthoCluster offers full control over the functionalities of OrthoCluster, such as specifying synteny block size, considering order and strandedness of genes within synteny blocks, including or excluding nested synteny blocks, handling one-to-many orthologous relationships, and comparing multiple genomes. In contrast, the View Synteny page gives access to perfect and imperfect synteny blocks precomputed for a large number of genomes, without the need for users to retrieve and format input data. Additionally, genes are cross-linked with public databases for effective browsing. For both Run OrthoCluster and View Synteny, identified synteny blocks can be browsed at the whole genome, chromosome, and individual gene level. OrthoClusterDB is freely accessible. CONCLUSION: We have developed an online system for the identification and visualization of synteny blocks among multiple genomes. The system is freely available at (http://genome.sfu.ca/orthoclusterdb/).
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.074 | 0.048 |
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".