Internet Contig Explorer (iCE)—A Tool for Visualizing Clone Fingerprint Maps
Bibliographic record
Abstract
Fingerprinted clone physical maps have proven useful in various applications, supporting both whole-genome and region-specific DNA sequencing as well as gene cloning studies. Fingerprint maps have been generated for several genomes, including those of human, mouse, rat, the nematodes Caenorhabditis elegans and Caenorhabditis briggsae, Arabidopsis thaliana and rice. Fingerprint maps of other genomes, including those of fungi, bacteria, poplar, and the cow, are being generated. The increasing use of fingerprint maps in genomic research has spawned a need in the research community for intuitive computer tools that facilitate viewing of the maps and the underlying fingerprint data. In this report we describe a new Java-based application called iCE (Internet Contig Explorer) that has been designed to provide views of fingerprint maps and associated data. Users can search for and display individual clones, contigs, clone fingerprints, clone insert sizes and markers. Users can also load into the software lists of particular clones of interest and view their fingerprints. iCE is being used at our Genome Centre to offer up to the research community views of the mouse, rat, bovine, C. briggsae, and several fungal genome bacterial artificial chromosome (BAC) fingerprint maps we have either completed or are currently constructing. We are also using iCE as part of the Rat Genome Sequencing Project to manage our provision of rat BAC clones for sequencing at the Human Genome Sequencing Center at the Baylor College of Medicine.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.005 |
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".