A fast and flexible approach to oligonucleotide probe design for genomes and gene families
Bibliographic record
Abstract
MOTIVATION: With hundreds of completely sequenced microbial genomes available, and advancements in DNA microarray technology, the detection of genes in microbial communities consisting of hundreds of thousands of sequences may be possible. The existing strategies developed for DNA probe design, geared toward identifying specific sequences, are not suitable due to the lack of coverage, flexibility and efficiency necessary for applications in metagenomics. METHODS: ProDesign is a tool developed for the selection of oligonucleotide probes to detect members of gene families present in environmental samples. Gene family-specific probe sequences are generated based on specific and shared words, which are found with the spaced seed hashing algorithm. To detect more sequences, those sharing some common words are re-clustered into new families, then probes specific for the new families are generated. RESULTS: The program is very flexible in that it can be used for designing probes for detecting many genes families simultaneously and specifically in one or more genomes. Neither the length nor the melting temperature of the probes needs to be predefined. We have found that ProDesign provides more flexibility, coverage and speed than other software programs used in the selection of probes for genomic and gene family arrays. AVAILABILITY: ProDesign is licensed free of charge to academic users. ProDesign and Supplementary Material can be obtained by contacting the authors. A web server for ProDesign is available at http://www.uhnresearch.ca/labs/tillier/ProDesign/ProDesign.html. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.010 |
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".