CryptoGenotyper: A new bioinformatics tool for rapid Cryptosporidium identification
Bibliographic record
Abstract
Cryptosporidium is a protozoan parasite that is transmitted to both humans and animals through zoonotic or anthroponotic means. When a host is infected with this parasite, it causes a gastrointestinal disease known as cryptosporidiosis. To understand the transmission dynamics of Cryptosporidium, the small subunit (SSU or 18S) rRNA and gp60 genes are commonly studied through PCR analysis and conventional Sanger sequencing. However, analyzing sequence chromatograms manually is both time consuming and prone to human error, especially in the presence of poorly resolved, heterozygous peaks and the absence of a validated database. For this study, we developed a Cryptosporidium genotyping tool, called CryptoGenotyper, which has the capability to read raw Sanger sequencing data for the two common Cryptosporidium gene targets (SSU rRNA and gp60) and classify the sequence data into standard nomenclature. The CryptoGenotyper has the capacity to perform quality control and properly classify sequences using a high quality, manually curated reference database, saving users' time and removing bias during data analysis. The incorporated heterozygous base calling algorithms for the SSU rRNA gene target resolves double peaks, therefore recovering data previously classified as inconclusive. The CryptoGenotyper successfully genotyped 99.3% (428/431) and 95.1% (154/162) of SSU rRNA chromatograms containing single and mixed sequences, respectively, and correctly subtyped 95.6% (947/991) of gp60 chromatograms without manual intervention. This new, user-friendly tool can provide both fast and reproducible analyses of Sanger sequencing data for the two most common Cryptosporidium gene targets.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.007 |
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".