<scp>MhGeneS</scp>: An Analytical Pipeline to Allow for Robust Microhaplotype Genotyping
Bibliographic record
Abstract
Microhaplotypes are small linked genomic regions comprising two or more single-nucleotide polymorphisms (SNPs) that are being applied in forensics and are emerging in wildlife monitoring studies and genomic epidemiology. Typically, targeted in non-coding regions, microhaplotypes in exonic regions can be designed with larger amplicons to capture functional non-synonymous sites and minimise insertion/deletion (indel) polymorphisms. Quality control is an important first step for high-confidence genotyping to counteract such false-positive variants. As genetic markers with higher polymorphism compared to biallelic SNPs, it is critical to ensure sequencing errors across the microhaplotype amplicon are filtered out to avoid introducing false-haplotypes. We developed the MhGeneS pipeline which works in tandem with Seq2Sat to help validate microhaplotype genotyping of the coding region of genes, with broader applicability to any microhaplotype profiling. We genotyped microhaplotype regions of the Zfx (≅ 160 bp) and Zfy (≅ 140 bp) genes, as well as an exon of the prion protein (Prnp) gene (≅ 370 bp) in caribou (Rangifer tarandus) using paired-end Illumina technology. As important quality metrics affecting microhaplotype calling, we identified the sequencing error rate profile related to the overlap or non-overlap of paired-end reads as well as the read depth as significant. In the case of Prnp, we achieved confident microhaplotype calling through MhGeneS by removing small sections of the 5' and 3' amplicons and using a minimum read depth of 20. Read depth and sequence trimming may be locus-specific, and validation of these parameters is recommended before the high-throughput profiling of samples.
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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) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.033 | 0.026 |
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".