Critical review of <scp>NGS</scp> analyses for de novo genotyping multigene families
Bibliographic record
Abstract
The genotyping of highly polymorphic multigene families across many individuals used to be a particularly challenging task because of methodological limitations associated with traditional approaches. Next-generation sequencing (NGS) can overcome most of these limitations, and it is increasingly being applied in population genetic studies of multigene families. Here, we critically review NGS bioinformatic approaches that have been used to genotype the major histocompatibility complex (MHC) immune genes, and we discuss how the significant advances made in this field are applicable to population genetic studies of gene families. Increasingly, approaches are introduced that apply thresholds of sequencing depth and sequence similarity to separate alleles from methodological artefacts. We explain why these approaches are particularly sensitive to methodological biases by violating fundamental genotyping assumptions. An alternative strategy that utilizes ultra-deep sequencing (hundreds to thousands of sequences per amplicon) to reconstruct genotypes and applies statistical methods on the sequencing depth to separate alleles from artefacts appears to be more robust. Importantly, the 'degree of change' (DOC) method avoids using arbitrary cut-off thresholds by looking for statistical boundaries between the sequencing depth for alleles and artefacts, and hence, it is entirely repeatable across studies. Although the advances made in generating NGS data are still far ahead of our ability to perform reliable processing, analysis and interpretation, the community is developing statistically rigorous protocols that will allow us to address novel questions in evolution, ecology and genetics of multigene families. Future developments in third-generation single molecule sequencing may potentially help overcome problems that still persist in de novo multigene amplicon genotyping when using current second-generation sequencing approaches.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".