Characterizing Immune Receptors from New Genome Sequences
Bibliographic record
Abstract
Genome sequences are quickly being generated from a variety of organisms and provide researchers with an abundance of previously inaccessible information and an important source of insight into immune mechanisms. There are a variety of methods to accurately characterize genes from new genome sequences, but immune receptors pose special challenges for these techniques. Immune receptors, particularly those that directly recognize pathogens, often diverge rapidly among species and are commonly found in large, complex multigene families. Because of these characteristics, immune receptors tend to be overlooked or misannotated in large-scale genomic surveys. We describe here a computational strategy to characterize homologs of immune receptors and also to identify putative novel receptors from newly assembled genome sequences. The description of these protocols is aimed at a typical immunologist, and a substantial knowledge of bioinformatics is not expected. The approach is based on using low-stringency sequence searches to identify divergent homologs. For receptors with multiple domains, the intersection of low-stringency searches can be used to identify divergent receptor sequences with high confidence. For multigene families, these predictions can be refined using sequence conservation among gene family paralogs. This strategy has recently been useful in identifying novel expansions in immune receptors in a number of animal genomes and will likely continue to revolutionize our view of animal immunity as new genomes emerge.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".