CompMap: an allele-specific expression read-counter based on competitive mapping
Bibliographic record
Abstract
ABSTRACT Summary Changes to regulatory sequences account for important phenotypic differences between species and populations. In heterozygote individuals, regulatory polymorphism typically manifests as allele-specific expression (ASE) of transcripts. ASE data from inter-species and inter-population hybrids, in conjunction with expression data from the parents, can be used to infer regulatory changes in cis and trans throughout the genome. Improper data handling, however, can create problems of mapping bias and excessive loss of information, which are prone to arise unintentionally from the cumbersome pipelines with multiple dependencies that are common among current methods. Here, we introduce a new, selfcontained method implemented in Python that generates allele-specific expression counts from genotype-specific map alignments. Rather than assessing individual SNPs, our approach sorts and counts reads within a given homologous region by comparing individual read-mapping statistics from each parental alignment. Reads that are aligned ambiguously to both references are resolved proportionally to the allele-specific matching read counts or statistically using a binomial distribution. Using simulations, we show CompMap has low error rates in assessing regulatory divergence. Availability The Python code with examples and installation instructions is available on the GitHub repository https://github.com/santiagosnchez/CompMap Contact santiago.snchez@gmail.com Supplementary information
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".