Key considerations for measuring allelic expression on a genomic scale using high‐throughput sequencing
Bibliographic record
Abstract
Differences in gene expression are thought to be an important source of phenotypic diversity, so dissecting the genetic components of natural variation in gene expression is important for understanding the evolutionary mechanisms that lead to adaptation. Gene expression is a complex trait that, in diploid organisms, results from transcription of both maternal and paternal alleles. Directly measuring allelic expression rather than total gene expression offers greater insight into regulatory variation. The recent emergence of high-throughput sequencing offers an unprecedented opportunity to study allelic transcription at a genomic scale for virtually any species. By sequencing transcript pools derived from heterozygous individuals, estimates of allelic expression can be directly obtained. The statistical power of this approach is influenced by the number of transcripts sequenced and the ability to unambiguously assign individual sequence fragments to specific alleles on the basis of transcribed nucleotide polymorphisms. Here, using mathematical modelling and computer simulations, we determine the minimum sequencing depth required to accurately measure relative allelic expression and detect allelic imbalance via high-throughput sequencing under a variety of conditions. We conclude that, within a species, a minimum of 500-1000 sequencing reads per gene are needed to test for allelic imbalance, and consequently, at least five to 10 millions reads are required for studying a genome expressing 10 000 genes. Finally, using 454 sequencing, we illustrate an application of allelic expression by testing for cis-regulatory divergence between closely related Drosophila species.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".