Improved sensitivity and resolution of ATAC-seq differential DNA accessibility analysis
Bibliographic record
Abstract
Abstract Eukaryotic genomes are packaged into chromatin, and the extent of its compaction must be modulated to allow several biological processes such as gene transcription. The regulatory elements of expressed genes are typically in relatively accessible chromatin, and several studies have revealed a reliable correlation between the abundance of mRNA transcripts and the degree of DNA accessibility at the regulatory elements of their coding genes. In consequence, the genome-wide profiling of DNA accessibility by methods such as ATAC-seq can help in the study of gene regulatory networks by serving as a proxy for gene expression and by helping identify important gene cis-regulatory elements and the trans-acting factors that bind them. The predominant approach used to identify differentially accessible genomic loci from ATAC-seq data obtained in two conditions of interest is comparable to that employed in RNA-seq gene expression profiling studies: accessible regions are identified through peak calling and treated like “genes”, then sequenced DNA fragments (originating from two neighboring transposase insertion events) that overlap them are counted and subjected to abundance modeling, which then allows to identify those that have a significant difference between the two conditions. We reasoned that this approach could be improved in terms of sensitivity and resolution by introducing two changes: bypassing peak calling, using instead a genome-wide sliding window quantification approach, and counting transposase insertion sites, instead of fragments originating from two neighboring insertion sites. We present the development of this approach, which we term “widaR”, for Window- and Insertion-based Differential Accessibility in R, using a murine skeletal myoblast differentiation dataset. Reproducible R code is provided.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".