HisTrader: A Tool to Identify Nucleosome Free Regions from ChIP-Seq of Histone Post-Translational Modifications
Bibliographic record
Abstract
ABSTRACT Motivation Chromatin immuno-precipitation sequencing (ChIP-Seq) of histone post-translational modifications coupled with de novo motif elucidation and enrichment analyses can identify transcription factors responsible for orchestrating transitions between cell-and disease-states. However, the identified regulatory elements can span several kilobases (kb) in length, which complicates motif-based analyses. Restricting the length of the target DNA sequence(s) can reduce false positives. Therefore, we present HisTrader, a computational tool to identify the regions accessible to transcription factors, nucleosome free regions (NFRs), within histone modification peaks to reduce the DNA sequence length required for motif analyses. Results HisTrader accurately identifies NFRs from H3K27Ac ChIP-seq profiles of the lung cancer cell line A549, which are validated by the presence of DNaseI hypersensitivity. In addition, HisTrader reveals that multiple NFRs are common within individual regulatory elements; an easily overlooked feature that should be considered to improve sensitivity of motif analyses using histone modification ChIP-seq data. Availability and implementation The HisTrader script is open-source and available on GitHub ( https://github.com/SvenBaileyLab/Histrader ) under a GNU general public license (GPLv3). HisTrader is written in PERL and can be run on any platform with PERL installed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.045 | 0.016 |
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".