Evidence map›Paper›PMID 42028238›Full record

ArticleComputational and structural biotechnology journal2026

Regulation Ratio: A Singular Multi-Omic Measurement of Gene Regulatory Mechanisms.

Alexander Krohannon, Mansi Srivastava, Neel Sangani, Sarath Chandra Janga

Abstract read
In one paragraph

Article in Computational and structural biotechnology journal, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Alexander KrohannonDepartment of Biomedical Engineering and Informatics, Luddy School of Informatics, Computing, and Engineering, Indiana University Indianapolis, Indianapolis, IN, USA.ORCID https://orcid.org/0000-0003-4449-587X
Mansi SrivastavaDepartment of Biology, College of Arts and Sciences, Indiana University, Bloomington, IN, USA.
Neel SanganiDepartment of Neurology, Indiana University School of Medicine, Indianapolis, IN, USA.ORCID https://orcid.org/0009-0000-1541-1388
Sarath Chandra JangaDepartment of Biomedical Engineering and Informatics, Luddy School of Informatics, Computing, and Engineering, Indiana University Indianapolis, Indianapolis, IN, USA.ORCID https://orcid.org/0000-0001-7351-6268

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Gene expression is regulated through a complex interplay between transcriptional and post-transcriptional mechanisms, yet their relative contributions and relationships remain incompletely understood. In this study, we propose normalized metrics to quantify regulatory density at each of these 2 levels by integrating assay for transposase-accessible chromatin sequencing (ATAC-seq), RNA sequencing (RNA-seq), and protein occupancy profiling sequencing (POP-seq) data across 3 human cell lines-HEK293, HepG2, and K562-to assess gene-specific regulatory variations. This analysis revealed 3 distinct regulatory classes: predominantly post-transcriptionally regulated, predominantly transcriptionally regulated, and balanced genes. Using this metric, prominent associations between regulatory strategies and gene properties were uncovered, with transcriptionally regulated genes exhibiting greater length, post-transcriptionally regulated genes displaying higher isoform diversity and expression levels, and specific transcript types showing consistent enrichment patterns across regulatory categories. Remarkably, 55.8% of genes maintained identical regulatory classification across the 3 cell lines examined, with functional pathway analysis demonstrating high conservation of regulatory-functional relationships despite different cellular origins. This study provides a novel framework for understanding gene regulatory strategies and demonstrates that the relationship between gene properties and regulatory mechanisms represents a fundamental organizational principle that could transcend cell type specificity, with important implications for understanding dysregulation in disease states.

Identifiers

PMID42028238
PMCPMC13100350

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.