2025 Hyper Recent •CC0 1.0 Universal

This work is dedicated to the public domain. No rights reserved.

Access Preprint From Server
April 5th, 2025
Version: 1
None
bioinformatics
biorxiv

PEAS: Detection of Clustered Differences in Genomic Data

Skola, D. D.Open in Google Scholar•Glass, C.Open in Google Scholar

Motivation: Studies of genetically-distinct individuals have shown that differences in marks of transcriptional regulation such as chromatin accessibility , transcription factor binding and histone modifications are often proximally clustered along the genome. These proximal clusters, which have been labeled as cis-regulatory domains (CRDs), are thought to reflect topological features of the genome and may demarcate functional units linking genetic variation to transcriptional regulation. The problem of distinguishing CRDs from background variation is computationally difficult and current methods rely on greedy approaches with ad-hoc parameters and do not provide an assessment of statistical significance, an important consideration for investigating CRDs in small sample cohorts. Results: We developed a software package, PEAS (Proximal Enrichment by Approximated Sampling), to identify CRDs from a small number of samples (as few as two distinct genetic backgrounds) using a robust statistical approach. PEAS uses methods for efficient and accurate estimation of empirical distributions to quantify the significance of enriched regions, followed by a dynamic programming algorithm to identify the minimum likelihood set of non-overlapping enriched regions. We used it to identify clusters of proximally-enriched differences in the histone mark H3K27ac between two mouse strains as well as proximally-enriched regions of correlation in this mark across five mouse strains. We find that differences in histone acetylation between two mouse strains form significant clusters that overlap closely with differences in the first principal component of their Hi-C correlation matrices. Availability: PEAS is written in Python and is available at https://pypi.org/project/PEAS/. Methods for approximating empirical distributions are implemented in C and Python and are available at https://pypi.org/project/empdist/,

Similar Papers

biorxiv
Sun Apr 06 2025
SnoBIRD: A tool to identify C/D box snoRNAs and refine their annotation across all eukaryotes
Small nucleolar RNAs (snoRNAs), a group of noncoding RNAs present amongst all eukaryotes, are most extensively characterized for their regulation of ribosome biogenesis and splicing. Despite their central roles, current snoRNA annotations remain incomplete. Several eukaryote genome annotations contain few or no snoRNAs, and none distinguish expressed snoRNAs from their pseudogenes, a recently char...
Fafard-Couture, E.
•
Boulanger, C.
•
Faucher-Giguere, L.
•
Sinagoga, V.
...•
Scott, M. S.
biorxiv
Sun Apr 06 2025
Enhancing variant detection in complex genomes: leveraging linked reads for robust SNP, Indel, and structural variant analysis
Accurate detection of genetic variants, including single nucleotide polymorphisms (SNPs), small insertions and deletions (INDELs), and structural variants (SVs), is critical for comprehensive genomic analysis. While traditional short-read sequencing performs well for SNP and INDEL detection, it struggles to resolve SVs, especially in complex genomic regions, due to inherent read length limitations...
Luo, C.
•
Liu, Y. H.
•
Liu, H.
•
Zhang, Z.
...•
Zhou, X. M.
biorxiv
Sun Apr 06 2025
Chemical Genomics Language Model toward Reliable and Explainable Compound-Protein Interaction Exploration
Deep learning-based compound-protein interaction (CPI) prediction models are promising in the field of molecular biology, particularly for facilitating the drug discovery process. In practical applications, CPI models should achieve a high generalization performance, quantify prediction confidence, and ensure explainability. Here, we propose ChemGLaM, a chemical genomics language model for reliabl...
Koyama, T.
•
Tsumura, H.
•
Okita, R.
•
Yamazaki, K.
...•
Okuno, Y.
biorxiv
Sun Apr 06 2025
SpatialAgent: An Autonomous AI Agent for Spatial Biology
Advances in AI are transforming scientific discovery, yet spatial biology, a field that deciphers the molecular organization within tissues, remains constrained by labor-intensive workflows. Here, we present SpatialAgent, a fully autonomous AI agent dedicated for spatial-biology research. SpatialAgent integrates large language models with dynamic tool execution and adaptive reasoning. SpatialAgent...
Wang, H.
•
He, Y.
•
Coelho, P. P.
•
Bucci, M.
...•
Regev, A.
biorxiv
Sun Apr 06 2025
SEHI-PPI: An End-to-End Sampling-Enhanced Human-Influenza Protein-Protein Interaction Prediction Framework with Double-View Learning
Influenza poses a persistent global health challenge, necessitating innovative approaches to predict host-virus interactions and inform antiviral strategies. Despite advancements, machine-learning-based computational methods typically struggle with limited high-quality negative samples and inadequate modeling of complex host-virus interactions, which hinder predictive accuracy and generalization. ...
Yang, Q.
•
Fan, X.
•
Zhao, H.
•
Ma, Z.
...•
Yin, R.
biorxiv
Sun Apr 06 2025
Unsupervised Whole-Genome Representation Learning Captures Bacterial Phenotypes
Shifting from hand-crafted to learned representations of data has revolutionized fields like natural language processing and computer vision. Despite this, current approaches to bacterial phenotype prediction from the genome rely on training machine learning models on hand-crafted features, often binary indicators or counts of the presence of different conserved genomic elements and protein domain...
Dufault, C.
•
Moses, A. M.
biorxiv
Sun Apr 06 2025
Functional prediction of DNA/RNA-binding proteins using deep learning based on gene expression correlations
Deep learning methods have been developed to predict gene expression levels across various cell and tissue types based on DNA-binding sites of protein identified by ChIP-seq. However, the experimental identification of DNA/RNA-binding sites and the functional characterization of certain proteins remain challenging. Here I predicted the regulatory target genes and functional roles of DNA/RNA-bindin...
Osato, N.
biorxiv
Sun Apr 06 2025
Learning interpretable representation for context-specific transcription regulatory networks using a foundation model
Gene expression is shaped by transcription regulatory networks (TRNs), where transcription regulators interact within regulatory elements in a context-specific manner. Despite significant efforts, understanding the intricate interactions of transcription regulators across different genomic regions and cell types remains a major challenge, largely due to data sparsity. Here, we introduce ChromBERT,...
Yu, Z.
•
Yang, D.
•
Chen, Q.
•
Zhang, Y.
...•
Zhang, Y.
biorxiv
Sun Apr 06 2025
Improving hifiasm haplotypes for autopolyploid genome assemblies using constraint programming
Actual versions of hifiasm often produce unbalanced haplotype contigs for autopolyploid genome assemblies. Adding a contraint on the distribution of proteins among haplotypes improves haplotype sizes and busco scores balances. Four species were used to benchmark this approach against hifiasm raw results. The script can be downloaded from https://github.com/chklopp/Separate_haplotypes_with_toulbar2
Schiex, T.
•
Klopp, C.
•
Givry, S. d.
•
Durante, V.
biorxiv
Sat Apr 05 2025
SCORCH2: a generalised heterogeneous consensus model for high-enrichment interaction-based virtual screening
The discovery of effective therapeutics remains a complex, costly, and time-consuming endeavor, characterized by high failure rates and significant resource investments. A central bottleneck in early-stage drug discovery is identifying suitable hit compounds with moderate affinity for known biological targets. Although advancements have been made, current in silico virtual screening methods are su...
Chen, L.
•
Blay, V.
•
Ballester, P. J.
•
Houston, D. R.