bioRxiv Science⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/083576

HBP: an integrative and flexible pipeline for the interaction analysis of Hi-C dataset

He, C.·Li, P.·Shi, M.·Zhang, Y.·Ye, B.·Xie, D.·Shen, W.·Zhao, Z.
View original source ↗Export metadata ↓

Abstract

BackgroundThe spatial organization of interphase chromatin in the nucleus play an important role in gene expression regulation and function. With the rapid development of revolutionized chromosome conformation capture technology and its genome-wide derivatives such as Hi-C, investigation of the genome folding becomes more efficient and convenient. How to robustly deal with these massive datasets and infer accurate 3D model and within-nucleus compartmentalization of chromosomes becomes a new challenge.\n\nResultThe implemented pipeline HBP (Hi-C BED file analysis Pipeline) integrates existing pipelines focusing on individual steps of Hi-C data processing into an all-in-one package with adjustable parameters to infer the consensus 3D structure of genome from raw Hi-C sequencing data. Whats more, HBP could assign statistical confidence estimation for chromatin interactions, and clustering interaction loci according to enrichment tracks or topological structure automatically.\n\nConclusionThe freely available HBP is an optimized and flexible pipeline for analyzing the folding of whole chromosome and interactions between some specific sites from the Hi-C raw sequencing reads to the partially processed datasets. The other complex genetic and epigenetic datasets from public sources such as GWAS, ENCODE consortiums etc. will also easily be integrated into HBP, hence the final output results of HBP could provide a comprehensive in-depth understanding for the specific chromatin interactions, potential molecular mechanisms and biological significance. We believe that HBP is a reliable tool for the rapidly analysis of Hi-C data and will be very useful for a wide range of researchers, particularly those who lack of background in computational biology. HBP is freely accessible at https://github.com/hechao0407/HBP/blob/master/HBP_1.0.tar.gz.

Explore related subjects

bioinformaticsPreprint

Keep this discovery

BibTeXRIS

He, C., Li, P., Shi, M., Zhang, Y., Ye, B., Xie, D., Shen, W., Zhao, Z.. 2016-10-26. HBP: an integrative and flexible pipeline for the interaction analysis of Hi-C dataset. https://doi.org/10.1101/083576

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

spatialMET: an open and scalable framework for spatial metabolomics analysis

Mass spectrometry imaging (MSI) enables spatially resolved metabolomics in intact tissue sections, but analysis remains challenging at scale. Existing MSI workflows often require users to combine multiple software tools, while others rely on proprietary vendor software that limits interoperability and reproducibility. To address these challenges, we developed spatialMET, an open-source framework that provides an end-to-end workflow for MSI analysis. spatialMET provides a unified platform for preprocessing, spatial domain detection, and visualization. Downstream analyses include differential abundance testing, spatial autocorrelation and gradient analysis, dimensionality reduction, and correlation network analysis. Spatial domain detection uses hcdist, a C-based hierarchical clustering implementation that substantially reduces runtime and memory use relative to existing R-based approaches. spatialMET can be run through an interactive R Shiny application or as a standalone command-line workflow for larger datasets or high-performance computing environments. Applied to mouse small cell lung cancer MALDI-MSI data containing 284,673 pixels, spatialMET identified tumor-associated, stromal, and adjacent lung spatial domains that aligned with matched histology. Differential abundance analysis identified 117 m/z features that differed between tumor and stromal regions, while spatial autocorrelation analyses revealed spatially structured abundance patterns. Applying spatialMET to mouse lung adenocarcinoma data from an entire lung lobe containing 338,477 pixels further demonstrated scalability and captured spatial heterogeneity across tumor and surrounding lung tissue. In summary, spatialMET provides a scalable, open-source framework for end-to-end spatial metabolomics analysis, and it is distributed as a Docker container for reproducible deployment. Source code and installation instructions are available at https://github.com/biodatalab/spatialMET.

bioinformatics↗
bioRxiv2026

Probing the transcriptome response to shivering in skeletal muscle using a multilayered bioinformatics approach

Cold acclimation holds therapeutic potential for improving metabolic health. We previously demonstrated that repeated cold-induced shivering enhances insulin sensitivity in humans. However, the molecular pathways that underlie the skeletal muscle shivering response, and how these relate to beneficial physiological effects, remain poorly understood. In this study, we combined complementary bioinformatics approaches to allow in-depth analysis of the transcriptomic response of human skeletal muscle to repeated shivering. We identified a robust transcriptional signature and show a sex-specific component in the shivering skeletal muscle response, which seemed to diminish following cold adaptation. Our findings provide mechanistic insights into cold-induced muscle adaptations, shed light on potential interesting molecular targets for further investigation, and emphasize the importance of including both sexes in future cold acclimation studies.

bioinformatics↗
bioRxiv2026

An Information Geometry approach to model topological trajectories and Gene Expression Radius from UMAP geometry.

Understanding the relationship between gene expression dynamics and cellular identity remains a central challenge in single cell biology. Here, we introduce a novel computational and mathematical framework that integrates information geometry, fuzzy topology, and UMAP analysis to model gene expression landscapes derived from single cell RNA sequencing data. We formalize gene expression data as a fuzzy topological space, where interactions between expression points are governed by probabilistic distributions inspired by manifold learning approaches such as UMAP. Within this framework, we define an information geometric structure through a Fisher metric induced by these distributions, enabling the computation of geodesic trajectories that capture cellular differentiation processes. A key contribution of this work is the derivation of analytical conditions, expressed as expression radius formulas, that characterize local neighborhoods in gene expression space. These conditions allow for the identification of genes associated with stem cell states and predictions in transitional cell types in future work. Application of the proposed framework to single cell datasets reveals biologically meaningful gene sets enriched in key regulatory pathways and transcription factors, demonstrating the capacity of our approach to uncover latent structure in complex gene expression data. Our results suggest that integrating differential geometry with statistical learning theory offers a powerful paradigm for modeling genotype and phenotype relationships and cellular state transitions, with potential implications for precision medicine and systems biology.

bioinformatics↗