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Comparative analysis of gene regulation in single cells using Compass

Cell Reports Methods 2025年 101035页

作者： Changxin Wan Yilong Qu Zhiyou Ye Tianbei Zhang Huifang Ma Ming Chen Wenpin Hou Zhicheng Ji Department of Biostatistics and Bioinformatics Duke University School of Medicine Durham NC USA Program of Computational Biology and Bioinformatics Duke University School of Medicine Durham NC USA Department of Biomedical Engineering Pratt School of Engineering Duke University Durham NC USA Department of Pathology Duke University School of Medicine Durham NC USA Duke Cancer Institute Duke University Durham NC USA Department of Biostatistics Columbia University Mailman School of Public Health New York City NY USA

Single-cell multi-omics is a transformative technology that measures both gene expression and chromatin accessibility in individual cells. However, most studies concentrate on a single tissue and are unable to determine whether a gene is regulated by a cis -regulatory element (CRE) in just one tissue or across multiple tissues. We developed Compass for comparative analysis of gene regulation across a large number of human and mouse tissues. Compass consists of a database, CompassDB, and an open-source R software package, CompassR. CompassDB contains processed single-cell multi-omics data of more than 2.8 million cells from hundreds of cell types. Building upon CompassDB, CompassR enables visualization and comparison of gene regulation across multiple tissues. We demonstrated that CompassR can identify CRE-gene linkages specific to a tissue type and their associated transcription factors in real examples.

关键词： single-cell multi-omics single-cell ATAC-seq gene regulation cis -regulatory elements CP: Systems biology

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ENERGY-BASED ARCHITECTURE FOR CLASSIFICATION OF PUBLICATION FIGURES

ENERGY-BASED ARCHITECTURE FOR CLASSIFICATION OF PUBLICATION ...

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Annual ORNL Biomedical Sciences and Engineering Conference on Collaborative Biomedical Innovations

作者： P. E. Barbano M. L. Nagy M. Krauthammer Department of Mathematics Yale University Program for Computational Biology and Bioinformatics Yale University

ISBN: (纸本)9781479921195

We present an implementation of the experimental and theoretical results obtained in the analysis of text and image content of biomedical publications. Particularly, we propose a novel optical recognition system using an adaptive algorithm for the classification and analysis of highly heterogeneous images in research papers. When compared with conventional algorithms, our technology substantially increases the probability of detection and classification of images buried in text or obscured by other images. We report successful testing of the new architecture using PubMed publications.

关键词： publications images ARCHITECTURE Classification of algorithms new buildings Taxonomy texts optical identification WWOX gene Image classification

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CIWARS: A Web Server for Antibiotic Resistance Surveillance Using Longitudinal Metagenomic Data

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Journal of Molecular biology 2025年

作者： Emon, Muhit Islam Cheung, Yat Fei Stoll, James Rumi, Monjura Afrin Brown, Connor Choi, Joung Min Moumi, Nazifa Ahmed Ahmed, Shafayat Song, Haoqiu Sein, Justin Yao, Shunyu Khan, Ahmad Gupta, Suraj Kulkarni, Rutwik Butt, Ali Vikesland, Peter Pruden, Amy Zhang, Liqing Department of Computer Science Virginia Tech Blacksburg 24060 VA United States Fralin Life Science Institute Virginia Tech Blacksburg 24060 VA United States Department of Civil and Environmental Engineering Virginia Tech Blacksburg 24060 VA United States The Interdisciplinary PhD Program in Genetics Bioinformatics and Computational Biology Virginia Tech Blacksburg 24060 VA United States

The rise of antibiotic resistance (AR) poses a substantial threat to human and animal health, food security, and economic stability. Wastewater-based surveillance (WBS) has emerged as a powerful strategy for population-level AR monitoring, providing valuable data to guide public health and policy decisions. Metagenomic sequencing is especially promising, as it can yield comprehensive profiles of antibiotic resistance genes (ARGs) and other genes relevant to AR in a single run. However, online analytical platforms to facilitate analysis of longitudinal metagenomic data are lacking. To address this, we introduce CyberInfrastructure for Waterborne Antibiotic Resistance Surveillance (CIWARS), a web server configured for characterizing key AR trends from longitudinal metagenomic WBS data. CIWARS offers comprehensive profiling of ARGs and taxonomic profiling of pathogen-associated bacterial taxonomic groups, identifies potential associations of ARGs with mobile genetic elements (MGEs) and pathogen-containing taxa, and assesses resistome risk based on the co-occurrence of ARGs, MGEs, and pathogen-like sequences. Additionally, it detects anomalous AR indicators over time, aiding in identifying potential events of concern, such as the emergence of resistant strains or outbreaks. Through interactive temporal data visualization, CIWARS enables AR monitoring and can serve as a tool to inform effective and timely interventions to mitigate the spread and transmission of AR. Here, CIWARS is demonstrated using longitudinal metagenomic data from a wastewater treatment plant (WWTP) influent and effluent, but it can be extended to any environment. CIWARS provides a valuable tool to support global efforts to combat the evolution and spread of AR, while also guiding agricultural and public health efforts aimed at optimizing antibiotic use. The web server is freely available at https://***/. © 2025 Elsevier Ltd

关键词： anomaly detection antibiotic resistance (AR) AR indicator profiling longitudinal metagenomics wastewater-based surveillance

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Elastic restricted Boltzmann machines for cancer data analysis

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Frontiers of Electrical and Electronic Engineering in China 2017年第2期5卷 159-172页

作者： Sai Zhang Muxuan Liang Zhongjun Zhou Chen Zhang Ning Chen Ting Chen Jianyang Zeng Institute for Interdisciplinary Information Sciences Tsinghua University Beijing 100084 China Department of Statistics University of Wisconsin-Madison Madison WI 53706-1685 USA Bioinformatics Division TNLIST Department of Computer Science and Technology Tsinghua University Beijing 100084 China Program in Computational Biology and Bioinformatics Univesity of Southern California Los Angeles CA 90089 USA

Background： Restricted Boltzmann machines （RBMs） are endowed with the universal power of modeling （binary） joint distributions. Meanwhile, as a result of their confining network structure, training RBMs confronts less difficulties when dealing with approximation and inference issues. But little work has been developed to fully exploit the capacity of these models to analyze cancer data, e.g., cancer genomic, transcriptomic, proteomic and epigenomic data. On the other hand, in the cancer data analysis task, the number of features/predictors is usually much larger than the sample size, which is known as the ＇~ 〉〉 N＂ problem and is also ubiquitous in other bioinformatics and computational biology fields. The ＂p 〉〉 N＂ problem puts the bias-variance trade-off in a more crucial place when designing statistical learning methods. However, to date, few RBM models have been particularly designed to address this issue. Methods： We propose a novel RBMs model, called elastic restricted Boltzmann machines （eRBMs）, which incorporates the elastic regularization term into the likelihood function, to balance the model complexity and sensitivity. Facilitated by the classic contrastive divergence （CD） algorithm, we develop the elastic contrastive divergence （eCD） algorithm which can train eRBMs efficiently. Results： We obtain several theoretical results on the rationality and properties of our model. We further evaluate the power of our model based on a challenging task -- predicting dichotomized survival time using the molecular profiling of tumors. The test results show that the prediction performance of eRBMs is much superior to that of the state-of-the-art methods. Conclusions： The proposed eRBMs are capable of dealing with the ＂p 〉〉 N＂ problems and have superior modeling performance over traditional methods. Our novel model is a promising method for future cancer data analysis.

关键词： RBMs regularization cancer data analysis survival time prediction

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Refinement of under-determined loops of human prion protein by database-derived distance constraints

Refinement of under-determined loops of human prion protein ...

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IEEE International Conference on bioinformatics and Biomedicine Workshops (BIBMW)

作者： Feng Cui Kriti Mukhopadhyay Won-Bin Young Robert L. Jernigan Zhijun Wu Program on Bioinformatics and Computational Biology Department of Mathematics USA University Research Park Ames IA USA Department of Biochemistry Biophysics and Molecular Biology Iowa State University USA Program on Bioinformatics and Computational Biology USA

computational simulations of the conversion from the normal cellular prion (PrP c ) to the scrapie prion (PrP Sc ) are usually based on the structures determined by NMR because of the difficulties in crystallizing prion protein. Due to insufficient experimental restraints, a biologically critical loop region in PrP c (residues 167-171), which is a potential binding site for Protein X, is under-determined in most mammalian species. Here, we show that by adding information about distance constraints derived from a database of high-resolution protein structures, this under-determined loop as well as other secondary structural elements of the E200K variant of human prion protein (hPrP c ), a disease-related isoform, can be refined into more realistic structures in the structural ensemble with improved quality and increased accuracy. In particular, the ensemble becomes more compact after the refinement and the percentage of residues in the most favourable region of the Ramachandran diagram is increased to about 90% in the refined structures from the 80 to 85% range in the previously reported structures. Our results not only provide significantly improved structures of the prion protein and hence would facilitate insights into its conversion in the spongiform encephalopathies, but also demonstrate the strong potential for using databases of known protein structures for structure determination and refinement.

关键词： Humans Proteins Databases Diseases Nuclear magnetic resonance Genetic mutations computational biology Brain Pathogens Amino acids

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Analysis of Protein Protein Dimeric Interfaces

Analysis of Protein Protein Dimeric Interfaces

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IEEE International Conference on bioinformatics and Biomedicine (BIBM)

作者： Feihong Wu Fadi Towfic Drena Dobbs Vasant Honavar Bioinformatics and Computational Biology Graduate Program Iowa State University Ames IA USA

We analyzed the structural properties and the local surface environment of surface amino acid residues of proteins using a large, non-redundant dataset of 2383 protein chains in dimeric complexes from PDB. We compared the interface residues and non-interface residues based on six properties: side chain orientation, surface roughness, solid angle, ex value, hydrophobicity and interface cluster size. The results of our analysis show that interface residues have side chains pointing inward; interfaces are rougher, tend to be flat, moderately convex or concave and protrude more relative to non-interface surface residues. Interface residues tend to be surrounded by hydrophobic neighbors and tend to form clusters consisting of three or more interfaces residues. These findings are consistent with previous published studies using much smaller datasets, while allowing for more qualitative conclusions due to our larger dataset. Preliminary results suggest the possibility of using the six the properties to identify putative interface residues.

关键词： Proteins Rough surfaces Surface roughness bioinformatics Amino acids Solids Sequences Biomedical computing Computer interfaces Biomedical measurements

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Robocop: Multivariate state space model integrating epigenomic accessibility data to elucidate genome-wide chromatin occupancy 24th

Robocop: Multivariate state space model integrating epigenom...

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24th Annual Conference on Research in computational Molecular biology, RECOMB 2020

作者： Mitra, Sneha Zhong, Jianling MacAlpine, David M. Hartemink, Alexander J. Department of Computer Science Duke University DurhamNC27708 United States Program in Computational Biology and Bioinformatics Duke University DurhamNC27708 United States Department of Pharmacology and Cancer Biology Duke University Medical Center DurhamNC27710 United States Center for Genomic and Computational Biology Duke University DurhamNC27708 United States

ISBN: (纸本)9783030452568

Chromatin is the tightly packaged structure of DNA and protein within the nucleus of a cell. The arrangement of different protein complexes along the DNA modulates and is modulated by gene expression. Measuring the binding locations and level of occupancy of different transcription factors (TFs) and nucleosomes is therefore crucial to understanding gene regulation. Antibody-based methods for assaying chromatin occupancy are capable of identifying the binding sites of specific DNA binding factors, but only one factor at a time. On the other hand, epigenomic accessibility data like ATAC-seq, DNase-seq, and MNase-seq provide insight into the chromatin landscape of all factors bound along the genome, but with minimal insight into the identities of those factors. Here, we present RoboCOP, a multivariate state space model that integrates chromatin information from epigenomic accessibility data with nucleotide sequence to compute genome-wide probabilistic scores of nucleosome and TF occupancy, for hundreds of different factors at once. RoboCOP can be applied to any epigenomic dataset that provides quantitative insight into chromatin accessibility in any organism, but here we apply it to MNase-seq data to elucidate the protein-binding landscape of nucleosomes and 150 TFs across the yeast genome. Using available protein-binding datasets from the literature, we show that our model more accurately predicts the binding of these factors genome-wide. © Springer Nature Switzerland AG 2020.

关键词： Hidden Markov models

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ExpHTS: Analysis of high throughput sequence data in an experimental framework 15

ExpHTS: Analysis of high throughput sequence data in an expe...

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6th ACM Conference on bioinformatics, computational biology, and Health Informatics, BCB 2015

作者： Streett, David A. Petersen, Kristen R. Gerritsen, Alida T. Hunter, Samuel S. Settles, Matthew L. Program in Bioinformatics and Computational Biology University of Idaho MoscowID83844-3051 United States Institute for Bioinformatics and Evolutionary Studies University of Idaho MoscowID83844-3051 United States Department of Biological Sciences University of Idaho MoscowID83844-3051 United States

ISBN: (纸本)9781450338530

Analysis time and storage space are critical bottlenecks when analyzing multi-sample, comparative high throughput sequence experiments. The experimental High Throughput Sequencing (expHTS) pipeline mitigates these bottlenecks by streaming external application data, speeding analysis, and reducing storage space by eliminating intermediate, un-analyzed files. This creates faster run times, less storage space on the machine, and improves uniformity of sample analysis. Copyright 2015 ACM.

关键词： Pipelines

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Super deduper, fast PCR duplicate detection in fastq files. 15

Super deduper, fast PCR duplicate detection in fastq files.

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6th ACM Conference on bioinformatics, computational biology, and Health Informatics, BCB 2015

作者： Petersen, Kristen R. Streett, David A. Gerritsen, Alida T. Hunter, Samuel S. Settles, Matthew L. Program in Bioinformatics and Computational Biology University of Idaho MoscowID83844-3051 United States Institute for Bioinformatics and Evolutionary Studies University of Idaho MoscowID83844-3051 United States Department of Biological Sciences University of Idaho MoscowID83844-3051 United States

ISBN: (纸本)9781450338530

Our goal was to explore the accuracy and utility of identifying and removing PCR duplicates from HTS data using Super Deduper. Super Deduper is a pre-alignment, sequence read based technique developed at the University of Idaho, which examines and uses only a small portion of each read's sequence in order to identify and remove PCR and/or optical duplicates. Through comparisons with well-known pre and post-alignment techniques, Super Deduper's parameters were optimized and its performance assessed. The results conclude that Super Deduper is a viable prealignment alternative to post-alignment techniques. Super Deduper is both independent of a reference genome and choice in alignment application, allowing for its use in a greater variety of HTS applications. Super Deduper is an open source application and can be found at https://***/dstreett/Super-Deduper. Copyright 2015 ACM.

关键词： Alignment

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Transcriptomic and Epigenomic Signatures of Liver Metabolism and Insulin Sensitivity in Aging Mice

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Mechanisms of ageing and development 2025年 225卷 112068页

作者： John T González Olivia H Scharfman Wanling Zhu Jessica Kasamoto Victoria Gould Rachel J Perry Albert T Higgins-Chen Department of Pathology Yale University School of Medicine New Haven CT USA. Department of Cellular & Molecular Physiology Yale School of Medicine New Haven CT USA Department of Endocrinology & Metabolism Yale School of Medicine New Haven CT USA. Program in Computational Biology and Bioinformatics Yale University New Haven CT USA. Altos Labs Institute of Computation San Diego CA 92114 USA. Department of Endocrinology & Metabolism Yale School of Medicine New Haven CT USA. Electronic address: rachel.perry@yale.edu. Department of Pathology Yale University School of Medicine New Haven CT USA Program in Computational Biology and Bioinformatics Yale University New Haven CT USA Department of Psychiatry Yale School of Medicine New Haven CT USA. Electronic address: a.higginschen@yale.edu.

Age-related declines in insulin sensitivity and glucose metabolism contribute to metabolic disease. Despite the liver's central role in glucose homeostasis, a comprehensive phenotypic characterization and concurrent molecular analysis of insulin resistance and metabolic dysfunction in the aging liver is lacking. We characterized hepatic insulin resistance and mitochondrial metabolic defects through metabolic cage, hyperinsulinemic-euglycemic clamp, and tracer studies paired with transcriptomic and DNA methylation analyses in young and aged male mice. Aged mice exhibited benchmark measures of whole body and liver insulin resistance. Aged mice showed lower pyruvate dehydrogenase flux, decreased fatty acid oxidation and citrate synthase fluxes, and increased pyruvate carboxylase flux under insulin-stimulated conditions. Molecular analysis revealed age-related changes in metabolic genes Pck1, Socs3, Tbc1d4, and Enpp1. Unsupervised network analysis identified an intercorrelated phenotype module (ME-Glucose), RNA module, and DNA methylation module. The DNA methylation module was enriched for lipid metabolism pathways and TCF-1 binding, while the RNA module was enriched for MZF-1 binding and regulation by miR-155-5p. Protein-protein interaction network analysis revealed interactions between module genes and canonical metabolic pathways, highlighting genes including Ets1, Ppp1r3b, and Enpp3. This study reveals novel genes underlying age-related hepatic insulin resistance as potential targets for metabolic interventions to promote healthy aging.

关键词： DNA methylation Insulin resistance Metabolic flux analysis Mitochondrial dysfunction Tricarboxylic acid (TCA) cycle

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