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Comparative analysis of gene regulation in single cells using Compass

Cell Reports Methods 2025年第5期5卷 101035页

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

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. © 2025 The Author(s)

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

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A normalization method for multiple reaction monitoring (MRM) data

A normalization method for multiple reaction monitoring (MRM...

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IEEE International Conference on bioinformatics and Biomedicine

作者： Jungsoo Gim Taesung Park Interdisciplinary Program for Bioinformatics and Department of Statistics Seoul National University

ISBN: (纸本)9781479956708

High accuracy and reproducibility provided by selected or multiple reaction monitoring (SRM/MRM) experiments suggest that it is likely to become the platform of choice for identifying and verifying candidate biomarkers. Although several methods have been developed to quantify and discover the significant changes in SRM/MRM expression, we show normalization continues to be an essential step in the differential expression analysis. Here, we first investigate what makes normalization across the samples difficult in SRM assay. Then, we suggest a simple but effective normalization method to overcome the difficulty. Both real and simulated data sets are examined and improved results, compared to an existing method, are shown for inferring differential expression.

关键词： Proteomics Selected reaction monitoring (SRM) Normalization Differential expression test

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Prediction of enzymatic activity of proteins based on structural and functional domains

Prediction of enzymatic activity of proteins based on struct...

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IEEE Symposium on bioinformatics and Bioengineering (BIBE)

作者： Theodoros G. Koutsandreas Eleftherios D. Pilalis Aristotelis A. Chatziioannou Metabolic Engineering and Bioinformatics Program Institute of Biology Athens Greece

The prediction of the putative enzymatic function of uncharacterized proteins is a major problem in the field of metagenomic research, where large amounts of sequences can be rapidly determined. In this work a machine-learning approach was developed, that attempts the prediction of enzymatic activity based on three protein domain databases, PFAM, CATH and SCOP, which contain functional and structural information of proteins as Hidden Markov Models. Separate and combined classifiers were trained by well-annotated data and their performance was assessed in order to compare the predictive power of different attribute sets corresponding to the three protein domain databases. All classifiers performed well, with an average accuracy of ~96% and an average AUC score of 0.84. As a conclusion, the classification procedure can be integrated to more extended metagenomic analysis workflows.

关键词： Proteins Hidden Markov models Training Databases Accuracy Biotechnology

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Prioritized functional analysis of biological experiments using resampling and noise control methodologies

Prioritized functional analysis of biological experiments us...

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IEEE Symposium on bioinformatics and Bioengineering (BIBE)

作者： Eleftherios D. Pilalis Aristotelis A. Chatziioannou Metabolic Engineering and Bioinformatics Program Institute of Biology Greece Athens

StRAnGER is a web application for the automated statistical analysis of annotated experiments, exploiting controlled biological vocabularies, like the Gene Ontology or the KEGG pathways terms. In the first version, StRAnGER featured various gene profiling platforms for functional analysis of genomic datasets, starting from a list of significant genes derived from statistical and empirical thresholds. In the current version, various major improvements have been implemented, namely a new ranking algorithm, the expansion of background distributions with protein annotations, the addition of a mode for batch experiments and a noise-control analysis that evaluates the robustness of the prioritized terms through iterative addition of random genes. Overall, StRAnGER enables a systems level functional interpretation through the utilization of bootstrapping techniques and the detection of distribution-independent term enrichments.

关键词： Finite element analysis Noise Proteins Ontologies Robustness Vocabulary

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A teragrid-enabled distributed discrete event agent-based epidemiological simulation 07

A teragrid-enabled distributed discrete event agent-based ep...

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Simulation Winter Conference

作者： Douglas J. Roberts Digho A. Simoni Bioinformatics Program RTI International Inc. NC USA

ISBN: (纸本)9781424413065

We discuss design issues related to the transformation of a mature Agent-Based Model (ABM) for computational epidemiology into a "grid-aware" version. EpiSims is a distributed discrete event ABM that has been in production for nearly a decade. Working under a grant from the National Science Foundation and the NIH (NIGMS) funded MIDAS project, we are reengineering EpiSims to run as a single job on multiple Linux clusters on the NSF TeraGrid.

关键词： Discrete event simulation Geophysics computing Computational modeling Grid computing Clustering algorithms Linux Engines Influenza bioinformatics Production

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Phytohormone signaling pathway analysis method for comparing hormone responses in plant-pest interactions

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BMC Research Notes 2012年第1期5卷 1-7页

作者： Studham, Matthew E. MacIntosh, Gustavo C. Bioinformatics and Computational Biology Program Iowa State University Ames IA 50011 United States Department of Biochemistry Biophysics and Molecular Biology Iowa State University Ames IA 50011 United States

Background: Phytohormones mediate plant defense responses to pests and pathogens. In particular, the hormones jasmonic acid, ethylene, salicylic acid, and abscisic acid have been shown to dictate and fine-tune defense responses, and identification of the phytohormone components of a particular defense response is commonly used to characterize it. Identification of phytohormone regulation is particularly important in transcriptome analyses. Currently there is no computational tool to determine the relative activity of these hormones that can be applied to transcriptome analyses in soybean. Findings: We developed a pathway analysis method that provides a broad measure of the activation or suppression of individual phytohormone pathways based on changes in transcript expression of pathway-related genes. The magnitude and significance of these changes are used to determine a pathway score for a phytohormone for a given comparison in a microarray experiment. Scores for individual hormones can then be compared to determine the dominant phytohormone in a given defense response. To validate this method, it was applied to publicly available data from previous microarray experiments that studied the response of soybean plants to Asian soybean rust and soybean cyst nematode. The results of the analyses for these experiments agreed with our current understanding of the role of phytohormones in these defense responses. Conclusions: This method is useful in providing a broad measure of the relative induction and suppression of soybean phytohormones during a defense response. This method could be used as part of microarray studies that include individual transcript analysis, gene set analysis, and other methods for a comprehensive defense response characterization. © 2012 Studham and MacIntosh;licensee BioMed Central Ltd.

关键词： Salicylic Acid Jasmonic Acid Soybean Cyst Nematode Soybean Aphid Salicylic Acid Pathway

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Elongation factor-G1A identified as a novel effector protein translocated into cells and a key modulator of Pseudomonas aeruginosa physiology and host cellular responses

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Microbial Pathogenesis 2025年 205卷 107720页

作者： Yeji Lee Yongxin Jin Weihui Wu Un-Hwan Ha Department of Biotechnology and Bioinformatics Korea University Sejong 30019 Republic of Korea State Key Laboratory of Medicinal Chemical Biology Key Laboratory of Molecular Microbiology and Technology of the Ministry of Education Department of Microbiology Nankai University Tianjin 300071 China Interdisciplinary Graduate Program for Artificial Intelligence Smart Convergence Technology Korea University Sejong 30019 Republic of Korea

Pseudomonas aeruginosa has two closely related elongation factors, EF-G1A and EF-G1B, which share 90 % sequence similarity. Despite their high sequence homology, the role of EF-G in P. aeruginosa pathogenesis remains not fully understood. In our study, we found that compared to EF-G1B, EF-G1A expression reduced bacterial growth and twitching motility, while increasing swimming motility. Notably, EF-G1A was translocated into host cells in a T6SS-dependent manner. This translocation was significantly reduced, though not completely abolished, in strains with mutations in both the T6SS spike protein VgrG1a and the tube protein Hcp1, suggesting that while EF-G1A translocation is influenced by T6SS, additional components are also involved. Moreover, EF-G1A expression reduced T3SS-mediated morphological alterations, as evidenced by the downregulation of T3SS effectors such as ExoS and ExoT. EF-G1A was also found to suppress activation of the NF-κB signaling pathway, leading to decreased production of inflammatory cytokines including IL-6, IL-8, and TNFα. These findings highlight EF-G1A as a key modulator, affecting both P. aeruginosa physiology and host cellular responses, thereby providing new insights into the complex role of EF-G1A in bacterial pathogenesis.

关键词： Elongation factor-G1A Pseudomonas aeruginosa T3SS T6SS Translocation

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Retroactivity Affects the Adaptive Robustness of Transcriptional Regulatory Networks

Retroactivity Affects the Adaptive Robustness of Transcripti...

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American Control Conference

作者： Junmin Wang Calin Belta Bioinformatics Graduate Program Boston University Boston MA 02215 USA Division of Systems Engineering and the Bioinformatics Graduate Program Boston University Boston MA 02215 USA

ISBN: (纸本)9781538679012;9781538679265

Adaptation refers to the system's ability to respond transiently to an input signal and subsequently recover to the initial states. Adaptive robustness, the ability of a network to achieve adaptation, is subject to the loading effects arising from modular interconnections, known as retroactivity. Studying the effects of retroactivity on adaptive robustness facilitates the employment of retroactivity to improve circuit performance in synthetic biology. In this paper, we developed a framework for quantifying adaptive robustness via statistical model checking and used this framework to investigate the effects of retroactivity on adaptive robustness. We found that increasing retroactivity tends to raise adaptive robustness in networks where the output protein does not perform regulatory functions, such as incoherent feedforward loops.

关键词： Self tuning input signal Robustness Circuit optimization Model checking Frameworks synthetic biology Network Gene Regulatory Networks Regulatory

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Literature based Bayesian analysis of gene expression data

Literature based Bayesian analysis of gene expression data

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IEEE International Conference on bioinformatics and Biomedicine Workshops (BIBMW)

作者： Lijing Xu Ramin Homayouni E. Olusegun George Bioinformatics Program University of Memphis Memphis USA Bioinformatics Program Department of Biological Sciences University of Memphis Memphis USA Department of Mathematical Sciences University of Memphis USA

Recent research has focused on incorporating biological function and pathway information into the analysis of gene expression data, partly as a means of compensating for insufficient experimental replications, low signal to noise, lack of reproducibility and/or multiple testing confounds. A Bayesian approach seems to be ideal for incorporating functional information into gene expression data analysis. In this study, we tested the feasibility of using literature derived gene relationships in a Bayesian model to analyze gene expression data. Prior distributions were constructed based on gene associations derived from the biomedical literature using Latent Semantic Indexing (LSI). The LSI model was built using more than 1 million Medline abstracts corresponding to 22,000 human and mouse genes. A key advantage of LSI is that both explicit and implicit gene relationships can be derived from the literature. Gene neighborhoods were determined using latent Gaussian Markov random fields and logistic transformation of the latent variables. We tested the procedure on a microarray dataset for interferon-stimulated genes in mouse embryonic fibroblasts. By integrating functional information from literature, Bayesian approach identified relevant genes that previously did not meet the 0.05 significance level. In comparison to a standard mixture model, spatial mixture model has more power for identifying direct and indirect interferon regulated genes. The spatial model enhanced the ranks of some genes which are known to be affected by interferon treatment, such as Nmi (NMI N-myc and STAT interactor) and ifi35 (interferon-induced protein 35). It also identified some genes that previously were ignored because of the marginal p-values, such as dpysl2, map2k1, msn, Psck5, and Il6st. Interestingly, these genes appear to be indirectly related to interferon treatment. In summary, we show that our procedure increases statistical power and produces more biologically meaningful gene lists. Th

关键词： Bayesian methods Gene expression bioinformatics Large scale integration Educational institutions Indexing

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An epigenetic clock for Xenopus tropicalis

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npj Aging 2025年第1期11卷 1-9页

作者： Ronan Bennett Marco Morselli Kseniya Petrova Leonid Peshkin Matteo Pellegrini Bioinformatics Interdepartmental Program University of California Los Angeles Los Angeles CA USA Department of Chemistry Life Sciences and Environmental Sustainability University of Parma Parma Italy Department of Systems Biology Harvard Medical School Boston MA USA Department of Molecular Cell and Developmental Biology University of California Los Angeles Los Angeles CA USA

DNA methylation clocks have been widely used for accurate age prediction, but most studies have been carried out on mammals. Here we present an epigenetic clock for the aquatic frogXenopus tropicalis, a widely used model organism in developmental biology and genomics. To construct the clock, we collected DNA methylation data from 192 frogs using targeted bisulfite sequencing at genomic regions containing CpG sites previously shown to have age-associated methylation inXenopus. We found highly positively and negatively age-correlated CpGs are enriched in heterochromatic regions marked with H4K20me3 and H3K9me3. Positively age-correlated CpGs are enriched in bivalent chromatin and gene bodies with H3K36me3, and tend to be proximal to lowly expressed genes. These epigenetic features of aging are similar to those found in mammals, suggesting evolutionary conservation of epigenetic aging mechanisms. Our clock enables future aging biology experiments that leverage the unique properties of amphibians.

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