PAGER Web APP: An Interactive, Online Gene Set and Network Interpretation Tool for Functional Genomics

Yue, Zongliang; Slominski, Radomir M.; Bharti, Samuel; Chen, Jake Y.

doi:10.3389/fgene.2022.820361

Cited by 5 publications

(4 citation statements)

References 61 publications

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“…To validate the patterns we identified by analyzing Mondrian Maps, we used a language model-based search engine called Perplexity AI (Perplexity AI, 2022). By querying the PAGER Web App (Yue et al, 2022) with specific PAG IDs, we obtained details about the pathways we were interested in. With this pathway information in hand, we then queried the search engine to explore any connections between the disease and these pathways.…”

Section: Methodsmentioning

confidence: 99%

Mondrian Abstraction and Language Model Embeddings for Differential Pathway Analysis

Al Abir,

Chen

2024

Preprint

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Motivation: Visualizing complex biological networks is a significant challenge, as traditional tools often struggle to represent data clearly and intuitively. Drawing inspiration from Piet Mondrian's abstract art style, we introduce Mondrian Map, a novel visualization tool that transforms the complexity of biological networks into a more organized, meaningful, and aesthetically appealing form. Results: In our case study of glioma progression, Mondrian Map reveals distinct pathway patterns across multiple patient profiles at different time points. Afterwards, the significance of these distinctive pathways in glioblastoma multiforme development is validated by recent literature. Mondrian Map's visually intuitive representation of complex biological networks enables researchers to easily identify crucial pathways and potential therapeutic targets. Moreover, The potential applications of Mondrian Map extend beyond biology, making it a versatile tool across domains. Availability and Implementation: The code was implemented in Python version 3.10 and is available through GitHub (github.com/fuad021/mondrian-map). The datasets used in this paper were retrieved from Glioma Longitudinal AnalySiS (GLASS) consortium.

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Section: Methodsmentioning

confidence: 99%

Mondrian Abstraction and Language Model Embeddings for Differential Pathway Analysis

Al Abir,

Chen

2024

Preprint

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“…The resulting networks were integrated with long noncoding RNAs (lncRNAs) and their proximal genes to identify the key pair relationships, which enabled the construction of lncRNA to messenger RNA (mRNA) interaction networks. BEERE can also be used to statistically evaluate the relevance of terms [105], which is an important application. For example, to validate the keywords of enriched pathways from a differentially expressed gene list in a particular disease such as "melanoma", users can leverage BEERE to assess the significance of the terms in the disease context.…”

Section: Beerementioning

confidence: 99%

“…Application: WIPER can be easily accessed using an API and is an effective method for evaluating network edge importance in any weighted network [117]. In the context of literature retrieval, WIPER uses the PubMed score as a measure of semantic validation [105]. The p-value evaluation in WIPER can be adapted to work with any ranking algorithm tools, such as the Polar Gini Curve [118].…”

Section: Wipermentioning

confidence: 99%

Biological Network Mining

Yue

Yan

Guo

et al. 2023

Biostatistics Research

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In this survey, we explore the latest methods and trends in constructing and mining biological networks. We delve into cutting-edge techniques such as weighted gene co-expression network analysis (WGCNA), step-level differential response (SLDR), Biomedical Entity Expansion, Ranking and Explorations (BEERE), Weighted In-Network Node Expansion and Ranking (WINNER), and Weighted In-Path Edge Ranking (WIPER) from the Bioinformatics community, as well as breakthroughs in graph mining methods like parallel subgraph mining systems, temporal graph algorithms, and deep learning. To ensure a solid foundation, we provide an introductory-level overview of six well-established network types in systems biology. In addition, we offer a concise and accessible overview of strategies for network construction, including gene co-expression networks (GCNs), gene regulatory networks (GRNs), and literature-mined biomedical networks. We explain biological network mining in interdisciplinary domains, catering to both biomedical researchers and data mining experts. Our goal is to provide a comprehensive guide that doesn't require a significant time investment. We believe that these current trends will help readers become familiar with the topic and the practical applications of these tools in real-world studies.

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“…To gain a profound understanding of the molecular mechanisms underpinning diseases or contributing to various disorders, comprehensive functional genome analysis has emerged, encompassing genomics, epigenomics, proteomics, and interactomics. In order to describe gene and protein functions under specific biological conditions or treatments, the integration of geneset, network, and pathway analysis (GNPA) has proven instrumental ( Yue et al, 2015 ; Yue et al, 2018 ; Yue et al, 2022 ). Pathway analysis, particularly topology-based approaches leveraging the knowledge about gene and protein interactions within pathways, has been developed to unveil mechanistic changes through pathway-level scoring and pathway significance assessment.…”

Section: Introductionmentioning

confidence: 99%

PAGER-scFGA: unveiling cell functions and molecular mechanisms in cell trajectories through single-cell functional genomics analysis

Huang,

Welner,

Chen

et al. 2024

Front. Bioinform.

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Background: Understanding how cells and tissues respond to stress factors and perturbations during disease processes is crucial for developing effective prevention, diagnosis, and treatment strategies. Single-cell RNA sequencing (scRNA-seq) enables high-resolution identification of cells and exploration of cell heterogeneity, shedding light on cell differentiation/maturation and functional differences. Recent advancements in multimodal sequencing technologies have focused on improving access to cell-specific subgroups for functional genomics analysis. To facilitate the functional annotation of cell groups and characterization of molecular mechanisms underlying cell trajectories, we introduce the Pathways, Annotated Gene Lists, and Gene Signatures Electronic Repository for Single-Cell Functional Genomics Analysis (PAGER-scFGA).Results: We have developed PAGER-scFGA, which integrates cell functional annotations and gene-set enrichment analysis into popular single-cell analysis pipelines such as Scanpy. Using differentially expressed genes (DEGs) from pairwise cell clusters, PAGER-scFGA infers cell functions through the enrichment of potential cell-marker genesets. Moreover, PAGER-scFGA provides pathways, annotated gene lists, and gene signatures (PAGs) enriched in specific cell subsets with tissue compositions and continuous transitions along cell trajectories. Additionally, PAGER-scFGA enables the construction of a gene subcellular map based on DEGs and allows examination of the gene functional compartments (GFCs) underlying cell maturation/differentiation. In a real-world case study of mouse natural killer (mNK) cells, PAGER-scFGA revealed two major stages of natural killer (NK) cells and three trajectories from the precursor stage to NK T-like mature stage within blood, spleen, and bone marrow tissues. As the trajectories progress to later stages, the DEGs exhibit greater divergence and variability. However, the DEGs in different trajectories still interact within a network during NK cell maturation. Notably, PAGER-scFGA unveiled cell cytotoxicity, exocytosis, and the response to interleukin (IL) signaling pathways and associated network models during the progression from precursor NK cells to mature NK cells.Conclusion: PAGER-scFGA enables in-depth exploration of functional insights and presents a comprehensive knowledge map of gene networks and GFCs, which can be utilized for future studies and hypothesis generation. It is expected to become an indispensable tool for inferring cell functions and detecting molecular mechanisms within cell trajectories in single-cell studies. The web app (accessible at https://au-singlecell.streamlit.app/) is publicly available.

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PAGER Web APP: An Interactive, Online Gene Set and Network Interpretation Tool for Functional Genomics

Cited by 5 publications

References 61 publications

Mondrian Abstraction and Language Model Embeddings for Differential Pathway Analysis

Mondrian Abstraction and Language Model Embeddings for Differential Pathway Analysis

Biological Network Mining

PAGER-scFGA: unveiling cell functions and molecular mechanisms in cell trajectories through single-cell functional genomics analysis

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