• 제목/요약/키워드: pathway analysis

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HisCoM-PCA: software for hierarchical structural component analysis for pathway analysis based using principal component analysis

  • Jiang, Nan;Lee, Sungyoung;Park, Taesung
    • Genomics & Informatics
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    • 제18권1호
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    • pp.11.1-11.3
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    • 2020
  • In genome-wide association studies, pathway-based analysis has been widely performed to enhance interpretation of single-nucleotide polymorphism association results. We proposed a novel method of hierarchical structural component model (HisCoM) for pathway analysis of common variants (HisCoM for pathway analysis of common variants [HisCoM-PCA]) which was used to identify pathways associated with traits. HisCoM-PCA is based on principal component analysis (PCA) for dimensional reduction of single nucleotide polymorphisms in each gene, and the HisCoM for pathway analysis. In this study, we developed a HisCoM-PCA software for the hierarchical pathway analysis of common variants. HisCoM-PCA software has several features. Various principle component scores selection criteria in PCA step can be specified by users who want to summarize common variants at each gene-level by different threshold values. In addition, multiple public pathway databases and customized pathway information can be used to perform pathway analysis. We expect that HisCoM-PCA software will be useful for users to perform powerful pathway analysis.

Iterative integrated imputation for missing data and pathway models with applications to breast cancer subtypes

  • Linder, Henry;Zhang, Yuping
    • Communications for Statistical Applications and Methods
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    • 제26권4호
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    • pp.411-430
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    • 2019
  • Tumor development is driven by complex combinations of biological elements. Recent advances suggest that molecularly distinct subtypes of breast cancers may respond differently to pathway-targeted therapies. Thus, it is important to dissect pathway disturbances by integrating multiple molecular profiles, such as genetic, genomic and epigenomic data. However, missing data are often present in the -omic profiles of interest. Motivated by genomic data integration and imputation, we present a new statistical framework for pathway significance analysis. Specifically, we develop a new strategy for imputation of missing data in large-scale genomic studies, which adapts low-rank, structured matrix completion. Our iterative strategy enables us to impute missing data in complex configurations across multiple data platforms. In turn, we perform large-scale pathway analysis integrating gene expression, copy number, and methylation data. The advantages of the proposed statistical framework are demonstrated through simulations and real applications to breast cancer subtypes. We demonstrate superior power to identify pathway disturbances, compared with other imputation strategies. We also identify differential pathway activity across different breast tumor subtypes.

Integration of a Large-Scale Genetic Analysis Workbench Increases the Accessibility of a High-Performance Pathway-Based Analysis Method

  • Lee, Sungyoung;Park, Taesung
    • Genomics & Informatics
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    • 제16권4호
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    • pp.39.1-39.3
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    • 2018
  • The rapid increase in genetic dataset volume has demanded extensive adoption of biological knowledge to reduce the computational complexity, and the biological pathway is one well-known source of such knowledge. In this regard, we have introduced a novel statistical method that enables the pathway-based association study of large-scale genetic dataset-namely, PHARAOH. However, researcher-level application of the PHARAOH method has been limited by a lack of generally used file formats and the absence of various quality control options that are essential to practical analysis. In order to overcome these limitations, we introduce our integration of the PHARAOH method into our recently developed all-in-one workbench. The proposed new PHARAOH program not only supports various de facto standard genetic data formats but also provides many quality control measures and filters based on those measures. We expect that our updated PHARAOH provides advanced accessibility of the pathway-level analysis of large-scale genetic datasets to researchers.

대규모 유전자 분석 기법을 이용한 육미지황원의 유전자 발현 연구 (Studies on Gene Expression of Yukmijihwang-tang using High-throughput Gene Expression Analysis Techniques)

  • 강봉주;김윤택;조동욱
    • 한국한의학연구원논문집
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    • 제8권2호통권9호
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    • pp.95-107
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    • 2002
  • Yukmijihwang-tang(YM) is a noted herbal prescription in Chinese and Korean traditional medicines, and it has been known to reinforce the vital essence and has been widely used for a variety of disease such as stroke, osteoporosis, anti-tumor, and hypothyrodism. Regarding its traditional use, YM has been known to reinforce the Yin (vital essence) of liver and kidney. Also it has been known to reinforce nutrition and biological function in brain. Recently, studies suggested that YM increase antioxidant activities and exert the protective effect against oxidant-induced liver cell injury. We investigated the high-throughput gene expression analysis on the Yukmijihwang-tang administrated in SD rats. Microarray data were validated on a limited number of genes by semiquantitative RT-PCR and Western blot analyses. The recent availability of microarrays provides an attractive strategy for elaborating an unbiased molecular profile of large number of genes in drug discovery This experimental approach offers the potential to identify molecules or cellular pathways not previously associated with herbal medicine. Total RNA from normal control brain and Yukmijihwang-tang administrated brain were hybridized to microarrays containing 10,000 rat genes. The 52 genes were found to be up-regulated(twice or more) excluding EST gene. The nine genes were found to be down-regulated(twice or more) excluding EST gene. Gene array technology was used to identify for the first time many genes expression pathway analysis that arecell cycle pathway, apoptosis pathway, electron transport chain pathway, cytoplasmic ribosomal protein pathway, fatty acid degradation pathway, and TGF-beta signaling pathway. These differentially expressed genes pathway analysis have not previously been iavestigated in the context of herbal medicine efficacy and represent novel factors for further study of the mechanism of herbal medicine efficacy.

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비선형시스템 관점으로부터 세포 신호전달경로의 동역학 분석 (Dynamical Analysis of Cellular Signal Transduction Pathways with Nonlinear Systems Perspectives)

  • 김현우;조광현
    • 제어로봇시스템학회논문지
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    • 제10권12호
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    • pp.1155-1163
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    • 2004
  • Extracellular signal-regulated kinase (ERK) signaling pathway is one of the mitogen-activated protein kinase (MAPK) signal transduction pathways. This pathway is known as pivotal in many signaling networks that govern proliferation, differentiation and cell survival. The ERK signaling pathway comprises positive and negative feedback loops, depending on whether the terminal kinase stimulates or inhibits the activation of the initial level. In this paper, we attempt to model the ERK pathway by considering both of the positive and negative feedback mechanisms based on Michaelis-Menten kinetics. In addition, we propose a fraction ratio model based on the mass action law. We first develop a mathematical model of the ERK pathway with fraction ratios. Secondly, we analyze the dynamical properties of the fraction ratio model based on simulation studies. Furthermore, we propose a concept of an inhibitor, catalyst, and substrate (ICS) controller which regulates the inhibitor, catalyst, and substrate concentrations of the ERK signal transduction pathway. The ICS controller can be designed through dynamical analysis of the ERK signaling transduction pathway within limited concentration ranges.

Pathway Retrieval for Transcriptome Analysis using Fuzzy Filtering Technique andWeb Service

  • Lee, Kyung-Mi;Lee, Keon-Myung
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • 제12권2호
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    • pp.167-172
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    • 2012
  • In biology the advent of the high-throughput technology for sequencing, probing, or screening has produced huge volume of data which could not be manually handled. Biologists have resorted to software tools in order to effectively handle them. This paper introduces a bioinformatics tool to help biologists find potentially interesting pathway maps from a transcriptome data set in which the expression levels of genes are described for both case and control samples. The tool accepts a transcriptome data set, and then selects and categorizes some of genes into four classes using a fuzzy filtering technique where classes are defined by membership functions. It collects and edits the pathway maps related to those selected genes without analyst' intervention. It invokes a sequence of web service functions from KEGG, which an online pathway database system, in order to retrieve related information, locate pathway maps, and manipulate them. It maintains all retrieved pathway maps in a local database and presents them to the analysts with graphical user interface. The tool has been successfully used in identifying target genes for further analysis in transcriptome study of human cytomegalovirous. The tool is very helpful in that it can considerably save analysts' time and efforts by collecting and presenting the pathway maps that contain some interesting genes, once a transcriptome data set is just given.

HisCoM-PAGE: software for hierarchical structural component models for pathway analysis of gene expression data

  • Mok, Lydia;Park, Taesung
    • Genomics & Informatics
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    • 제17권4호
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    • pp.45.1-45.3
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    • 2019
  • To identify pathways associated with survival phenotypes using gene expression data, we recently proposed the hierarchical structural component model for pathway analysis of gene expression data (HisCoM-PAGE) method. The HisCoM-PAGE software can consider hierarchical structural relationships between genes and pathways and analyze multiple pathways simultaneously. It can be applied to various types of gene expression data, such as microarray data or RNA sequencing data. We expect that the HisCoM-PAGE software will make our method more easily accessible to researchers who want to perform pathway analysis for survival times.

Pathway Analysis of Metabolic Syndrome Using a Genome-Wide Association Study of Korea Associated Resource (KARE) Cohorts

  • Shim, Unjin;Kim, Han-Na;Sung, Yeon-Ah;Kim, Hyung-Lae
    • Genomics & Informatics
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    • 제12권4호
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    • pp.195-202
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    • 2014
  • Metabolic syndrome (MetS) is a complex disorder related to insulin resistance, obesity, and inflammation. Genetic and environmental factors also contribute to the development of MetS, and through genome-wide association studies (GWASs), important susceptibility loci have been identified. However, GWASs focus more on individual single-nucleotide polymorphisms (SNPs), explaining only a small portion of genetic heritability. To overcome this limitation, pathway analyses are being applied to GWAS datasets. The aim of this study is to elucidate the biological pathways involved in the pathogenesis of MetS through pathway analysis. Cohort data from the Korea Associated Resource (KARE) was used for analysis, which include 8,842 individuals (age, $52.2{\pm}8.9years$ ; body mass index, $24.6{\pm}3.2kg/m^2$). A total of 312,121 autosomal SNPs were obtained after quality control. Pathway analysis was conducted using Meta-analysis Gene-Set Enrichment of Variant Associations (MAGENTA) to discover the biological pathways associated with MetS. In the discovery phase, SNPs from chromosome 12, including rs11066280, rs2074356, and rs12229654, were associated with MetS (p < $5{\times}10^{-6}$), and rs11066280 satisfied the Bonferroni-corrected cutoff (unadjusted p < $1.38{\times}10^{-7}$, Bonferroni-adjusted p < 0.05). Through pathway analysis, biological pathways, including electron carrier activity, signaling by platelet-derived growth factor (PDGF), the mitogen-activated protein kinase kinase kinase cascade, PDGF binding, peroxisome proliferator-activated receptor (PPAR) signaling, and DNA repair, were associated with MetS. Through pathway analysis of MetS, pathways related with PDGF, mitogen-activated protein kinase, and PPAR signaling, as well as nucleic acid binding, protein secretion, and DNA repair, were identified. Further studies will be needed to clarify the genetic pathogenesis leading to MetS.

Reconstruction and Exploratory Analysis of mTORC1 Signaling Pathway and Its Applications to Various Diseases Using Network-Based Approach

  • Buddham, Richa;Chauhan, Sweety;Narad, Priyanka;Mathur, Puniti
    • Journal of Microbiology and Biotechnology
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    • 제32권3호
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    • pp.365-377
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    • 2022
  • Mammalian target of rapamycin (mTOR) is a serine-threonine kinase member of the cellular phosphatidylinositol 3-kinase (PI3K) pathway, which is involved in multiple biological functions by transcriptional and translational control. mTOR is a downstream mediator in the PI3K/Akt signaling pathway and plays a critical role in cell survival. In cancer, this pathway can be activated by membrane receptors, including the HER (or ErbB) family of growth factor receptors, the insulin-like growth factor receptor, and the estrogen receptor. In the present work, we congregated an electronic network of mTORC1 built on an assembly of data using natural language processing, consisting of 470 edges (activations/interactions and/or inhibitions) and 206 nodes representing genes/proteins, using the Cytoscape 3.6.0 editor and its plugins for analysis. The experimental design included the extraction of gene expression data related to five distinct types of cancers, namely, pancreatic ductal adenocarcinoma, hepatic cirrhosis, cervical cancer, glioblastoma, and anaplastic thyroid cancer from Gene Expression Omnibus (NCBI GEO) followed by pre-processing and normalization of the data using R & Bioconductor. ExprEssence plugin was used for network condensation to identify differentially expressed genes across the gene expression samples. Gene Ontology (GO) analysis was performed to find out the over-represented GO terms in the network. In addition, pathway enrichment and functional module analysis of the protein-protein interaction (PPI) network were also conducted. Our results indicated NOTCH1, NOTCH3, FLCN, SOD1, SOD2, NF1, and TLR4 as upregulated proteins in different cancer types highlighting their role in cancer progression. The MCODE analysis identified gene clusters for each cancer type with MYC, PCNA, PARP1, IDH1, FGF10, PTEN, and CCND1 as hub genes with high connectivity. MYC for cervical cancer, IDH1 for hepatic cirrhosis, MGMT for glioblastoma and CCND1 for anaplastic thyroid cancer were identified as genes with prognostic importance using survival analysis.

바이오 패스웨이 다차원 분석 시스템 개발 (Development of Multidimensional Analysis System for Bio-pathways)

  • 서동민;최윤수;전선희;이민호
    • 한국콘텐츠학회논문지
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    • 제14권11호
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    • pp.467-475
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    • 2014
  • 최근 유전체학의 발전, 웨어러블 디바이스의 확산, IT/NT의 발전 등에 따라 방대한 양의 바이오-메디컬 데이터가 생산되고, 이에 따라 빅데이터를 활용한 헬스케어 산업이 급속히 발달하고 있으며, 이와 관련된 빅데이터 기술은 국민의 건강 증대와 건강한 고령 삶을 제공하는 핵심 기술로 급부상하고 있다. 패스웨이(Pathway)는 단백질, 유전자, 세포 등의 생체적 요소 간의 역학관계 혹은 상호작용 등을 네트워크 형식으로 표현한 생물학적 심층지식으로, 바이오-메디컬 빅데이터 분석에 있어서 널리 활용되고 있다. 하지만 패스웨이는 매우 다양한 형태를 갖고 용량이 매우 큰 빅데이터로 이를 분석하는데 많은 시간이 소요되며, 현재까지도 다양한 패스웨이를 통합 분석할 수 있는 시스템은 전무하다. 그래서 본 논문에서는 세계적으로 가장 우수하고 방대한 양의 패스웨이를 제공하는 KEGG 패스웨이 데이터베이스로부터 사용자가 관심 갖는 패스웨이만을 자동 수집하고 패스웨이 간 계층구조를 기반으로 네트워크를 구성 후, 해당 패스웨이 네트워크에 대한 클러스터링과 핵심 패스웨이 선정을 통해 패스웨이 간의 역학관계 또는 상호작용을 직관적으로 분석할 수 시스템을 제안했다. 마지막으로, 다양한 성능 평가 결과를 통해 개발한 분석 시스템의 우수성을 입증한다.