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Inverse Langmuir method for oligonucleotide microarray analysis

BMC BIOINFORMATICS 2009年第1期10卷 64-64页

作者： Mulders, Geert C. W. M. Barkema, Gerard T. Carlon, Enrico Katholieke Univ Leuven Inst Theoret Phys B-3000 Louvain Belgium Univ Utrecht Inst Theoret Phys NL-3584 CE Utrecht Netherlands

Background: An algorithm for the analysis of Affymetrix Genechips is presented. This algorithm, referred to as the Inverse Langmuir Method (ILM), estimates the binding of transcripts to complementary probes using DNA/RNA hybridization free energies, and the hybridization between partially complementary transcripts in solution using RNA/RNA free energies. The balance between these two competing reactions allows for the translation of background-subtracted intensities into transcript concentrations. Results: To validate the ILM, it is applied to publicly available microarray data from a multi-lab comparison study. Here, microarray experiments are performed on samples which deviate only in few genes. The log(2) fold change between these two samples, as obtained from RT-PCR experiments, agrees well with the log(2) fold change as obtained with the ILM, indicating that the ILM determines changes in the expression level accurately. We also show that the ILM allows for the identification of outlying probes, as it yields independent concentration estimates per probe. Conclusion: The ILM is robust and offers an interesting alternative to purely statistical algorithms for microarray data analysis.

关键词： Outlier Probe microarray data analysis Log2 Fold Change Median Absolute Deviation Complementary Probe

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Identification of key candidate genes and biological pathways in neuropathic pain

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COMPUTERS IN BIOLOGY AND MEDICINE 2022年 150卷 106135-106135页

作者： Cui, Chun-Yan Liu, Xiao Peng, Ming-Hui Liu, Qing Zhang, Ying Southwest Med Univ Dept Pain Hosp TCM Luzhou 646000 Sichuan Peoples R China Southwest Med Univ Dept Anesthesiol Hosp TCM Luzhou 646000 Sichuan Peoples R China Hejiang Tradit Chinese Med Hosp Luzhou 646000 Sichuan Peoples R China

Background: Neuropathic pain is a common chronic pain, characterized by spontaneous pain and mechanical allodynia. The incidence of neuropathic pain is on the rise due to infections, higher rates of diabetes and stroke, and increased use of chemotherapy drugs in cancer patients. At present, due to its pathophysiological process and molecular mechanism remaining unclear, there is a lack of effective treatment and prevention methods in clinical practice. Now, we use bioinformatics technology to integrate and filter hub genes that may be related to the pathogenesis of neuropathic pain, and explore their possible molecular mechanism by functional annotation and pathway enrichment analysis. Methods: The expression profiles of GSE24982, GSE2884, GSE2636 and GSE30691 were downloaded from the Gene Expression Omnibus(GEO)database, and these datasets include 93 neuropathic pain Rattus norvegicus and 59 shame controls. After the four datasets were all standardized by quantiles, the differentially expressed genes (DEGs) between NPP Rattus norvegicus and the shame controls were finally identified by the robust rank aggregation (RRA) analysis method. In order to reveal the possible underlying biological function of DEGs, the Gene Ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathway enrichment analysis of DEGs were performed. In addition, a Protein-protein Interaction (PPI) network was also established. At the end of our study, a high throughput sequencing dataset GSE117526 was used to corroborate our calculation ***: Through RRA analysis of the above four datasets GSE24982, GSE2884, GSE2636, and GSE30691, we finally obtained 231 DEGs, including 183 up-regulated genes and 47 down-regulated genes. Arranging 231 DEGs in descending order according to |log2 fold change (FC)|, we found that the top 20 key genes include 14 up -regulated genes and 6 down-regulated genes. The most down-regulated hub gene abnormal expressed in NPP was E

关键词： Neuropathic pain Hub genes Robust rank aggregation microarray data analysis Protein-protein interaction (PPI) network analysis

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TC-VGC: A Tumor Classification System using Variations in Genes' Correlation

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COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2011年第3期104卷 E87-E101页

作者： Shin, Eunji Yoon, Youngmi Ahn, Jaegyoon Park, Sanghyun Yonsei Univ Dept Comp Sci Seoul 120749 South Korea Gachon Univ Med & Sci Div Informat Technol Inchon 5342 South Korea

Classification analysis of microarray data is widely used to reveal biological features and to diagnose various diseases, including cancers. Most existing approaches improve the performance of learning models by removing most irrelevant and redundant genes from the data. They select the marker genes which are expressed differently in normal and tumor tissues. These techniques ignore the importance of the complex functional-dependencies between genes. In this paper, we propose a new method for cancer classification which uses distinguished variations of gene-gene correlation in two sample groups. The cancer specific genetic network composed of these gene pairs contains many literature-curated prostate cancer genes, and we were successful in identifying new candidate prostate cancer genes inferred by them. Furthermore, this method achieved a high accuracy with a small number of genes in cancer classification. (C) 2011 Elsevier Ireland Ltd. All rights reserved.

关键词： Tumor classification microarray data analysis Gene-gene correlation Cancer specific genetic network

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From genes to functional classes in the study of biological systems

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BMC BIOINFORMATICS 2007年第1期8卷 114-114页

作者： Al-Shahrour, Fatima Arbiza, Leonardo Dopazo, Hernan Huerta-Cepas, Jaime Minguez, Pablo Montaner, David Dopazo, Joaquin Bioinformat Dept E-46013 Valencia Spain CIPF INB Funct Genom Node E-46013 Valencia Spain

Background: With the popularisation of high-throughput techniques, the need for procedures that help in the biological interpretation of results has increased enormously. Recently, new procedures inspired in systems biology criteria have started to be developed. Results: Here we present FatiScan, a web-based program which implements a threshold-independent test for the functional interpretation of large-scale experiments that does not depend on the pre-selection of genes based on the multiple application of independent tests to each gene. The test implemented aims to directly test the behaviour of blocks of functionally related genes, instead of focusing on single genes. In addition, the test does not depend on the type of the data used for obtaining significance values, and consequently different types of biologically informative terms (gene ontology, pathways, functional motifs, transcription factor binding sites or regulatory sites from CisRed) can be applied to different classes of genome-scale studies. We exemplify its application in microarray gene expression, evolution and interactomics. Conclusion: Methods for gene set enrichment which, in addition, are independent from the original data and experimental design constitute a promising alternative for the functional profiling of genome-scale experiments. A web server that performs the test described and other similar ones can be found at: http://***.

关键词： Gene Ontology Functional Classis KEGG Pathway microarray data analysis Functional Term

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EzArray: A web-based highly automated Affymetrix expression array data management and analysis system

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BMC BIOINFORMATICS 2008年第1期9卷 46-46页

作者： Zhu, Yuerong Zhu, Yuelin Xu, Wei BioInfoRx Inc Middleton WI 53562 USA Georgetown Univ Dept Oncol Washington DC 20057 USA Univ Wisconsin Dept Oncol Madison WI 53706 USA

Background: Though microarray experiments are very popular in life science research, managing and analyzing microarray data are still challenging tasks for many biologists. Most microarray programs require users to have sophisticated knowledge of mathematics, statistics and computer skills for usage. With accumulating microarray data deposited in public databases, easy-to-use programs to re-analyze previously published microarray data are in high demand. Results: EzArray is a web-based Affymetrix expression array data management and analysis system for researchers who need to organize microarray data efficiently and get data analyzed instantly. EzArray organizes microarray data into projects that can be analyzed online with predefined or custom procedures. EzArray performs data preprocessing and detection of differentially expressed genes with statistical methods. All analysis procedures are optimized and highly automated so that even novice users with limited pre-knowledge of microarray data analysis can complete initial analysis quickly. Since all input files, analysis parameters, and executed scripts can be downloaded, EzArray provides maximum reproducibility for each analysis. In addition, EzArray integrates with Gene Expression Omnibus (GEO) and allows instantaneous re-analysis of published array data. Conclusion: EzArray is a novel Affymetrix expression array data analysis and sharing system. EzArray provides easy-to-use tools for re-analyzing published microarray data and will help both novice and experienced users perform initial analysis of their microarray data from the location of data storage. We believe EzArray will be a useful system for facilities with microarray services and laboratories with multiple members involved in microarray data analysis. EzArray is freely available from http://***/.

关键词： microarray data Gene Expression Omnibus microarray data analysis Bioconductor Package Novice User

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FEATURE SELECTION BY WEIGHTED-SNR FOR CANCER microarray data CLASSIFICATION

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INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL 2009年第12A期5卷 4627-4635页

作者： Hengpraprohm, Supoj Chongstitvatana, Prabhas Nakhon Pathom Rajabhat Univ Fac Sci & Technol Nakhon Pathom 73000 Thailand Chulalongkorn Univ Dept Comp Engn Bangkok 10330 Thailand

Feature selection technique is widely used to improve the data analysis of high dimensional data especially in a classification. task. Cancer microarray data classification task belongs to this category. Many researches studied the feature selection for,microarray data classification, The major problem. is that many feature selection methods must pre-define the number of feature. Unfortunately, the number of feature which is suitable is not known a priori. In this paper, we present a method to weight the value of each feature by SNR score. It is not necessary to pre-define the number of feature. Genetic Programming is employed as a classifier. The experimental results indicate that the proposed method yields good prediction accuracy.

关键词： microarray data analysis Cancer classification Feature selection Signal to noise ratio Genetic programming

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A novel biclustering algorithm of binary microarray data: BiBinCons and BiBinAlter

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BIOdata MINING 2015年第1期8卷 38-38页

作者： Ben Saber, Haifa Elloumi, Mourad Tunis Time Univ ENSIT Latice Lab Tunis Tunisia Tunis Univ Tunis El Manar Ensit Latice Lab Tunis Tunisia

The biclustering of microarray data has been the subject of a large research. No one of the existing biclustering algorithms is perfect. The construction of biologically significant groups of biclusters for large microarray data is still a problem that requires a continuous work. Biological validation of biclusters of microarray data is one of the most important open issues. So far, there are no general guidelines in the literature on how to validate biologically extracted biclusters. In this paper, we develop two biclustering algorithms of binary microarray data, adopting the Iterative Row and Column Clustering Combination (IRCCC) approach, called BiBinCons and BiBinAlter. However, the BiBinAlter algorithm is an improvement of BiBinCons. On the other hand, BiBinAlter differs from BiBinCons by the use of the EvalStab and IndHomog evaluation functions in addition to the CroBin one (Bioinformatics 20: 1993-2003, 2004). BiBinAlter can extracts biclusters of good quality with better p-values.

关键词： Biclustering Algorithm Evaluation function microarray data analysis

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EXTRACTION OF CO-BEHAVING GENES BY SIMILARITY ENSEMBLES

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JOURNAL OF BIOLOGICAL SYSTEMS 2017年第3期25卷 479-494页

作者： Mohammadi-Jenghara, Moslem Ebrahimpour-Komleh, Hossein Univ Kashan Dept Comp & Elect Engn Kashan Iran

microarray technology is used as a source of data for a wide range of biology studies. Useful biological information can be extracted from the analysis of microarray data, namely, the impact of a particular gene expression on the expression of other genes or the determination of expressed genes under different conditions. The purpose of this paper is to find co-behavioral genes in different data sets for different times and conditions. In other words, genes with similar behavior, same increase or decrease, under different medical, stress, and time conditions in terms of expression are determined. Multi-valued discretization of expression data was used for extracting genes with identical behavior. The algorithm proposed in this study is based on data and methods ensemble. The data ensemble technique was used to extract candidate genes with identical behavior. Other methods were also applied on all the data sets;as a result, many co-behavioral candidate genes with different similarity and correlation values were identified. Finally, the ultimate output was created from the ensemble of different methods. By applying the algorithm on yeast gene expression data, meaningful relations among genes were extracted.

关键词： Co-Behavior Genes Gene Expression microarray data analysis microarray Discretization Gene Similarity

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CeDAR: incorporating cell type hierarchy improves cell type-specific differential analyses in bulk omics data

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GENOME BIOLOGY 2023年第1期24卷 37-37页

作者： Chen, Luxiao Li, Ziyi Wu, Hao Emory Univ Dept Biostat & Bioinformat Atlanta GA 30322 USA Univ MD Anderson Canc Ctr Dept Biostat Houston TX 77030 USA Chinese Acad Sci Shenzhen Inst Adv Technol Fac Comp Sci & Control Engn 1068 Xueyuan Ave Shenzhen 518055 Peoples R China

Bulk high-throughput omics data contain signals from a mixture of cell types. Recent developments of deconvolution methods facilitate cell type-specific inferences from bulk data. Our real data exploration suggests that differential expression or methylation status is often correlated among cell types. Based on this observation, we develop a novel statistical method named CeDAR to incorporate the cell type hierarchy in cell type-specific differential analyses of bulk data. Extensive simulation and real data analyses demonstrate that this approach significantly improves the accuracy and power in detecting cell type-specific differential signals compared with existing methods, especially in low-abundance cell types.

关键词： Cell type-specific differential analysis Cell type hierarchy Hierarchical Bayesian model microarray data analysis

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Bioinformatics and its applications in plant biology

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ANNUAL REVIEW PLANT BIOLOGY 2006年第1期57卷 335-360页

作者： Rhee, Seung Yon Dickerson, Julie Xu, Dong Carnegie Inst Dept Plant Biol Stanford CA 94305 USA Iowa State Univ Baker Ctr Computat Biol Elect & Comp Engn Ames IA 50011 USA Univ Missouri Digital Biol Lab Dept Comp Sci Columbia MO 65211 USA Univ Missouri Life Sci Ctr Columbia MO 65211 USA

Bioinformatics plays an essential role in today's plant science. As the amount of data grows exponentially, there is a parallel growth in the demand for tools and methods in data management, visualization, integration, analysis, modeling, and prediction. At the same time, many researchers in biology are unfamiliar with available bioinformatics methods, tools, and databases, which could lead to missed opportunities or misinterpretation of the information. In this review, we describe some of the key concepts, methods, software packages, and databases used in bioinformatics, with an emphasis on those relevant to plant science. We also cover some fundamental issues related to biological sequence analyses, transcriptome analyses, computational proteomics, computational metabolomics, bio-ontologies, and biological databases. Finally, we explore a few emerging research topics in bioinformatics.

关键词： sequence analysis computational proteomics microarray data analysis bio-ontology biological database

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