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Promoter-motif extraction from co-regulated genes and their relevance to co-expression using E. coli as a model

BRIEFINGS IN FUNCTIONAL GENOMICS 2023年第2期22卷 204-216页

作者： Nayarisseri, Anuraj Bhrdwaj, Anushka Khan, Arshiya Sharma, Khushboo Shaheen, Uzma Selvaraj, Chandrabose Khan, Mohammad Aqueel Abhirami, Rajaram Pravin, Muthuraja Arun Shri, Gurunathan Rubha Raje, Dhanjay Singh, Sanjeev Kumar Eminent Biosci 91 Sect AMahalakshmi Nagar Indore 452010 Madhya Pradesh India Sil Res Lab Eminent Biosci Mahalakshmi Nagar Indore 452010 India LeGene Biosci Pvt Ltd Bioinformat Res Lab 91 Sect AMahalakshmi Nagar Indore 452010 India Alagappa Univ Dept Bioinformat Comp Aided Drug Designing & Mol Modeling Lab Karaikkudi 630003 India Ctr Biomed Res Dept Data Sci SGPGIMS Campus Raebareli Rd Lucknow 226014 India

Gene expression varies due to the intrinsic stochasticity of transcription or as a reaction to external perturbations that generate cellular mutations. Co-regulation, co-expression and functional similarity of substances have been employed for indoctrinating the process of the transcriptional paradigm. The difficult process of analysing complicated proteomes and biological switches has been made easier by technical improvements, and microarray technology has flourished as a viable platform. Therefore, this research enables microarray to cluster genes that are co-expressed and co-regulated into specific segments. Copious search algorithms have been employed to ascertain diacritic motifs or a combination of motifs that are performing regular expression, and their relevant information corresponding to the gene patterns is also documented. The associated genes co-expression and relevant cis-elements are further explored by engaging Escherichia coli as a model organism. Various clustering algorithms have also been used to generate classes of genes with similar expression profiles. A promoter database 'EcoPromDB' has been developed by referring RegulonDB database;this promoter database is freely available at and is divided into two sub-groups, depending upon the results of co-expression and co-regulation analyses.

关键词： Gene regulation Co-expression Motif discovery microarray data analysis E coli coli promoter database EcopromDB

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Computational analysis of microarray data ofArabidopsis thalianachallenged withAlternaria brassicicolafor identification of key genes inBrassica

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JOURNAL OF GENETIC ENGINEERING AND BIOTECHNOLOGY 2020年第1期18卷 17-17页

作者： Pathak, Rajesh Kumar Baunthiyal, Mamta Pandey, Dinesh Kumar, Anil Govind Ballabh Pant Inst Engn & Technol Dept Biotechnol Pauri Garhwal 246194 Uttarakhand India GB Pant Univ Agr & Technol Dept Mol Biol & Genet Engn Coll Basic Sci & Humanities Pantnagar 263145 Uttarakhand India Rani Lakshmi Bai Cent Agr Univ Jhansi 284003 Uttar Pradesh India

Background Alternariablight, a recalcitrant disease caused byAlternaria brassicaeandAlternaria brassicicola, has been recognized for significant losses of oilseed crops especially rapeseed-mustard throughout the world. Till date, no resistance source is available against the disease;hence, plant breeding methods cannot be used to develop disease-resistant varieties. Therefore, in the present study, efforts have been made to identify resistance and defense-related genes as well as key components ofJA-SA-ET-mediated pathway involved in resistance againstAlternaria brasscicolathrough computational analysis of microarray data and network biology approach. microarray profiling data from wild type and mutantArabidopsisplants challenged withAlternaria brassicicolaalong with control plant were obtained from the Gene Expression Omnibus (GEO) database. The data analysis, including DEGs extraction, functional enrichment, annotation, and network analysis, was used to identify genes associated with disease resistance and defense response. Results A total of 2854 genes were differentially expressed in WT9C9;among them, 1327 genes were upregulated and 1527 genes were downregulated. A total of 1159 genes were differentially expressed in JAM9C9;among them, 809 were upregulated and 350 were downregulated. A total of 2516 genes were differentially expressed in SAM9C9;among them, 1355 were upregulated and 1161 were downregulated. A total of 1567 genes were differentially expressed in ETM9C9;among them, 917 were upregulated and 650 were downregulated. Besides, a total of 2965 genes were differentially expressed in contrast WT24C24;among them, 1510 genes were upregulated and 1455 genes were downregulated. A total of 4598 genes were differentially expressed in JAM24C24;among them, 2201 were upregulated and 2397 were downregulated. A total of 3803 genes were differentially expressed in SAM24C24;among them, 1819 were upregulated and 1984 were downregulated. A total of 4164 genes were differ

关键词： microarray data analysis GO analysis Network analysis Hubs Oilseeds Brassica

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Scalable Non-Linear Graph Fusion for Prioritizing Cancer-Causing Genes

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022年第2期19卷 1130-1143页

作者： Shah, Ekta Maji, Pradipta Indian Stat Inst Machine Intelligence Unit Biomed Imaging & Bioinformat Lab Kolkata 700108 India

In the past few decades, both gene expression data and protein-protein interaction (PPI)networks have been extensively studied, due to their ability to depict important characteristics of disease-associated genes. In this regard, the paper presents a new gene prioritization algorithm to identify and prioritize cancer-causing genes, integrating judiciously the complementary information obtained from two data sources. The proposed algorithm selects disease-causing genes by maximizing the importance of selected genes and functional similarity among them. A new quantitative index is introduced to evaluate the importance of a gene. It considers whether a gene exhibits a differential expression pattern across sick and healthy individuals, and has a strong connectivity in the PPI network, which are the important characteristics of a potential biomarker. As disease-associated genes are expected to have similar expression profiles and topological structures, a scalable non-linear graph fusion technique, termed as ScaNGraF, is proposed to learn a disease-dependent functional similarity network from the co-expression and common neighbor based similarity networks. The proposed ScaNGraF, which is based on message passing algorithm, efficiently combines the shared and complementary information provided by different data sources with significantly lower computational cost. A new measure, termed as DiCoIN, is introduced to evaluate the quality of a learned affinity network. The performance of the proposed graph fusion technique and gene selection algorithm is extensively compared with that of some existing methods, using several cancer data sets.

关键词： Diseases Gene expression Proteins Protein engineering Indexes data integration Disease gene identification microarray data analysis protein-protein interaction network network fusion

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A Voting based Assimilation Method for the Winning Neurons in Multi-Level SOM to Cluster the Convoluted Biomarkers of a Time Varying 'Omic data 15

A Voting based Assimilation Method for the Winning Neurons i...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Dey, Anirban Sharma, Kaushik Das Bhattacharjee, Pritha Chatterjee, Amitava University of Calcutta Department of Applied Physics Kolkata India University of Calcutta Department of Environmental Science Kolkata India Jadavpur University Department of Electrical Engineering Kolkata India

ISBN: (纸本)9798350370249

Early detection and modelling of disease development are crucial for effective healthcare. Diseases develop over time, with changes in 'omic expressions, leading to genotypic or phenotypic changes in the sample. These changes are unique to disease samples and can develop in various parts of the body and continue to grow, if not addressed in a timely fashion. However, due to the availability of a small population of very high dimensional 'omic samples, this presents a significant challenge. Thus, dimensionality reduction techniques are essential to simplify the data while preserving its core structure. This work focuses on the technique of Non-Negative Matrix Factorization (NMF), where the decomposed basis and coefficient matrices are component-wise non-negative. NMF has recently gained prominence for analyzing time-varying 'omic data and the present work uses a contemporary variant of NMF, called flexible NMF (FNMF). In a bid to effective extract differentially expressed biomarkers using NMF, this work proposes a voting based assimilation method for factorized convoluted biomarkers obtained from FNMF using a hybrid MLSOM (Hy-MLSOM) architecture. This results in an enhanced representation of the 'omic data, thereby facilitating more effective and precise disease detection. © 2024 IEEE.

关键词： Biomarker Identification Feature Representational Learning microarray data analysis Non Negative Matrix Factorization Self-Organizing Maps

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microarray analysis Workflow Based on a Genetic Algorithm to Discover Potential Hub Genes

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CURRENT BIOINFORMATICS 2022年第9期17卷 787-792页

作者： Carballido, Jessica Andrea UNS Inst CS & Engn Dept CS & Engn CONICET Bahia Blanca Argentina

This paper presents a sequence of steps oriented to gain biological knowledge from microarray gene expression data. The pipeline's core is a canonical multi-objective Genetic Algorithm (GA), which takes a gene expression matrix and a factor as input. The factor groups samples according to different criteria, e.g., healthy tissue and diseased tissue samples. The result of one run of the GA is a gene set with good properties both at the individual level, in terms of differential expression, and at the aggregate level, in terms of correlation between expression profiles. microarray experiment data are obtained from GEO (Gene Expression Omnibus dataset). As for the pipeline structure, independent runs of the GA are analyzed, genes in common between all the runs are collected, and over-representation analysis is performed. At the end of the process, a small number of genes of interest arise. The methodology is exemplified with a leukemia benchmark dataset, and a group of genes of interest is obtained for the illustrative example.

关键词： Functional genomics genetic algorithm microarray data analysis gene ontology over-representation analysis mutual information

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maGUI: A Graphical User Interface for analysis and Annotation of DNA microarray data

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The Open Bioinformatics Journal 2019年 12卷 40-44页

作者： Dhammapal Bharne Praveen Kant Vaibhav Vindal Department of Biotechnology and Bioinformatics School of Life Sciences University of Hyderabad Hyderabad India .

Summary maGUI is a graphical user interface designed to analyze microarray data produced from experiments performed on various platforms such as Affymetrix, Agilent, Illumina, and Nimblegen and so on, automatically. It follows an integrated workflow for pre-processing and analysis of the microarray data. The user may proceed from loading of microarray data to normalization, quality check, filtering, differential gene expression, principal component analysis, clustering and classification. It also provides miscellaneous applications such as gene set test and enrichment analysis for identifying gene symbols using Bioconductor packages. Further, the user can build a co-expression network for differentially expressed genes. Tables and figures generated during the analysis can be viewed and exported to local disks. The graphical user interface is very friendly especially for the biologists to perform the most microarray data analyses and annotations without much need of learning R command line programming. Availability and Implementation maGUI is an R package which can be downloaded freely from Comprehensive R Archive Network resource. It can be installed in any R environment with version 3.0.2 or above.

关键词： Graphical user interface R programming language Bioconductor Comprehensive R Archive Network microarray data analysis Gene set test analysis Gene set enrichment analysis

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Hybrid ant lion mutated ant colony optimizer technique for Leukemia prediction using microarray gene data

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2021年第2期12卷 2965-2973页

作者： Santhakumar, D. Logeswari, S. CK Coll Engn & Technol Comp Sci Engn Cuddalore Tamil Nadu India Bannari Amman Inst Technol Comp Sci Engn Satyamangalam Tamil Nadu India

The classification of cancers is one of the most vital functions of microarray data analysis. The classification of the gene expression profile is treated as a NP-Hard problem since it is a very demanding job. Compared to the individual search utilized by conventional algorithms, the population search utilized by Evolutionary Algorithm (EA) is visibly more beneficial. In feasible search areas, EA algorithms also are more likely to detect various optimums instantly. Evolutionary techniques which are inspired by nature perform exceptionally well and are extensively used for microarray data analysis. Ant Colony Optimization (ACO) is a distinct intelligent optimization algorithm based on iterative optimization which uses ideas like evolution and group. ACO algorithm was developed by studying how ants identify paths while food foraging. Ant Lion Optimization (ALO) algorithm is proposed and employed as muted selection process and the ant lions to hunt process is simulated. A hybrid ant lion mutated ant colony optimizer technique is proposed in this work.

关键词： microarray data analysis Ant Colony Optimization (ACO) Ant Lion Optimization (ALO) algorithm A hybrid ant lion mutated ant colony optimizer technique

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Molecular differential analysis of uterine leiomyomas and leiomyosarcomas through weighted gene network and pathway tracing approaches

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SYSTEMS BIOLOGY IN REPRODUCTIVE MEDICINE 2021年第3期67卷 209-220页

作者： Sahly, Nora Naif Banaganapalli, Babajan Sahly, Ahmed N. Aligiraigri, Ali H. Nasser, Khalidah K. Shinawi, Thoraia Mohammed, Arif Alamri, Abdulhakeem S. Bondagji, Nabeel Elango, Ramu Shaik, Noor Ahmad King Abdulaziz Univ Fac Med Dept Obstet & Gynecol Jeddah Saudi Arabia King Abdulaziz Univ Fac Med Dept Genet Med Jeddah Saudi Arabia King Abdulaziz Univ Princess Al Jawhara Al Brahim Ctr Excellence Res Jeddah Saudi Arabia King Faisal Specialist Hosp & Res Ctr Dept Neurosci Jeddah Saudi Arabia King Abdulaziz Univ Hosp Dept Hematol Jeddah Saudi Arabia King Abdulaziz Univ Fac Appl Med Sci Dept Med Lab Technol Jeddah Saudi Arabia Univ Jeddah Coll Sci Dept Biol Jeddah Saudi Arabia Taif Univ Coll Appl Med Sci Dept Clin Labs Sci At Taif Saudi Arabia Taif Univ Ctr Biomed Sci Res CBSR Deanship Sci Res At Taif Saudi Arabia

Uterine smooth muscular neoplastic growths like benign leiomyomas (UL) and metastatic leiomyosarcomas (ULMS) share similar clinical symptoms, radiological and histological appearances making their clinical distinction a difficult task. Therefore, the objective of this study is to identify key genes and pathways involved in transformation of UL to ULMS through molecular differential analysis. Global gene expression profiles of 25 ULMS, 25 UL, and 29 myometrium (Myo) tissues generated on Affymetrix U133A 2.0 human genome microarrays were analyzed by deploying robust statistical, molecular interaction network, and pathway enrichment methods. The comparison of expression signals across Myo vs UL, Myo vs ULMS, and UL vs ULMS groups identified 249, 1037, and 716 significantly expressed genes, respectively (p <= 0.05). The analysis of 249 DEGs from Myo vs UL confirms multistage dysregulation of various key pathways in extracellular matrix, collagen, cell contact inhibition, and cytokine receptors transform normal myometrial cells to benign leiomyomas (p value <= 0.01). The 716 DEGs between UL vs ULMS were found to affect cell cycle, cell division related Rho GTPases and PI3K signaling pathways triggering uncontrolled growth and metastasis of tumor cells (p value <= 0.01). Integration of gene networking data, with additional parameters like estimation of mutation burden of tumors and cancer driver gene identification, has led to the finding of 4 hubs (JUN, VCAN, TOP2A, and COL1A1) and 8 bottleneck genes (PIK3R1, MYH11, KDR, ESR1, WT1, CCND1, EZH2, and CDKN2A), which showed a clear distinction in their distribution pattern among leiomyomas and leiomyosarcomas. This study provides vital clues for molecular distinction of UL and ULMS which could further assist in identification of specific diagnostic markers and therapeutic targets.

关键词： Uterine leiomyosarcoma protein interaction network microarray data analysis differentially expressed genes

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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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Identifying Necroptosis Signature Genes Based on Machine Learning for Evaluating COVID-19 Progression 23

Identifying Necroptosis Signature Genes Based on Machine Lea...

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Proceedings of the 2023 12th International Conference on Bioinformatics and Biomedical Science

作者： Tingting Feng Yitong Li Yue Wu Jue Wang Qi Kong Institute of Laboratory Animal Sciences Chinese Academy of Medical Sciences & Peking Union Medical College China

ISBN: (纸本)9798400716140

Backgrounds: COVID-19 has impaired the health of many people around the world since 2019. In recent years, necroptosis has been defined as a crucial cell death in many diseases as well as has an association with COVID-19. However, the mechanism of necroptosis in COVID-19 remains to be excavated. Methods: The GEO series was used to get different expression genes. Intersected with necroptosis genes, necroptosis-related genes were got and to work on enrichment analysis. The LASSO regression algorithm was applied to selected signature genes associated with progression. Then we analyzed the correlation and tissue locations of these genes. Immune infiltration was analyzed with the use of an R script. To find the regulatory targets, networks of TF and miRNA genes were built. LINCS database was carried out to get potential drugs. Results: 17 necroptosis-related DEGs were enriched in several progress represented by cytoplasmic transcription. We obtained 9 signature genes through LASSO regression algorithm, including TP53I3, CDK1, MAPK14, AURKA, PGLYRP1, VIL1, TRAF5, ZBP1, and CYLD. These 9 genes showed a strong correlation. analysis showed differences in immune infiltration in patients with non-severe and severe COVID-19. One transcription factor and 5 miRNAs were identified through regulatory networks. 10 drugs were predicted to be useful. Conclusion: Our results revealed 9 necroptosis signature genes that could evaluate the development of COVID-19, and identified potential targets and drugs that have treatment potential. These findings provide novel information for the treatment of COVID-19.

关键词： COVID-19 Gene identification Machine learning in bioinformatics microarray data analysis Necroptosis

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