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Development of a novel microarray data analysis tool without normalization for genotyping degraded forensic DNA

FORENSIC SCIENCE INTERNATIONAL-GENETICS 2023年第1期65卷 102885-102885页

作者： Yagasaki, Kayoko Nishida, Nao Mabuchi, Akihiko Tokunaga, Katsushi Fujimoto, Akihiro Univ Tokyo Grad Sch Med Dept Human Genet 73-1 Hongo Tokyo 1130033 Japan Natl Ctr Global Hlth & Med Genome Med Sci Project 1-21-1 Toyama Tokyo 1628655 Japan Natl Ctr Global Hlth & Med Genome Med Sci Project 1-7-1 Kohnodai Ichikawa Chiba 2728516 Japan Tokyo Metropolitan Police Dept Forens Sci Lab 3-35-21 Shakujiidai Tokyo 1770045 Japan

Since the arrest of the Golden State Killer in the US in April 2018, forensic geneticists have been increasingly interested in the investigative genetic genealogy (IGG) method. While this method has already been in practical use as a powerful tool for criminal investigation, we have yet to know well the limitations and potential risks. In this current study, we performed an evaluation study focusing on degraded DNA using the Affymetrix GenomeWide Human SNP Array 6.0 platform (Thermo Fisher Scientific). We revealed one of the potential problems that occur during SNP genotype determination using a microarray-based platform. Our analysis results indicated that the SNP profiles derived from degraded DNA contained many false heterozygous SNPs. In addition, it was confirmed that the total amount of probe signal intensity on microarray chips derived from degraded DNA decreased significantly. Because the conventional analysis algorithm performs normalization during genotype determination, we concluded that noise signals could be genotype-called. To address this issue, we proposed a novel microarray data analysis method without normalization (nMAP). Although the nMAP algorithm resulted in a low call rate, it substantially improved genotyping accuracy. Finally, we confirmed the usefulness of the nMAP algorithm for kinship inferences. These findings and the nMAP algorithm will make a contribution to the advance of the IGG method.

关键词： Investigative genetic genealogy Kinship analysis microarray data analysis Degraded DNA KING software Normalization

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Identification of Adriamycin Resistance Genes in Breast Cancer based on microarray data analysis

Identification of Adriamycin Resistance Genes in Breast Canc...

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2022CCTB中国肿瘤标志物学术大会暨中国整合肿瘤学大会暨第十六届肿瘤标志物青年科学家论坛暨中国肿瘤标志物产业创新大会

作者： Zhaolei Cui Yan Chen Fujian Cancer Hospital

Objective:Breast cancer is a common malignant tumor with increasing incidence *** study aimed to investigate the molecular mechanisms of the adriamycin(ADR)resistance in breast ***:The GSE76540 dataset downloaded from the National Center for Biotechnology Information(NCBI) Gene Expression Omnibus(GEO) database was adopted for *** expressed genes(DEGs) in chemo-sensitive cases and chemo-resistant cases were identified using the GEO2R online tool *** analysis and KEGG enrichment analysis of DEGs were carried out by using the DAVID online *** protein-protein interaction(PPI) network was constructed using the Search Tool for the Retrieval of Interacting Genes(STRING) and visualized with Cytoscape *** impact of key tumor genes on the survival and prognosis were ***:A total of 1481 DEGs were excavated,including 549 up-regulated genes and 932down-regulated *** to the GO analysis,the DEGs were significantly enriched in:extracellular matrix organization,positive regulation of transcription from RNA polymerase II promoter,lung development,positive regulation of gene expression,axon quidance and so *** results of KEGG pathway enrichment analysis showed that the most enriched DEGs can be detected in:pathways in cancer,PI3K AKT signaling pathway,focal adhesion,ras signaling pathway and so *** the PPI network analysis,hub genes of CDH1,ESR1,SOX2,AR,GATA3,FOXA1,KRT19,CLDN7,AGR2,ESRP1,RAB25,CLDN4,IGF1R,CLDN3 and IRS1 were ***,there is a correlation filter out these hub genes in resistance of ***:Hub genes associated with ADR resistance were identified using bioinformatic *** results of this study may contribute to the development of targeted therapy for breast cancer.

关键词： breast cancer microarray data analysis adriamycin resistance

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microarray data analysis on gene and miRNA expression to identify biomarkers in non-small cell lung cancer

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BMC CANCER 2020年第1期20卷 329-329页

作者： Jin, Xiang Guan, Yinghui Zhang, Zhen Wang, Hongyue First Hosp Jilin Univ Dept Respirat 1 Xinminda St Changchun 130021 Peoples R China First Hosp Jilin Univ PICU Changchun 130021 Peoples R China First Hosp Jilin Univ Dept Nephrol Changchun 130021 Peoples R China

Background The aim of this study was to gain further investigation of non-small cell lung cancer (NSCLC) tumorigenesis and identify biomarkers for clinical management of patients through comprehensive bioinformatics analysis. Methods miRNA and mRNA microarray datasets were downloaded from GEO (Gene Expression Omnibus) database under the accession number GSE102286 and GSE101929, respectively. Genes and miRNAs with differential expression were identified in NSCLC samples compared with controls, respectively. The interaction between differentially expressed genes (DEGs) and differentially expressed miRNAs (DEmiRs) was predicted, followed by functional enrichment analysis, and construction of miRNA-gene regulatory network, protein-protein interaction (PPI) network, and competing endogenous RNA (ceRNA) network. Through comprehensive bioinformatics analysis, we anticipate to find novel therapeutic targets and biomarkers for NSCLC. Results A total of 123 DEmiRs (5 up- and 118 down-regulated miRNAs) and 924 DEGs (309 up- and 615 down-regulated genes) were identified. These genes and miRNAs were significantly involved in different pathways including adherens junction, relaxin signaling pathway, and axon guidance. Furthermore, hsa-miR-9-5p, has-miR-196a-5p and hsa-miR-31-5p, as well as hsa-miR-1, hsa-miR-218-5p and hsa-miR-135a-5p were shown to have higher degree in the miRNA-gene regulatory network and ceRNA network, respectively. Furthermore, BIRC5 and FGF2, as well as RTKN2 and SLIT3 were hubs in the PPI network and ceRNA network, respectively. Conclusion Several pathways (adherens junction, relaxin signaling pathway, and axon guidance) miRNAs (hsa-miR-9-5p, has-miR-196a-5p, hsa-miR-31-5p, hsa-miR-1, hsa-miR-218-5p and hsa-miR-135a-5p) and genes (BIRC5, FGF2, RTKN2 and SLIT3) may play important roles in the pathogenesis of NSCLC.

关键词： Non-small cell lung cancer Differentially expressed genes miRNA Regulatory network microarray data analysis

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Fuzzy mixed-prototype clustering algorithm for microarray data analysis

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NEUROCOMPUTING 2018年 276卷 42-54页

作者： Liu, Jin Pham, Tuan D. Yan, Hong Liang, Zhizhen China Univ Min & Technol Sch Comp Sci Xuzhou 221008 Jiangsu Peoples R China Linkoping Univ Dept Biomed Engn S-58183 Linkoping Sweden City Univ Hong Kong Dept Elect Engn Kowloon Hong Kong Peoples R China

Being motivated by combining the advantages of hyperplane-based pattern analysis and fuzzy clustering techniques, we present in this paper a fuzzy mix-prototype (FMP) clustering for microarray data analysis. By integrating spherical and hyper-planar cluster prototypes, the FMP is capable of capturing latent data models with both spherical and non-spherical geometric structures. Our contributions of the paper can be summarized into three folds: first, the objective function of the FMP is formulated. Second, an iterative solution which minimizes the objective function under given constraints is derived. Third, the effectiveness of the proposed FMP is demonstrated through experiments on yeast and leukemia data sets. (c) 2017 Elsevier B.V. All rights reserved.

关键词： FMP microarray data analysis Fuzzy clustering

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microarray data analysis with Support Vector Machine 3rd

Microarray Data Analysis with Support Vector Machine

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3rd International Conference on Intelligent Technologies and Engineering Systems (ICITES)

作者： Du, Si-Hao Jeng, Jin-Tsong Su, Shun-Feng Chang, Sheng-Chieh Natl Taiwan Univ Sci & Technol Dept Elect Engn Taipei Taiwan Natl Formosa Univ Dept Comp Sci & Informat Engn Huwei Township Yunlin Taiwan Natl Chung Shan Inst Sci & Technol Aeronaut Syst Res Div Taichung Taiwan

ISBN: (纸本)9783319173146;9783319173139

microarray data analysis approach has became a widely used tool for disease detection. It uses tens of thousands of genes as input dimension that would be a huge computational problem for data analysis. In this chapter, the proposed approach deals with selection of feature genes and classification of microarray data under support vector machine (SVM) approach. Feature genes can be finding out according to the adjustable epsilon-support vector regression (epsilon-SVR) and then to select high ranked genes after all microarray data. Moreover, multi-class support vector classification (multi-class SVC) and cross-validation methods apply to acquire great prediction classification accuracy and less computing time.

关键词： Support vector machine Support vector regression Multi-class support vector classification Feature genes microarray data analysis

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Optimizing cancer classification: a hybrid RDO-XGBoost approach for feature selection and predictive insights

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CANCER IMMUNOLOGY IMMUNOTHERAPY 2024年第12期73卷 261页

作者： Yaqoob, Abrar Verma, Navneet Kumar Aziz, Rabia Musheer Shah, Mohd Asif VIT Bhopal Univ Sch Adv Sci & Language Bhopal 466114 India State Planning Inst Planning Dept New Div Lucknow 226001 Utter Pradesh India Kardan Univ Dept Econ Kabul 1001 Afghanistan Lovely Profess Univ Div Res & Dev Phagwara 144001 Punjab India Chitkara Univ Inst Engn & Technol Ctr Res Impact & Outcome Rajpura 140401 Punjab India

The identification of relevant biomarkers from high-dimensional cancer data remains a significant challenge due to the complexity and heterogeneity inherent in various cancer types. Conventional feature selection methods often struggle to effectively navigate the vast solution space while maintaining high predictive accuracy. In response to these challenges, we introduce a novel feature selection approach that integrates Random Drift Optimization (RDO) with XGBoost, specifically designed to enhance the performance of cancer classification tasks. Our proposed framework not only improves classification accuracy but also offers valuable insights into the underlying biological mechanisms driving cancer progression. Through comprehensive experiments conducted on real-world cancer datasets, including Central Nervous System (CNS), Leukemia, Breast, and Ovarian cancers, we demonstrate the efficacy of our method in identifying a smaller subset of unique and relevant genes. This selection results in significantly improved classification efficiency and accuracy. When compared with popular classifiers such as Support Vector Machine, K-Nearest Neighbor, and Naive Bayes, our approach consistently outperforms these models in terms of both accuracy and F-measure metrics. For instance, our framework achieved an accuracy of 97.24% in the CNS dataset, 99.14% in Leukemia, 95.21% in Ovarian, and 87.62% in Breast cancer, showcasing its robustness and effectiveness across different types of cancer data. These results underline the potential of our RDO-XGBoost framework as a promising solution for feature selection in cancer data analysis, offering enhanced predictive performance and valuable biological insights.

关键词： Random drift optimization XGBoost Feature selection Cancer classification microarray data analysis

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Genome-wide identification and characterization of the dof gene family in mango (Mangifera indica L.)

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GENETIC RESOURCES AND CROP EVOLUTION 2024年第6期71卷 2749-2765页

作者： Alghanem, Suliman M. S. Alnusairi, Ghalia S. H. Alkhateeb, Mariam Abdulaziz Alwutayd, Khairiah Mubarak Alaklabi, Abdullah Alharbi, Basmah M. Albalawi, Doha Abdullah Alshehri, Dikhnah Al-Harbi, Nadi Awad Al-Qahtani, Salem Mesfir Soliman, Mona H. Qassim Univ Coll Sci Dept Biol Buraydah 52571 Saudi Arabia Jouf Univ Coll Sci Dept Biol Sakaka 2014 Saudi Arabia Princess Nourah bint Abdulrahman Univ Coll Sci Dept Biol 84428 Riyadh 11671 Saudi Arabia Univ Bisha Fac Sci Dept Biol 551 Bisha 61922 Saudi Arabia Univ Tabuk Fac Sci Dept Biol Tabuk 71491 Saudi Arabia Univ Tabuk Univ Coll Tayma Biol Dept Tabuk 47512 Saudi Arabia Cairo Univ Fac Sci Bot & Microbiol Dept Giza 12613 Egypt Taibah Univ Fac Sci Biol Dept Al Sharm 46429 Yanbu El Bahr Saudi Arabia

DOF transcription factors are essential plant-specific gene regulators that are crucial in plant growth and stress responses. Mangifera indica, a tropical fruit species with diverse applications, undergoes complex genetic regulation during fruit ripening, influenced by climacteric conditions. This study focuses on DNA-binding one zinc finger genes (MiDofs) in Mangifera indica (mango) and their response to drought stress during fruit development. We found 36 MiDof genes in mango, categorizing them into six distinct clusters based on evolutionary relationships. Despite variations in various characteristics, the fundamental structures of these MiDof transcription factors remained consistent. Notably, two MiDof genes were outside the nucleus. Gene structure analysis and motif identification revealed common features among all MiDofs, with certain genes like MiDof5.3 and MiDof2.2 performing unique functions. Our analysis of conserved domains highlighted crucial functional sites and protein-protein interaction networks, with the zf-Dof domain being the most conserved. Additionally, we explored cis-acting regulatory elements, shedding light on the mechanisms by which MiDofs respond to light and drought stress. Expression analysis demonstrated the involvement of MiDofs in mango fruit ripening and development under light conditions. Quantitative reverse transcription-polymerase chain reaction (qRT-PCR) confirmed the induction of MiDofs under drought stress when compared to a control group. This study contributes valuable insights to the field of fruit biology and provides a deeper understanding of the role of MiDof genes in mango fruit development, offering potential avenues for crop improvement strategies.

关键词： DOF Genome-wide analysis Mangifera indica microarray data analysis

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Filtering non-balanced data using an evolutionary approach

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LOGIC JOURNAL OF THE IGPL 2023年第2期31卷 271-286页

作者： Carballido, Jessica A. Ponzoni, Ignacio Cecchini, Rocio L. Univ Nacl Sur UNS CONICET Inst Comp Sci & EngnDept Comp Sci & Engn San Andres 800 RA-8000 Bahia Blanca Buenos Aires Argentina

Matrices that cannot be handled using conventional clustering, regression or classification methods are often found in every big data research area. In particular, datasets with thousands or millions of rows and less than a hundred columns regularly appear in biological so-called omic problems. The effectiveness of conventional data analysis approaches is hampered by this matrix structure, which necessitates some means of reduction. An evolutionary method called PreCLAS is presented in this article. Its main objective is to find a submatrix with fewer rows that exhibits some group structure. Three stages of experiments were performed. First, a benchmark dataset was used to assess the correct functionality of the method for clustering purposes. Then, a microarray gene expression data matrix was used to analyze the method's performance in a simple classification scenario, where differential expression was carried out. Finally, several classification methods were compared in terms of classification accuracy using an RNA-seq gene expression dataset. Experiments showed that the new evolutionary technique significantly reduces the number of rows in the matrix and intelligently performs unsupervised row selection, improving classification and clustering methods.

关键词： Clustering tendency data reduction techniques evolutionary algorithm classification strategies unsupervised data filtering microarray data analysis

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Identification of potential biomarkers for melanoma cancer (black tumor) using bioinformatics strategy: a study based on GEO and SRA datasets

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JOURNAL OF APPLIED GENETICS 2024年第1期65卷 83-93页

作者： Almatrafi, Tahani Ahmad Mohammedsaleh, Zuhair M. Moawadh, Mamdoh S. Bassfar, Zaid Jalal, Mohammed M. Badahdah, Fatima Ahmed Alghamdi, Youssef S. Almasoudi, Hassan Hussain Hakami, Mohammed Ageeli Binshaya, Abdulkarim S. Almohaimeed, Hailah M. Soliman, Mona H. King Saud Univ Coll Med Anat Dept Riyadh Saudi Arabia Univ Tabuk Fac Appl Med Sci Dept Med Lab Technol Tabuk 71491 Saudi Arabia Univ Tabuk Fac Comp & Informat Technol Tabuk Saudi Arabia Prince Sultan Mil Med City Surg Dept PSMMC Riyadh Saudi Arabia Taif Univ Turabah Univ Coll Dept Biol Taif 21995 Saudi Arabia Najran Univ Coll Appl Med Sci Dept Clin Lab Sci Najran Saudi Arabia Shaqra Univ Coll Appl Med Sci Dept Clin Lab Sci Riyadh Saudi Arabia Prince Sattam Bin Abdulaziz Univ Coll Appl Med Sci Dept Med Lab Sci AlKharj 11942 Saudi Arabia Princess Nourah bint Abdulrahman Univ Coll Med Dept Basic Sci POB 84428 Riyadh 11671 Saudi Arabia Cairo Univ Fac Sci Bot & Microbiol Dept Giza 12613 Egypt Taibah Univ Fac Sci Biol Dept Yanbu El Bahr 46429 Yanbu Saudi Arabia

Melanoma, a highly invasive type of skin cancer that penetrates the entire dermis layer, is associated with increased mortality rates. Excessive exposure of the skin to sunlight, specifically ultraviolet radiation, is the underlying cause of this malignant condition. The appearance of unique skin moles represents a visible clue, referred to as the "ugly duckling" sign, indicating the presence of melanoma and its association with cellular DNA damage. This research aims to explore potential biomarkers derived from microarray data, employing bioinformatics techniques and methodologies, for a thorough investigation of melanoma skin cancer. The microarray dataset for melanoma skin cancer was obtained from the GEO database, and thorough data analysis and quality control measures were performed to identify differentially expressed genes (DEGs). The top 14 highly expressed DEGs were identified, and their gene information and protein sequences were retrieved from the NCBI gene and protein database. These proteins were further analyzed for domain identification and network analysis. Gene expression analysis was conducted to visualize the upregulated and downregulated genes. Additionally, gene metabolite network analysis was carried out to understand the interactions between highly interconnected genes and regulatory transcripts. Molecular docking was employed to investigate the ligand-binding sites and visualize the three-dimensional structure of proteins. Our research unveiled a collection of genes with varying expression levels, some elevated and others reduced, which could function as promising biomarkers closely linked to the development and advancement of melanoma skin cancer. Through molecular docking analysis of the GINS2 protein, we identified two natural compounds (PubChem-156021169 and PubChem-60700) with potential as inhibitors against melanoma. This research has implications for early detection, treatment, and understanding the molecular basis of melanoma.

关键词： Melanoma cancer Molecular docking microarray data analysis SRA data analysis

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Identification of common genes in obesity and cancer through network interaction and targeting those genes by virtual screening approach

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JOURNAL OF BIOMOLECULAR STRUCTURE & DYNAMICS 2023年第3期41卷 1109-1127页

作者： Modanwal, Shristi Mishra, Nidhi Indian Inst Informat Technol Allahabad Dept Appl Sci Prayagraj 211015 UP India

Obesity may have an effect on cancer outcomes, resulting in global inequalities in cancer survival and death. microarray data analysis was done to identify differentially expressed genes (DEGs) in obese and cancer patients. Total 1977 differentially expressed genes among obesity and gastric cancer, breast cancer, pancreatic cancer, and colorectal cancer were used to build a gene interaction network, which was then analyzed by using Cytoscape software. It has been identified that JUN, CXCL12, and LEP genes show a higher degree and stress, and play an important role in obesity and cancer progression. Further, CXCL12 and LEP were taken for virtual screening study with coumarin and its derivatives to develop a drug against obesity and cancer. The interactions of CXCL12 and LEP with coumarins were studied by molecular docking and it shows good interaction as well as docking score as compared to the standard one. The ADME properties were predicted to check the drug-likeness activity of coumarins and the most of the drug-likeness activities are in admire range. The Binding free energy of the docked complex was calculated by performing MM-GBSA. The molecular docking, ADME properties prediction, and MM-GBSA was performed on Maestro 12.6. The top docked score compounds were further subjected to molecular dynamic simulation to check the stability by using GROMACS. The MM-PBSA study was performed to calculate the binding energy components as well as the energy contributions of specific amino acids. The resultant compounds could be a potent anti-obesity and anti-cancer drug.

关键词： Obesity cancer microarray data analysis protein-protein interaction MD Simulation

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