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Chen, Ray, Hon Wong, and Brendan Burns. "New Approaches to Detect Biosynthetic Gene Clusters in the Environment." Medicines 6, no. 1 (February 25, 2019): 32. http://dx.doi.org/10.3390/medicines6010032.
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Microorganisms in the environment can produce a diverse range of secondary metabolites (SM), which are also known as natural products. Bioactive SMs have been crucial in the development of antibiotics and can also act as useful compounds in the biotechnology industry. These natural products are encoded by an extensive range of biosynthetic gene clusters (BGCs). The developments in omics technologies and bioinformatic tools are contributing to a paradigm shift from traditional culturing and screening methods to bioinformatic tools and genomics to uncover BGCs that were previously unknown or transcriptionally silent. Natural product discovery using bioinformatics and omics workflow in the environment has demonstrated an extensive distribution of BGCs in various environments, such as soil, aquatic ecosystems and host microbiome environments. Computational tools provide a feasible and culture-independent route to find new secondary metabolites where traditional approaches cannot. This review will highlight some of the advances in the approaches, primarily bioinformatic, in identifying new BGCs, especially in environments where microorganisms are rarely cultured. This has allowed us to tap into the huge potential of microbial dark matter.
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Quan, Yuan, Zhong-Yi Wang, Min Xiong, Zheng-Tao Xiao, and Hong-Yu Zhang. "Dissecting Traditional Chinese Medicines by Omics and Bioinformatics." Natural Product Communications 9, no. 9 (September 2014): 1934578X1400900. http://dx.doi.org/10.1177/1934578x1400900942.
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Traditional Chinese medicines (TCM) are a rich source of potential leads for drug development. However, there are fundamental differences between traditional Chinese medical concepts and modern pharmacology, which greatly hinder the modern development of TCM. To address this challenge, new techniques associated with genomics, transcriptomics, proteomics, metabolomics and bioinformatics have been used to dissect the pharmacological mechanisms of TCM. This review article provides an overview of the current research in this area, and illustrates the potential of omic and bioinformatic methods in TCM-based drug discovery.
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Droit, Arnaud, Guy G. Poirier, and Joanna M. Hunter. "Experimental and bioinformatic approaches for interrogating protein–protein interactions to determine protein function." Journal of Molecular Endocrinology 34, no. 2 (April 2005): 263–80. http://dx.doi.org/10.1677/jme.1.01693.
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An ambitious goal of proteomics is to elucidate the structure, interactions and functions of all proteins within cells and organisms. One strategy to determine protein function is to identify the protein–protein interactions. The increasing use of high-throughput and large-scale bioinformatics-based studies has generated a massive amount of data stored in a number of different databases. A challenge for bioinformatics is to explore this disparate data and to uncover biologically relevant interactions and pathways. In parallel, there is clearly a need for the development of approaches that can predict novel protein–protein interaction networks in silico. Here, we present an overview of different experimental and bioinformatic methods to elucidate protein–protein interactions.
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Lang, E. "Section 2: Patient Records: Integrating Bioinformatics into Clinical Practice: Progress and Evaluation." Yearbook of Medical Informatics 16, no. 01 (August 2007): 106–8. http://dx.doi.org/10.1055/s-0038-1638534.
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SummaryTo summarize current excellent research in the field of bioinformatics.Synopsis of the articles selected for the IMIA Yearbook 2007.Current research in the field of bioinformatics is characterized by careful evaluation of methods and by improved integration of methods into clinical workflows. Ongoing research on genetic causes of diseases is performed on more and better sources of reference data (genome sets and respective annotations), but is still hampered by insufficient, lacking or biased patient data. The application area of bioinformatics has been broadened, leading to amendment or even replacement of traditional methods in fields like characterization of microorganisms. Researchers carry out thorough statistical analyses in order to ensure quality and methodological correctness of new methods based on bioinformatic approaches which are more and more competitive compared to well-established techniques.The best paper selection of articles on bioinformatics shows examples of excellent research on methods concerning original development as well as quality assurance of previously reported studies. The crucial role of reliable and comprehensive data sources is affirmed, while technical development draws attention to the increasing problem of comparability of data derived some years ago with weaker equipment and those that are of up-to-date quality.
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Soanes, Darren M., Wendy Skinner, John Keon, John Hargreaves, and Nicholas J. Talbot. "Genomics of Phytopathogenic Fungi and the Development of Bioinformatic Resources." Molecular Plant-Microbe Interactions® 15, no. 5 (May 2002): 421–27. http://dx.doi.org/10.1094/mpmi.2002.15.5.421.
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Genomic resources available to researchers studying phytopathogenic fungi are limited. Here, we briefly review the genomic and bioinformatic resources available and the current status of fungal genomics. We also describe a relational database containing sequences of expressed sequence tags (ESTs) from three phytopathogenic fungi, Blumeria graminis, Magnaporthe grisea, and Mycosphaerella graminicola, and the methods and underlying principles required for its construction. The database contains significant annotation for each EST sequence and is accessible at http://cogeme.ex.ac.uk . An easy-to-use interface allows the user to identify gene sequences by using simple text queries or homology searches. New querying functions and large sequence sets from a variety of phytopathogenic species will be incorporated in due course.
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Hynst, Jakub, Veronika Navrkalova, Karol Pal, and Sarka Pospisilova. "Bioinformatic strategies for the analysis of genomic aberrations detected by targeted NGS panels with clinical application." PeerJ 9 (March 31, 2021): e10897. http://dx.doi.org/10.7717/peerj.10897.
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Molecular profiling of tumor samples has acquired importance in cancer research, but currently also plays an important role in the clinical management of cancer patients. Rapid identification of genomic aberrations improves diagnosis, prognosis and effective therapy selection. This can be attributed mainly to the development of next-generation sequencing (NGS) methods, especially targeted DNA panels. Such panels enable a relatively inexpensive and rapid analysis of various aberrations with clinical impact specific to particular diagnoses. In this review, we discuss the experimental approaches and bioinformatic strategies available for the development of an NGS panel for a reliable analysis of selected biomarkers. Compliance with defined analytical steps is crucial to ensure accurate and reproducible results. In addition, a careful validation procedure has to be performed before the application of NGS targeted assays in routine clinical practice. With more focus on bioinformatics, we emphasize the need for thorough pipeline validation and management in relation to the particular experimental setting as an integral part of the NGS method establishment. A robust and reproducible bioinformatic analysis running on powerful machines is essential for proper detection of genomic variants in clinical settings since distinguishing between experimental noise and real biological variants is fundamental. This review summarizes state-of-the-art bioinformatic solutions for careful detection of the SNV/Indels and CNVs for targeted sequencing resulting in translation of sequencing data into clinically relevant information. Finally, we share our experience with the development of a custom targeted NGS panel for an integrated analysis of biomarkers in lymphoproliferative disorders.
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Coy, Samantha, Eric Gann, Helena Pound, Steven Short, and Steven Wilhelm. "Viruses of Eukaryotic Algae: Diversity, Methods for Detection, and Future Directions." Viruses 10, no. 9 (September 11, 2018): 487. http://dx.doi.org/10.3390/v10090487.
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The scope for ecological studies of eukaryotic algal viruses has greatly improved with the development of molecular and bioinformatic approaches that do not require algal cultures. Here, we review the history and perceived future opportunities for research on eukaryotic algal viruses. We begin with a summary of the 65 eukaryotic algal viruses that are presently in culture collections, with emphasis on shared evolutionary traits (e.g., conserved core genes) of each known viral type. We then describe how core genes have been used to enable molecular detection of viruses in the environment, ranging from PCR-based amplification to community scale “-omics” approaches. Special attention is given to recent studies that have employed network-analyses of -omics data to predict virus-host relationships, from which a general bioinformatics pipeline is described for this type of approach. Finally, we conclude with acknowledgement of how the field of aquatic virology is adapting to these advances, and highlight the need to properly characterize new virus-host systems that may be isolated using preliminary molecular surveys. Researchers can approach this work using lessons learned from the Chlorella virus system, which is not only the best characterized algal-virus system, but is also responsible for much of the foundation in the field of aquatic virology.
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Han, Xiaoyan, Lei Cai, Yi Lu, Dan Li, and Jin Yang. "Identification of tRNA-derived fragments and their potential roles in diabetic cataract rats." Epigenomics 12, no. 16 (August 2020): 1405–18. http://dx.doi.org/10.2217/epi-2020-0193.
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Aim: To illustrate the expression profile of transfer RNA-derived fragments and reveal their putative role in the pathogenesis of diabetic cataract (DC) rats. Materials & methods: Small RNA sequencing was conducted in the lens epithelium of rats lens. The data were validated by quantitative real-time PCR, and bioinformatic analysis was performed to explore the roles of the fragments in DC pathogenesis. Results: A total of 213 differentially expressed tRNA-related fragments were identified, in which 111 were upregulated and 102 were downregulated in DC rats. Bioinformatics analysis revealed that several associated pathways might participate in the development of DC rats. Conclusion: tRNA-derived fragments may be involved in the pathogenesis of DC rats.
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Waite, David W., Lia Liefting, Catia Delmiglio, Anastasia Chernyavtseva, Hye Jeong Ha, and Jeremy R. Thompson. "Development and Validation of a Bioinformatic Workflow for the Rapid Detection of Viruses in Biosecurity." Viruses 14, no. 10 (September 30, 2022): 2163. http://dx.doi.org/10.3390/v14102163.
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The field of biosecurity has greatly benefited from the widespread adoption of high-throughput sequencing technologies, for its ability to deeply query plant and animal samples for pathogens for which no tests exist. However, the bioinformatics analysis tools designed for rapid analysis of these sequencing datasets are not developed with this application in mind, limiting the ability of diagnosticians to standardise their workflows using published tool kits. We sought to assess previously published bioinformatic tools for their ability to identify plant- and animal-infecting viruses while distinguishing from the host genetic material. We discovered that many of the current generation of virus-detection pipelines are not adequate for this task, being outperformed by more generic classification tools. We created synthetic MinION and HiSeq libraries simulating plant and animal infections of economically important viruses and assessed a series of tools for their suitability for rapid and accurate detection of infection, and further tested the top performing tools against the VIROMOCK Challenge dataset to ensure that our findings were reproducible when compared with international standards. Our work demonstrated that several methods provide sensitive and specific detection of agriculturally important viruses in a timely manner and provides a key piece of ground truthing for method development in this space.
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SONG, JIANGNING, HAO TAN, SARAH E. BOYD, HONGBIN SHEN, KHALID MAHMOOD, GEOFFREY I. WEBB, TATSUYA AKUTSU, JAMES C. WHISSTOCK, and ROBERT N. PIKE. "BIOINFORMATIC APPROACHES FOR PREDICTING SUBSTRATES OF PROTEASES." Journal of Bioinformatics and Computational Biology 09, no. 01 (February 2011): 149–78. http://dx.doi.org/10.1142/s0219720011005288.
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Proteases have central roles in "life and death" processes due to their important ability to catalytically hydrolyze protein substrates, usually altering the function and/or activity of the target in the process. Knowledge of the substrate specificity of a protease should, in theory, dramatically improve the ability to predict target protein substrates. However, experimental identification and characterization of protease substrates is often difficult and time-consuming. Thus solving the "substrate identification" problem is fundamental to both understanding protease biology and the development of therapeutics that target specific protease-regulated pathways. In this context, bioinformatic prediction of protease substrates may provide useful and experimentally testable information about novel potential cleavage sites in candidate substrates. In this article, we provide an overview of recent advances in developing bioinformatic approaches for predicting protease substrate cleavage sites and identifying novel putative substrates. We discuss the advantages and drawbacks of the current methods and detail how more accurate models can be built by deriving multiple sequence and structural features of substrates. We also provide some suggestions about how future studies might further improve the accuracy of protease substrate specificity prediction.
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Jayanto, Herdhanu, and Budi Setiadi Daryono. "DEVELOPMENT OF SPECIFIC PRIMERS FOR INTER SPECIES PHYLOGENY RELATIONSHIP ON Crocodilian sp." KnE Life Sciences 2, no. 1 (September 20, 2015): 301. http://dx.doi.org/10.18502/kls.v2i1.163.
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<p>Poaching, trafficking, and illegal product trading are classic activities which frequently faced by Crocodilian group. To overcome, laws need supporting methods for a decision of these all activities which threaten crocodile species. This will require species identification that associated to taxonomy classification. Crocodilian species are very similar in morphology. This may result to a false identification especially when working on incomplete specimen. Currently, twenty-four existing Crocodilian species are continuously revised to improve the precise placement and/or acceptance of certain species on Crocodilian classification. Herein we address this issue using Cytochrome-b. The idea was to obtain genus specific primer from Cytochrome-b and then tested the precision of the designed primers using bioinformatics tools’ Primer-BLAST and CLC sequence Viewer 6. The designed primers showed a highly specificity on species level. The phylogenetic tree constructed by is relatively precise compared to reported phylogenetic trees. These specific primers together with the genus specific primers may give valuable and important support for the effective and efficient identification of Crocodilian group.</p><p><br /><strong>Keywords</strong>: Crocodilian, illegal trading, Cytochrome-b , specific primer, bioinformatic</p>
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Vakhrushev, Yu A., T. I. Vershinina, P. A. Fedotov, A. A. Kozyreva, A. M. Kiselev, Yu V. Fomicheva, E. S. Vasichkina, T. M. Pervunina, and A. A. Kostareva. "Left ventricular noncompaction associated with titin-truncating variants in the TTN gene." Russian Journal of Cardiology 25, no. 10 (November 18, 2020): 4027. http://dx.doi.org/10.15829/1560-4071-2020-4027.
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Aim. To study the association of genetic variants in the titin gene (TTN) with the development and clinical course of left ventricular noncompaction in different age groups.Material and methods. The article discusses three clinical cases of patients with left ventricular noncompaction who were treated at theAlmazovNationalMedicalResearchCenter. We performed a new-generation sequencing of 108 genes associated with cardiomyopathies, as well as whole exome sequencing and Sanger sequencing.Results. We identified genetic variants in the TTN gene leading to the synthesis of truncated protein: in the first two cases, the cause of noncompaction was a thirteen nucleotide deletion with a reading frame shift, in the second, a nonsense mutation. An algorithm for assessing the pathogenicity of the identified variants and a scheme of diagnostic genetic search are presented.Conclusion. Causal role of TTN-truncating variants in development of cardiomyopathies and, in particular, left ventricular noncompaction, requires a comprehensive clinical, segregation and bioinformatic analysis using international databases and the use of bioinformatics software.
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Bi, Siwei, Ruiqi Liu, Beiyi Wu, Linfeng He, and Jun Gu. "Bioinformatic Analysis of Key Genes and Pathways Related to Keloids." BioMed Research International 2021 (March 23, 2021): 1–11. http://dx.doi.org/10.1155/2021/5897907.
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Background. The pathophysiology of keloids is complex, and the treatment for keloids is still an unmet medical need. Our study is aimed at identifying the hub genes among the differentially expressed genes (DEGs) between normal skin tissue and keloids and key pathways in the development of keloids. Materials and Methods. We downloaded the GSE92566 and GSE90051 microarray data, which contain normal skin tissue and keloid gene expression data. GSE92566 was treated as a discovery dataset for summarizing the significantly DEGs, and GSE90051 served as a validation dataset. Gene Ontology, Kyoto Encyclopedia of Genes and Genomes pathway, Reactome enrichment analysis, gene set enrichment analysis, and gene set variation analysis were performed for the key functions and pathways enriched in DEGs. Moreover, we also validated the hub genes identified from the protein-protein interaction network and predicted miRNA-hub gene interactions. Results. 117 downregulated DEGs and 204 upregulated DEGs in GSE92566 were identified. Extracellular and collagen-related pathways were prominent in upregulated DEGs, while the keratinization-related pathway was associated with downregulated DEGs. The hub genes included COL5A1, COL5A2, and SERPINH1, which were also validated in GSE90051. Conclusion. This study identified several hub genes and provided insights for the underlying pathways and miRNA-hub gene interactions for keloid development through bioinformatic analysis of two microarray datasets. Additionally, our results would support the development of future therapeutic strategies.
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Li, Xuebing, Chunxia Wang, Heng Yang, Dongxu Pei, Yuchun Liu, Sha Yan, and Yongwei Li. "Screening and verification of genes related to polycystic ovary syndrome." Journal of International Medical Research 51, no. 1 (January 2023): 030006052211474. http://dx.doi.org/10.1177/03000605221147444.
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Objective To identify key genes involved in occurrence and development of polycystic ovary syndrome (PCOS). Methods By downloading the GSE85932 dataset from the GEO database, we used bioinformatical analysis to analyse differentially expressed genes (DEGs) from blood samples of eight women with PCOS and eight matched controls. Following bioinformatic analysis, we performed a cross-sectional study of serum samples taken from 79 women with PCOS and 36 healthy controls. Results From the 178 DEGs identified by bioinformatical analysis, 15 genes were identified as significant, and of these, ORM1 and ORM2 were selected for further verification as potential biomarkers for PCOS. Serum ORM1 and ORM2 levels were significantly increased in women with PCOS, and had a high diagnostic value. ORM1 and ORM2 were positively correlated with testosterone, cholesterol, and triglycerides. ORM1 levels were negatively correlated with high density lipoprotein (HDL) while ORM2 levels showed no significant correlation. Conclusions ORM may be an effective biomarker for the diagnosis of PCOS and its monitoring may be a useful therapeutic strategy.
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Zhang, Yuan, Lei Yang, Jia Shi, Yunfei Lu, Xiaorong Chen, and Zongguo Yang. "The Oncogenic Role of CENPA in Hepatocellular Carcinoma Development: Evidence from Bioinformatic Analysis." BioMed Research International 2020 (April 8, 2020): 1–8. http://dx.doi.org/10.1155/2020/3040839.
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Objective. This study is aimed at investigating the predictive value of CENPA in hepatocellular carcinoma (HCC) development. Methods. Using integrated bioinformatic analysis, we evaluated the CENPA mRNA expression in tumor and adjacent tissues and correlated it with HCC survival and clinicopathological features. A Cox regression hazard model was also performed. Results. CENPA mRNA was significantly upregulated in tumor tissues compared with that in adjacent tissues, which were validated in The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) series (all P<0.01). In the Kaplan-Meier plotter platform, the high level of CENPA mRNA was significantly correlated with overall survival (OS), disease-free survival (DFS), recurrence-free survival (RFS), and progression-free survival (PFS) in HCC patients (all log rank P<0.01). For validation in GSE14520 and pan-TCGA dataset, HCC patients with CNEPA mRNA overexpression had poor OS compared with those with low CENPA mRNA (log rank P=0.025 and P<0.0001, respectively), and those with high CENPA had poor DFS in TCGA (log rank P=0.0001). Additionally, CENPA mRNA were upregulated in HCC patients with alpha-fetoprotein (AFP) elevation, advanced TNM stage, larger tumor size, advanced AJCC stage, advanced pathology grade, and vascular invasion (all P<0.05). A Cox regression model including CENPA, OIP5, and AURKB could predict OS in HCC patients effectively (AUC=0.683). Conclusion. Overexpressed in tumors, CENPA might be an oncogenic factor in the development of HCC patients.
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Li, Xiaoying, Xin Lin, Huiling Ren, and Jinjing Guo. "Ontological Organization and Bioinformatic Analysis of Adverse Drug Reactions From Package Inserts: Development and Usability Study." Journal of Medical Internet Research 22, no. 7 (July 20, 2020): e20443. http://dx.doi.org/10.2196/20443.
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Background Licensed drugs may cause unexpected adverse reactions in patients, resulting in morbidity, risk of mortality, therapy disruptions, and prolonged hospital stays. Officially approved drug package inserts list the adverse reactions identified from randomized controlled clinical trials with high evidence levels and worldwide postmarketing surveillance. Formal representation of the adverse drug reaction (ADR) enclosed in semistructured package inserts will enable deep recognition of side effects and rational drug use, substantially reduce morbidity, and decrease societal costs. Objective This paper aims to present an ontological organization of traceable ADR information extracted from licensed package inserts. In addition, it will provide machine-understandable knowledge for bioinformatics analysis, semantic retrieval, and intelligent clinical applications. Methods Based on the essential content of package inserts, a generic ADR ontology model is proposed from two dimensions (and nine subdimensions), covering the ADR information and medication instructions. This is followed by a customized natural language processing method programmed with Python to retrieve the relevant information enclosed in package inserts. After the biocuration and identification of retrieved data from the package insert, an ADR ontology is automatically built for further bioinformatic analysis. Results We collected 165 package inserts of quinolone drugs from the National Medical Products Administration and other drug databases in China, and built a specialized ADR ontology containing 2879 classes and 15,711 semantic relations. For each quinolone drug, the reported ADR information and medication instructions have been logically represented and formally organized in an ADR ontology. To demonstrate its usage, the source data were further bioinformatically analyzed. For example, the number of drug-ADR triples and major ADRs associated with each active ingredient were recorded. The 10 ADRs most frequently observed among quinolones were identified and categorized based on the 18 categories defined in the proposal. The occurrence frequency, severity, and ADR mitigation method explicitly stated in package inserts were also analyzed, as well as the top 5 specific populations with contraindications for quinolone drugs. Conclusions Ontological representation and organization using officially approved information from drug package inserts enables the identification and bioinformatic analysis of adverse reactions caused by a specific drug with regard to predefined ADR ontology classes and semantic relations. The resulting ontology-based ADR knowledge source classifies drug-specific adverse reactions, and supports a better understanding of ADRs and safer prescription of medications.
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Lavezzo, E., A. Sinigaglia, S. Toppo, L. Barzon, G. Palu', R. Cardin, C. Carlotto, and F. Farinati. "OC.03.1 ANALYSIS OF MICRORNA EXPRESSION PROFILES IN CHRONIC HEPATITIS AND DEVELOPMENT OF BIOINFORMATIC METHODS FOR PREDICTION OF MICRORNA TARGETS." Digestive and Liver Disease 42 (March 2010): S75. http://dx.doi.org/10.1016/s1590-8658(10)60039-4.
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Zacharias, Helena, Michael Altenbuchinger, and Wolfram Gronwald. "Statistical Analysis of NMR Metabolic Fingerprints: Established Methods and Recent Advances." Metabolites 8, no. 3 (August 28, 2018): 47. http://dx.doi.org/10.3390/metabo8030047.
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In this review, we summarize established and recent bioinformatic and statistical methods for the analysis of NMR-based metabolomics. Data analysis of NMR metabolic fingerprints exhibits several challenges, including unwanted biases, high dimensionality, and typically low sample numbers. Common analysis tasks comprise the identification of differential metabolites and the classification of specimens. However, analysis results strongly depend on the preprocessing of the data, and there is no consensus yet on how to remove unwanted biases and experimental variance prior to statistical analysis. Here, we first review established and new preprocessing protocols and illustrate their pros and cons, including different data normalizations and transformations. Second, we give a brief overview of state-of-the-art statistical analysis in NMR-based metabolomics. Finally, we discuss a recent development in statistical data analysis, where data normalization becomes obsolete. This method, called zero-sum regression, builds metabolite signatures whose estimation as well as predictions are independent of prior normalization.
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Zeng, Yu, Nanhong Li, Zhenzhen Zheng, Riken Chen, Min Peng, Wang Liu, Jinru Zhu, Mingqing Zeng, Junfen Cheng, and Cheng Hong. "Screening of Hub Genes Associated with Pulmonary Arterial Hypertension by Integrated Bioinformatic Analysis." BioMed Research International 2021 (March 22, 2021): 1–16. http://dx.doi.org/10.1155/2021/6626094.
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Background. Pulmonary arterial hypertension (PAH) is a disease or pathophysiological syndrome which has a low survival rate with abnormally elevated pulmonary artery pressure caused by known or unknown reasons. In addition, the pathogenesis of PAH is not fully understood. Therefore, it has become an urgent matter to search for clinical molecular markers of PAH, study the pathogenesis of PAH, and contribute to the development of new science-based PAH diagnosis and targeted treatment methods. Methods. In this study, the Gene Expression Omnibus (GEO) database was used to downloaded a microarray dataset about PAH, and the differentially expressed genes (DEGs) between PAH and normal control were screened out. Moreover, we performed the functional enrichment analyses and protein-protein interaction (PPI) network analyses of the DEGs. In addition, the prediction of miRNA and transcriptional factor (TF) of hub genes and construction miRNA-TF-hub gene network were performed. Besides, the ROC curve was used to evaluate the diagnostic value of hub genes. Finally, the potential drug targets for the 5 identified hub genes were screened out. Results. 69 DEGs were identified between PAH samples and normal samples. GO and KEGG pathway analyses revealed that these DEGs were mostly enriched in the inflammatory response and cytokine-cytokine receptor interaction, respectively. The miRNA-hub genes network was conducted subsequently with 131 miRNAs, 7 TFs, and 5 hub genes (CCL5, CXCL12, VCAM1, CXCR1, and SPP1) which screened out via constructing the PPI network. 17 drugs interacted with 5 hub genes were identified. Conclusions. Through bioinformatic analysis of microarray data sets, 5 hub genes (CCL5, CXCL12, VCAM1, CXCR1, and SPP1) were identified from DEGs between control samples and PAH samples. Studies showed that the five hub genes might play an important role in the development of PAH. These 5 hub genes might be potential biomarkers for diagnosis or targets for the treatment of PAH. In addition, our work also indicated that paying more attention on studies based on these 5 hub genes might help to understand the molecular mechanism of the development of PAH.
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Hou, Liangjuan, Xin Zeng, Xuan Li, Chune Zhao, Juan Zou, Yukun Li, and Gang Liu. "MCM6 Promotes Hepatocellular Carcinoma Progression via the Notch Pathway: Clinical, Functional, and Genomic Insights." Computational and Mathematical Methods in Medicine 2022 (June 9, 2022): 1–21. http://dx.doi.org/10.1155/2022/3116303.
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Objective. To evaluate the expression profile of MCM6 in HCC and the relationship between MCM6 level and clinicopathological parameters through bioinformatics analysis of several databases. Methods. MCM expression level, clinical parameters, survival data, and gene set enrichment analysis were analyzed by bioinformatics database, including Oncomine™, UALCAN, HCCDB, TCGA, cBioPortal, and LinkedOmics. Real-time PCR, western blotting, and IHC staining were conducted to identify the expression of MCM6 in HCC compared to normal liver tissues. Results. Bioinformatics analysis indicated that the mRNA of MCM6 was obviously increased in multiple cancer types, especially in HCC. MCM6 level was positively associated with multiple clinical parameters (stage 3 and grades 3 and 4) and negatively associated with patient outcomes (overall survival). Moreover, enrichment of functions and signaling pathways analysis of MCM6 suggested that MCM6 might mediate DNA replication and cellular metabolism to promote the development and progression of HCC. Furthermore, IHC staining and western blotting indicated that the MCM6 was enhanced in HCC tissue, and MCM6 could promote HCC proliferation in activating Notch pathway via WB and bioinformatic analysis. Conclusion. This study actually revealed the expression and related functions of MCM6 in HCC. Furthermore, MCM6 is a carcinogenic role in activating Notch pathway to promote HCC cell proliferation, which may be a new prognostic biomarker and therapeutic target for HCC patients.
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De Las Rivas, Javier, and Alberto de Luis. "Interactome Data and Databases: Different Types of Protein Interaction." Comparative and Functional Genomics 5, no. 2 (2004): 173–78. http://dx.doi.org/10.1002/cfg.377.
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In recent years, the biomolecular sciences have been driven forward by overwhelming advances in new biotechnological high-throughput experimental methods and bioinformatic genome-wide computational methods. Such breakthroughs are producing huge amounts of new data that need to be carefully analysed to obtain correct and useful scientific knowledge. One of the fields where this advance has become more intense is the study of the network of ‘protein–protein interactions’, i.e. the ‘interactome’. In this short review we comment on the main data and databases produced in this field in last 5 years. We also present a rationalized scheme of biological definitions that will be useful for a better understanding and interpretation of ‘what a protein–protein interaction is’ and ‘which types of protein–protein interactions are found in a living cell’. Finally, we comment on some assignments of interactome data to defined types of protein interaction and we present a new bioinformatic tool called APIN (Agile Protein Interaction Network browser), which is in development and will be applied to browsing protein interaction databases.
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Xu, Yuexia, Yifeng Wang, Baomei He, Yuhui Yao, Qianqian Cai, and Lihui Wu. "Identification of the Shared Gene Signatures between Autism Spectrum Disorder and Epilepsy via Bioinformatic Analysis." Computational and Mathematical Methods in Medicine 2022 (December 16, 2022): 1–17. http://dx.doi.org/10.1155/2022/9883537.
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Purpose. To identify gene signatures that are shared by autism spectrum disorder (ASD) and epilepsy (EP) and explore the potential molecular mechanism of the two diseases using WGCNA analysis. Additionally, to verify the effects of the shared molecular mechanism on ADHD, which is another neurological comorbidity. Methods. We screened the crosstalk genes between ASD and EP based on WGCNA and differential expression analysis from GEO and DisGeNET database and analyzed the function of the genes’ enrichment by GO and KEGG analyses. Then, with combination of multiple datasets and multiple bioinformatic analysis methods, the shared gene signatures were identified. Moreover, we explored whether the shared gene signature had influence on the other neurological disorder like ADHD by analyzing the difference of the relative genes’ expression based on bioinformatic analysis and molecular experiment. Results. By comprehensive bioinformatic analysis for multiple datasets, we found that abnormal immune response and abnormal lipid metabolic pathway played important roles in coincidence of ASD and EP. Base on the results of WGCNA, we got the hub genes in ASD and EP. In attention deficit and hyperactivity disorder (ADHD) animal model, we also found a significant difference of gene expression related to sulfatide metabolism, indicating that the abnormal sphingolipid metabolism was common in multiple neurological disorders. Conclusion. This study reveals shared gene signatures between ASD and EP and identifies abnormal sphingolipid metabolism as an important participant in the development of ASD, EP, and ADHD.
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Wang, Ying, Yinhui Yao, Jingyi Zhao, Chunhua Cai, Junhui Hu, and Yanwu Zhao. "Development of an Autophagy-Related Gene Prognostic Model and Nomogram for Estimating Renal Clear Cell Carcinoma Survival." Journal of Oncology 2021 (February 18, 2021): 1–13. http://dx.doi.org/10.1155/2021/8810849.
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Background. Kidney renal clear cell carcinoma (KIRC) is a fatal malignancy of the urinary system. Autophagy is implicated in KIRC occurrence and development. Here, we evaluated the prognostic value of autophagy-related genes (ARGs) in kidney renal clear cell carcinoma. Materials and Methods. We analyzed RNA sequencing and clinical KIRC patient data obtained from TCGA and ICGC to develop an ARG prognostic signature. Differentially expressed ARGs were further evaluated by functional assessment and bioinformatic analysis. Next, ARG score was determined in 215 KIRC patients using univariable Cox and LASSO regression analyses. An ARG nomogram was built based on multivariable Cox analysis. The prognosis nomogram model based on the ARG signatures and clinicopathological information was evaluated for discrimination, calibration, and clinical usefulness. Results. A total of 47 differentially expressed ARGs were identified. Of these, 8 candidates that significantly correlated with KIRC overall survival were subjected to LASSO analysis and an ARG score built. Functional enrichment and bioinformatic analysis were used to reveal the differentially expressed ARGs in cancer-related biological processes and pathways. Multivariate Cox analysis was used to integrate the ARG nomogram with the ARG signature and clinicopathological information. The nomogram exhibited proper calibration and discrimination (C-index = 0.75, AUC = >0.7). Decision curve analysis also showed that the nomogram was clinically useful. Conclusions. KIRC patients and doctors could benefit from ARG nomogram use in clinical practice.
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Puig, Oscar, Eugene Joseph, Malgorzata Jaremko, Gregory Kellogg, Robert Wisotzkey, Roman Shraga, Bonny Patel, et al. "Comprehensive next generation sequencing assay and bioinformatic pipeline for identifying pathogenic variants associated with hereditary cancers." Journal of Clinical Oncology 35, no. 15_suppl (May 20, 2017): e13105-e13105. http://dx.doi.org/10.1200/jco.2017.35.15_suppl.e13105.
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e13105 Background: Diagnosis of hereditary cancer syndromes involves time-consuming comprehensive clinical and laboratory work-up, however, timely and accurate diagnosis is pivotal to the clinical management of cancer patients. Germline genetic testing has shown to facilitate the diagnostic process, allowing for identification and management of individuals at risk for inherited cancers. However, the laboratory diagnostics process requires not only development and validation of comprehensive gene panels to improve diagnostic yields, but a quality driven workflow including an end-to-end bioinformatics pipeline, and a robust process for variant classification. We will present a gene panel for the evaluation of hereditary cancer syndromes, conducted utilizing our novel end-to-end workflow, and validated in the CLIA-approved environment. Methods: A targeted Next-Generation Sequencing (NGS) panel consisting of 130 genes, including exons, promoters, 5’-UTRs, 3’-UTRs and selected introns, was designed to include genes associated with hereditary cancers. The assay was validated using samples from the 1000 genomes project and samples with known pathogenic variants. Elements software was utilized for end-to-end bioinformatic process ensuring adherence with the CLIA quality standards, and supporting manual curation of sequence variants. Results: Preliminary data from our current panel of genes associated with hereditary cancer syndromes revealed high sensitivity, specificity, and positive predictive value. Accuracy was confirmed by analysis of known SNVs, indels, and CNVs using 1000 Genomes and samples carrying pathogenic variants. The bioinformatics software allowed for an end-to-end quality controlled process of handling and analyzing of the NGS data, showing applicability for a clinical laboratory workflow. Conclusions: We have developed a comprehensive and accurate genetic testing process based on an automated and quality driven bioinformatics workflow that can be used to identify clinically important variants in genes associated with hereditary cancers. It's performance allows for implementation in the clinical laboratory setting.
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Kotlyarov, Stanislav, and Anna Kotlyarova. "Bioinformatic Analysis of ABCA1 Gene Expression in Smoking and Chronic Obstructive Pulmonary Disease." Membranes 11, no. 9 (August 31, 2021): 674. http://dx.doi.org/10.3390/membranes11090674.
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Smoking is a key modifiable risk factor for developing the chronic obstructive pulmonary disease (COPD). When smoking, many processes, including the reverse transport of cholesterol mediated by the ATP binding cassette transporter A1 (ABCA1) protein are disrupted in the lungs. Changes in the cholesterol content in the lipid rafts of plasma membranes can modulate the function of transmembrane proteins localized in them. It is believed that this mechanism participates in increasing the inflammation in COPD. Methods: Bioinformatic analysis of datasets from Gene Expression Omnibus (GEO) was carried out. Gene expression data from datasets of alveolar macrophages and the epithelium of the respiratory tract in smokers and COPD patients compared with non-smokers were used for the analysis. To evaluate differentially expressed genes, bioinformatic analysis was performed in comparison groups using the limma package in R (v. 4.0.2), and the GEO2R and Phantasus tools (v. 1.11.0). Results: The conducted bioinformatic analysis showed changes in the expression of the ABCA1 gene associated with smoking. In the alveolar macrophages of smokers, the expression levels of ABCA1 were lower than in non-smokers. At the same time, in most of the airway epithelial datasets, gene expression did not show any difference between the groups of smokers and non-smokers. In addition, it was shown that the expression of ABCA1 in the epithelial cells of the trachea and large bronchi is higher than in small bronchi. Conclusions: The conducted bioinformatic analysis showed that smoking can influence the expression of the ABCA1 gene, thereby modulating lipid transport processes in macrophages, which are part of the mechanisms of inflammation development.
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Navarrete, Paula, María José Garzón, Sheila Lorente-Pozo, Salvador Mena-Mollá, Máximo Vento, Federico V. Pallardó, Jesús Beltrán-García, Rebeca Osca-Verdegal, Eva García-López, and José Luis García-Giménez. "Use of Two Complementary Bioinformatic Approaches to Identify Differentially Methylated Regions in Neonatal Sepsis." Open Bioinformatics Journal 14, no. 1 (November 25, 2021): 144–52. http://dx.doi.org/10.2174/1875036202114010144.
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Background: Neonatal sepsis is a heterogeneous condition affecting preterm infants whose underlying mechanisms remain unknown. The analysis of changes in the DNA methylation pattern can contribute to improving the understanding of molecular pathways underlying disease pathophysiology. Methylation EPIC 850K BeadChip technology is an excellent tool for genome-wide methylation analyses and the detection of differentially methylated regions (DMRs). Objective: The aim is to identify DNA methylation traits in complex diseases, such as neonatal sepsis, using data from Methylation EPIC 850K BeadChip arrays. Methods: Two different bioinformatic methods, DMRcate (a supervised approach) and mCSEA (an unsupervised approach), were used to identify DMRs using EPIC data from leukocytes of neonatal septic patients. Here, we describe with detail the implementation of both methods as well as their applicability, briefly discussing the results obtained for neonatal sepsis. Results: Differences in methylation levels were observed in neonatal sepsis patients. Moreover, differences were identified between the two subsets of the disease: Early-Onset neonatal Sepsis (EOS) and Late-Onset Neonatal Sepsis (LOS). Conclusion: This approach by using DMRcate and mCSA helped us to gain insight into the intricate mechanisms that may drive EOS and LOS development and progression in newborns.
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Kim, Byung-Chul, Kyu Hwan Kwack, Jeewan Chun, and Jae-Hyung Lee. "Comparative Transcriptome Analysis of Human Adipose-Derived Stem Cells Undergoing Osteogenesis in 2D and 3D Culture Conditions." International Journal of Molecular Sciences 22, no. 15 (July 26, 2021): 7939. http://dx.doi.org/10.3390/ijms22157939.
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Human adipose-derived stem cells (hADSCs) are types of mesenchymal stem cells (MSCs) that have been used as tissue engineering models for bone, cartilage, muscle, marrow stroma, tendon, fat and other connective tissues. Tissue regeneration materials composed of hADSCs have the potential to play an important role in reconstituting damaged tissue or diseased mesenchymal tissue. In this study, we assessed and investigated the osteogenesis of hADSCs in both two-dimensional (2D) and three-dimensional (3D) culture conditions. We confirmed that the hADSCs successfully differentiated into bone tissues by ARS staining and quantitative RT–PCR. To gain insight into the detailed biological difference between the two culture conditions, we profiled the overall gene expression by analyzing the whole transcriptome sequencing data using various bioinformatic methods. We profiled the overall gene expression through RNA-Seq and further analyzed this using various bioinformatic methods. During differential gene expression testing, significant differences in the gene expressions between hADSCs cultured in 2D and 3D conditions were observed. The genes related to skeletal development, bone development and bone remodeling processes were overexpressed in the 3D culture condition as compared to the 2D culture condition. In summary, our RNA-Seq-based study proves effective in providing new insights that contribute toward achieving a genome-wide understanding of gene regulation in mesenchymal stem cell osteogenic differentiation and bone tissue regeneration within the 3D culture system.
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Salzer, Liesa, and Michael Witting. "Quo Vadis Caenorhabditis elegans Metabolomics—A Review of Current Methods and Applications to Explore Metabolism in the Nematode." Metabolites 11, no. 5 (April 29, 2021): 284. http://dx.doi.org/10.3390/metabo11050284.
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Metabolomics and lipidomics recently gained interest in the model organism Caenorhabditis elegans (C. elegans). The fast development, easy cultivation and existing forward and reverse genetic tools make the small nematode an ideal organism for metabolic investigations in development, aging, different disease models, infection, or toxicology research. The conducted type of analysis is strongly depending on the biological question and requires different analytical approaches. Metabolomic analyses in C. elegans have been performed using nuclear magnetic resonance (NMR) spectroscopy, direct infusion mass spectrometry (DI-MS), gas-chromatography mass spectrometry (GC-MS) and liquid chromatography mass spectrometry (LC-MS) or combinations of them. In this review we provide general information on the employed techniques and their advantages and disadvantages in regard to C. elegans metabolomics. Additionally, we reviewed different fields of application, e.g., longevity, starvation, aging, development or metabolism of secondary metabolites such as ascarosides or maradolipids. We also summarised applied bioinformatic tools that recently have been used for the evaluation of metabolomics or lipidomics data from C. elegans. Lastly, we curated metabolites and lipids from the reviewed literature, enabling a prototypic collection which serves as basis for a future C. elegans specific metabolome database.
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Cao, Bang Phi. "Genome-wide analysis of NIN-like protein (NLP) family in maize (Zea mays L.) by using bioinformatic methods." Science and Technology Development Journal - Natural Sciences 1, T2 (June 30, 2017): 39–47. http://dx.doi.org/10.32508/stdjns.v1it2.450.
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The NIN-like proteins (NLP) belong to RWP-RK transcription factor family and possess the similarity characteristics of NIN (Nodules INception). The NLPs regulate the expression of genes which are involved in nitrate signaling in plants. In this work, we have performed a genome-wide analysis of the NLP gene family in maize (Zea mays L.) through the bioinformatic methods. We identified a total of nine NLP encoding genes in whole genome of maize. The genomic sequences of these genes were from 2855 to 8092 nucleotides in length and contained three or four introns. Their predicted protein sizes ranged in size from 786 to 945 amino acids. The theoretical isoelectric point values of most deduced protein were less than 7. The maize NLP proteins possessed conserved regions of plant NLP at N-terminal as well as at C-terminal including the RWP-RK and PB1 domains. Based on the phylogenic analysis, we detected three current whole-genome gene duplication events which occurred in maize genome apter speciation point. All of NLP genes expressed in tissues at different development stages, from germinating seed to maturation seed were examined. The ZmNLP5, ZmNLP6 and ZmNLP7 were weakly expressed in comparison to others genes in most examined tissues.
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Dai, Fangfang, Jinglin Wu, Zhimin Deng, Hengxing Li, Wei Tan, Mengqin Yuan, Dongyong Yang, et al. "Integrated Bioinformatic Analysis of DNA Methylation and Immune Infiltration in Endometrial Cancer." BioMed Research International 2022 (June 20, 2022): 1–13. http://dx.doi.org/10.1155/2022/5119411.
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Background. Endometrial cancer greatly threatens the health of female. Emerging evidences have demonstrated that DNA methylation and immune infiltration are involved in the occurrence and development of endometrial cancer. However, the mechanism and prognostic biomarkers of endometrial cancer are still unclear. In this study, we assess DNA methylation and immune infiltration via bioinformatic analysis. Methods. The latest RNA-Seq, DNA methylation data, and clinical data related to endometrial cancer were downloaded from the UCSC Xena dataset. The methylation-driven genes were selected, and then the risk score was obtained using “MethylMix” and “corrplot” R packages. The connection between methylated genes and the expression of screened driven genes were explored using “survminer” and “beeswarm” packages, respectively. Finally, the role of VTCN1in immune infiltration was analyzed using “CIBERSORT” package. Results. In this study, 179 upregulated genes, and 311 downregulated genes were identified and found to be related to extracellular matrix organization, cell–cell junctions, and cell adhesion molecular binding. The methylation-driven gene VTCN1 was selected, and patients classified to the hypomethylation and high expression group displayed poor prognosis. The VTCN1 gene exhibited highest correlation coefficient between methylation and expression. More importantly, the hypomethylation of promoter of VTCN1 led to its high expression, thereby induce tumor development by inhibiting CD8+ T cell infiltration. Conclusions. Overall, our study was the first to reveal the mechanism of endometrial cancer by assessing DNA methylation and immune infiltration via integrated bioinformatic analysis. In addition, we found a pivotal prognostic biomarker for the disease. Our study provides potential targets for the diagnosis and prognosis of endometrial cancer in the future.
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Xiong, Xin-gui, Qinghua Liang, Chunhu Zhang, Yang Wang, Wei Huang, Weijun Peng, Zhe Wang, and Zi-an Xia. "Serum Proteome Alterations in Patients with Cognitive Impairment after Traumatic Brain Injury Revealed by iTRAQ-Based Quantitative Proteomics." BioMed Research International 2017 (2017): 1–13. http://dx.doi.org/10.1155/2017/8572509.
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Background. Cognitive impairment is the leading cause of traumatic brain injury- (TBI-) related disability; however, the underlying pathogenesis of this dysfunction is not completely understood. Methods. Using an isobaric tagging for relative and absolute quantitation- (iTRAQ-) based quantitative proteomic approach, serum samples from healthy control subjects, TBI patients with cognitive impairment, and TBI patients without cognitive impairment were analysed to identify differentially expressed proteins (DEPs) related to post-TBI cognitive impairment. In addition, DEPs were further analysed using bioinformatic platforms and validated using enzyme-linked immunosorbent assays (ELISA). Results. A total of 56 DEPs were identified that were specifically related to TBI-induced cognitive impairment. Bioinformatic analysis revealed that a wide variety of cellular and metabolic processes and some signaling pathways were involved in the pathophysiology of cognitive deficits following TBI. Five randomly selected DEPs were validated using ELISA in an additional 105 cases, and the results also supported the experimental findings. Conclusions. Despite limitations, our findings will facilitate further studies of the pathological mechanisms underlying TBI-induced cognitive impairment and provide new methods for the research and development of neuroprotective agents. However, further investigation on a large cohort is warranted.
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Hynst, Jakub, Karla Plevova, Lenka Radova, Vojtech Bystry, Karol Pal, and Sarka Pospisilova. "Bioinformatic pipelines for whole transcriptome sequencing data exploitation in leukemia patients with complex structural variants." PeerJ 7 (June 12, 2019): e7071. http://dx.doi.org/10.7717/peerj.7071.
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Background Extensive genome rearrangements, known as chromothripsis, have been recently identified in several cancer types. Chromothripsis leads to complex structural variants (cSVs) causing aberrant gene expression and the formation of de novo fusion genes, which can trigger cancer development, or worsen its clinical course. The functional impact of cSVs can be studied at the RNA level using whole transcriptome sequencing (total RNA-Seq). It represents a powerful tool for discovering, profiling, and quantifying changes of gene expression in the overall genomic context. However, bioinformatic analysis of transcriptomic data, especially in cases with cSVs, is a complex and challenging task, and the development of proper bioinformatic tools for transcriptome studies is necessary. Methods We designed a bioinformatic workflow for the analysis of total RNA-Seq data consisting of two separate parts (pipelines): The first pipeline incorporates a statistical solution for differential gene expression analysis in a biologically heterogeneous sample set. We utilized results from transcriptomic arrays which were carried out in parallel to increase the precision of the analysis. The second pipeline is used for the identification of de novo fusion genes. Special attention was given to the filtering of false positives (FPs), which was achieved through consensus fusion calling with several fusion gene callers. We applied the workflow to the data obtained from ten patients with chronic lymphocytic leukemia (CLL) to describe the consequences of their cSVs in detail. The fusion genes identified by our pipeline were correlated with genomic break-points detected by genomic arrays. Results We set up a novel solution for differential gene expression analysis of individual samples and de novo fusion gene detection from total RNA-Seq data. The results of the differential gene expression analysis were concordant with results obtained by transcriptomic arrays, which demonstrates the analytical capabilities of our method. We also showed that the consensus fusion gene detection approach was able to identify true positives (TPs) efficiently. Detected coordinates of fusion gene junctions were in concordance with genomic breakpoints assessed using genomic arrays. Discussion Byapplying our methods to real clinical samples, we proved that our approach for total RNA-Seq data analysis generates results consistent with other genomic analytical techniques. The data obtained by our analyses provided clues for the study of the biological consequences of cSVs with far-reaching implications for clinical outcome and management of cancer patients. The bioinformatic workflow is also widely applicable for addressing other research questions in different contexts, for which transcriptomic data are generated.
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Rosolowski, M., H. Berger, C. Schwaenen, S. Wessendorf, M. Loeffler, D. Hasenclever, and M. Kreuz. "Development and Implementation of an Analysis Tool for Array-based Comparative Genomic Hybridization." Methods of Information in Medicine 46, no. 05 (2007): 608–13. http://dx.doi.org/10.1160/me9064.
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Summary Objectives: Array-comparative genomic hybridization (aCGH) is a high-throughput method to detect and map copy number aberrations in the genome. Multi-step analysis of high-dimensional data requires an integrated suite of bioinformatic tools. In this paperwe detail an analysis pipeline for array CGH data. Methods: We developed an analysis tool for array CGH data which supports single and multi-chip analyses as well as combined analyses with paired mRNA gene expression data. The functions supporting relevant steps of analysis were implemented using the open source software R and combined as package aCGHPipeline. Analysis methods were illustrated using 189 CGH arrays of aggressive B-cell lymphomas. Results: The package covers data input, quality control, normalization, segmentation and classification. For multi-chip analysis aCGHPipeline offers an algorithm for automatic delineation of recurrent regions. This task was performed manuallyup to now. The package also supports combined analysis with mRNA gene expression data. Outputs consist of HTML documents to facilitate communication with clinical partners. Conclusions: The R package aCGHPipeline supports basic tasks of single and multi-chip analysis of array CGH data.
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Mahmoud, Medhat, Alejandro Rafael Gener, Michael M. Khayat, Adam C. English, Advait Balaji, Anbo Zhou, Andreas Hehn, et al. "Methods developed during the first National Center for Biotechnology Information Structural Variation Codeathon at Baylor College of Medicine." F1000Research 9 (September 16, 2020): 1141. http://dx.doi.org/10.12688/f1000research.23773.1.
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In October 2019, 46 scientists from around the world participated in the first National Center for Biotechnology Information (NCBI) Structural Variation (SV) Codeathon at Baylor College of Medicine. The charge of this first annual working session was to identify ongoing challenges around the topics of SV and graph genomes, and in response to design reliable methods to facilitate their study. Over three days, seven working groups each designed and developed new open-sourced methods to improve the bioinformatic analysis of genomic SVs represented in next-generation sequencing (NGS) data. The groups’ approaches addressed a wide range of problems in SV detection and analysis, including quality control (QC) assessments of metagenome assemblies and population-scale VCF files, de novo copy number variation (CNV) detection based on continuous long sequence reads, the representation of sequence variation using graph genomes, and the development of an SV annotation pipeline. A summary of the questions and developments that arose during the daily discussions between groups is outlined. The new methods are publicly available at https://github.com/NCBI-Codeathons/, and demonstrate that a codeathon devoted to SV analysis can produce valuable new insights both for participants and for the broader research community.
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OSOLODKIN, DMITRY I., NATALIA V. ZAKHAREVICH, VLADIMIR A. PALYULIN, VALERY N. DANILENKO, and NIKOLAY S. ZEFIROV. "Bioinformatic analysis of glycogen synthase kinase 3: human versus parasite kinases." Parasitology 138, no. 6 (February 24, 2011): 725–35. http://dx.doi.org/10.1017/s0031182011000151.
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SUMMARYObjective. Glycogen synthase kinase 3 (GSK-3) is a promising target for the treatment of various human diseases such as type 2 diabetes, Alzheimer's disease and inflammation. Successful inhibition of the homologues of this kinase in Plasmodium falciparum, Trypanosoma brucei and Leishmania donovani makes the kinase an attractive target for the treatment of malaria, trypanosomiasis and leishmaniasis, respectively. The aim of this work was to compare the binding sites of the GSK-3 kinases of different parasites and to analyse them as possible targets for therapeutic compounds. Methods. Both a sequence alignment and homology models of the structure of 21 different GSK-3 homologues belonging to mammals, insects, pathogenic fungi, nematodes, trematodes and protozoa have been analysed, 17 of them being studied for the first time. Results. The structure of the kinases and, in particular, their binding sites, were found to be rather conserved, possessing small insertions or deletions and conserved amino acid substitutions. Nevertheless, the kinases of most species of parasite did have some amino acid differences from the human kinase, which could be exploited for the design of selective drugs. Conclusion. Comparison of the human and parasite GSK-3 ATP binding site models has shown that the development of selective drugs affecting parasite GSK-3 is possible. Known inhibitors of human GSK-3 can also be used as starting scaffolds for the search for drugs acting against parasitic diseases.
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Ziemann, Mark, Mrinal Bhave, and Sabine Zachgo. "Bioinformatic studies of the wheat glutaredoxin gene family and functional analysis of the ROXY1 orthologues." Functional Plant Biology 38, no. 1 (2011): 25. http://dx.doi.org/10.1071/fp10185.
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CC-type glutaredoxins comprise a large land plant-specific class of oxidoreductases. Previous research shows roles for two such proteins in developmental processes in Arabidopsis; ROXY1 mediates petal initiation and morphogenesis, and ROXY1 and ROXY2 are required for normal anther development. In the present work, the broader glutaredoxin family was investigated in hexaploid wheat with bioinformatic methods, revealing a large and multifunctional gene family. With a PCR based method, three wheat ROXY homeoalleles were isolated. Complementation analyses show that these three isoforms fully complemented the roxy1 mutation in Arabidopsis. Further, yeast two-hybrid experiments demonstrate that one such wheat ROXY protein interacts strongly with TGA3, an Arabidopsis TGA transcription factor previously shown to associate with ROXY1. Deletion analyses show that TaROXY-α3 docks to a glutamine rich region of TGA3, a putative transcriptional activation domain. These results suggest a conserved molecular role of Arabidopsis and wheat ROXY proteins in inflorescence/spike development, most likely in the post-translational regulation of TGA proteins including HBP-1b (the wheat PERIANTHIA orthologue), which likely exerts also a developmental function by activating histone gene transcription in highly proliferating tissues such as the SAM and root tip.
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Sun, Jing, Tianyu Zhao, Di Zhao, Xin Qi, Xuanwen Bao, Run Shi, and Chuan Su. "Development and validation of a hypoxia-related gene signature to predict overall survival in early-stage lung adenocarcinoma patients." Therapeutic Advances in Medical Oncology 12 (January 2020): 175883592093790. http://dx.doi.org/10.1177/1758835920937904.
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Background: Patients with early-stage lung adenocarcinoma (LUAD) exhibit significant heterogeneity in overall survival. The current tumour-node-metastasis staging system is insufficient to provide precise prediction for prognosis. Methods: We quantified the levels of various hallmarks of cancer and identified hypoxia as the primary risk factor for overall survival in early-stage LUAD. Different bioinformatic and statistical methods were combined to construct a robust hypoxia-related gene signature for prognosis. Furthermore, a decision tree and a nomogram were constructed based on the gene signature and clinicopathological features to improve risk stratification and quantify risk assessment for individual patients. Results: The hypoxia-related gene signature discriminated high-risk patients at an early stage in our investigated cohorts. Survival analyses demonstrated that our gene signature served as an independent risk factor for overall survival. The decision tree identified risk subgroups powerfully, and the nomogram exhibited high accuracy. Conclusions: Our study might contribute to the optimization of risk stratification for survival and personalized management of early-stage LUAD.
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Li, Wei Vivian, and Jingyi Jessica Li. "A statistical simulator scDesign for rational scRNA-seq experimental design." Bioinformatics 35, no. 14 (July 2019): i41—i50. http://dx.doi.org/10.1093/bioinformatics/btz321.
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Abstract Motivation Single-cell RNA sequencing (scRNA-seq) has revolutionized biological sciences by revealing genome-wide gene expression levels within individual cells. However, a critical challenge faced by researchers is how to optimize the choices of sequencing platforms, sequencing depths and cell numbers in designing scRNA-seq experiments, so as to balance the exploration of the depth and breadth of transcriptome information. Results Here we present a flexible and robust simulator, scDesign, the first statistical framework for researchers to quantitatively assess practical scRNA-seq experimental design in the context of differential gene expression analysis. In addition to experimental design, scDesign also assists computational method development by generating high-quality synthetic scRNA-seq datasets under customized experimental settings. In an evaluation based on 17 cell types and 6 different protocols, scDesign outperformed four state-of-the-art scRNA-seq simulation methods and led to rational experimental design. In addition, scDesign demonstrates reproducibility across biological replicates and independent studies. We also discuss the performance of multiple differential expression and dimension reduction methods based on the protocol-dependent scRNA-seq data generated by scDesign. scDesign is expected to be an effective bioinformatic tool that assists rational scRNA-seq experimental design and comparison of scRNA–seq computational methods based on specific research goals. Availability and implementation We have implemented our method in the R package scDesign, which is freely available at https://github.com/Vivianstats/scDesign. Supplementary information Supplementary data are available at Bioinformatics online.
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Kotlyarov, Stanislav, and Anna Kotlyarova. "Analysis of ABC Transporter Gene Expression in Atherosclerosis." Cardiogenetics 11, no. 4 (November 4, 2021): 204–20. http://dx.doi.org/10.3390/cardiogenetics11040021.
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ABC transporters are a large family of membrane proteins that transport chemically diverse substrates across the cell membrane. Disruption of transport mechanisms mediated by ABC transporters causes the development of various diseases, including atherosclerosis. Methods: A bioinformatic analysis of a dataset from Gene Expression Omnibus (GEO) was performed. A GEO dataset containing data on gene expression levels in samples of atherosclerotic lesions and control arteries without atherosclerotic lesions from carotid, femoral, and infrapopliteal arteries was used for analysis. To evaluate differentially expressed genes, a bioinformatic analysis was performed in comparison groups using the limma package in R (v. 4.0.2) and the GEO2R and Phantasus tools (v. 1.11.0). Results: The obtained data indicate the differential expression of many ABC transporters belonging to different subfamilies. The differential expressions of ABC transporter genes involved in lipid transport, mechanisms of multidrug resistance, and mechanisms of ion exchange are shown. Differences in the expression of transporters in tissue samples from different arteries are established. Conclusions: The expression of ABC transporter genes demonstrates differences in atherosclerotic samples and normal arteries, which may indicate the involvement of transporters in the pathogenesis of atherosclerosis.
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Hughes, Riley L., Maria L. Marco, James P. Hughes, Nancy L. Keim, and Mary E. Kable. "The Role of the Gut Microbiome in Predicting Response to Diet and the Development of Precision Nutrition Models—Part I: Overview of Current Methods." Advances in Nutrition 10, no. 6 (June 21, 2019): 953–78. http://dx.doi.org/10.1093/advances/nmz022.
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ABSTRACT Health care is increasingly focused on health at the individual level. In the rapidly evolving field of precision nutrition, researchers aim to identify how genetics, epigenetics, and the microbiome interact to shape an individual's response to diet. With this understanding, personalized responses can be predicted and dietary advice can be tailored to the individual. With the integration of these complex sources of data, an important aspect of precision nutrition research is the methodology used for studying interindividual variability in response to diet. This article stands as the first in a 2-part review of current research investigating the contribution of the gut microbiota to interindividual variability in response to diet. Part I reviews the methods used by researchers to design and carry out such studies as well as the statistical and bioinformatic methods used to analyze results. Part II reviews the findings of these studies, discusses gaps in our current knowledge, and summarizes directions for future research. Taken together, these reviews summarize the current state of knowledge and provide a foundation for future research on the role of the gut microbiome in precision nutrition.
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Piombo, Edoardo, Ahmed Abdelfattah, Samir Droby, Michael Wisniewski, Davide Spadaro, and Leonardo Schena. "Metagenomics Approaches for the Detection and Surveillance of Emerging and Recurrent Plant Pathogens." Microorganisms 9, no. 1 (January 16, 2021): 188. http://dx.doi.org/10.3390/microorganisms9010188.
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Globalization has a dramatic effect on the trade and movement of seeds, fruits and vegetables, with a corresponding increase in economic losses caused by the introduction of transboundary plant pathogens. Current diagnostic techniques provide a useful and precise tool to enact surveillance protocols regarding specific organisms, but this approach is strictly targeted, while metabarcoding and shotgun metagenomics could be used to simultaneously detect all known pathogens and potentially new ones. This review aims to present the current status of high-throughput sequencing (HTS) diagnostics of fungal and bacterial plant pathogens, discuss the challenges that need to be addressed, and provide direction for the development of methods for the detection of a restricted number of related taxa (specific surveillance) or all of the microorganisms present in a sample (general surveillance). HTS techniques, particularly metabarcoding, could be useful for the surveillance of soilborne, seedborne and airborne pathogens, as well as for identifying new pathogens and determining the origin of outbreaks. Metabarcoding and shotgun metagenomics still suffer from low precision, but this issue can be limited by carefully choosing primers and bioinformatic algorithms. Advances in bioinformatics will greatly accelerate the use of metagenomics to address critical aspects related to the detection and surveillance of plant pathogens in plant material and foodstuffs.
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Chandelier, Anne, Julie Hulin, Gilles San Martin, Frédéric Debode, and Sébastien Massart. "Comparison of qPCR and Metabarcoding Methods as Tools for the Detection of Airborne Inoculum of Forest Fungal Pathogens." Phytopathology® 111, no. 3 (March 2021): 570–81. http://dx.doi.org/10.1094/phyto-02-20-0034-r.
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Forest diseases caused by invasive fungal pathogens are becoming more common, sometimes with dramatic consequences to forest ecosystems. The development of early detection systems is necessary for efficient surveillance and to mitigate the impact of invasive pathogens. Windborne spores are an important pathway for introduction of fungal pathogens into new areas; the design of spore trapping devices adapted to forests, capable of collecting different types of spores, and aligned with development of efficient molecular methods for detection of the pathogen, should help forest managers anticipate new disease outbreaks. Two types of Rotorod samplers were evaluated for the collection of airborne inoculum of forest fungal pathogens with a range of spore sizes in five forest types. Detection was by specific quantitative PCR (qPCR) and by high-throughput sequencing (HTS) of amplified internal transcribed spacer sequences using a new bioinformatic pipeline, FungiSearch, developed for diagnostic purposes. Validation of the pipeline was conducted on mock communities of 10 fungal species belonging to different taxa. Although the sensitivity of the new HTS pipeline was lower than the specific qPCR, it was able to detect a wide variety of fungal pathogens. FungiSearch is easy to use, and the reference database is updatable, making the tool suitable for rapid identification of new pathogens. This new approach combining spore trapping and HTS detection is promising as a diagnostic tool for invasive fungal pathogens.
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Alatortseva, G. I., A. V. Sidorov, L. N. Nesterenko, L. N. Luhverchik, V. V. Dotsenko, I. I. Amiantova, M. V. Zhukina, et al. "DEVELOPMENT OF HEPATITIS E 3 GENOTYPE RECOMBINANT PROTEIN CAPSID OF: CLONING, EXPRESSION, PURIFICATION, EVALUATION OF THE ANTIGENIC PROPERTIES." Journal of microbiology epidemiology immunobiology 1, no. 1 (August 23, 2019): 10–17. http://dx.doi.org/10.36233/0372-9311-2019-1-10-17.
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Aim. The development of the hepatitis E virus (HEV) genotype 3 recombinant capsid protein.Materials and methods. E.coli strains, plasmid vectors, serological and clinical samples, ELISA reagent kits, molecular biological, bioinformatic, biotechnological, biochemical and serological methods.Results. Using viruscontaining material from pigs of Belgorod region (Russian Federation) we made E.coli strains producing recombinant capsid protein, containing C-terminal of viral ORF2 protein fragment fused to E.coli β-galactosidase. Recombinant protein ORF2 had been isolated from the bacterial inclusion bodies and purified by size exclusion chromatography. Antigenic specificity of the recombinant polypeptide was confirmed by ELISA and Western blotting with sera of hepatitis E patients and reference groups (healthy donors, patients with hepatitis A, B, C, infectious mononucleosis, cytomegalovirus infection and HIV-infected patients). Conclusion. HEV genotype 3 ORF2 recombinant antigen had been developed, and the possibility to use it in diagnostic tests had been experimentally shown.
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Alatortseva, G. I., A. V. Sidorov, L. N. Nesterenko, L. N. Luhverchik, M. V. Zhukina, I. I. Amiantova, A. V. Milovanova, et al. "DESIGN OF HEPATITIS E VIRUS GENOTYPE 1 RECOMBINANT ORF3 PROTEIN BY CODON OPTIMIZATION METHOD." Journal of microbiology epidemiology immunobiology, no. 6 (December 28, 2017): 63–72. http://dx.doi.org/10.36233/0372-9311-2017-6-63-72.
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Aim. The development of the hepatitis E virus (HEV) genotype 1 full-size ORF3 recombinant polypeptide. Materials and methods. Escherichia coli strains, plasmid vectors, serological and clinical samples, ELISA reagent kits, molecular biological, bioinformatic, biotechnological, biochemical and serological methods. Results. HEV genotype 1 RNA had been isolated from clinical samples collected in Kyrgyzstan. DNA copy of subgenomic virus RNA had been cloned and used for further development of E.coli strains producing full-size recombinant protein ORF3 fused to E.coli beta-galactosidase. Codons optimization method was used in aim to increase expression level of recombinant protein. Recombinant protein ORF3 had been isolated from the inclusion bodies of the E.coli biomass and purified by size exclusion chromatography. Antigenic specificity of recombinant polypeptide had been confirmed by enzyme-linked immunosorbent assay and Western blotting with the specific sera. Conclusion. HEVgenotype 1 ORF3 recombinant antigen had been designed, and it’s applicability in diagnostic tests had been experimentally confirmed.
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Yang, Su-jin, Dan-dan Wang, Si-ying Zhou, Qian Zhang, Jin-yan Wang, Shan-liang Zhong, He-da Zhang, et al. "Identification of circRNA–miRNA networks for exploring an underlying prognosis strategy for breast cancer." Epigenomics 12, no. 2 (January 2020): 101–25. http://dx.doi.org/10.2217/epi-2019-0058.
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Aim: Circular RNAs (circRNAs) still have many potential functions in the process of tumor development that are not completely understood. The study aims to explore novel circRNAs and their mechanisms of action in breast cancer (BCa). Materials & methods: A combination strategy of RNA-sequencing (RNA-seq) technique, quantitative real-time PCR and bioinformatic analysis was employed to identify the potential mechanisms involving differentially expressed circRNAs in the serum exosomes and tissues of BCa patients. Results: The expression levels of hsa-circRNA-0005795 and hsa-circRNA-0088088 were significantly different both in serum exosomes and tissues and might function as competing endogenous RNAs and play vital roles in BCa development. Conclusion: We constructed two circRNA–miRNA networks and provided new insight into the prognosis and therapy of BCa using circRNAs from serum exosomes.
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Bartas, Martin, Václav Brázda, Jiří Červeň, and Petr Pečinka. "Characterization of p53 Family Homologs in Evolutionary Remote Branches of Holozoa." International Journal of Molecular Sciences 21, no. 1 (December 18, 2019): 6. http://dx.doi.org/10.3390/ijms21010006.
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The p53 family of transcription factors plays key roles in development, genome stability, senescence and tumor development, and p53 is the most important tumor suppressor protein in humans. Although intensively investigated for many years, its initial evolutionary history is not yet fully elucidated. Using bioinformatic and structure prediction methods on current databases containing newly-sequenced genomes and transcriptomes, we present a detailed characterization of p53 family homologs in remote members of the Holozoa group, in the unicellular clades Filasterea, Ichthyosporea and Corallochytrea. Moreover, we show that these newly characterized homologous sequences contain domains that can form structures with high similarity to the human p53 family DNA-binding domain, and some also show similarities to the oligomerization and SAM domains. The presence of these remote homologs demonstrates an ancient origin of the p53 protein family.
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Sun, Xue-Jiao, Ming-Xing Li, Chen-Zi Gong, Jing Chen, Mohammad Nasb, Sayed Zulfiqar Ali Shah, Muhammad Rehan, Ya-Jie Li, and Hong Chen. "Temporal expression profiles of lncRNA and mRNA in human embryonic stem cell-derived motor neurons during differentiation." PeerJ 8 (November 13, 2020): e10075. http://dx.doi.org/10.7717/peerj.10075.
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Background Human embryonic stem cells (hESC) have been an invaluable research tool to study motor neuron development and disorders. However, transcriptional regulation of multiple temporal stages from ESCs to spinal motor neurons (MNs) has not yet been fully elucidated. Thus, the goals of this study were to profile the time-course expression patterns of lncRNAs during MN differentiation of ESCs and to clarify the potential mechanisms of the lncRNAs that are related to MN differentiation. Methods We utilized our previous protocol which can harvest motor neuron in more than 90% purity from hESCs. Then, differentially expressed lncRNAs (DElncRNAs) and mRNAs (DEmRNAs) during MN differentiation were identified through RNA sequencing. Bioinformatic analyses were performed to assess potential biological functions of genes. We also performed qRT-PCR to validate the DElncRNAs and DEmRNAs. Results A total of 441 lncRNAs and 1,068 mRNAs at day 6, 443 and 1,175 at day 12, and 338 lncRNAs and 68 mRNAs at day 18 were differentially expressed compared with day 0. Bioinformatic analyses identified that several key regulatory genes including POU5F1, TDGF1, SOX17, LEFTY2 and ZSCAN10, which involved in the regulation of embryonic development. We also predicted 283 target genes of DElncRNAs, in which 6 mRNAs were differentially expressed. Significant fold changes in lncRNAs (NCAM1-AS) and mRNAs (HOXA3) were confirmed by qRT-PCR. Then, through predicted overlapped miRNA verification, we constructed a lncRNA NCAM1-AS-miRNA-HOXA3 network.
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Akin Bali, Dilara Fatma. "Why Cytoskeletal Associated Proteins are Important in Colorectal Cancer Patients: Molecular & Bioinformatic Analysis." Lokman Hekim Health Sciences 1, no. 1 (2021): 14–31. http://dx.doi.org/10.14744/lhhs.2021.70004.
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Introduction: It has been aimed to analyze the role of pathogenic effects of mutation and expression anomalies occurring on diaphanous-related formin 1 (DIAPH1), WASP actin nucleation-promoting factor (WASP), myosin heavy chain 9 (MYH9), actinin alpha 1 (ACNT1), filamin A (FLNA), and tubulin beta 1 class VI (TUBB1), which are known as fundamental cellular skeleton proteins, on the development and progression of cancer via bioinformatic tools. Methods: The genome sequence and expression profiles of 594 Colorectal Cancer (CRC) patients were obtained via bioinformatic tools, which provide data for The Cancer Genome Atlas. The mutation patterns of six genes were determined in detail, and for the prediction of pathogenic properties of identified changes for CRC, Polymorphism Phenotyping v2, Screening for Non-Acceptable Polymorphisms, and the Catalogue Of Somatic Mutations In Can- cer were utilized. Apart from the mutation profile, the effects of existing mutations on messenger ribonucleic acid (mRNA) expression and survival were also identified. Moreover, the Search Tool for the Retrieval of Interacting Genes/Proteins network analysis was realized to further comprehend the functional relations of proteins in cellu- lar processes. Results: There have been 142 distinct point mutations, gene amplification, and deep deletions identified on DIAPH1, WAS, MYH9, ACNT1, FLNA, and TUBB1 genes. ACTN1 and FLNA low mRNA expression levels for DIAPH1 increased, and the mRNA expression level was statistically significant (p<0.05). Prognosis-wise, the effect of mRNA expression on survival in the absence of disease was meaningful for FLNA (p=0.011). Discussion and Conclusion: Bioinformatic analysis data in DIAPH1, WASP, MYH9, ACNT1, FLNA, and TUBB1 genes, which are important in CRC pathogenesis revealed in this study, will be a guide for future laboratory studies.
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Zhang, Xiuzhi, Chunyan Kang, Ningning Li, Xiaoli Liu, Jinzhong Zhang, Fenglan Gao, and Liping Dai. "Identification of special key genes for alcohol-related hepatocellular carcinoma through bioinformatic analysis." PeerJ 7 (February 6, 2019): e6375. http://dx.doi.org/10.7717/peerj.6375.
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Background Alcohol-related hepatocellular carcinoma (HCC) was reported to be diagnosed at a later stage, but the mechanism was unknown. This study aimed to identify special key genes (SKGs) during alcohol-related HCC development and progression. Methods The mRNA data of 369 HCC patients and the clinical information were downloaded from the Cancer Genome Atlas project (TCGA). The 310 patients with certain HCC-related risk factors were included for analysis and divided into seven groups according to the risk factors. Survival analyses were applied for the HCC patients of different groups. The patients with hepatitis B virus or hepatitis C virus infection only were combined into the HCC-V group for further analysis. The differentially expressed genes (DEGs) between the HCCs with alcohol consumption only (HCC-A) and HCC-V tumors were identified through limma package in R with cutoff criteria│log2 fold change (logFC)|>1.0 and p < 0.05. The DEGs between eight alcohol-related HCCs and their paired normal livers of GSE59259 from the Gene Expression Omnibus (GEO) were identified through GEO2R (a built-in tool in GEO database) with cutoff criteria |logFC|> 2.0 and adj.p < 0.05. The intersection of the two sets of DEGs was considered SKGs which were then investigated for their specificity through comparisons between HCC-A and other four HCC groups. The SKGs were analyzed for their correlations with HCC-A stage and grade and their prognostic power for HCC-A patients. The expressional differences of the SKGs in the HCCs in whole were also investigated through Gene Expression Profiling Interactive Analysis (GEPIA). The SKGs in HCC were validated through Oncomine database analysis. Results Pathological stage is an independent prognostic factor for HCC patients. HCC-A patients were diagnosed later than HCC patients with other risk factors. Ten SKGs were identified and nine of them were confirmed for their differences in paired samples of HCC-A patients. Three (SLC22A10, CD5L, and UROC1) and four (SLC22A10, UROC1, CSAG3, and CSMD1) confirmed genes were correlated with HCC-A stage and grade, respectively. SPP2 had a lower trend in HCC-A tumors and was negatively correlated with HCC-A stage and grade. The SKGs each was differentially expressed between HCC-A and at least one of other HCC groups. CD5L was identified to be favorable prognostic factor for overall survival while CSMD1 unfavorable prognostic factor for disease-free survival for HCC-A patients and HCC patients in whole. Through Oncomine database, the dysregulations of the SKGs in HCC and their clinical significance were confirmed. Conclusion The poor prognosis of HCC-A patients might be due to their later diagnosis. The SKGs, especially the four stage-correlated genes (CD5L, SLC22A10, UROC1, and SPP2) might play important roles in HCC development, especially alcohol-related HCC development and progression. CD5L might be useful for overall survival and CSMD1 for disease-free survival predication in HCC, especially alcohol-related HCC.
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Lin, Jinghan, Shanshan Shi, Qihui Chen, and Yonghui Pan. "Differential Expression and Bioinformatic Analysis of the circRNA Expression in Migraine Patients." BioMed Research International 2020 (October 7, 2020): 1–14. http://dx.doi.org/10.1155/2020/4710780.
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Background. CircRNAs are noncoding RNA molecules that have recently been described and shown to regulate miRNA functionality. While recent studies have suggested such circRNAs to be associated with pain related diseases in humans, no comprehensive migraine-related circRNA profiles have been generated, and there is currently no clear understanding of whether they can serve as regulators of migraine pathology. Methods. We initially conducted a circRNA microarray analysis of the plasma of migraine patients and healthy controls. Based upon these data, we then selected 8 differentially expressed circRNAs and confirmed their expression in more migraine patient plasma samples via real-time PCR. We then performed functional and pathway enrichment analyses. Lastly, using a robust rank aggregation approach, we constructed a ceRNA network according to predicted circRNA–miRNA and miRNA–mRNA pairs in these migraine patient samples. Results. We were able to detect 2039 circRNAs in our patient samples, with 794 of 1245 these circRNAs being up- and downregulated in migraine patients relative to controls, respectively ( fold change ≥ 1.5 , p < 0.01 ). A qRT-PCR analysis confirmed that the expression of hsa_circRNA_100236, hsa_circRNA_102413, and hsa_circRNA_000367 was significantly enhanced in migraine patients, whereas the expression of hsa_circRNA_103809, hsa_circRNA_103670, and hsa_circRNA_101833 was significantly reduced in these individuals relative to healthy controls. We found these differentially regulated circRNAs to be associated with numerous predicted biological processes, with enrichment analyses suggesting that they may modulate the PI3K-Akt signaling so as to promote inflammation to drive migraine development. However, further research will be needed to formally test these mechanistic possibilities and to validate these circRNAs as potential biomarkers of migraine patients. Conclusions. Our results offer new potential insights into the mechanistic basis of this condition and suggest that hsa_circRNA_000367 and hsa_circRNA_102413 may offer value as regulators of migraine pathology.