scholarly journals Bioinformatics Methods in Medical Genetics and Genomics

2020 ◽  
Vol 21 (17) ◽  
pp. 6224
Author(s):  
Yuriy L. Orlov ◽  
Ancha V. Baranova ◽  
Tatiana V. Tatarinova
Keyword(s):  
Systems Biology ◽  
Protein Interactions ◽  
Gene Networks ◽  
Drug Targets ◽  
Molecular Mechanisms ◽  
Medical Genetics ◽  
Biomedical Literature ◽  
Literature Mining ◽  
Proteomics Data ◽  
Medical Genomics

Medical genomics relies on next-gen sequencing methods to decipher underlying molecular mechanisms of gene expression. This special issue collects materials originally presented at the “Centenary of Human Population Genetics” Conference-2019, in Moscow. Here we present some recent developments in computational methods tested on actual medical genetics problems dissected through genomics, transcriptomics and proteomics data analysis, gene networks, protein–protein interactions and biomedical literature mining. We have selected materials based on systems biology approaches, database mining. These methods and algorithms were discussed at the Digital Medical Forum-2019, organized by I.M. Sechenov First Moscow State Medical University presenting bioinformatics approaches for the drug targets discovery in cancer, its computational support, and digitalization of medical research, as well as at “Systems Biology and Bioinformatics”-2019 (SBB-2019) Young Scientists School in Novosibirsk, Russia. Selected recent advancements discussed at these events in the medical genomics and genetics areas are based on novel bioinformatics tools.

scholarly journals Search for New Candidate Genes Involved in the Comorbidity of Asthma and Hypertension Based on Automatic Analysis of Scientific Literature

2018 ◽  
Vol 15 (4) ◽  
Author(s):  
Olga V. Saik ◽  
Pavel S. Demenkov ◽  
Timofey V. Ivanisenko ◽  
Elena Yu. Bragina ◽  
Maxim B. Freidin ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Gene Networks ◽  
Molecular Mechanisms ◽  
Molecular Genetic ◽  
Genetic Networks ◽  
Positive Sign ◽  
Phenotypic Traits ◽  
Protein Protein Interactions ◽  
Scientific Publications ◽  
Associative Gene Networks

AbstractComorbid states of diseases significantly complicate diagnosis and treatment. Molecular mechanisms of comorbid states of asthma and hypertension are still poorly understood. Prioritization is a way for identifying genes involved in complex phenotypic traits. Existing methods of prioritization consider genetic, expression and evolutionary data, molecular-genetic networks and other. In the case of molecular-genetic networks, as a rule, protein-protein interactions and KEGG networks are used. ANDSystem allows reconstructing associative gene networks, which include more than 20 types of interactions, including protein-protein interactions, expression regulation, transport, catalysis, etc. In this work, a set of genes has been prioritized to find genes potentially involved in asthma and hypertension comorbidity. The prioritization was carried out using well-known methods (ToppGene and Endeavor) and a cross-talk centrality criterion, calculated by analysis of associative gene networks from ANDSystem. The identified genes, including IL1A, CD40LG, STAT3, IL15, FAS, APP, TLR2, C3, IL13 and CXCL10, may be involved in the molecular mechanisms of comorbid asthma/hypertension. An analysis of the dynamics of the frequency of mentioning the most priority genes in scientific publications revealed that the top 100 priority genes are significantly enriched with genes with increased positive dynamics, which may be a positive sign for further studies of these genes.

scholarly journals HMNPPID—human malignant neoplasm protein–protein interaction database

2019 ◽  
Vol 13 (S1) ◽  
Author(s):  
Qingqing Li ◽  
Zhihao Yang ◽  
Zhehuan Zhao ◽  
Ling Luo ◽  
Zhiheng Li ◽  
...  
Keyword(s):  
Protein Interaction ◽  
Protein Interactions ◽  
Molecular Mechanisms ◽  
Malignant Neoplasm ◽  
Biomedical Literature ◽  
Malignant Neoplasms ◽  
Protein Protein Interactions ◽  
Protein Protein Interaction ◽  
Interaction Database ◽  
Protein Interaction Database

Abstract Background Protein–protein interaction (PPI) information extraction from biomedical literature helps unveil the molecular mechanisms of biological processes. Especially, the PPIs associated with human malignant neoplasms can unveil the biology behind these neoplasms. However, such PPI database is not currently available. Results In this work, a database of protein–protein interactions associated with 171 kinds of human malignant neoplasms named HMNPPID is constructed. In addition, a visualization program, named VisualPPI, is provided to facilitate the analysis of the PPI network for a specific neoplasm. Conclusions HMNPPID can hopefully become an important resource for the research on PPIs of human malignant neoplasms since it provides readily available data for healthcare professionals. Thus, they do not need to dig into a large amount of biomedical literatures any more, which may accelerate the researches on the PPIs of malignant neoplasms.

scholarly journals Parasites, proteomes and systems: has Descartes’ clock run out of time?

Parasitology ◽  
2012 ◽  
Vol 139 (9) ◽  
pp. 1103-1118 ◽  
Author(s):  
J. M. WASTLING ◽  
S. D. ARMSTRONG ◽  
R. KRISHNA ◽  
D. XIA
Keyword(s):  
Systems Biology ◽  
Protein Interactions ◽  
Biological Data ◽  
Protein Protein Interactions ◽  
Proteomics Data ◽  
Data Types ◽  
Quality Of Data ◽  
Post Translational Modifications ◽  
Systems Modelling ◽  
New Generation

SUMMARYSystems biology aims to integrate multiple biological data types such as genomics, transcriptomics and proteomics across different levels of structure and scale; it represents an emerging paradigm in the scientific process which challenges the reductionism that has dominated biomedical research for hundreds of years. Systems biology will nevertheless only be successful if the technologies on which it is based are able to deliver the required type and quality of data. In this review we discuss how well positioned is proteomics to deliver the data necessary to support meaningful systems modelling in parasite biology. We summarise the current state of identification proteomics in parasites, but argue that a new generation of quantitative proteomics data is now needed to underpin effective systems modelling. We discuss the challenges faced to acquire more complete knowledge of protein post-translational modifications, protein turnover and protein-protein interactions in parasites. Finally we highlight the central role of proteome-informatics in ensuring that proteomics data is readily accessible to the user-community and can be translated and integrated with other relevant data types.

scholarly journals Toward a Proteome-Complete Computational Model of the Human Red Blood Cell

Blood ◽  
2018 ◽  
Vol 132 (Supplement 1) ◽  
pp. 4888-4888
Author(s):  
James T Yurkovich ◽  
Laurence Yang ◽  
Bernhard O Palsson
Keyword(s):  
Systems Biology ◽  
Blood Cell ◽  
Computational Model ◽  
Red Blood Cell ◽  
Molecular Mechanisms ◽  
Conflicts Of Interest ◽  
Protein Complexes ◽  
Proteomics Data ◽  
Hemoglobin Binding ◽  
Starting Point

Abstract The human red blood cell has served as a starting point for the application and development of systems biology approaches due to its simplicity, intrinsic experimental accessibility, and importance in human health applications. Here, we present a multi-scale computational model of the human red blood cell that accounts for metabolism and macromolecules. Proteomics data are used to place quantitative constraints on individual protein complexes that catalyze metabolic reactions, as well as a total proteome capacity constraint. We explicitly describe molecular mechanisms-such as hemoglobin binding and the formation and detoxification of reactive oxygen species-and account for sequence variations between individuals, allowing for personalized physiological predictions. This model allows for direct computation of the oxyhemoglobin curve as a function of model species and a more accurate computation of the flux state of the metabolic network. More broadly, this work represents another step toward building increasingly more complete systems biology whole-cell models. Disclosures No relevant conflicts of interest to declare.

scholarly journals Systems Biology Unraveled the Relationship of lncRNA OIP5-AS1 with CD25 and its Co-Expression Analysis in Cancers

2020 ◽  
Author(s):  
Moein Dehbashi ◽  
Zohreh Hojati ◽  
Majid Motovali-bashi ◽  
C. S. Cho ◽  
Akihiro Shimosaka ◽  
...  
Keyword(s):  
Systems Biology ◽  
Signaling Pathways ◽  
Expression Analysis ◽  
Protein Interactions ◽  
Molecular Mechanisms ◽  
Treg Cells ◽  
Rna Seq ◽  
Cd25 Expression ◽  
High Level ◽  
The Relationship

Abstract Background: Treg cells function in the immune homeostasis, these cells express high level of CD25. Even though the molecular mechanisms of CD25-mediated signaling pathways has been reported, some questions are still unclear, e.g. the relationship and function of the relative lncRNA. It is known that the CD25 expression levels are various among different cancers. Thus, we intended to dissect systems biology of a lncRNA pertained to CD25 and CD25 protein interactors-targeting miRNAs. Methods: Apart from using the available RNA-seq data, the co-expression analysis of the lncRNA pertained to some cancers was performed. Our analysis was done for protein interactors of CD25 by STRING 11.0, ShinyGO v0.60 and KEGG web servers were used for enrichment and network analysis of CD25. TargetScan 7.2, miRTargetLink Human and mirDIP were applied for determining the CD25 and CD25 interactors-targeting miRNAs. To find the lncRNA-miRNA and lncRNA-protein interactions, starBase v3.0, LncBase Predicted v.2 and SFPEL-LPI were recruited, respectively. Also, using Co-LncRNA, the co-expressed lncRNA analysis and the relative signaling pathways in some cancers including bladder, breast, head and neck, kidney, liver, lung, prostate and thyroid cancers using RNA-seq data were achieved. Results: OIP5-AS1 was shown to have the interaction with CD25 and CD25 protein interactors-targeting miRNAs. In addition, the co-expression of OIP5-AS1 in cancers and their signaling pathways was identified. Conclusions: Possibly, OIP5-AS1 can effect on CD25 expression in all relative signaling pathways of these cancers.

scholarly journals Proteomic Analysis of Anticancer TCMs Targeted at Mitochondria

2015 ◽  
Vol 2015 ◽  
pp. 1-14 ◽  
Author(s):  
Yang Wang ◽  
Ru-Yuan Yu ◽  
Qing-Yu He
Keyword(s):  
Protein Interactions ◽  
Drug Targets ◽  
Molecular Mechanisms ◽  
New Technology ◽  
Subcellular Fractionation ◽  
Natural Compounds ◽  
Systematic Investigation ◽  
Antitumor Effects ◽  
Action Mechanisms ◽  
Mechanistic Investigation

Traditional Chinese medicine (TCM) is a rich resource of anticancer drugs. Increasing bioactive natural compounds extracted from TCMs are known to exert significant antitumor effects, but the action mechanisms of TCMs are far from clear. Proteomics, a powerful platform to comprehensively profile drug-regulated proteins, has been widely applied to the mechanistic investigation of TCMs and the identification of drug targets. In this paper, we discuss several bioactive TCM products including terpenoids, flavonoids, and glycosides that were extensively investigated by proteomics to illustrate their antitumor mechanisms in various cancers. Interestingly, many of these natural compounds isolated from TCMs mostly exert their tumor-suppressing functions by specifically targeting mitochondria in cancer cells. These TCM components induce the loss of mitochondrial membrane potential, the release of cytochrome c, and the accumulation of ROS, initiating apoptosis cascade signaling. Proteomics provides systematic views that help to understand the molecular mechanisms of the TCM in tumor cells; it bears the inherent limitations in uncovering the drug-protein interactions, however. Subcellular fractionation may be coupled with proteomics to capture and identify target proteins in mitochondria-enriched lysates. Furthermore, translating mRNA analysis, a new technology profiling the drug-regulated genes in translatome level, may be integrated into the systematic investigation, revealing global information valuable for understanding the action mechanism of TCMs.

scholarly journals Identification of prognostic risk factors for pancreatic cancer using bioinformatics analysis

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e9301
Author(s):  
Dandan Jin ◽  
Yujie Jiao ◽  
Jie Ji ◽  
Wei Jiang ◽  
Wenkai Ni ◽  
...  
Keyword(s):  
Gene Expression ◽  
Pancreatic Cancer ◽  
Gene Networks ◽  
Molecular Mechanisms ◽  
Functional Module ◽  
Disease Free Survival ◽  
Ductal Adenocarcinoma ◽  
Literature Mining ◽  
Ppi Network ◽  
Hub Genes

Background Pancreatic cancer is one of the most common malignant cancers worldwide. Currently, the pathogenesis of pancreatic cancer remains unclear; thus, it is necessary to explore its precise molecular mechanisms. Methods To identify candidate genes involved in the tumorigenesis and proliferation of pancreatic cancer, the microarray datasets GSE32676, GSE15471 and GSE71989 were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between Pancreatic ductal adenocarcinoma (PDAC) and nonmalignant samples were screened by GEO2R. The Database for Annotation Visualization and Integrated Discovery (DAVID) online tool was used to obtain a synthetic set of functional annotation information for the DEGs. A PPI network of the DEGs was established using the Search Tool for the Retrieval of Interacting Genes (STRING) database, and a combination of more than 0.4 was considered statistically significant for the PPI. Subsequently, we visualized the PPI network using Cytoscape. Functional module analysis was then performed using Molecular Complex Detection (MCODE). Genes with a degree ≥10 were chosen as hub genes, and pathways of the hub genes were visualized using ClueGO and CluePedia. Additionally, GenCLiP 2.0 was used to explore interactions of hub genes. The Literature Mining Gene Networks module was applied to explore the cocitation of hub genes. The Cytoscape plugin iRegulon was employed to analyze transcription factors regulating the hub genes. Furthermore, the expression levels of the 13 hub genes in pancreatic cancer tissues and normal samples were validated using the Gene Expression Profiling Interactive Analysis (GEPIA) platform. Moreover, overall survival and disease-free survival analyses according to the expression of hub genes were performed using Kaplan-Meier curve analysis in the cBioPortal online platform. The relationship between expression level and tumor grade was analyzed using the online database Oncomine. Lastly, the eight snap-frozen tumorous and adjacent noncancerous adjacent tissues of pancreatic cancer patients used to detect the CDK1 and CEP55 protein levels by western blot. Conclusions Altogether, the DEGs and hub genes identified in this work can help uncover the molecular mechanisms underlying the tumorigenesis of pancreatic cancer and provide potential targets for the diagnosis and treatment of this disease.

scholarly journals Ontology-based validation and identification of regulatory phenotypes

2018 ◽  
Author(s):  
Maxat Kulmanov ◽  
Paul N Schofield ◽  
Georgios V Gkoutos ◽  
Robert Hoehndorf
Keyword(s):  
Protein Interactions ◽  
Research Group ◽  
Drug Targets ◽  
Molecular Mechanisms ◽  
Computational Method ◽  
Gene Products ◽  
Protein Protein Interactions ◽  
Functional Annotations ◽  
Disease Associations ◽  
Annotation Quality

AbstractMotivationFunction annotations of gene products, and phenotype annotations of genotypes, provide valuable information about molecular mechanisms that can be utilized by computational methods to identify functional and phenotypic relatedness, improve our understanding of disease and pathobiology, and lead to discovery of drug targets. Identifying functions and phenotypes commonly requires experiments which are time-consuming and expensive to carry out; creating the annotations additionally requires a curator to make an assertion based on reported evidence. Support to validate the mutual consistency of functional and phenotype annotations as well as a computational method to predict phenotypes from function annotations, would greatly improve the utility of function annotations.ResultsWe developed a novel ontology-based method to validate the mutual consistency of function and phenotype annotations. We apply our method to mouse and human annotations, and identify several inconsistencies that can be resolved to improve overall annotation quality. Our method can also be applied to the rule-based prediction of phenotypes from functions. We show that the predicted phenotypes can be utilized for identification of protein-protein interactions and gene-disease associations. Based on experimental functional annotations, we predict phenotypes for 1,986 genes in mouse and 7,301 genes in human for which no experimental phenotypes have yet been determined.Availabilityhttps://github.com/bio-ontology-research-group/[email protected]

Sign in / Sign up

Export Citation Format

Share Document