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RSwitch: A Novel bioinformatics database on Riboswitches as Antibacterial Drug Targets

IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND bioinformatics 2021年第2期18卷 804-808页

作者： Penchovsky, Robert Pavlova, Nikolet Kaloudas, Dimitrios Sofia Univ Dept Genet Fac Biol St Kliment Ohridski 11648 Dragan Tsankov Blvd Sofia Bulgaria

The RSwitch is a MySQL database implemented on a PHP-based server, which also provides various useful tools for analyses of DNA, RNA, and protein sequences applying a user-friendly interface. The RSwitch database currently contains information and annotations of 215 bacterial riboswitches from 16 different types found in 50 human pathogenic bacteria. The riboswitch classes include those sensing FMN, glmS, Cobalamin, Lysine, SAH, SAM, Purine, TPP, c-di-GMPI, c-di-GMPII, Moco, PreQ1, Fluoride, Glycine, Mg2+, and Mn2+ type of bacterial riboswitches. The database provides information about the riboswitch aptamer sequences, the thermodynamic ensemble of the RNA structures on partition function and on free minimum energy function. Additionally, the database presents the centroid structure and the positional entropy for each position of the aptamer sequences. The database also provides the biochemical pathways in which the riboswitches are involved in, as well as multiple sequence alignments, multi-drug resistance bacterial strains and consensus motifs for each type of the switches. The RSwitch database is permanently available online without any restrictions. This bioinformatics database provides for the first time all information needed for assessing the suitability of the presented riboswitches as antibacterial drug targets.

关键词： databases Microorganisms Antibacterial activity RNA Strain bioinformatics bioinformatics database human pathogenic bacteria riboswitches RNA secondary structures biochemical pathways

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Data and AI-driven synthetic binding protein discovery

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TRENDS IN PHARMACOLOGICAL SCIENCES 2025年第2期46卷 132-144页

作者： Li, Yanlin Duan, Zixin Li, Zhenwen Xue, Weiwei Chongqing Univ Sch Pharmaceut Sci Chongqing 401331 Peoples R China Western Chongqing Collaborat Innovat Ctr Intellige Chongqing Natl Biomed Ind Pk Chongqing 401329 Peoples R China

Synthetic binding proteins (SBPs) are a class of protein binders that are artificially created and do not exist naturally. Their broad applications in tackling challenges of research, diagnostics, and therapeutics have garnered significant interest. Traditional protein engineering is pivotal to the discovery of SBPs. Recently, this discovery has been significantly accelerated by computational approaches, such as molecular modeling and artificial intelligence (AI). Furthermore, while numerous bioinformatics databases offer a wealth of resources that fuel SBP discovery, the full potential of these data has not yet been fully exploited. In this review, we present a comprehensive overview of SBP data ecosystem and methodologies in SBP discovery, highlighting the critical role of high-quality data and AI technologies in accelerating the discovery of innovative SBPs with promising applications in pharmacological sciences.

关键词： synthetic binding proteins protein design bioinformatics database molecular modeling artificial intelligence

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Repurposing haloperidol for the treatment of rheumatoid arthritis: an integrative approach using data mining techniques

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THERAPEUTIC ADVANCES IN MUSCULOSKELETAL DISEASE 2021年第13期13卷

作者： Nakagawa, Chihiro Yokoyama, Satoshi Hosomi, Kouichi Takada, Mitsutaka Kindai Univ Sch Pharm Div Drug Informat 3-4-1 Kowakae Higashiosaka Osaka 5778502 Japan

Introduction: Treatment of rheumatoid arthritis (RA) has advanced with the introduction of biological disease-modifying antirheumatic drugs. However, more than 20% of patients with RA still have moderate or severe disease activity. Hence, novel antirheumatic drugs are required. Recently, drug repurposing, a process of identifying new indications for existing drugs, has received great attention. Furthermore, a few reports have shown that antipsychotics are capable of affecting several cytokines that are also modulated by existing antirheumatic drugs. Therefore, we investigated the association between antipsychotics and RA by data mining using real-world data and bioinformatics databases. Methods: Disproportionality and sequence symmetry analyses were employed to identify the associations between the investigational drugs and RA using the US Food and Drug Administration Adverse Event Reporting System (2004-2016) and JMDC administrative claims database (January 2005-April 2017;JMDC Inc., Tokyo, Japan), respectively. The reporting odds ratio (ROR) and information component (IC) were used in the disproportionality analysis to indicate a signal. The adjusted sequence ratio (SR) was used in the sequence symmetry analysis to indicate a signal. The bioinformatics analysis suite, BaseSpace Correlation Engine (Illumina, CA, USA) was employed to explore the molecular mechanisms associated with the potential candidates identified by the drug-repurposing approach. Results: A potential inverse association between the antipsychotic haloperidol and RA, which exhibited significant inverse signals with ROR, IC, and adjusted SR, was found. Furthermore, the results suggested that haloperidol may exert antirheumatic effects by modulating various signaling pathways, including cytokine and chemokine signaling, major histocompatibility complex class-II antigen presentation, and Toll-like receptor cascade pathways. Conclusion: Our drug-repurposing approach using data mining techniques identi

关键词： bioinformatics database data mining drug repurposing haloperidol real-world data rheumatoid arthritis

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circVAR database: genome-wide archive of genetic variants for human circular RNAs

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BMC GENOMICS 2020年第1期21卷 750-750页

作者： Zhao, Min Qu, Hong Univ Sunshine Coast Sch Sci & Engn Maroochydore Qld 4558 Australia Peking Univ Ctr Bioinformat State Key Lab Prot & Plant Gene Res Coll Life Sci Beijing 100871 Peoples R China

Background: Circular RNAs (circRNAs) play important roles in regulating gene expression through binding miRNAs and RNA binding proteins. Genetic variation of circRNAs may affect complex traits/diseases by changing their binding efficiency to target miRNAs and proteins. There is a growing demand for investigations of the functions of genetic changes using large-scale experimental evidence. However, there is no online genetic resource for circRNA genes. Results: We performed extensive genetic annotation of 295,526 circRNAs integrated from circBase, circNet and circRNAdb. All pre-computed genetic variants were presented at our online resource, circVAR, with data browsing and search functionality. We explored the chromosome-based distribution of circRNAs and their associated variants. We found that, based on mapping to the 1000 Genomes and ClinVAR databases, chromosome 17 has a relatively large number of circRNAs and associated common and health-related genetic variants. Following the annotation of genome wide association studies (GWAS)-based circRNA variants, we found many non-coding variants within circRNAs, suggesting novel mechanisms for common diseases reported from GWAS studies. For cancer-based somatic variants, we found that chromosome 7 has many highly complex mutations that have been overlooked in previous research. Conclusion: We used the circVAR database to collect SNPs and small insertions and deletions (INDELs) in putative circRNA regions and to identify their potential phenotypic information. To provide a reusable resource for the circRNA research community, we have published all the pre-computed genetic data concerning circRNAs and associated genes together with data query and browsing functions at http://***/circvar.

关键词： bioinformatics database Circular RNA Genetic variant Systems biology

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Identification of novel miRNAs with potential role in Gastric Cancer diagnosis: In silico procedure

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META GENE 2019年 19卷 246-252页

作者： Delshad, Encieh Shafiee, Mohammad Maghsoudi, Hossein Shamsabadi, Fatemeh Bahramian, Shabbou Payame Noor Univ Dept Biotechnol Tehran Iran Golestan Univ Med Sci Golestan Res Ctr Gastroenterol & Hepatol Gorgan Iran Golestan Univ Med Sci Sch Adv Technol Med Dept Med Biotechnol Gorgan Iran

Gastric Cancer (GC) is the third most common cause of cancer-related deaths in the world. Due to the lack of enough symptoms in early stages, it is diagnosed in advanced stages in the majority of patients and causes great rate of mortality. Early recognition of GC significantly raises the lucks for successful treatment. Molecular mechanisms of GC are still poorly understood. MiRNAs are small non-coding RNAs which regulate gene expression in post-transcription levels. In cancer cells, miRNAs have been found to be severely dysregulated. Using high-throughput (HTP) technologies such as RNA- Seq, the effects of miRNAs on cancers can be investigated. In this study, we retrieved miRNAs obtained by HTP method from OncoLnc database. Consequently, retrieved miRNAs were compared in literature-based databases such as PubMed. As a result, two lists including experimentally validated and predicted miRNAs were provided. We found 28 predicted miRNAs that so far had not been experimentally validated in GC. In the following, further bioinformatics analyses were performed to obtain the expression profile of both validated and predicted miRNAs in tumor and normal tissues. Also, the role of predicted miRNAs in other cancers, and their possible targets in apoptosis, metastasis and angiogenesis retrieved from related databases. By recognizing the miRNAs involved in initiation and progression of GC, they may be considered as potential biomarkers in GC early diagnosis or targeted treatment and lead to novel therapeutic strategies. We introduce 28 predicted miRNAs involved in GC pathogenesis by in silico analysis.

关键词： Gastric Cancer Predicted miRNAs In silico analysis bioinformatics database High-throughput technology

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bioinformatics Analysis of Protein Phosphorylation in Plant Systems Biology Using P3DB

Bioinformatics Analysis of Protein Phosphorylation in Plant ...

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作者： Qiuming Yao Dong Xu

Protein phosphorylation is one of the most pervasive protein post-translational modification events in plant cells. It is involved in many plant biological processes, such as plant growth, organ development, and plant immunology, by regulating or switching signaling and metabolic pathways. High-throughput experimental methods like mass spectrometry can easily characterize hundreds to thousands of phosphorylation events in a single experiment. With the increasing volume of the data sets, Plant Protein Phosphorylation database (P3DB, http://*** ) provides a comprehensive, systematic, and interactive online platform to deposit, query, analyze, and visualize these phosphorylation events in many plant species. It stores the protein phosphorylation sites in the context of identified mass spectra, phosphopeptides, and phosphoproteins contributed from various plant proteome studies. In addition, P3DB associates these plant phosphorylation sites to protein physicochemical information in the protein charts and tertiary structures, while various protein annotations from hierarchical kinase phosphatase families, protein domains, and gene ontology are also added into the database. P3DB not only provides rich information, but also interconnects and provides visualization of the data in networks, in systems biology context. Currently, P3DB includes the KiC (Kinase Client) assay network, the protein-protein interaction network, the kinase-substrate network, the phosphatase-substrate network, and the protein domain co-occurrence network. All of these are available to query for and visualize existing phosphorylation events. Although P3DB only hosts experimentally identified phosphorylation data, it provides a plant phosphorylation prediction model for any unknown queries on the fly. P3DB is an entry point to the plant phosphorylation community to deposit and visualize any customized data sets within this systems biology framework. Nowadays, P3DB has become one of

关键词： Mass Spectrometry Statistical Calculation Proteomics Cytoscape Metabolomics Jmol Pathway Or Network Visualization Arabidopsis thaliana Medicago truncatula Oryza sativa Apis Systems biology Protein–protein interaction network bioinformatics database Domain co-occurrence network Kinase–substrate network Phosphatase-substrate network Plant protein phosphorylation

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Visual Composition of Complex Queries on an Integrative Genomic and Proteomic Data Warehouse

Visual Composition of Complex Queries on an Integrative Geno...

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The 7th International Conference on Management and Service Science(MASS 2013)

作者： Francesco Pessina Marco Masseroli Arif Canakoglu Dipartimento di Elettronica e Informazione Politecnico di MilanoMilanItaly

Biomedical questions are usually complex and regard several different life science *** valuable and heterogeneous data are increasingly available to answer such ***,they are dispersedly stored and difficult to be queried *** created a Genomic and Proteomic Data Warehouse(GPDW) that integrates data provided by some of the main bioinformatics *** adopts a modular integrated data schema and several metadata to describe the integrated data,their sources and their location in the ***,we present the Web application that we developed to enable any user to easily compose queries,although complex,on all data integrated in the *** is publicly available at http:***/GPKB/.Through a visual interface,the user is only required to select the types of data to be included in the query and the conditions on their values to be ***,the Web application leverages the metadata and modular schema of the GPDW to automatically compose an efficient SQL query,run it on the GPDW and show the extracted requested data,enriched with links to external data *** tests demonstrated efficiency and usability of the developed Web application,and showed its and GPDW relevance in supporting answering biomedical questions,also difficult.

关键词： SQL Query Composition Visual Interface Integrated Data Extraction Data Warehousing bioinformatics database

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New Challenges for Biological Text-Mining in the Next Decade

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Journal of Computer Science & Technology 2010年第1期25卷 169-178,F0003页

作者： Yen-Ching Chang Richard Tzong-Han Tsai Wen-Lian Hsu Institute of Information Science "Academia Sinica" Department of Computer Science and Engineering Yuan Ze University Department of Computer Science "National Tsing-Hua University"

The massive flow of scholarly publications from traditional paper journals to online outlets has benefited biologists because of its ease to access. However, due to the sheer volume of available biological literature, researchers are finding it increasingly difficult to locate needed information. As a result, recent biology contests, notably JNLPBA and BioCreAtIvE, have focused on evaluating various methods in which the literature may be navigated. Among these methods, text-mining technology has shown the most promise. With recent advances in text-mining technology and the fact that publishers are now making the full texts of articles available in XML format, TMSs can be adapted to accelerate literature curation, maintain the integrity of information, and ensure proper linkage of data to other resources. Even so, several new challenges have emerged in relation to full text analysis, life-science terminology, complex relation extraction, and information fusion. These challenges must be overcome in order for text-mining to be more effective. In this paper, we identify the challenges, discuss how they might be overcome, and consider the resources that may be helpful in achieving that goal.

关键词： bioinformatics database mining method and algorithm text mining

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Bio2RDF: Towards a mashup to build bioinformatics knowledge systems

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JOURNAL OF BIOMEDICAL INFORMATICS 2008年第5期41卷 706-716页

作者： Belleau, Francois Nolin, Marc-Alexandre Tourigny, Nicole Rigault, Philippe Morissette, Jean Univ Laval Dept Informat & Genie Logiciel Quebec City PQ G1K 7P4 Canada Univ Laval CHUL Ctr Rech Quebec City PQ G1V 4G2 Canada Univ Laval Dept Anat Physiol Quebec City PQ G1K 7P4 Canada

Presently, there are numerous bioinformatics databases available on different websites. Although RDF was proposed as a standard format for the web, these databases are still available in various formats. With the increasing popularity of the semantic web technologies and the ever growing number of databases in bioinformatics, there is a pressing need to develop mashup systems to help the process of bioinformatics knowledge integration. Bio2RDF is such a system, built from rdfizer programs written in JSP, the Sesame open Source triplestore technology and an OWL ontology. With Bio2RDF, documents from public bioinformatics databases Such as Kegg, PDB, MGI, HGNC and several of NCBI's databases can now be made available in RDF format through a unique URL in the form of http://***/namespace:id. The Bio2RDF project has successfully applied the semantic web technology to publicly available databases by creating a knowledge space of RDF documents linked together with normalized URIs and sharing a common ontology. Bio2RDF is based on a three-step approach to build mashups of bioinformatics data. The present article details this new approach and illustrates the building of a mashup used to explore the implication Of four transcription factor genes in Parkinson's disease. The Bio2RDF repository can be queried at http://***. (C) 2008 Elsevier Inc. All rights reserved.

关键词： Knowledge integration bioinformatics database Semantic web Mashup Ontology

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Critical evaluation of the JDO API for the persistence and portability requirements of complex biological databases

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BMC bioinformatics 2005年第1期6卷 1-15页

作者： Srdanovic, M Schenk, U Schwieger, M Campagne, F Weill Cornell Med Coll New York NY 10021 USA FastObjects Inc San Rafael CA 94903 USA

Background: Complex biological database systems have become key computational tools used daily by scientists and researchers. Many of these systems must be capable of executing on multiple different hardware and software configurations and are also often made available to users via the Internet. We have used the Java Data Object (JDO) persistence technology to develop the database layer of such a system known as the SigPath information management system. SigPath is an example of a complex biological database that needs to store various types of information connected by many relationships. Results: Using this system as an example, we perform a critical evaluation of current JDO technology;discuss the suitability of the JDO standard to achieve portability, scalability and performance. We show that JDO supports portability of the SigPath system from a relational database backend to an object database backend and achieves acceptable scalability. To answer the performance question, we have created the SigPath JDO application benchmark that we distribute under the Gnu General Public License. This benchmark can be used as an example of using JDO technology to create a complex biological database and makes it possible for vendors and users of the technology to evaluate the performance of other JDO implementations for similar applications. Conclusions: The SigPath JDO benchmark and our discussion of JDO technology in the context of biological databases will be useful to bioinformaticians who design new complex biological databases and aim to create systems that can be ported easily to a variety of database backends.

关键词： Relational database Application Programming Interface Code Base Biological database bioinformatics database

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