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Yao D, Mei S, Tang W, Xu X, Lu Q, Shi Z. AAAKB: A manually curated database for tracking and predicting genes of Abdominal aortic aneurysm (AAA). PLoS One 2023; 18:e0289966. [PMID: 38100461 PMCID: PMC10723669 DOI: 10.1371/journal.pone.0289966] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Accepted: 07/31/2023] [Indexed: 12/17/2023] Open
Abstract
Abdominal aortic aneurysm (AAA), an extremely dangerous vascular disease with high mortality, causes massive internal bleeding due to aneurysm rupture. To boost the research on AAA, efforts should be taken to organize and link the information about AAA-related genes and their functions. Currently, most researchers screen through genetic databases manually, which is cumbersome and time-consuming. Here, we developed "AAAKB" a manually curated knowledgebase containing genes, SNPs and pathways associated with AAA. In order to facilitate researchers to further explore the mechanism network of AAA, AAAKB provides predicted genes that are potentially associated with AAA. The prediction is based on the protein interaction information of genes collected in the database, and the random forest algorithm (RF) is used to build the prediction model. Some of these predicted genes are differentially expressed in patients with AAA, and some have been reported to play a role in other cardiovascular diseases, illustrating the utility of the knowledgebase in predicting novel genes. Also, AAAKB integrates a protein interaction visualization tool to quickly determine the shortest paths between target proteins. As the first knowledgebase to provide a comprehensive catalog of AAA-related genes, AAAKB will be an ideal research platform for AAA. Database URL: http://www.lqlgroup.cn:3838/AAAKB/.
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Affiliation(s)
- Di Yao
- Institute of Industrial Internet and Internet of Things, China Academy of Information and Communications Technology (CAICT), China
| | - Shuyuan Mei
- Key Laboratory of Cardiovascular and Cerebrovascular Medicine, School of Pharmacy, Nanjing Medical University, Nanjing, China
| | - Wangyang Tang
- School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China
| | - Xingyu Xu
- School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China
| | - Qiulun Lu
- Key Laboratory of Cardiovascular and Cerebrovascular Medicine, School of Pharmacy, Nanjing Medical University, Nanjing, China
| | - Zhiguang Shi
- Key Laboratory of Cardiovascular and Cerebrovascular Medicine, School of Pharmacy, Nanjing Medical University, Nanjing, China
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Gosset S, Glatigny A, Gallopin M, Yi Z, Salé M, Mucchielli-Giorgi MH. APPINetwork: an R package for building and computational analysis of protein-protein interaction networks. PeerJ 2022; 10:e14204. [PMID: 36353604 PMCID: PMC9639416 DOI: 10.7717/peerj.14204] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Accepted: 09/19/2022] [Indexed: 11/06/2022] Open
Abstract
Background Protein-protein interactions (PPIs) are essential to almost every process in a cell. Analysis of PPI networks gives insights into the functional relationships among proteins and may reveal important hub proteins and sub-networks corresponding to functional modules. Several good tools have been developed for PPI network analysis but they have certain limitations. Most tools are suited for studying PPI in only a small number of model species, and do not allow second-order networks to be built, or offer relevant functions for their analysis. To overcome these limitations, we have developed APPINetwork (Analysis of Protein-protein Interaction Networks). The aim was to produce a generic and user-friendly package for building and analyzing a PPI network involving proteins of interest from any species as long they are stored in a database. Methods APPINetwork is an open-source R package. It can be downloaded and installed on the collaborative development platform GitLab (https://forgemia.inra.fr/GNet/appinetwork). A graphical user interface facilitates its use. Graphical windows, buttons, and scroll bars allow the user to select or enter an organism name, choose data files and network parameters or methods dedicated to network analysis. All functions are implemented in R, except for the script identifying all proteins involved in the same biological process (developed in C) and the scripts formatting the BioGRID data file and generating the IDs correspondence file (implemented in Python 3). PPI information comes from private resources or different public databases (such as IntAct, BioGRID, and iRefIndex). The package can be deployed on Linux and macOS operating systems (OS). Deployment on Windows is possible but it requires the prior installation of Rtools and Python 3. Results APPINetwork allows the user to build a PPI network from selected public databases and add their own PPI data. In this network, the proteins have unique identifiers resulting from the standardization of the different identifiers specific to each database. In addition to the construction of the first-order network, APPINetwork offers the possibility of building a second-order network centered on the proteins of interest (proteins known for their role in the biological process studied or subunits of a complex protein) and provides the number and type of experiments that have highlighted each PPI, as well as references to articles containing experimental evidence. Conclusion More than a tool for PPI network building, APPINetwork enables the analysis of the resultant network, by searching either for the community of proteins involved in the same biological process or for the assembly intermediates of a protein complex. Results of these analyses are provided in easily exportable files. Examples files and a user manual describing each step of the process come with the package.
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Affiliation(s)
- Simon Gosset
- Université Paris-Saclay, CNRS, INRAE, Université Evry, Institute of Plant Sciences Paris-Saclay (IPS2), Gif-sur-Yvette, France
- Université de Paris, Institute of Plant Sciences Paris-Saclay (IPS2), Gif-sur-Yvette, France
| | - Annie Glatigny
- Université Paris-Saclay, CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC), Gif-sur-Yvette, France
| | - Mélina Gallopin
- Université Paris-Saclay, CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC), Gif-sur-Yvette, France
| | - Zhou Yi
- Université Paris-Saclay, CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC), Gif-sur-Yvette, France
| | - Marion Salé
- Université Paris-Saclay, CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC), Gif-sur-Yvette, France
| | - Marie-Hélène Mucchielli-Giorgi
- Université Paris-Saclay, CNRS, INRAE, Université Evry, Institute of Plant Sciences Paris-Saclay (IPS2), Gif-sur-Yvette, France
- Université de Paris, Institute of Plant Sciences Paris-Saclay (IPS2), Gif-sur-Yvette, France
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Improving cancer treatments via dynamical biophysical models. Phys Life Rev 2021; 39:1-48. [PMID: 34688561 DOI: 10.1016/j.plrev.2021.10.001] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Accepted: 10/13/2021] [Indexed: 12/17/2022]
Abstract
Despite significant advances in oncological research, cancer nowadays remains one of the main causes of mortality and morbidity worldwide. New treatment techniques, as a rule, have limited efficacy, target only a narrow range of oncological diseases, and have limited availability to the general public due their high cost. An important goal in oncology is thus the modification of the types of antitumor therapy and their combinations, that are already introduced into clinical practice, with the goal of increasing the overall treatment efficacy. One option to achieve this goal is optimization of the schedules of drugs administration or performing other medical actions. Several factors complicate such tasks: the adverse effects of treatments on healthy cell populations, which must be kept tolerable; the emergence of drug resistance due to the intrinsic plasticity of heterogeneous cancer cell populations; the interplay between different types of therapies administered simultaneously. Mathematical modeling, in which a tumor and its microenvironment are considered as a single complex system, can address this complexity and can indicate potentially effective protocols, that would require experimental verification. In this review, we consider classical methods, current trends and future prospects in the field of mathematical modeling of tumor growth and treatment. In particular, methods of treatment optimization are discussed with several examples of specific problems related to different types of treatment.
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Grzesik P, Augustyn DR, Wyciślik Ł, Mrozek D. Serverless computing in omics data analysis and integration. Brief Bioinform 2021; 23:6367629. [PMID: 34505137 PMCID: PMC8499876 DOI: 10.1093/bib/bbab349] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Revised: 06/28/2021] [Accepted: 08/06/2021] [Indexed: 11/30/2022] Open
Abstract
A comprehensive analysis of omics data can require vast computational resources and access to varied data sources that must be integrated into complex, multi-step analysis pipelines. Execution of many such analyses can be accelerated by applying the cloud computing paradigm, which provides scalable resources for storing data of different types and parallelizing data analysis computations. Moreover, these resources can be reused for different multi-omics analysis scenarios. Traditionally, developers are required to manage a cloud platform’s underlying infrastructure, configuration, maintenance and capacity planning. The serverless computing paradigm simplifies these operations by automatically allocating and maintaining both servers and virtual machines, as required for analysis tasks. This paradigm offers highly parallel execution and high scalability without manual management of the underlying infrastructure, freeing developers to focus on operational logic. This paper reviews serverless solutions in bioinformatics and evaluates their usage in omics data analysis and integration. We start by reviewing the application of the cloud computing model to a multi-omics data analysis and exposing some shortcomings of the early approaches. We then introduce the serverless computing paradigm and show its applicability for performing an integrative analysis of multiple omics data sources in the context of the COVID-19 pandemic.
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Affiliation(s)
- Piotr Grzesik
- Silesian University of Technology, Department of Applied Informatics, Gliwice 44-100, Poland
| | - Dariusz R Augustyn
- Silesian University of Technology, Department of Applied Informatics, Gliwice 44-100, Poland
| | - Łukasz Wyciślik
- Silesian University of Technology, Department of Applied Informatics, Gliwice 44-100, Poland
| | - Dariusz Mrozek
- Corresponding author: Dariusz Mrozek, Department of Applied Informatics, Silesian University of Technology, Gliwice 44-100, Poland. E-mail:
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Zhou J, Wang H, Zhao Z, Xu R, Lu Q. CNNH_PSS: protein 8-class secondary structure prediction by convolutional neural network with highway. BMC Bioinformatics 2018; 19:60. [PMID: 29745837 PMCID: PMC5998876 DOI: 10.1186/s12859-018-2067-8] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
BACKGROUND Protein secondary structure is the three dimensional form of local segments of proteins and its prediction is an important problem in protein tertiary structure prediction. Developing computational approaches for protein secondary structure prediction is becoming increasingly urgent. RESULTS We present a novel deep learning based model, referred to as CNNH_PSS, by using multi-scale CNN with highway. In CNNH_PSS, any two neighbor convolutional layers have a highway to deliver information from current layer to the output of the next one to keep local contexts. As lower layers extract local context while higher layers extract long-range interdependencies, the highways between neighbor layers allow CNNH_PSS to have ability to extract both local contexts and long-range interdependencies. We evaluate CNNH_PSS on two commonly used datasets: CB6133 and CB513. CNNH_PSS outperforms the multi-scale CNN without highway by at least 0.010 Q8 accuracy and also performs better than CNF, DeepCNF and SSpro8, which cannot extract long-range interdependencies, by at least 0.020 Q8 accuracy, demonstrating that both local contexts and long-range interdependencies are indeed useful for prediction. Furthermore, CNNH_PSS also performs better than GSM and DCRNN which need extra complex model to extract long-range interdependencies. It demonstrates that CNNH_PSS not only cost less computer resource, but also achieves better predicting performance. CONCLUSION CNNH_PSS have ability to extracts both local contexts and long-range interdependencies by combing multi-scale CNN and highway network. The evaluations on common datasets and comparisons with state-of-the-art methods indicate that CNNH_PSS is an useful and efficient tool for protein secondary structure prediction.
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Affiliation(s)
- Jiyun Zhou
- School Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, HIT Campus Shenzhen University Town, Xili, Shenzhen, Guangdong 518055 China
- Department of Computing, the Hong Kong Polytechnic University, Hung Hom, Hong Kong
| | - Hongpeng Wang
- School Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, HIT Campus Shenzhen University Town, Xili, Shenzhen, Guangdong 518055 China
| | - Zhishan Zhao
- School Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, HIT Campus Shenzhen University Town, Xili, Shenzhen, Guangdong 518055 China
| | - Ruifeng Xu
- School Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, HIT Campus Shenzhen University Town, Xili, Shenzhen, Guangdong 518055 China
| | - Qin Lu
- Department of Computing, the Hong Kong Polytechnic University, Hung Hom, Hong Kong
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Dagliati A, Tibollo V, Cogni G, Chiovato L, Bellazzi R, Sacchi L. Careflow Mining Techniques to Explore Type 2 Diabetes Evolution. J Diabetes Sci Technol 2018; 12:251-259. [PMID: 29493360 PMCID: PMC5851241 DOI: 10.1177/1932296818761751] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
In this work we describe the application of a careflow mining algorithm to detect the most frequent patterns of care in a type 2 diabetes patients cohort. The applied method enriches the detected patterns with clinical data to define temporal phenotypes across the studied population. Novel phenotypes are discovered from heterogeneous data of 424 Italian patients, and compared in terms of metabolic control and complications. Results show that careflow mining can help to summarize the complex evolution of the disease into meaningful patterns, which are also significant from a clinical point of view.
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Affiliation(s)
- Arianna Dagliati
- Istituti Clinici Scientifici Maugeri, Pavia, Italy
- University of Manchester, Manchester, UK
| | | | - Giulia Cogni
- Istituti Clinici Scientifici Maugeri, Pavia, Italy
| | | | - Riccardo Bellazzi
- Istituti Clinici Scientifici Maugeri, Pavia, Italy
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
| | - Lucia Sacchi
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
- Lucia Sacchi, PhD, Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Via Ferrata, 5, Pavia, 27100, Italy.
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Dovrolis N, Kolios G, Spyrou G, Maroulakou I. Laying in silico pipelines for drug repositioning: a paradigm in ensemble analysis for neurodegenerative diseases. Drug Discov Today 2017; 22:805-813. [PMID: 28363518 DOI: 10.1016/j.drudis.2017.03.009] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2016] [Revised: 02/17/2017] [Accepted: 03/21/2017] [Indexed: 12/22/2022]
Abstract
When faced with time- and money-consuming problems, new practices in pharmaceutical R&D arose when trying to alleviate them. Drug repositioning has great promise and when combined with today's computational power and intelligence it becomes more precise and potent. This work showcases current approaches of creating a computational pipeline for drug repositioning, along with an extensive example of how researchers can influence therapeutic approaches and further understanding, through either single or multiple disease studies. This paradigm is based on three neurodegenerative diseases with pathophysiological similarities. It is our goal to provide the readers with all the information needed to enrich their research and note expectations along the way.
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Affiliation(s)
- Nikolas Dovrolis
- Laboratory of Pharmacology, Department of Medicine, Democritus University of Thrace, Greece
| | - George Kolios
- Laboratory of Pharmacology, Department of Medicine, Democritus University of Thrace, Greece
| | - George Spyrou
- Bioinformatics ERA Chair, The Cyprus Institute of Neurology and Genetics, Cyprus
| | - Ioanna Maroulakou
- Department of Molecular Biology & Genetics, Democritus University of Thrace, Greece.
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Protein-protein interaction analysis for functional characterization of helicases. Methods 2016; 108:56-64. [PMID: 27090004 DOI: 10.1016/j.ymeth.2016.04.014] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2016] [Revised: 04/11/2016] [Accepted: 04/13/2016] [Indexed: 11/22/2022] Open
Abstract
Helicases are enzymes involved in nucleic acid metabolism, playing major roles in replication, transcription, and repair. Defining helicases oligomerization state and transient and persistent protein interactions is essential for understanding of their function. In this article we review current methods for the protein-protein interaction analysis, and discuss examples of its application to the study of helicases: Pif1 and DDX3. Proteomics methods are our main focus - affinity pull-downs and chemical cross-linking followed by mass spectrometry. We review advantages and limitations of these methods and provide general guidelines for their implementation in the functional analysis of helicases.
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