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  1. Article ; Online: Investigating underlying human immunity genes, implicated diseases and their relationship to COVID-19.

    Ahmed, Zeeshan / Renart, Eduard Gibert / Zeeshan, Saman

    Personalized medicine

    2022  Volume 19, Issue 3, Page(s) 229–250

    Abstract: Aim: ...

    Abstract Aim:
    MeSH term(s) Angiotensin-Converting Enzyme 2/genetics ; COVID-19/genetics ; Genome ; Humans ; Membrane Transport Proteins/genetics ; SARS-CoV-2/genetics ; Whole Exome Sequencing
    Chemical Substances Membrane Transport Proteins ; SLC6A20 protein, human ; Angiotensin-Converting Enzyme 2 (EC 3.4.17.23)
    Language English
    Publishing date 2022-03-09
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2299146-3
    ISSN 1744-828X ; 1741-0541
    ISSN (online) 1744-828X
    ISSN 1741-0541
    DOI 10.2217/pme-2021-0132
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

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    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
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  2. Article: Genomics pipelines to investigate susceptibility in whole genome and exome sequenced data for variant discovery, annotation, prediction and genotyping.

    Ahmed, Zeeshan / Renart, Eduard Gibert / Zeeshan, Saman

    PeerJ

    2021  Volume 9, Page(s) e11724

    Abstract: Over the last few decades, genomics is leading toward audacious future, and has been changing our views about conducting biomedical research, studying diseases, and understanding diversity in our society across the human species. The whole genome and ... ...

    Abstract Over the last few decades, genomics is leading toward audacious future, and has been changing our views about conducting biomedical research, studying diseases, and understanding diversity in our society across the human species. The whole genome and exome sequencing (WGS/WES) are two of the most popular next-generation sequencing (NGS) methodologies that are currently being used to detect genetic variations of clinical significance. Investigating WGS/WES data for the variant discovery and genotyping is based on the nexus of different data analytic applications. Although several bioinformatics applications have been developed, and many of those are freely available and published. Timely finding and interpreting genetic variants are still challenging tasks among diagnostic laboratories and clinicians. In this study, we are interested in understanding, evaluating, and reporting the current state of solutions available to process the NGS data of variable lengths and types for the identification of variants, alleles, and haplotypes. Residing within the scope, we consulted high quality peer reviewed literature published in last 10 years. We were focused on the standalone and networked bioinformatics applications proposed to efficiently process WGS and WES data, and support downstream analysis for gene-variant discovery, annotation, prediction, and interpretation. We have discussed our findings in this manuscript, which include but not are limited to the set of operations, workflow, data handling, involved tools, technologies and algorithms and limitations of the assessed applications.
    Language English
    Publishing date 2021-07-26
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2703241-3
    ISSN 2167-8359
    ISSN 2167-8359
    DOI 10.7717/peerj.11724
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: Advancing clinical genomics and precision medicine with GVViZ: FAIR bioinformatics platform for variable gene-disease annotation, visualization, and expression analysis.

    Ahmed, Zeeshan / Renart, Eduard Gibert / Zeeshan, Saman / Dong, XinQi

    Human genomics

    2021  Volume 15, Issue 1, Page(s) 37

    Abstract: Background: Genetic disposition is considered critical for identifying subjects at high risk for disease development. Investigating disease-causing and high and low expressed genes can support finding the root causes of uncertainties in patient care. ... ...

    Abstract Background: Genetic disposition is considered critical for identifying subjects at high risk for disease development. Investigating disease-causing and high and low expressed genes can support finding the root causes of uncertainties in patient care. However, independent and timely high-throughput next-generation sequencing data analysis is still a challenge for non-computational biologists and geneticists.
    Results: In this manuscript, we present a findable, accessible, interactive, and reusable (FAIR) bioinformatics platform, i.e., GVViZ (visualizing genes with disease-causing variants). GVViZ is a user-friendly, cross-platform, and database application for RNA-seq-driven variable and complex gene-disease data annotation and expression analysis with a dynamic heat map visualization. GVViZ has the potential to find patterns across millions of features and extract actionable information, which can support the early detection of complex disorders and the development of new therapies for personalized patient care. The execution of GVViZ is based on a set of simple instructions that users without a computational background can follow to design and perform customized data analysis. It can assimilate patients' transcriptomics data with the public, proprietary, and our in-house developed gene-disease databases to query, easily explore, and access information on gene annotation and classified disease phenotypes with greater visibility and customization. To test its performance and understand the clinical and scientific impact of GVViZ, we present GVViZ analysis for different chronic diseases and conditions, including Alzheimer's disease, arthritis, asthma, diabetes mellitus, heart failure, hypertension, obesity, osteoporosis, and multiple cancer disorders. The results are visualized using GVViZ and can be exported as image (PNF/TIFF) and text (CSV) files that include gene names, Ensembl (ENSG) IDs, quantified abundances, expressed transcript lengths, and annotated oncology and non-oncology diseases.
    Conclusions: We emphasize that automated and interactive visualization should be an indispensable component of modern RNA-seq analysis, which is currently not the case. However, experts in clinics and researchers in life sciences can use GVViZ to visualize and interpret the transcriptomics data, making it a powerful tool to study the dynamics of gene expression and regulation. Furthermore, with successful deployment in clinical settings, GVViZ has the potential to enable high-throughput correlations between patient diagnoses based on clinical and transcriptomics data.
    MeSH term(s) Computational Biology ; Databases, Factual ; Gene Expression Profiling ; Genetic Predisposition to Disease ; Genomics ; High-Throughput Nucleotide Sequencing ; Humans ; Molecular Sequence Annotation ; Precision Medicine ; RNA-Seq ; Software ; Transcriptome/genetics ; User-Computer Interface
    Language English
    Publishing date 2021-06-26
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2147618-4
    ISSN 1479-7364 ; 1479-7364
    ISSN (online) 1479-7364
    ISSN 1479-7364
    DOI 10.1186/s40246-021-00336-1
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: JWES: a new pipeline for whole genome/exome sequence data processing, management, and gene-variant discovery, annotation, prediction, and genotyping.

    Ahmed, Zeeshan / Renart, Eduard Gibert / Mishra, Deepshikha / Zeeshan, Saman

    FEBS open bio

    2021  Volume 11, Issue 9, Page(s) 2441–2452

    Abstract: Whole genome and exome sequencing (WGS/WES) are the most popular next-generation sequencing (NGS) methodologies and are at present often used to detect rare and common genetic variants of clinical significance. We emphasize that automated sequence data ... ...

    Abstract Whole genome and exome sequencing (WGS/WES) are the most popular next-generation sequencing (NGS) methodologies and are at present often used to detect rare and common genetic variants of clinical significance. We emphasize that automated sequence data processing, management, and visualization should be an indispensable component of modern WGS and WES data analysis for sequence assembly, variant detection (SNPs, SVs), imputation, and resolution of haplotypes. In this manuscript, we present a newly developed findable, accessible, interoperable, and reusable (FAIR) bioinformatics-genomics pipeline Java based Whole Genome/Exome Sequence Data Processing Pipeline (JWES) for efficient variant discovery and interpretation, and big data modeling and visualization. JWES is a cross-platform, user-friendly, product line application, that entails three modules: (a) data processing, (b) storage, and (c) visualization. The data processing module performs a series of different tasks for variant calling, the data storage module efficiently manages high-volume gene-variant data, and the data visualization module supports variant data interpretation with Circos graphs. The performance of JWES was tested and validated in-house with different experiments, using Microsoft Windows, macOS Big Sur, and UNIX operating systems. JWES is an open-source and freely available pipeline, allowing scientists to take full advantage of all the computing resources available, without requiring much computer science knowledge. We have successfully applied JWES for processing, management, and gene-variant discovery, annotation, prediction, and genotyping of WGS and WES data to analyze variable complex disorders. In summary, we report the performance of JWES with some reproducible case studies, using open access and in-house generated, high-quality datasets.
    MeSH term(s) Computational Biology/methods ; Data Management ; Databases, Genetic ; Exome ; Genetic Variation ; Genome ; Genomics/methods ; Humans ; Molecular Sequence Annotation ; Reproducibility of Results ; Sequence Analysis, DNA/methods ; Software ; Whole Exome Sequencing ; Whole Genome Sequencing ; Workflow
    Language English
    Publishing date 2021-08-11
    Publishing country England
    Document type Journal Article
    ZDB-ID 2651702-4
    ISSN 2211-5463 ; 2211-5463
    ISSN (online) 2211-5463
    ISSN 2211-5463
    DOI 10.1002/2211-5463.13261
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Article: JWES: a new pipeline for whole genome/exome sequence data processing, management, and gene‐variant discovery, annotation, prediction, and genotyping

    Ahmed, Zeeshan / Renart, Eduard Gibert / Mishra, Deepshikha / Zeeshan, Saman

    FEBS Open Bio. 2021 Sept., v. 11, no. 9

    2021  

    Abstract: Whole genome and exome sequencing (WGS/WES) are the most popular next‐generation sequencing (NGS) methodologies and are at present often used to detect rare and common genetic variants of clinical significance. We emphasize that automated sequence data ... ...

    Abstract Whole genome and exome sequencing (WGS/WES) are the most popular next‐generation sequencing (NGS) methodologies and are at present often used to detect rare and common genetic variants of clinical significance. We emphasize that automated sequence data processing, management, and visualization should be an indispensable component of modern WGS and WES data analysis for sequence assembly, variant detection (SNPs, SVs), imputation, and resolution of haplotypes. In this manuscript, we present a newly developed findable, accessible, interoperable, and reusable (FAIR) bioinformatics‐genomics pipeline Java based Whole Genome/Exome Sequence Data Processing Pipeline (JWES) for efficient variant discovery and interpretation, and big data modeling and visualization. JWES is a cross‐platform, user‐friendly, product line application, that entails three modules: (a) data processing, (b) storage, and (c) visualization. The data processing module performs a series of different tasks for variant calling, the data storage module efficiently manages high‐volume gene‐variant data, and the data visualization module supports variant data interpretation with Circos graphs. The performance of JWES was tested and validated in‐house with different experiments, using Microsoft Windows, macOS Big Sur, and UNIX operating systems. JWES is an open‐source and freely available pipeline, allowing scientists to take full advantage of all the computing resources available, without requiring much computer science knowledge. We have successfully applied JWES for processing, management, and gene‐variant discovery, annotation, prediction, and genotyping of WGS and WES data to analyze variable complex disorders. In summary, we report the performance of JWES with some reproducible case studies, using open access and in‐house generated, high‐quality datasets.
    Keywords automation ; computer software ; data collection ; data visualization ; genome ; genotyping ; haplotypes ; information storage ; prediction
    Language English
    Dates of publication 2021-09
    Size p. 2441-2452.
    Publishing place John Wiley & Sons, Ltd
    Document type Article
    Note JOURNAL ARTICLE
    ZDB-ID 2651702-4
    ISSN 2211-5463
    ISSN 2211-5463
    DOI 10.1002/2211-5463.13261
    Database NAL-Catalogue (AGRICOLA)

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  6. Book ; Online: Challenges in designing edge-based middlewares for the Internet of Things

    Renart, Eduard Gibert / Balouek-thomert, Daniel / Parashar, Manish

    A survey

    2019  

    Abstract: The Internet of Things paradigm connects edge devices via the Internet enabling them to be seamlessly integrated with a wide variety of applications. In recent years, the number of connected devices has grown significantly, along with the volume and ... ...

    Abstract The Internet of Things paradigm connects edge devices via the Internet enabling them to be seamlessly integrated with a wide variety of applications. In recent years, the number of connected devices has grown significantly, along with the volume and variety of data that is being generated by these devices at the edge of the network. An edge-based middleware is defined as a software that serves as an interface between the computational resources and the IoT devices, making communication possible among elements. Such middleware is required to provide the necessary functional components for sensor registration, discovery, workflow composition, and data pre-processing. In recent years, the landscape of the edge middleware platforms has grown exponentially, each of them with different platform requirements, architectures, and features. The core of this survey is a comprehensive review of existing edge middleware solutions. In this regard, we propose a four-layer architecture for the design of edge-based middleware, along with some design goals for each of the proposed layer. The paper concludes with some open challenges and possible future research directions.
    Keywords Computer Science - Distributed ; Parallel ; and Cluster Computing
    Publishing date 2019-12-13
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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