For Publishers
DiscoveryMetadataPeer reviewHostingPublishing
For Researchers
JoinPublishReviewCollect
Register
Blog
About
Search
Advanced search
My ScienceOpen
Search
For Publishers
For Researchers
Blog
About
29
views
128
references
 Top references
cited by
7
    
0 reviews
 Review
0
comments
 Comment
0
recommends
+1 Recommend
0 collections
    0
    shares
      145
      similar
       All similar
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Review on Databases and Bioinformatic Approaches on Pharmacogenomics of Adverse Drug Reactions

      review-article

      Read this article at

      ScienceOpenPublisherPMC
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Pharmacogenomics has been used effectively in studying adverse drug reactions by determining the person-specific genetic factors associated with individual response to a drug. Current approaches have revealed the significant importance of sequencing technologies and sequence analysis strategies for interpreting the contribution of genetic variation in developing adverse reactions. Advance in next generation sequencing and platform brings new opportunities in validating the genetic candidates in certain reactions, and could be used to develop the preemptive tests to predict the outcome of the variation in a personal response to a drug. With the highly accumulated available data recently, the in silico approach with data analysis and modeling plays as other important alternatives which significantly support the final decisions in the transformation from research to clinical applications such as diagnosis and treatments for various types of adverse responses.

          Related collections

          Most cited references128

          • Record: found
          • Abstract: found
          • Article: not found

          KEGG: kyoto encyclopedia of genes and genomes.

          M Kanehisa (2000)
          KEGG (Kyoto Encyclopedia of Genes and Genomes) is a knowledge base for systematic analysis of gene functions, linking genomic information with higher order functional information. The genomic information is stored in the GENES database, which is a collection of gene catalogs for all the completely sequenced genomes and some partial genomes with up-to-date annotation of gene functions. The higher order functional information is stored in the PATHWAY database, which contains graphical representations of cellular processes, such as metabolism, membrane transport, signal transduction and cell cycle. The PATHWAY database is supplemented by a set of ortholog group tables for the information about conserved subpathways (pathway motifs), which are often encoded by positionally coupled genes on the chromosome and which are especially useful in predicting gene functions. A third database in KEGG is LIGAND for the information about chemical compounds, enzyme molecules and enzymatic reactions. KEGG provides Java graphics tools for browsing genome maps, comparing two genome maps and manipulating expression maps, as well as computational tools for sequence comparison, graph comparison and path computation. The KEGG databases are daily updated and made freely available (http://www. genome.ad.jp/kegg/).
          Article 0 comments Cited 8441 times – based on 0 reviews     Review now
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Standards and Guidelines for the Interpretation of Sequence Variants: A Joint Consensus Recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology

            Sue Richards, Nazneen Aziz, Sherri Bale … (2015)
            The American College of Medical Genetics and Genomics (ACMG) previously developed guidance for the interpretation of sequence variants. 1 In the past decade, sequencing technology has evolved rapidly with the advent of high-throughput next generation sequencing. By adopting and leveraging next generation sequencing, clinical laboratories are now performing an ever increasing catalogue of genetic testing spanning genotyping, single genes, gene panels, exomes, genomes, transcriptomes and epigenetic assays for genetic disorders. By virtue of increased complexity, this paradigm shift in genetic testing has been accompanied by new challenges in sequence interpretation. In this context, the ACMG convened a workgroup in 2013 comprised of representatives from the ACMG, the Association for Molecular Pathology (AMP) and the College of American Pathologists (CAP) to revisit and revise the standards and guidelines for the interpretation of sequence variants. The group consisted of clinical laboratory directors and clinicians. This report represents expert opinion of the workgroup with input from ACMG, AMP and CAP stakeholders. These recommendations primarily apply to the breadth of genetic tests used in clinical laboratories including genotyping, single genes, panels, exomes and genomes. This report recommends the use of specific standard terminology: ‘pathogenic’, ‘likely pathogenic’, ‘uncertain significance’, ‘likely benign’, and ‘benign’ to describe variants identified in Mendelian disorders. Moreover, this recommendation describes a process for classification of variants into these five categories based on criteria using typical types of variant evidence (e.g. population data, computational data, functional data, segregation data, etc.). Because of the increased complexity of analysis and interpretation of clinical genetic testing described in this report, the ACMG strongly recommends that clinical molecular genetic testing should be performed in a CLIA-approved laboratory with results interpreted by a board-certified clinical molecular geneticist or molecular genetic pathologist or equivalent.
            Article 0 comments Cited 8334 times – based on 0 reviews     Review now
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              From FastQ data to high confidence variant calls: the Genome Analysis Toolkit best practices pipeline.

              Geraldine Auwera, Mauricio Carneiro, Christopher Hartl … (2013)
              This unit describes how to use BWA and the Genome Analysis Toolkit (GATK) to map genome sequencing data to a reference and produce high-quality variant calls that can be used in downstream analyses. The complete workflow includes the core NGS data processing steps that are necessary to make the raw data suitable for analysis by the GATK, as well as the key methods involved in variant discovery using the GATK.
              Article 0 comments Cited 1682 times – based on 0 reviews     Review now
                Bookmark
                All references

                Author and article information

                Journal
                Journal ID (nlm-ta): Pharmgenomics Pers Med
                Journal ID (iso-abbrev): Pharmgenomics Pers Med
                Journal ID (publisher-id): pgpm
                Journal ID (pmc): ppm
                Title: Pharmacogenomics and Personalized Medicine
                Publisher: Dove
                ISSN (Electronic): 1178-7066
                Publication date (Electronic): 13 January 2021
                Publication date Collection: 2021
                Volume: 14
                Pages: 61-75
                Affiliations
                [1 ]School of Biotechnology, International University , Ho Chi Minh City, Vietnam
                [2 ]Vietnam National University , Ho Chi Minh City, Vietnam
                [3 ]Department of Translational Biomedical Informatics, Vingroup Big Data Institute , Hanoi, Vietnam
                [4 ]Department of Respiratory, Allergy and Clinical Immunology, Vinmec International Hospital , Hanoi, Vietnam
                [5 ]College of Health Sciences, VinUniversity , Hanoi, Vietnam
                Author notes
                Correspondence: Ly Le; Nam S Vo Email ly.le@hcmiu.edu.vn; v.namvs@vinbdi.org
                Article
                Publisher ID: 290781
                DOI: 10.2147/PGPM.S290781
                PMC ID: 7812041
                PubMed ID: 33469342
                SO-VID: 1b1c056a-6f1d-4cd4-84ed-dbde5f4aa944
                Copyright © © 2021 Tong et al.
                License:

                This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License ( http://creativecommons.org/licenses/by-nc/3.0/). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms ( https://www.dovepress.com/terms.php).

                History
                Date received : 06 November 2020
                Date accepted : 26 December 2020
                Page count
                Figures: 1, Tables: 5, References: 131, Pages: 15
                Categories
                Subject: Review

                ScienceOpen disciplines: Pharmacology & Pharmaceutical medicine
                Keywords: pharmacogenomics,adverse drug reactions,next generation sequencing,genome-wide association study,candidate gene approach
                Data availability:
                ScienceOpen disciplines: Pharmacology & Pharmaceutical medicine
                Keywords: pharmacogenomics, adverse drug reactions, next generation sequencing, genome-wide association study, candidate gene approach

                Comments

                Comment on this article

                scite_

                Similar content145

                See all similar

                Cited by6

                See all cited by

                Most referenced authors2,449

                See all reference authors