Bioinformatic challenges for pharmacogenomic study: tools for genomic data analysis
The sequencing of the human genome in 2003 marked a transformative shift from a one-size-fits-all approach to personalized medicine, emphasizing patient-specific molecular and physiological characteristics. Advances in sequencing technologies, from Sanger methods to Next-Generation Sequencing (NGS),...
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Frontiers Media S.A.
2025-04-01
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| Series: | Frontiers in Pharmacology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fphar.2025.1548991/full |
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| author | Mariamena Arbitrio Marianna Milano Maria Lucibello Emanuela Altomare Nicoletta Staropoli Nicoletta Staropoli Pierfrancesco Tassone Pierfrancesco Tassone Pierosandro Tagliaferri Pierosandro Tagliaferri Mario Cannataro Giuseppe Agapito |
| author_facet | Mariamena Arbitrio Marianna Milano Maria Lucibello Emanuela Altomare Nicoletta Staropoli Nicoletta Staropoli Pierfrancesco Tassone Pierfrancesco Tassone Pierosandro Tagliaferri Pierosandro Tagliaferri Mario Cannataro Giuseppe Agapito |
| author_sort | Mariamena Arbitrio |
| collection | DOAJ |
| description | The sequencing of the human genome in 2003 marked a transformative shift from a one-size-fits-all approach to personalized medicine, emphasizing patient-specific molecular and physiological characteristics. Advances in sequencing technologies, from Sanger methods to Next-Generation Sequencing (NGS), have generated vast genomic datasets, enabling the development of tailored therapeutic strategies. Pharmacogenomics (PGx) has played a pivotal role in elucidating how the genetic make-up influences inter-individual variability in drug efficacy and toxicity discovering predictive and prognostic biomarkers. However, challenges persist in interpreting polymorphic variants and translating findings into clinical practice. Multi-omics data integration and bioinformatics tools are essential for addressing these complexities, uncovering novel molecular insights, and advancing precision medicine. In this review, starting from our experience in PGx studies performed by DMET microarray platform, we propose a guideline combining machine learning, statistical, and network-based approaches to simplify and better understand complex DMET PGx data analysis which can be adapted for broader PGx applications, fostering accessibility to high-performance bioinformatics, also for non-specialists. Moreover, we describe an example of how bioinformatic tools can be used for a comprehensive integrative analysis which could allow the translation of genetic insights into personalized therapeutic strategies. |
| format | Article |
| id | doaj-art-e460ca22163b437daa98e55e66309ed3 |
| institution | DOAJ |
| issn | 1663-9812 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Pharmacology |
| spelling | doaj-art-e460ca22163b437daa98e55e66309ed32025-08-20T03:08:21ZengFrontiers Media S.A.Frontiers in Pharmacology1663-98122025-04-011610.3389/fphar.2025.15489911548991Bioinformatic challenges for pharmacogenomic study: tools for genomic data analysisMariamena Arbitrio0Marianna Milano1Maria Lucibello2Emanuela Altomare3Nicoletta Staropoli4Nicoletta Staropoli5Pierfrancesco Tassone6Pierfrancesco Tassone7Pierosandro Tagliaferri8Pierosandro Tagliaferri9Mario Cannataro10Giuseppe Agapito11Institute for Biomedical Research and Innovation, National Research Council, Catanzaro, ItalyDepartment of Experimental and Clinical Medicine, University Magna Græcia, Catanzaro, ItalyInstitute for Biomedical Research and Innovation, National Research Council, Catanzaro, ItalyDepartment of Health Science, University Magna Græcia, Catanzaro, ItalyDepartment of Experimental and Clinical Medicine, University Magna Græcia, Catanzaro, ItalyMedical Oncology Unit, R. Dulbecco (Mater Domini Facility), Teaching Hospital, Magna Græcia University and Cancer Center, Campus Salvatore Venuta, Catanzaro, ItalyDepartment of Experimental and Clinical Medicine, University Magna Græcia, Catanzaro, ItalyMedical Oncology Unit, R. Dulbecco (Mater Domini Facility), Teaching Hospital, Magna Græcia University and Cancer Center, Campus Salvatore Venuta, Catanzaro, ItalyDepartment of Experimental and Clinical Medicine, University Magna Græcia, Catanzaro, ItalyMedical Oncology Unit, R. Dulbecco (Mater Domini Facility), Teaching Hospital, Magna Græcia University and Cancer Center, Campus Salvatore Venuta, Catanzaro, ItalyDepartment of Medical and Surgical Sciences, University Magna Græcia, Catanzaro, ItalyDepartment of Law, Economics and Social Sciences, University Magna Græcia, Catanzaro, ItalyThe sequencing of the human genome in 2003 marked a transformative shift from a one-size-fits-all approach to personalized medicine, emphasizing patient-specific molecular and physiological characteristics. Advances in sequencing technologies, from Sanger methods to Next-Generation Sequencing (NGS), have generated vast genomic datasets, enabling the development of tailored therapeutic strategies. Pharmacogenomics (PGx) has played a pivotal role in elucidating how the genetic make-up influences inter-individual variability in drug efficacy and toxicity discovering predictive and prognostic biomarkers. However, challenges persist in interpreting polymorphic variants and translating findings into clinical practice. Multi-omics data integration and bioinformatics tools are essential for addressing these complexities, uncovering novel molecular insights, and advancing precision medicine. In this review, starting from our experience in PGx studies performed by DMET microarray platform, we propose a guideline combining machine learning, statistical, and network-based approaches to simplify and better understand complex DMET PGx data analysis which can be adapted for broader PGx applications, fostering accessibility to high-performance bioinformatics, also for non-specialists. Moreover, we describe an example of how bioinformatic tools can be used for a comprehensive integrative analysis which could allow the translation of genetic insights into personalized therapeutic strategies.https://www.frontiersin.org/articles/10.3389/fphar.2025.1548991/fullpharmacogenomicsgenomic data analysisbioinformaticsbiological pathwaysnetwork analysis, pathway enrichment analysis |
| spellingShingle | Mariamena Arbitrio Marianna Milano Maria Lucibello Emanuela Altomare Nicoletta Staropoli Nicoletta Staropoli Pierfrancesco Tassone Pierfrancesco Tassone Pierosandro Tagliaferri Pierosandro Tagliaferri Mario Cannataro Giuseppe Agapito Bioinformatic challenges for pharmacogenomic study: tools for genomic data analysis Frontiers in Pharmacology pharmacogenomics genomic data analysis bioinformatics biological pathways network analysis, pathway enrichment analysis |
| title | Bioinformatic challenges for pharmacogenomic study: tools for genomic data analysis |
| title_full | Bioinformatic challenges for pharmacogenomic study: tools for genomic data analysis |
| title_fullStr | Bioinformatic challenges for pharmacogenomic study: tools for genomic data analysis |
| title_full_unstemmed | Bioinformatic challenges for pharmacogenomic study: tools for genomic data analysis |
| title_short | Bioinformatic challenges for pharmacogenomic study: tools for genomic data analysis |
| title_sort | bioinformatic challenges for pharmacogenomic study tools for genomic data analysis |
| topic | pharmacogenomics genomic data analysis bioinformatics biological pathways network analysis, pathway enrichment analysis |
| url | https://www.frontiersin.org/articles/10.3389/fphar.2025.1548991/full |
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