Abstract
Microarrays are broadly used in the omic investigation and have several areas of applications in biology and medicine, providing a significant amount of data for a single experiment. Different kinds of microarrays are available, identifiable by characteristics such as the type of probes, the surface used as support, and the method used for the target detection. To better deal with microarray datasets, the development of microarray data analysis protocols simple to use as well as able to produce accurate reports, and comprehensible results arise. The object of this paper is to provide a general protocol showing how to choose the best software tool to analyze microarray data, allowing to efficiently figure out genomic/pharmacogenomic biomarkers.
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References
Watson JD, Crick FHC (1953) Molecular structure of nucleic acids: a structure for deoxyribose nucleic acid. Nature 171:737
Sanger F, Nicklen S, Coulson AR (1977) Dna sequencing with chain-terminating inhibitors. Proc Natl Acad Sci U S A 74(12):5463. https://doi.org/10.1073/pnas.74.12.5463
Heller MJ (2002) DNA microarray technology: devices, systems, and applications. Annu Rev Biomed Eng 4(1):129–153
Blohm DH, Guiseppi-Elie A (2001) New developments in microarray technology. Curr Opin Biotechnol 12(1):41–47
Hoheisel JD (2006) Microarray technology: beyond transcript profiling and genotype analysis. Nat Rev Genet 7(3):200–210
Guzzi PH, Agapito G, Di Martino MT, Arbitrio M, Tassone P, Tagliaferri P, Cannataro M (2012) DMET-analyzer: automatic analysis of affymetrix dmet data. BMC Bioinformatics 13(1):258
Agapito G, Guzzi PH, Cannataro M (2017) Parallel extraction of association rules from genomics data. Appl Math Comput 350:434–446
Agapito G, Guzzi PH, Cannataro M (2018) Parallel and distributed association rule mining in life science: a novel parallel algorithm to mine genomics data. Inf Sci. https://doi.org/10.1016/j.ins.2018.07.055
Agapito G, Botta C, Guzzi PH, Arbitrio M, Di Martino MT, Tassone P, Tagliaferri P, Cannataro M (2016) OS-analyzer: a bioinformatics tool for the analysis of gene polymorphisms enriched with clinical outcomes. Microarrays 5(4):24
Agapito G, Guzzi PH, Cannataro M (2015) DMET-miner: efficient discovery of association rules from pharmacogenomic data. J Biomed Inform 56:273
Agapito G, Cannataro M, Guzzi PH, Marozzo F, Talia D, Trunfio P (2013) In: Proceedings of the international conference on bioinformatics, computational biology and biomedical informatics
Marozzo F, Talia D, Trunfio P (2011) A cloud framework for parameter sweeping data mining applications. In: 2011 IEEE third international conference on cloud computing technology and science. IEEE, pp 367–374
Marozzo F, Talia D, Trunfio P (2012) Using clouds for scalable knowledge discovery applications. In: European conference on parallel processing. Springer, Berlin, pp 220–227
Agapito G, Guzzi PH, Cannataro M (2019) Pathway analysis for SNP microarray data. In: 2019 IEEE international conference on bioinformatics and biomedicine (BIBM). IEEE, pp 2244–2250
Di Martino MT, Arbitrio M, Guzzi PH et al (2011) A peroxisome proliferator-activated receptor gamma (PPARG) polymorphism is associated with zoledronic acid-related osteonecrosis of the jaw in multiple myeloma patients: analysis by DMET microarray profiling. Br J Haematol 154(4):529
Di Martino MT, Arbitrio M, Leone E et al (2011) Single nucleotide polymorphisms of ABCC5 and ABCG1 transporter genes correlate to irinotecan-associated gastrointestinal toxicity in colorectal cancer patients: a DMET microarray profiling study. Cancer Biol Ther 12(9):780–787
Arbitrio M, Di Martino MT, Barbieri V et al (2016) Identification of polymorphic variants associated with erlotinib-related skin toxicity in advanced non-small cell lung cancer patients by DMET microarray analysis. Cancer Chemother Pharmacol 77(1):205–209
Di Martino MT, Scionti F, Sestito S et al (2016) Genetic variants associated with gastrointestinal symptoms in Fabry disease. Oncotarget 7(52):85895
Scionti F, Di Martino MT, Sestito S et al (2017) Genetic variants associated with Fabry disease progression despite enzyme replacement therapy. Oncotarget 8(64):107558
Arbitrio M, Scionti F, Altomare E et al (2019) Polymorphic variants in NR 1I3 and UGT 2B7 predict taxane neurotoxicity and have prognostic relevance in patients with breast cancer: a case-control study. Clin Pharmacol Ther 106(2):422–431
Arbitrio M, Scionti F, Di Martino MT et al (2021) Pharmacogenomics biomarker discovery and validation for translation in clinical practice. Clin Transl Sci 14(1):113–119
Agapito G, Settino M, Scionti F et al (2020) DMETTM genotyping: tools for biomarkers discovery in the era of precision medicine. High Throughput 9(2):8
Arbitrio M, Di Martino MT, Scionti F et al (2018) Pharmacogenomic profiling of adme gene variants: current challenges and validation perspectives. High Throughput 7(4):40
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Agapito, G., Arbitrio, M. (2022). Microarray Data Analysis Protocol. In: Agapito, G. (eds) Microarray Data Analysis. Methods in Molecular Biology, vol 2401. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-1839-4_17
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DOI: https://doi.org/10.1007/978-1-0716-1839-4_17
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