{"title":"组学数据的高性能分析:卡坦萨罗麦格纳希腊大学的经验","authors":"Giuseppe Agapito, P. Guzzi, M. Cannataro","doi":"10.1109/HPCS.2017.157","DOIUrl":null,"url":null,"abstract":"Several omics disciplines, such as genomics, proteomics, and interactomics , are gaining an increasing interest in the scientific community due to the availability of high throughput experimental platforms (e.g. next generation sequencing, microarray, mass spectrometry, to cite a few), that are producing an overwhelming amount of experimental omics data. However, efficient analysis of omics data requires large data stores as well as novel algorithms and data structures for data preprocessing, analysis, and integration. As a result, parallel bioinformatics tools for the analysis of omics data, often made available on the Cloud, start to be available. This paper surveys some parallel and distributed bioinformatics tools for the preprocessing and analysis of omics data. The description includes some tools developed at the Bioinformatics Laboratory of the University Magna Graecia of Catanzaro and validated using real data made available by the University Hospital.","PeriodicalId":115758,"journal":{"name":"2017 International Conference on High Performance Computing & Simulation (HPCS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"High Performance Analysis of Omics Data: Experiences at University Magna Graecia of Catanzaro\",\"authors\":\"Giuseppe Agapito, P. Guzzi, M. Cannataro\",\"doi\":\"10.1109/HPCS.2017.157\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Several omics disciplines, such as genomics, proteomics, and interactomics , are gaining an increasing interest in the scientific community due to the availability of high throughput experimental platforms (e.g. next generation sequencing, microarray, mass spectrometry, to cite a few), that are producing an overwhelming amount of experimental omics data. However, efficient analysis of omics data requires large data stores as well as novel algorithms and data structures for data preprocessing, analysis, and integration. As a result, parallel bioinformatics tools for the analysis of omics data, often made available on the Cloud, start to be available. This paper surveys some parallel and distributed bioinformatics tools for the preprocessing and analysis of omics data. The description includes some tools developed at the Bioinformatics Laboratory of the University Magna Graecia of Catanzaro and validated using real data made available by the University Hospital.\",\"PeriodicalId\":115758,\"journal\":{\"name\":\"2017 International Conference on High Performance Computing & Simulation (HPCS)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 International Conference on High Performance Computing & Simulation (HPCS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/HPCS.2017.157\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 International Conference on High Performance Computing & Simulation (HPCS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/HPCS.2017.157","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
High Performance Analysis of Omics Data: Experiences at University Magna Graecia of Catanzaro
Several omics disciplines, such as genomics, proteomics, and interactomics , are gaining an increasing interest in the scientific community due to the availability of high throughput experimental platforms (e.g. next generation sequencing, microarray, mass spectrometry, to cite a few), that are producing an overwhelming amount of experimental omics data. However, efficient analysis of omics data requires large data stores as well as novel algorithms and data structures for data preprocessing, analysis, and integration. As a result, parallel bioinformatics tools for the analysis of omics data, often made available on the Cloud, start to be available. This paper surveys some parallel and distributed bioinformatics tools for the preprocessing and analysis of omics data. The description includes some tools developed at the Bioinformatics Laboratory of the University Magna Graecia of Catanzaro and validated using real data made available by the University Hospital.