a quick guide to large-scale genomic data mining大规模基因组数据挖掘的快速指南.pdfVIP

Education A Quick Guide to Large-Scale Genomic Data Mining Curtis Huttenhower*, Oliver Hofmann Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts, United States of America Introduction coexpress in high-throughput data as great or greater a concern as data repositories? Under what experimental processing: plain text or XML storage For the first several hundred years of conditions, or in which tissues? formats, while conveniently human-read- research in cellular biology, the main able, can waste unsustainable amounts of bottleneck to scientific progress was data Bringing large quantities of genomic space for large repositories. collection. Our newfound data-richness, data to bear on such questions involves Solutions to these technical issues in- however, has shifted this bottleneck from three main tasks: establishing methodology clude software and data access methodol- collection to analysis [1]. While a variety for efficiently querying large data collec- ogies specifically tailored to large-scale of options exists for examining any one tions; assembling data from appropriate data manipulation. Three broad catego- experimental dataset, we are still discov- repositories; and integrating information ries of solutions exist: Web applications ering what new biological questions can be from a variety of experimental data types. that aggregate information from multiple answered by mining thousands of