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- W4213020439 abstract "This chapter highlights several challenges and discusses data management strategies and solutions for each stage of a large genetic analysis project. It presents tools for storage, access, and manipulation of a single dataset rather than more complex designs. Storing normalized data in a series of text files or spreadsheet documents would quickly become overwhelming, and retrieving, updating, or deleting data from such a series of files would be extremely slow and cumbersome. A diverse array of data types is collected in any genetic study, but all this information can be broadly classified into two ways: sequence-level data and sample-level data. Sample-level data should also be stored in a database indexed by sample. Hardware and software requirements should be considered before implementing and using a database to manage information in a genetic analysis project. Relational database management systems usually come with a tool to facilitate interacting with the data." @default.
- W4213020439 created "2022-02-24" @default.
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- W4213020439 date "2021-10-29" @default.
- W4213020439 modified "2023-09-27" @default.
- W4213020439 title "Data Management" @default.
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- W4213020439 doi "https://doi.org/10.1002/9781119104100.ch7" @default.
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