Efficient Management and Manipulation of Genetic Data Using Microsoft GUI Interfaces

摘要

Background: In the absence of user-friendly interfaces developed to manage and manipulate genetic data, geneticists are faced with the challenge of having to learn programming. The mostly common and recommended programming language is Perl. Perl is a program of choice because it is especially valuable in manipulating text files, which is generally the format of most input and output of most statistical genetic software. However, in research environments with strong computing capacity, in-house programs can be developed by computer scientists or bioinformaticians to meet this need of statistical geneticists. Also, the need for efficient, time-saving and user-friendly interfaces is heightened by the lack of standard data formats and the fact that analysis of genetic data calls for the use of multiple computer programs each with unique data input. Objectives: Using genotype data from the Jackson Heart Study (JHS) a suite of Microsoft Windows-based GUI Interfaces was developed to manage complex genetic data prior to analysis. JHS is the largest single-site observational cohort study designed to assess the etiology and progression of CVD in African Americans. The primary objective of the project was to develop a suite of GUI interfaces (SGUII) to perform the following functions: 1) Count the number of times a specific marker (SNP or Microsatellite) appears in a non-conventional dataset and output the frequencies; 2) concatenate columns and provide counts in the entire dataset. Also concatenate two columns (name of a marker and its frequency) and output a string to another file; and 3) Read names of markers (files in a particular folder) and format them for linkage analysis. Methods: C#.Net and win-forms were utilized in the development of SGUII. The GUI features of Micosoft.Net were the primary rationale for selecting these set of programs. Also, the coding and deployment features were a plus. Results: The SGUII developed enable the user to: 1) generate the frequencies of specific makers such as SNPs; 2) combined data elements originally separated in separate columns; 3) transform data in the original format into the appropriate format for a given statistical genetics software. Discussion: Developing such applications as SGUII provides statistical geneticists with time-saving and effective tools to manage these complex genetic data files prior to analysis. Also, the use of dot.net applications in the Microsoft environment could be an efficient and comparable alternative to the Unix-based programs such as in Perl.