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Polyglot programming in applications used for genetic data analysis.

Nowak RM - Biomed Res Int (2014)

Bottom Line: High performance, flexibility, and a user interface with a web browser are required by these solutions, which can be achieved by using multiple programming languages.In this study, I developed a freely available framework for building software to analyze genetic data, which uses C++, Python, JavaScript, and several libraries.This system was used to build a number of genetic data processing applications and it reduced the time and costs of development.

View Article: PubMed Central - PubMed

Affiliation: Institute of Electronic Systems, Warsaw University of Technology, Nowowiejska 15/19, 00-665 Warsaw, Poland.

ABSTRACT
Applications used for the analysis of genetic data process large volumes of data with complex algorithms. High performance, flexibility, and a user interface with a web browser are required by these solutions, which can be achieved by using multiple programming languages. In this study, I developed a freely available framework for building software to analyze genetic data, which uses C++, Python, JavaScript, and several libraries. This system was used to build a number of genetic data processing applications and it reduced the time and costs of development.

Show MeSH
Three-layer application deployment models: desktop application (a), database server (b), thin client (c), and web application (d). This solution supports the creation of applications using a web application architecture.
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fig1: Three-layer application deployment models: desktop application (a), database server (b), thin client (c), and web application (d). This solution supports the creation of applications using a web application architecture.

Mentions: Four possible deployment models were considered for the three-layer architecture: the desktop, the database server, the thin client, and the web application, as shown in Figure 1. The desktop architecture (Figure 1(a)) was rejected because the framework was designed to support multiuser applications. Collaboration features were hard to implement in this architecture because of the lack of central data server that could be accessed by multiple users. The offline mode is rarely used because the Internet is available almost everywhere and the transmission costs are negligible compared with the costs of maintaining the system. Furthermore, sequence databases are publicly available via the Internet, so an Internet connection is essential for the analysis of genetic data.


Polyglot programming in applications used for genetic data analysis.

Nowak RM - Biomed Res Int (2014)

Three-layer application deployment models: desktop application (a), database server (b), thin client (c), and web application (d). This solution supports the creation of applications using a web application architecture.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4150456&req=5

fig1: Three-layer application deployment models: desktop application (a), database server (b), thin client (c), and web application (d). This solution supports the creation of applications using a web application architecture.
Mentions: Four possible deployment models were considered for the three-layer architecture: the desktop, the database server, the thin client, and the web application, as shown in Figure 1. The desktop architecture (Figure 1(a)) was rejected because the framework was designed to support multiuser applications. Collaboration features were hard to implement in this architecture because of the lack of central data server that could be accessed by multiple users. The offline mode is rarely used because the Internet is available almost everywhere and the transmission costs are negligible compared with the costs of maintaining the system. Furthermore, sequence databases are publicly available via the Internet, so an Internet connection is essential for the analysis of genetic data.

Bottom Line: High performance, flexibility, and a user interface with a web browser are required by these solutions, which can be achieved by using multiple programming languages.In this study, I developed a freely available framework for building software to analyze genetic data, which uses C++, Python, JavaScript, and several libraries.This system was used to build a number of genetic data processing applications and it reduced the time and costs of development.

View Article: PubMed Central - PubMed

Affiliation: Institute of Electronic Systems, Warsaw University of Technology, Nowowiejska 15/19, 00-665 Warsaw, Poland.

ABSTRACT
Applications used for the analysis of genetic data process large volumes of data with complex algorithms. High performance, flexibility, and a user interface with a web browser are required by these solutions, which can be achieved by using multiple programming languages. In this study, I developed a freely available framework for building software to analyze genetic data, which uses C++, Python, JavaScript, and several libraries. This system was used to build a number of genetic data processing applications and it reduced the time and costs of development.

Show MeSH