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.
Highlights
The number of computer programs for the analysis of genetic data is increasing significantly, but it still needs to be improved greatly because of the importance of result analysis with appropriate methods and the exponential growth in the volume of genetic data.Genetic data are typically represented by a set of strings [1], where each string is a sequence of symbols from a given alphabet
The secondary, tertiary, and quaternary structures need to be considered to understand the interactions among nucleotides or amino acids, but they are used less frequently in computer programs
This technology is being replaced by HTML5, which is supported directly by web browsers, so HTML5 and JavaScript are recommended for use in new applications
Summary
The number of computer programs for the analysis of genetic data is increasing significantly, but it still needs to be improved greatly because of the importance of result analysis with appropriate methods and the exponential growth in the volume of genetic data. The representations of molecules are extended based on connections between sequences or subsequences, which denotes similarity from various perspectives These data are supplemented with human-readable descriptions, which facilitate an understanding of the biological meanings of the sequence, that is, its function and/or its structure. Of particular importance in this field are dynamic programming algorithms, which allow us to find the optimal alignment of biological sequences (i.e., arranging the sequences by inserting gaps to identify regions of similarity [1]) in polynomial time, the search space grows exponentially. I describe the bioweb framework, including application architecture, the programming languages, libraries, and tools, used to develop applications for processing genetic data. The use of appropriate and tested architectures, libraries, and tools decreases the risk of failure in software system development as well as reduces the costs and time requirements. The use of appropriate systems facilitates rapid prototyping, which allows us to verify concepts by obtaining the requisite information from end users: biologists and doctors
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