Comparing Ease of Programming in C++, Go, and Java for Implementing a Next-Generation Sequencing Tool.

Area of Science:

• Bioinformatics
• Computational Biology
• Software Engineering

Background:

• Next-generation sequencing (NGS) pipelines require efficient processing of large Sequence Alignment/Map (SAM) and Binary Alignment/Map (BAM) files.
• Memory management is a critical challenge for tools handling substantial genomic datasets.

Purpose of the Study:

• To evaluate and compare the performance and programming ease of C++, Go, and Java for implementing the elPrep software framework.
• To determine the optimal programming language for developing and extending elPrep, focusing on computational efficiency and maintainability.

Main Methods:

• Implemented a significant subset of elPrep in C++, Go, and Java.
• Benchmarked runtime performance and memory usage across the three language implementations.
• Assessed the ease of programming, considering language constructs and standard library support.

Main Results:

• Go demonstrated competitive runtime performance and memory efficiency compared to C++ and Java.
• The study identified specific programming challenges and advantages associated with each language's constructs and libraries.
• Go was selected as the preferred implementation language due to its balance of performance and ease of development.

Conclusions:

• Go provides a robust and efficient solution for developing bioinformatics tools like elPrep that handle large-scale sequencing data.
• The choice of Go facilitates ongoing development and extension of elPrep, balancing performance with programmer productivity.
• This work provides insights into language selection for computationally intensive bioinformatics software.