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

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This summary is machine-generated.

Go programming language offers efficient memory management for processing large sequencing data files (SAM/BAM). This study benchmarks Go against C++ and Java, highlighting Go

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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.