Enhancing non-Perl bioinformatic applications with Perl: Building novel, component based applications using Object Orientation, PDL, Alien, FFI, Inline and OpenMP

TL;DR

This paper demonstrates how Perl's modern features and integrations can be used to rapidly develop high-performance, component-based bioinformatics applications, revitalizing Perl's role in data-intensive biological research.

Contribution

It showcases novel methods for enhancing bioinformatics tools using Perl's object orientation, PDL, FFI, Inline, and OpenMP, enabling efficient, modular, and parallel bioinformatics workflows.

Findings

Perl can be effectively used to create high-performance bioinformatics modules.

Parallel processing can be integrated into Perl-based bioinformatics tools using OpenMP and MCE.

The approach facilitates rapid development of complex, data-intensive bioinformatics applications.

Abstract

Component-Based Software Engineering (CBSE) is a methodology that assembles pre-existing, re-usable software components into new applications, which is particularly relevant for fast moving, data-intensive fields such as bioinformatics. While Perl was used extensively in this field until a decade ago, more recent applications opt for a Bioconductor/R or Python. This trend represents a significantly missed opportunity for the rapid generation of novel bioinformatic applications out of pre-existing components since Perl offers a variety of abstractions that can facilitate composition. In this paper, we illustrate the utility of Perl for CBSE through a combination of Object Oriented frameworks, the Perl Data Language and facilities for interfacing with non-Perl code through Foreign Function Interfaces and inlining of foreign source code. To do so, we enhance Polyester, a RNA sequencing simulator written in R, and edlib a fast sequence similarity search library based on the edit distance. The first case study illustrates the near effortless authoring of new, highly performant Perl modules for the simulation of random numbers using the GNU Scientific Library and PDL, and proposes Perl and Perl/C alternatives to the Python tool cutadapt that is used to "trim" polyA tails from biological sequences. For the edlib case, we leverage the power of metaclass programming to endow edlib with coarse, process based parallelism, through the Many Core Engine (MCE) module and fine grained parallelism through OpenMP, a C/C++/Fortran Application Programming Interface for shared memory multithreaded processing. These use cases provide proof-of-concept for the Bio::SeqAlignment framework, which can organize heterogeneous components in complex memory and command-line based workflows for the construction of novel bionformatic tools to analyze data from long-read sequencing, e.g. Nanopore, sequencing platforms.