نیلوفر رنجبر

Biopython and BioPerl are widely adopted, actively maintained open- source toolkits developed to address recurring computational problems in bioinformatics and the life sciences. Python and Perl, the two general-purpose, high- level languages underlying these toolkits, are both extensively used across academic, educational, and commercial computing contexts. A persistent challenge facing computational biologists is the selection of an appropriate programming language for in silico simulation of biological systems a decision that has direct downstream consequences for genomic sequence analysis, three-dimensional protein structure prediction, genome-scale functional annotation, database design and maintenance, and the mathematical modeling of biological processes. The choice of language can materially affect output quality, development time, execution speed, and memory overhead. This review synthesizes the core architectural and design differences between Python and Perl, situates them within the broader context of scripting- language use in computational biology, and revisits an empirical benchmarking exercise in which both languages were used to implement two canonical bioinformatics algorithms global pairwise sequence alignment and Neighbor-Joining phylogenetic tree reconstruction as well as a large-scale BLAST output parsing task, executed under both Windows and Linux operating systems. The results indicate that Perl offers superior performance for input/output-bound operations in terms of both execution time and memory consumption, and outperforms Python specifically in parsing large BLAST report files. Conversely, for algorithms requiring intensive in-memory manipulation of character strings namely global alignment and Neighbor-Joining Python demonstrates comparatively higher efficiency. These findings suggest that language selection in bioinformatics workflows should be task-dependent rather than driven by a single, universal preference, and this review further