Editing Portal:Databases (section)

=== Perl, data science, and scientific data ===

Perl has a long history in scientific and technical data processing, especially in bioinformatics, systems biology, astronomy support tools, high-performance-computing workflows, and research data cleanup. In bioinformatics, Perl became popular because biological data often arrived as large text files in formats such as FASTA, GenBank, BLAST output, tabular annotations, and custom lab formats. Perl was good at parsing these formats, extracting meaningful fields, and loading results into databases.

Perl’s scientific data role often overlaps with relational databases. A Perl script might parse raw instrument output, normalize sample identifiers, store results in PostgreSQL or MySQL, generate summary files, and produce reports. The database stores the durable structure; Perl handles the messy edges.

Although Python and R later became more dominant in interactive data science, Perl remains capable in production data pipelines. Its strengths are less about notebooks and more about reliability, text processing, repeatable scripts, database loading, and integration with Unix tools.