I remember many years ago, outside programming I had a moderate interest in biology. I recall looking into bioinformatics, and reading about Python, but also about how popular Perl was in field. This was 5-10 years ago, though the sources I was reading very likely could have been 15 years old or more.
Perl was extremely popular 20+ years ago in biology; I made the jump from the lab bench to bioinformatics with Perl and Bioperl (https://bioperl.org/).
But, after a while I discovered Ruby, and later Python, and moved on, also switching to other fields.
The presence of Perl and a "proper" terminal on Mac OS X back then was a big draw in encouraging bioinformaticians to use it (well, that is my recollection, anyway).
That first was a good book - there's still a copy on my shelf in the office, I think, though I've not had chance to go there in person to check recently.
The first thing that occurs to me, and this may be superficial, is that bioinformatics has a linguistic character to it, with pattern matching of sequences and so forth, and this is something where Perl is more adept than most other languages.
Bioinformatics today is much more about complex statistical models applied to sequencing data. There’s some pattern matching when doing low-level FASTQ processing for your fancy combinatorial barcoding. But other than that you need tools capable of processing huge datasets. Python ecosystem fits better in modern era.
Yup. I’ll occasionally still break out Perl for quickly parsing FASTQs/SAM alignments/VCFs on the command line when sed/awk/grep won’t cut it, but for everything else it’s Pandas, NumPy/SciPy, Torch, and TensorFlow.
