As a continuation of my previous posts comparing the three major scripting languages used in bioinformatics I wanted to take a look at the regular expression performance of the three languages. A common use case of regular expressions in bioinformatics is searching for restriction enzyme cut sites in a genome of interest. To benchmark this case I downloaded a list of known restriction enzymes from REBASE in the simple bionet format, then parsed that format and converted it into regular expressions with the this code.
This gives one a list of restriction enzymes and a regular expression for each enzyme, which is used as the input for the searching scripts for each language. To test the relative performance I randomly sampled 10 restriction enzymes and searched human chromosome 10 for them. Here are the implementations for each language.
Perl
Python
Ruby
To sample the enzymes and run them for each language
sort -R enzymes | head > top
regex.pl top Hg18.fa >> out
regex.py top Hg18.fa >> out
regex.rb top Hg18.fa >> outAnd the results
perl: 0.4926 secs per enzyme
python: 7.6647 secs per enzyme
ruby: 0.4685 secs per enzymeClearly python’s regular expression engine leaves a lot to be desired compared to that of perl’s and ruby’s, with speed more than 15 fold slower. These results are very consistent with repeated runs of different enzymes. This clearly shows a lack of suitability for python to complete this common task.
Addendum: using google’s re2 regular expression engine and the python extension which uses it in python brings the performance much closer to the other languages
python: 0.8421 secs per enzymeI cannot easily test the re2 bindings in perl and ruby, the perl package requires perl 5.10 or newer, which we do not have installed, and the ruby bindings do not implement global matching from what I can tell. It is not a huge issue there however as both languages regex engines are natively fast.
