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Why JSON Will Save Bioinformatics (Well, Sort Of…)

I’ve been doing a lot of work recently with rather large (i.e. 55,000+ records) sets of biological data, both for fun (well, I am a geek) and to help out a friend who’s using Python for some work in the field of Bioinformatics. One thing that’s pretty much impossible not to notice is that a lot of data is provided in one of two formats: XML (usually without any kind of schema or even informal documentation), or some ad hoc unspecified plaintext format where you have to write a 32-page long regular expression to parse the first line, and can’t even be sure that it’ll work on the other 54,999 lines in the file.

One of the documents which shines as an example of an AHUF (ad hoc unspecified format) is the list of Human Single Amino Acid Variants, a document containing a large list of the known single-amino-acid polymorphisms in human proteins. A polymorphism is one of the particular forms a protein may be found in (for the physicists, polymorphisms are to proteins as isotopes are to elements). A single-amino-acid polymorphism is a polymorphism wherein a single amino acid has been changed from one to another at a single locus (position) along the protein’s sequence. In order for a polymorphism to be classified as such (as opposed to just an arbitrary mutation), it needs to be present in a significant portion of the population.

But I digress. So if you take a look at that file, you’ll notice that it’s made up of an English ‘header’, followed by column definitions, and finally the records themselves — 54,424 to be exact. Each of these records has several columns which are delimited by their position; for example, the ‘Main gene name’ column goes from the 0th character on the line to the 8th character (inclusive), and so on and so forth. Anyone who has had to parse this file has had to figure out the widths and positions of each column, and write a parser in their programming language of choice which could handle it.

The particular task I was working on required me to use data from this file. Problem was, I so did not want to have to whip out my custom parser every time I used it! Speed and performance issues aside, I was going to need to manipulate the data and store it again. I asked myself: why am I going to massive lengths to work with this format? There had to be an easier way!

And it turned out there was. JSON is a data format which is several things at once. On one hand, it’s kind of like a cross-language Pickle (Pickle being the Pythonic way of writing native Python objects to a file or string and being able to read them back out again as if they were the original objects). In this sense I could parse the file, take these abstract objects I’d created, in memory, from each line of the file, and write them out again to JSON format. This new JSON file now contained a list of objects in a pretty human-readable format (to some extent), which I could read back into memory and get a list of dictionaries, with keys like main_gene_name pointing to the corresponding entry’s Main gene name column. So from this point of view it was good; I didn’t have to write my own parser, I could use the simplejson library for Python 2.5 or the json built-in module in Python 2.6 (which is, incidentally, simplejson packaged into the standard library and renamed).

But the other benefit that JSON gives me is that I can share the data with almost any other language I want. Whether there’s the professor on the other side of the world using BioPerl, or some hacker using LISP, or someone using Ruby or PHP or Smalltalk or Visual Basic, it doesn’t matter—parsers for these languages are readily available as free software, and the basic datatypes of JSON (numbers, strings, lists, dictionaries, booleans and null) have representations in almost every language yet invented.

A benefit of JSON over XML (and believe me, there is a war raging on the forums and mailing lists as we speak) is that it is a lot quicker to manipulate. There’s no Document Object Model or SaX parser to worry about: just plain old objects. JSON is schema-less (although there are working groups working on how to define JSON schema right now), so no validation of input is necessary. JSON is far more terse than XML, which means faster over-the-wire transfers. All-in-all, it’s a helluva lot easier to just get in and start working with the data without having to fuss about with parsing either AHUF or XML.

Another thing which concerns me with Bioinformatics in Python especially is that I still see a lot of the bad habits of Perl creeping into Python programmers’ code. The tell-tale signs of this style of Write-Only programming are all there: short (almost cryptically so) variable and function names, a severe lack of whitespace (which both programmers and designers will tell you is massively important), a very low comment-to-code ratio (it’s a good idea to keep it around 15–25%, which sounds like a lot but is definitely worth it) and an abundance of regular expressions where they aren’t even necessary. I don’t think I need to explain myself on the first three of these.

On the subject of the fourth, I shall paraphrase Jamie Zawinski, one of the early Netscape engineers, who succinctly stated in other words that using regular expressions to solve a problem simply gives you another problem. Why do I agree with this statement? Well, when you look at code, it’s immensely helpful (if not vital) to grok two things: what it does—i.e. that code’s purpose—and how it does it—i.e. that code’s design. But when a programmer looks at a long regular expression, it offers no clue as to one nor the other. No amount of syntax highlighting can mitigate the cloud of obfuscation created by the unintelligible string of asterisks, brackets, parentheses, backslashes and question marks. Yes, I’m sure it made perfect sense when you wrote it. But just shut your eyes for five minutes, or go and have a coffee—when you come back, you won’t be able to understand what you’ve written, let alone why you wrote it. This is how regular expressions, over time, become their own ‘black boxes’; these complex systems that we don’t dare touch, lest we aggravate the beast. They are the access control list which make Perl a write-only language.

So what are your other solutions? For one, I’d recommend the use of a BNF grammar parser. I’ve used pyparsing and found it to be brilliant—it’s fast, terse and comprehensive. BNF grammars are a clean, modular and maintainable way of expressing formal grammars of any complexity. One of the main arguments for the use regular expressions was speed, but computing performance is definitely at the stage now where your applications are going to be I/O-bound, not CPU-bound, so you can definitely afford to spend the extra CPU cycles making your program maintainable. On the other hand, you can still make those disposable apps using regular expressions: just make sure that whatever you parse, you serialize to JSON (or some other well-known format) so that you can skip that nastiness in the future. This was the route I went down with the aforementioned list of polymorphisms; I wrote an application which parsed the file by columns of characters and output a dictionary for each line, and then I serialized the resulting list of dictionaries to a JSON file. When I want to manipulate the data, I simply open up the Python interpreter, import json, run data = json.load(open('humsavar.json')) and I’m away.

So that’s why JSON (along with a dash of BNF) will save Bioinformatics. Well, sort of…