Four Years Remaining

"Hello world" in Flask

Over the recent years I happen to have made several small personal projects using Python's Flask web framework. The framework is designed to provide a very minimalistic "bottom-up" approach. It feels slightly less cluttered and imposing than some of the popular alternatives, thus fitting nicely for the projects a single person might typically want to hack up in a spare weekend. Minimalism of Flask does not mean it is somehow limited or unsuitable for larger projects - perhaps on the contrary, small size of the framework means there are fewer restrictions on what and how you can do with it.

What a small framework needs to be applied comfortably beyond its 6-line "Hello-World" use case, however, is a decent starter project template that would include some of the most common bells and whistles. And indeed, there happen to be several such templates available. I used parts of them over time in my own projects, however I always ended redoing/rewriting/renaming bits and pieces to fit my personal aesthetic needs. Eventually I got tired of renaming and packaged a Flask application template in the way I consistently prefer to use it. I am not sure whether it is objectively better or worse than the alternatives. None the less, if at some point you decide to give Flask a try, let me suggest you try this template of mine as your point of origin.

There is a popular claim that "human DNA is 50% similar to the DNA of a banana", which is often cited in the context of "science popularization" as well as in the various "OMG did you know that" articles. It sounds funny, scientific and "plausible", hence I've seen many smart people repeat it, perhaps as a joke, without giving it too much thought. It is worth giving a thought, though.

Note that depending on how you phrase the statement, it may imply different things, some of them could be more, and some less exciting. The following examples are completely different in their meaning:

1. If you change 50% of human DNA you can obtain the DNA of a banana.
2. 50% of human DNA nucleotides are present in the DNA of a banana.
3. 50% of human's DNA regions have approximate matches in the banana DNA (or vice versa, which would be a different statement)
4. 50% of human genes have orthologs among banana genes (or vice versa).

The first one is obviously false, due to the fact that the total length of the human DNA is about 10 times that of the banana. You could include the whole banana sequence verbatim into a human genome, and it would only make 10% of it. The second one is also false, because, strictly speaking, not 50% but all 100% of human DNA nucleotides are also present in the DNA of a banana. Indeed, any two organisms on Earth have their DNA composed as a sequence of exactly the same four nucleotides. Moreover, if you start comparing random positions of two random DNA's, you will get a match once every four attempts by pure chance. There's a 25% basepair-wise similarity of any DNA to a random sequence!

The last two (or four, if you include the "reversed" versions) claims are more informative. In fact, claim #4 is probably the most interesting and is what could be meant if the presumed "50% similarity" was indeed ever found. Given the wide availability of genomic data, this claim be checked to some extent by anyone with a computer and a desire to finally make sure, how much of a banana he is, after all. Let us do it.

What proportion of human genes could be very similar to banana genes?

Although there is a lot of data about gene orthology among various organisms, as far as humans and bananas are concerned, I could not find any proper precomputed alignments. Creating a full-genome alignment for two organisms from scratch is a procedure way too tedious for a single Sunday's evening, so let us try a simplified measurement approach instead. Consider all possible 20-nucleotide reads taken from the gene-associated regions in the reference human genome. We shall say that a human gene "is somewhat bananas" if at least 5% of its 20-bp reads match somewhere on the banana genome. Given this definition, we shall ask: what proportion of the known human genes "are somewhat bananas"?

At this point some passionate readers could argue that, for example, 20-nucleotide reads are not long/short enough for the purposes of this question, or that the cutoff of 5% is too low or too high, or that approximate matching should be used instead along with some proper string alignment techniques, etc. As noted, we shall leave those aspects to dedicated researchers in the field of human bananology.

The computation took about an hour to run and came back with the following conclusion:

Only 33 out of the 24624 human genes (of at least 1000bp in length) are "somewhat bananas".

In other words, no, you are not "50% similar" to a banana according to any simple definition of such similarity. Not even 1% similar! Of course, there could still be means to torture the data and squeeze the "50%" value out, but those must be some quite nontrivial means and the interpretation of the resulting similarity would be far from straightforward.

When it comes to data analysis, there are hundreds of exciting approaches: simple summary statistics and hypothesis tests, various clustering methods, linear and nonlinear regression or classification techniques, neural networks of various types and depths, decision rules and frequent itemsets, feature extractors and dimension reductors, ensemble methods, bayesian approaches and graphical models, logic-based approaches and fuzzy stuff, ant colonies, genetic algorithms and other optimization methods, monte-carlo algorithms, sampling and density estimation, logic-based and graph methods. Don't even get me started on the numerous visualization techniques.

This sheer number of options is, however, both a blessing and a curse at the same time. In many practical situations just having those methods at your disposal may pose more problems than solutions. First you need to pick one of the approaches that might possibly fit your purpose. Then you will try to adapt it appropriately, spend several iterations torturing the data only to obtain very dubious first results, come to the conclusion that most probably you are doing something wrong, reconvince yourself that you need to try harder in that direction, spend some more iterations testing various parameter settings. Nothing works as you want it to, so you start everything from scratch with another method to find yourself obtaining new, even more dubious results, torturing the data even further, getting tired of that and finally settling on something "intermediately decent", which "probably makes sense", although you are not so sure any more and feel frustrated.

I guess life of a statistician was probably way simpler back in the days when you could run a couple of t-tests, or an F-test from a linear regression and call it a day. In fact, it seems that many experimental (e.g. wetlab) scientists still live in that kind of world, when it comes to analyzing their experimental results. The world of T-tests is cozy and safe. They don't get you frustrated. Unfortunately, t-tests can feel ad-hockish, because they force you to believe that something "is normally distributed". Also, in practice, they are mainly used to confirm the obvious rather than discover something new from the data. A simple scatterplot will most often be better than a t-test as an analysis method. Hence, I am not a big fan of T-tests. However, I do have my own favourite statistical method, which always feels cozy and safe, and never gets me frustrated. I tend to apply it whenever I see a chance. It is the Fisher exact test in the particular context of feature selection.

My appreciation of it stems from my background in bioinformatics and some experience with motif detection in particular. Suppose you have measured the DNA sequences for a bunch of genes. What can you do to learn something new about the sequence structure from that data? One of your best bets is to first group your sequences according to some known criteria. Suppose you know from previous experiments that some of the genes are cancer-related whereas others are not. As soon as you have specified those groups, you can start making observations like the following: "It seems that 10 out of my 20 cancer-related genes have the subsequence GATGAG in their DNA code. The same sequence is present in only 5 out of 100 non-cancer-related ones. How probable would it be to obtain similar counts of GATGAG, if the two groups were picked randomly?" If the probability to get those counts at random is very low, then obviously there is something fishy about GATGAG and cancer - perhaps they are related. To compute this probability you will need to use the hypergeometric distribution, and the resulting test (i.e. the question "how probable is this situation in a random split?") is known as the Fishers' exact test.

This simple logic (with a small addition of a multiple testing correction on top) has worked wonders for finding actually important short sequences on the DNA. Of course it is not limited to sequence search. One of our research group's most popular web tools uses the same approach to discover functional annotations, that are "significantly overrepresented" in a given group of genes. The same approach can be used to construct decision trees, and in pretty much any other "supervised learning" situation, where you have groups of objects and want to find binary features of those objects, associated with the groups.

Although in general the Fisher test is just one particular measure of association, it is, as I noted above, rather "cozy and comfortable". It does not force me to make any weird assumptions, there is no "ad-hoc" aspect to it, it is simple to compute and, most importantly, in my experience it nearly always produces "relevant" results.

Words overrepresented in the speeches of Greece MPs

A week ago me, Ilya and Alex happened to take part in a small data analysis hackathon, dedicated to the analysis of speech transcripts from the European Parliament. Somewhat analogously to DNA sequences, speeches can be grouped in various ways: you can group them by the speaker who gave them, by country, gender or political party of that speaker, by the month or year when the speech was given or by any combination of such groupings. The obvious "features" of a speech are words, which can be either present or not present in it. Once you view the problem this way the task of finding group-specific words becomes self-evident and the Fisher test is the natural solution to it. We implemented this idea and extracted "country-specific" and "time-specific" words from the speeches (other options were left out due to time constraints). As is usual the case with my favourite method, the obtained results look relevant, informative and, when shown in the form of a word cloud, fun. Check them out.

The complete source code of the analysis scripts and the visualization application is available on Github.