Sunday, 23 July 2017

Faster toolkit, faster!

After an extended hiatus, I've been back doing a bit of work on Open Babel, and specifically I've been working on improving performance. Basic reading and writing of molecules should be limited by disk I/O speed and not CPU, but this is not (yet) the case with Open Babel. Through a combination of avoiding unneccessary work, trimming inefficiencies in existing code and using more efficient algorithms, I'm hoping to speed things up.

1. Replace slower algorithms with faster algorithms.

This is perhaps the hardest one, as it takes time to get grips with the existing code and figure out whether and where it can be improved. So far the only thing I've done in this line is replace our previous kekulisation procedure with a perfect matching algorithm. Though also, I guess, in this category are the changes I made to replace the original set of SMARTS patterns for aromaticity 'atom-typing' with logic to do the typing directly in code.

2. Streamline existing code.

This can be tedious and doesn't give a big win, but it's a case of avoiding what Roger refers to as Death by a Thousand Cuts. Individually, they don't count for much (and Stack Overflow would have you believe you shouldn't worry about them), but you should think of reading a molecule as the inner loop and consider that it might be done millions of times (e.g. when processing ChEMBL).

In particular, in the context of file format reading and writing, unnecessary string copies are to be avoided. This can be everything from a function that takes a std::string as a parameter, copying part of the input buffer unneccessarily, or concatenating strings with strcat.

3. Avoid unneccesary work by considering the OBMol to be a container for the contents of the file format.

This is a roundabout way of saying that the file format reader should not worry too much about the chemical content of the described molecule and shouldn't spend time checking and validating it. If there's a carbon atom with a +20 charge, fine. If there's a septuple bond between hydrogens, sure, go right ahead. Just read it in and bung it in an OBMol. That's not to say that there isn't a role for validation, but it should be an option as it takes time, may be completely unneccessary (e.g. you have just written out this molecule yourself) and is, strictly speaking, distinct from file format reading.

We already did this to a certain extent, but we didn't follow through completely. If the user said an atom was aromatic, there was no way to preserve this and avoid reperception. This has now been fixed in the current master, and the SMILES reader has an option to preserve aromaticity. Similarly, we currently reperceive stereocenters rather than accept them at face value as present in SMILES for example. This is next on my list of things to change.

Related pull requests:
* Improve performance of element handling
* Improve performance of SMILES parser
* Keep count of implicit hydrogens instead of inferring them
* Change the OBAromTyper from using SMARTS patterns to a switch statement

Image credit:
Renato Carvalho on Flickr

Friday, 5 May 2017

Using WebLogo3 to create a sequence logo from Python

A sequence logo is a way to display the variation at particular positions of multiple-aligned sequences. Here I used WebLogo 3 to do this. The documentation is not great, even once you find it, and working out how to do my specific use case required a certain amount of looking at the source code of the library. Hence this post for future me.

My sequences are peptides, and use lowercase to indicate D forms of the amino acids. So I needed to create my own 'alphabet' as all of those provided by the library uppercase everything. I also wanted to highlight a reference sequence in a particular colour. This is made easy by the RefSeqColor rule, but I needed to override it, as again it wanted to uppercase everything.
import os
import StringIO
import weblogolib as w

class RefSeqColor(w.ColorRule):
    Color the given reference sequence in its own color, so you can easily see 
    which positions match that sequence and which don't.

    def __init__(self, ref_seq, color, description=None):
        self.ref_seq = ref_seq
        self.color = w.Color.from_string(color)
        self.description = description

    def symbol_color(self, seq_index, symbol, rank):
        if symbol == self.ref_seq[seq_index]:
            return self.color

baserules = [
            w.SymbolColor("GSTYC", "green", "polar"),
            w.SymbolColor("NQ", "purple", "neutral"),
            w.SymbolColor("KRH", "blue", "basic"),
            w.SymbolColor("DE", "red", "acidic"),
            w.SymbolColor("PAWFLIMV", "black", "hydrophobic")

protein_alphabet = w.Alphabet('ACDEFGHIKLMNOPQRSTUVWYBJZX*-adefghiklmnopqrstuvwybjzx', [])

def plotseqlogo(refseq, mseqs, name):
    fasta = "> \n" + "\n> \n".join(mseqs)
    seqs = w.read_seq_data(StringIO.StringIO(fasta), alphabet=protein_alphabet)

    colorscheme = w.ColorScheme([RefSeqColor(refseq, "orange", "refseq")] + baserules,
                                alphabet = protein_alphabet)

    data = w.LogoData.from_seqs(seqs)
    options = w.LogoOptions()
    # options.logo_title = name
    options.show_fineprint = False
    options.yaxis_label = ""
    options.color_scheme = colorscheme
    mformat = w.LogoFormat(data, options)

    fname = "%s.pdf" % name
    with open(fname, "wb") as f:
        f.write(w.pdf_formatter(data, mformat))

if __name__ == "__main__":
    testdata = ["ACDF", "ACDF", "ACDE", "CCDE"]
    plotseqlogo("ACDF", testdata, "testdata")
Notes: The code above writes the logo out to a PDF file, which I subsequently converted to SVG with Inkscape at the commandline:
"C:\Program Files (x86)\Inkscape\" --without-gui --file=fname.pdf --export-plain-svg=svg/fname.svg

Monday, 23 January 2017

Whiskas statistics and the pitfalls of mean rank

In the fingerprint similarity paper I published last year, I made the following observation: "However the use of the mean rank is itself problematic as the pairwise similarity of two methods can be altered (and even inverted) by adding additional methods to an evaluation." I thought about providing an example illustrating this, but I didn't want to digress too much. And besides, that's perfect blog fodder...

Let's say we want to compare N methods, and we have their scores for 10 diverse datasets. Because they are diverse, we think to ourselves that it wouldn't be an accurate evaluation if we used the scores directly, e.g. taking the mean scores or some such, because some of the datasets are easy (all of the scores are high but spread out) and some are hard (all of the scores are low, but close together). As I reference in the paper, Bob Sheridan has discussed this problem, and among other solutions suggested using the mean rank.

So here's the thing. Consider two methods, A and B, where A outperforms B on 9 out of the 10 datasets, i.e. A has rank 1 nine times and rank 2 once, while vice versa for B. So the mean rank for A is 1.1 while that for B is 1.9, implying that A is better than B. So far so good.

Now let's suppose I start tweaking the parameters of B to generate 9 additional methods (B1 to B9). Unfortunately, while they have similar performance to B, they are all slightly worse on each dataset. So the rank order for 9 of the 10 datasets in now A > B > B1...B9, and that for the 10th dataset is B > B1...B9 > A. So A has rank 1 nine times (as before) and rank 11 once (giving a mean rank of 2.0), while B has the same rank as before. So now B is better than A.

Wait a second. Neither A nor B has changed, so how can our evaluation of their relative performance have changed? And therein lies the problem.

So what's the solution? Even a cat knows the answer: as I stated at the start, on 9 out of 10 datasets A outperformed B. So A is better than B, or A>B. It's as simple as that. We don't need to calculate the mean anything. Our goal is not to find a summary value for the overall performance for a method, and then to compare those. It is to evaluate relative performance, and for diverse datasets each dataset has an equal vote towards that answer. So on dataset 1 (taking a different imaginary dataset), each method dukes it out giving A>B, A>C, A>D, B>C, B>D, C=D. Then round 2, on dataset 2. Toting up the values over all datasets will yield something like A>B 10 times, while B>A 5 times, and A=B twice, so A>B overall (and similar results for other pairwise comparisons).

The nice thing about all of these pairs is that you can then plot a Hasse diagram to summarise the info. There are some niggly details like handling incomparable values (e.g. A>B, B>C, C>A) but when it's all sorted out, the resulting diagram is quite information-rich.

I didn't mention statistical significance, but the assessment of whether you can say A>B overall requires a measurement of significance. In my case, I could generate multiple datasets from the same population, and so I used a a basic T-test over the final results from each of the datasets (and corrected for multiple testing).

I've gone on and gone on a bit about what's really quite a simple idea. In fact, it's so simple that I was sure it must be how people already carry out performance evaluations across diverse datasets. However, I couldn't find any evidence of this, nor could I find any existing description of this method. Any thoughts?

Saturday, 14 January 2017

Counting hydrogens in a SMILES string - The Rules

Hydrogens are not usually listed explicitly in a SMILES string, but instead can be inferred from a set of rules. To be precise, when reading a SMILES, the position of every hydrogen is known unambiguously once you know the rules. Oh - did I forget to mention the catch? - those rules are not written down anywhere. Nice.

So let's fix the world.

What is written down (i.e. on the Daylight website, the OpenSMILES spec) is the following:
1. For atoms in brackets, the number of hydrogens is either listed or zero. e.g. [Na] has 0 zeros, [NaH5] has 5.
2. For atoms outside brackets (which means they must be in the so-called 'organic subset'), if they are not written lowercase (indicating aromaticity), the number of hydrogens is described by the SMILES valence model (see the OpenSMILES specification for the details), e.g. the carbons in CC have 3 implicit hydrogens.

What is not written down (apart from references to pyrrole-/pyridine-type hydrogens) is what to do about (unbracketed) aromatic atoms when reading. These should be handled as follows: [Updated 03/Aug/2017]
1. Calculate the bond order sum by treating aromatic bonds as single bonds
2. Apply normal SMILES implicit valence rules using this sum
3. Subtract one from the number of implicit hydrogens, if there are any

As an example, consider aromatic C and N as in pyridine, c1ccncc1. The bond order sum for both the c and the n is 2. Their implicit valence is 4 and 3 respectively, and so the number of implicit hydrogens would be 4-2 and 3-2 respectively. After subtracting one, this gives 1 and 0, respectively. As another example, consider 'cn'. Without worrying too much about whether this is a sensible SMILES string, we can still read off the number of hydrogens based on the rules above: 4-1 on the carbon, and 3-1 on the nitrogen, then subtract one from each, and so [CH2] and [NH] connected by an aromatic bond (if kekulized, this should give H2C=NH). As a final example, consider the nitrogen of c1ccn(C)c1: 3-3 on the nitrogen gives 0, and we can't do any further subtraction.

Probably because of the lack of clarity around these latter rules, not every writer or reader follows them, and Wikipedia in particular is rife with generated SMILES from REDACTED which writes lowercase n whether or not the nitrogen has a hydrogen present. Some toolkits fail to read such SMILES, or interpret them differently than intended.

This raises the question as to what to do when presented with, for example, the SMILES c1cncc1? A correct SMILES for pyrrole would be c1c[nH]cc1. As written, the first SMILES is not kekulisable according to the Daylight aromaticity model (a neutral 'n' without a hydrogen must have a double bond, or alternatively, radicals contibute only a single electron) but one could infer that the structure intended was pyrrole. This is a slippery slope, though, once you consider aromatic rings with multiple nitrogens where it may not be possible to unambiguously assign hydrogens. Also, I would argue that it is not the job of a reader to change the structure because "it knows best".

For related reading, see John Mayfield's posts on SMILES implicit valence of aromatic atoms and New SMILES behaviour - parsing (CDK 1.5.4). Also worth noting is that at no point in identifying hydrogen locations was determination of aromatic systems or kekulisation required. Of course, if you go down the route of editing erroneous structures that may be a different story.

Image credit: Licensed CC-BY-NC by Sean Davis (image on Flickr)