Bioinformatics

[EDIT 01-01-2010] this is now available at bitbucket . I've written a simple, fast, python version of Needleman-Wunsch as I couldn't find one to use. It uses Cython and specifically cython-numpy goodness. It's easy-installable as: sudo easy_install -UZ http://bpbio.googlecode.com/svn/trunk/nwalign/ or via svn from: svn co http://bpbio.googlecode.com/svn/trunk/nwalign/ it will put an executable 'nwalign' into /usr/bin/ which when run will give this message: Usage: nwalign [options] seq1 seq2 Options: -h, --help show this help message and exit --gap=GAP gap extend penalty (must be integer --gap_init=GAP_INIT gap start penalty (must be integer --match=MATCH match score (must be integer > 0) --mismatch=MISMATCH gap penalty (must be integer --matrix=MATRIX scoring matrix in ncbi/data/ format, if not specificied, match/mismatch are used where the matrix is optional but can be the full path ...

On the Cython mailing list, I saw this mentioned for avoiding init overhead, so i wrote up some code to try it. Basically, instead of using an __init__, it uses the PY_NEW macro (which I don't pretend to understand fully). I ran a benchmark with 5 cases: PY_NEW macro (still has python overhead for each call to the creator function) regular python init python init using __slots__ cython init (cdef'ed class) batch PY_NEW: calling PY_NEW from inside cython to avoid python call overhead batch init on cython class the timings look like this: PY_NEW on Cython class: 1.160 __init__ on Python class: 30.414 __init__ on Python class with slots: 10.242 __init__ on Cython class 1.185 batch PY_NEW total: 0.855 , interval only: 0.383 batch __init__ on Cython class total 0.998 , interval_only: 0.540 So, the PY_NEW is .383 compared to .540 for using a __init__ on a Cython class, but both are much faster than python. I was surprised that using slots gives a 3x speed improvement over a regular...

I'm partly writing this because I think such valuable info is too hard to find, I'd read a lot that if you want php, you have to use mpm-prefork but it's not true! I've been using slicehost for dev server for almost a year now. Since my 1 year deal is up, I decided to switch to their affiliate mosso . I really like slicehost, but Mosso "cloud servers" seem a good fit for a server that goes through spurts of development and use followed by weeks of non-use. So now, I can keep it as a 256MB instance at about $10/month and update to a larger instance when doing real dev. I built it today as a 1024MB instance -- installed all my usual stuff, and updated my build script for ubuntu. That's here . The machine I'm on is extremely fast, normally I set GDAL building and leave, but it finished before I had a chance. After all was built, I resized it to a 256MB server -- that took 12minutes, but my instance was accessible for at least 10 of those. After that, I ...

This is a quick project I did a while back but, I've seen people interested in similar ideas, so I'll post my implementation here. Geohash encodes latitude/longtitude locations into a string such that "nearby places will often present similar prefixes" and the longer the string, the greater the precision. Using this python implementation by Schuyler as a reference, I ported the concept to a "biohash" which can encode intervals. It works in a similar fashion, starting with the extremes and halving the space until it finds the smallest space that contains the interval. The use to allow efficient search of intervals using a BTree index, as in any relational db. It's implemented with only a dumps() and loads() function after the pickle interface. The dumps function takes start and end args and returns a 1/0 encoded string. The loads takes a 1/0 encoded string and returns the tightest interval it can given that string. Both functions take a rng kwarg, whic...

In 2008, I was good enough at programming to get my ass kicked by hard problems. I think that's the most positive way to say it. My main bioinformatics project was a long annotation pipeline. It takes days to run, often using 8 CPUs. It's driven by a big ol' Makefile. I made the mistake of passing data between steps in un-structured text files or python pickles. I'd create one at the beginning of the pipeline and not notice it was messed up in a way that affected other parts until the entire pipeline was done, days later. Toward the end of the pipeline, I'd need something simple, like the strand of a BLAST hit, but I'd have to parse through an entire GFF file, or load some huge pickle into memory just to get to that. Then I'd need some annotation, and I'd have to add a slow step of doing a lookup in a script that'd otherwise run very quickly. I was passing around data in arrays and tuples, so then when I changed the order or added another element in...

One of the things I like the least about my real job, and much of the contract work that I do is that i'm usually the only programmer working on each task. So, it's been very fun to work on a project with Josh Livni ( His writeup ). We got together one afternoon, and by the time we left, we had a reasonable start of what we call landsummary , we've since put in a fair bit of work sprinkled here and there. Josh set up an AWS server--it's nice for me to have fewer sys-admin duties too. What's actually on display is fairly modest. What it does is takes a user-drawn square, circle, or arbitrary polygon, and uses that to summarize the NLCD dataset along with some census data. The things that make it more than lame are that it's very fast, it can easily be extended to summarize any raster dataset, and we have a sorta cool API (not documented) which allows us or anyone to query the data with a WKT Polygon and request a particular service -- currently nlcd, population,...

EDIT: added a couple points inline. I'm obsessed with trees lately -- of the CS variety, not the plant variety. Although we are studying poplar, so I'll be using trees to study trees . I'd tried a couple times to implement an interval tree from scratch following the Wikipedia entry . Today I more or less did that in python. It's the simplest possible form. There's no insertion (though that's trivial to add), it just takes a list of 'things' with start and stop attributes and creates a tree with a .find() method. The wikipedia entry that baffled me was about storing 2 copies of each node's intervals --one sorted by start and the other by stop. I didn't do that as I think in most cases it won't improve lookup time. I figure if you have 1 million elements and a tree of depth 16, then you have on average 15 intervals per node (actually fewer since there are the non-leaf nodes). So I just brute force each of those nodes and move to the next. I th...

Since I couldn't find this anywhere, I'll add it here for those who have the same problem: I was trying to test a website with twill and got this at the end of my traceback: raise BrowserStateError("not viewing HTML") BrowserStateError: not viewing HTML After spending a bunch of time making sure that, yes, it was spitting out HTML, I figured out that it specifically means that twill (actually mechanize) doesnt like X HTML. You can likely fix it by adding this at the top of the script: b = twill.get_browser() b._browser._factory.is_html = True twill.browser = b Presumably, there's a real reason that check is in place, but works-4-me...

[NOTE: see the 2nd comment below about using a tuple as a key. better to just use pickle.dumps] I've been playing with google appengine lately. I'm working on a fun, pointless side project. Here's what I came up with for a cache decorator that pulls from memcache based on the args, kwargs and function name if no explicit key is given. The code for creating a key from those is from the recipe linked in the docstring. """ a decorator to use memcache on google appengine. optional arguments: `key`: the key to use for the memcache store `time`: the time to expiry sent to memcache if no key is given, the function name, args, and kwargs are used to create a unique key so that the same function can return different results when called with different arguments (as expected). usage: NOTE: actual usage is simpler as: @gaecache() def some_function(): ... but doctest doesnt seem to like that. >>> import time >>> def slow_fn(): ... time...

EDIT: added job posting to comments. I've been working a bit on genedex , I'm still not happy with the way it stores features. Which is a huge pickle of dictionaries where every dictionary is a 'feature' that looks like: {'name':'At2g26540', 'start': 1234, 'stop': 3456, 'strand': 1, 'chr': 2}. So the only way to do a search is by location--and that is _very_ fast, thanks to rtree , but there's no way to search by name or any other attribute--and an entire organism is loaded into memory at once--that part actually works out ok, but it feels dirty. I quickly wrote an SQLAlchemy backed interface to a simple db schema do allow this sort of searching here: http://code.google.com/p/genedex/source/browse/trunk/genedex/models/sqla.py . That already supports Feature.upstream(), downstream(), etc. methods, but it will work nicely once python supports sqlite rtree without any extra work--for now, it just uses BTree indexes on th...

I prefer sqlalchemy and genshi (or mako) and was therefore looking at using turbogears, but I saw a demo of the django admin, and that sold me. Certainly the templating language did not. Before this, I'd only used web.py in my projects. These are the things I've liked/noted: The first and most important: community . (Oddly enough, as I write this there are 666 projects tagged as django. 'turbogears', 'tg2', 'tg' give less than 50 projects combined. Think someone might have already written what you need? yep. Also, a great site: http://www.djangosnippets.org/ , where I've learned a lot just by reading, and saved myself a lot of time, by extending ideas there. And the development is active . Second, django.contrib.* User authentication is simple, and check google-code for various alterations on the theme. admin. This was what first made me decide on django. And now, new-forms admin is in trunk. This gives you a pretty nice CRUD interface for models ...

UPDATE 7-10-08: + add example for scatter plot + link to ken-ichi === Figuring how to make a client side image map from a matplotlib image has stumped me more than once. Andrew Dalke does have a working example. Below, I have the minimal example. It's simple once you get the steps right: just use mpl's transform() to convert the data into the image's coordinate system. Then flip the y-axis as required by the imagemap, then do the normal imagemap stuff and save the html. The only real gotcha, is to make sure to put the dpi in the call to savefig(). import pylab import sys import random name = 'imap' # make some fake data xs = range(15) ys = [random.choice(xs) for i in range(len(xs))] xys = zip(xs, ys) # can also use : f = pylab.subplot(121) f, = pylab.plot(xs, ys, 'ro') dpi = f.figure.get_dpi() height = f.figure.get_figheight() * dpi # convert the x,y coords into image coords. transform = f.get_transform() icoords = transform.transform(xys) # the minimal ...

[EDIT: update location of code repo] This isn't particularly advanced or clever, it's just a simple implementation--less code than anything else I could come up with. Binary search is easy. Just look at the python std library implementation (and the C API version ). When you play the game with a friend of guessing a number between 0 and 100, you guess 50, your friend tells you "higher", you guess 75. That's pretty much binary search. It takes about 7 guesses max to guess a number between 0 and 100. It just requires that the numbers be in order. Interval search is more difficult. It's not just looking for a single value, but rather for a range of values that overlap a given interval. Also, you can't sort on just start, because some intervals are longer than others, so when sorting by start, the stops may be out of order. So, you have to arrange some protocol. In all the examples I've seen, including the explanation here , that means storing not only t...

I agree with every report I've seen. Wherecamp was awesome. I've been telling people, and I'm still sure it's true, that I met exactly zero people who I'd physically seen before. Ordinarily, I avoid meetings, but this is a good format and seems to attract good people. It's fun to meet and work with people who are really into what they do. The talks are less "talky" and more like chat sessions--which is possible when the groups are small. There was also plenty of time to hack, which was the original reason I went. During and after, I learned some simple things which I'm trying to incorporate into my usual workflow: In the shell, background a job with "ctrl + z" then get back to it with %i where i is the number shown in the output from "jobs". That's a trick from jlivni. From crschmidt , I added: alias doctest="nosetests --with-doctest --doctest-extension=.txt" to my .bash_aliases. Which let's me do: doctest tes...

I have a development "server" here beside me. It's actually a budget laptop that sold for $799 2 years ago. It's a xubuntu machine, hosting a trac instance, a development server for mapping stuff, postgresql/postgis, mapserver, mysql, and couple svn repos, anything I do for contracting, etc. Oh, and it's also hosting a couple of sites for the multi-national company that my gf works for! all of their servers are windows machines (long rant suppressed). It used to get warm, so propped it up on 4 tuna cans, 1 for each corner, now it stays cooler. Yep, it's a sweet setup. Anyway, I pay AT&T or SBC --or whatever they are now called-- for static IP's and a supposedly faster internet connection. My 1 year contract for that is nearly up, so I'm switching to slicehost . I'm not a sys-admin, I sorta do that for 4 gentoo (not my choice) machines at $work, and my strategy is to set up, rsnapshot and never, ever emerge -u world. ever. So far, it's most...

In response to Titus' post on using imagemaps for genomic visualization: Why are imagemaps so popular in genomics? As an extreme and unfair comparison, just imagine if http://maps.google.com was an image map. Given a CGI script that can accept a url like &start=1024&stop=2048&chr=3 and return an appropriate image, you can provide a substantial set of tools using openlayers , which is developed by what must be one of the largest and active developer communities in GIS. (Yes, I am an openlayers fan-boy.) You can do that with a small addition to openlayers which I updated a couple weeks ago to OL version 2.6. In that update, I removed > 140 lines of code . So, it's now even less of a change to OL. Maybe when 2.7 comes out, I'll figure out how to provide a patch that allows an extra argument to the OpenLayers.Map constructor that limits panning to the horizontal direction -- in which case genome-browser will cease to exist and only the single file containing ...

I've copied this recipe that I modified before, and added the BK Tree structure in cython. It's in my repo here . Check it out with: svn checkout http://bpbio.googlecode.com/svn/trunk/seqfind or easy_install with sudo easy_install http://bpbio.googlecode.com/svn/trunk/seqfind It's now using the Damerau-Levenshtein distance which is more sensible for bioinformatics where transpositions are frequent. Bearophile's original implementation used a tuple, which made sense, but in Cython, it's more efficient to use an object where the properties can be typed--as a class is converted to a c-struct--so there is no conversion when appending to a python array -- if i understand the generated c code correctly. Using an object also allows arbitrary info to be passed along with the word when creating the tree, again, this is important for bio-informatics when the string is something like "actgcc ... acgtc" and it's useful to attach some annotation to it like: word...

The PNAS article linked here found subjects could improve IQ with training. I've written a simple flash version of their protocol over a couple long evenings in haxe /flash. The article methods list 2 stimuli, a moving box, and spoken letters. The test subject is to respond (click in my case) when the box position or the spoken letter is the same as it was 2 time steps ago. Where 2 is increased as the subject gets better. I didn't do sound, I just show a big letter. Clearly, the logical thing to do is use it for a couple weeks and then implement the sound when I'm smarter. (I've never really used swfmill , but I think that'd be useful here...) The article is ambiguous about when the letter is to sound, I've made both the letter and the box appear at the same time. The default, as in the article is to have 3 seconds between events, and to show the box for 0.5 seconds. I also add some indication of whether the answer was correct (green +) or not (red -). That act...

EDIT(2): fix markup for <char *> casts ... fix malloc (see comments. thanks Bao). NOTE: using a kwarg for limit slows things down. setting that to a required arg and using calloc for m2 speed things up to nearly as fast as the pylevenshtein. Well, it seems to be popular to code up the levenshtein . I actually have a use for this and wanted to practice some Cython , so I've written a version. I used bearophile's recipe , wikibooks , this (from k4st on reddit) and this for reference. It follows bearophile's code closely, using only O(m) space instead of O(mn). cdef extern from "stdlib.h": ctypedef unsigned int size_t size_t strlen(char *s) void *malloc(size_t size) void free(void *ptr) int strcmp(char *a, char *b) cdef inline size_t imin(int a, int b, int c): if a if c return c return a if c return c return b cpdef int levenshtein(char *a, char *b, int limit=100): cdef int m = strlen(a),...

EDIT: 7 months later I came back to this and found an error. update in the code below with old line commented out. Rather than venting about a project I've recently decoupled myself from, I'll try to do something constructive... I also posted this to the gispython mailing list, but I've had to figure it out a couple times, so I'll put it here for the record. Given an N * N numpy array, and a bounding box, it's actually fairly simple to make a georeferenced tiff: from osgeo import gdal, gdal_array import numpy from osgeo.gdalconst import GDT_Float64 xsize, ysize = 10, 10 a = numpy.random.random((xsize, ysize)).astype(numpy.float64) xmin, xmax = -121., -119. ymin, ymax = 41., 43. driver = gdal.GetDriverByName('GTiff') # bad: out = driver.Create('a.tiff', a.shape[0], a.shape[1], 1, GDT_Float64) # the args to Create are 'name', xsize, ysize. and .shape[0] is rows, which is y. driver.Create('a.tiff', a.shape[1], a.shape[0], 1, GDT_Float64...