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Tag: Python

Telephone keypad combinations

Problem: Given a sequence of numbers, show all possible letter combinations in a telephone keypad.

Recursive solution in Python:

keyboard = {
  '1': [],
  '2': ['a','b','c'],
  '3': ['d','e','f'],
  '4': ['g','h','i'],
  '5': ['j','k','l'],
  '6': ['m','n','o'],
  '7': ['p','q','r','s'],
  '8': ['t','u','v'],
  '9': ['w','x','y','z'],
  '0': []

def printkeys(numbers, prefix=""):
    if len(numbers)==0:
        print prefix

    for letter in keyboard[numbers[0]]:
        printkeys(numbers[1:], prefix+letter)




permutations implemented in Python

In case you can’t use Python’s itertools or in case you want a simple, recursive python implementation for a permutation of a list:

def perm(a,k=0):
      print a
      for i in xrange(k,len(a)):
         a[k],a[i] = a[i],a[k]
         perm(a, k+1)
         a[k],a[i] = a[i],a[k]



[1, 2, 3]
[1, 3, 2]
[2, 1, 3]
[2, 3, 1]
[3, 2, 1]
[3, 1, 2]

This Python implementation is based in the algorithm presented in the book Computer Algorithms by Horowitz, Sahni and Rajasekaran.

GWU Computer Science Graduate Classes Graph

In order to help me to take decisions about which class to take every semester I did a web scrapping from the graduate and undergraduate bulletin. For every class I could get classe name, prerequisites, credits, teacher, program, description, etc, in a formated tabular document.

Using Python CSV library I could read the tables and parse the data to other formats. One format very useful to handle graph structures is the DOT language script (included in the Graphviz project), in which you can describe both the graph structure and the elements of the graph layout.

Here is the Python source-code to convert the tables to graphs at Github.

The final result (click to view in full size):

Limitations and comments:

  • Prerequisites are only displayed using AND logic. It’s not showing other logics as OR (equivalent classes).
  • Errors may exists due to the scrapping process, conversions, or in the errors in the original source.
  • In the sources there is also a function to convert the graph in Dracula (a JavaScript interactive graph representation) but the current result is too tangled.


Substitutions in a phylogenetic tree file

The newick tree

The Newick tree format is a way of representing a graph trees with edge lengths using parentheses and commas.

A newick tree example:

(((Espresso:2,(Milk Foam:2,Espresso Macchiato:5,((Steamed Milk:2,Cappucino:2,(Whipped Cream:1,Chocolate Syrup:1,Cafe Mocha:3):5):5,Flat White:2):5):5):1,Coffee arabica:0.1,(Columbian:1.5,((Medium Roast:1,Viennese Roast:3,American Roast:5,Instant Coffee:9):2,Heavy Roast:0.1,French Roast:0.2,European Roast:1):5,Brazilian:0.1):1):1,Americano:10,Water:1);

A graphical representation for the newick tree above (using the library):

The Newick format is commonly used for store phylogenetic trees.

The problem

A phylogenetic tree can be highly branched and dense and even using proper visualization software can be difficult to analyse it. Additionally, as a tree are produced by a chain of different software with data from the laboratory, the label for each leaf/node can be something not meaningful for a human reader.

For this particular problem, an example of a node label could be SXS_3014_Albula_vulpes_id_30.

There was a spreadsheet with more meaningful information where a node label could be used as a primary key. Example for the node above:

Taxon Order Family Genus Species ID
Albuliformes Albulidae Albula vulpes SXS_3014_Albula_vulpes_id_30

The problem consists in using the tree and the spreadsheet to produce a new tree with the same structure, where each node have a more meaningful label.

The approach

The new tree can be mounted by substituting each label of the initial tree with the respective information from the spreadsheet. A script can be used to automate this process.

The solution

After converting the spreadsheet to a CSV file that could be more easily handled by a CSV Python library the problem is reduced to a file handling and string substitution. Fortunately, due the simplicity of the Newick format and its limited vocabulary, a tree parser is not necessary.

Source-code at Github.

Difficulties found

The spreadsheet was originally in a Microsoft Office Excel 2007 (.xlsx) and the conversion to CSV provided by Excel was not good and there was no configuration option available. Finally, the conversion provided by LibreOffice Productivity Suite was more configurable and was easier to read by the CSV library.

In the script, the DictReader class showed in the the long-term much more reliable and tolerant to changes in the spreadsheet as long the names of the columns remain the same.

P.S. due to the nature of the original sources for the tree and spreadsheet I don’t have the authorization for public publishing their complete and original content. The artificial data displayed here is merely illustrative.

GenBank renaming
DNA inspired sculpture by Charles Jencks. Creative Commons photo by Maria Keays.

What is GenBank?

The GenBank sequence database is a widely used collection of nucleotide sequences and their protein translations. A GenBank sequence record file typically has a .gbk or .gb extension and is filled with plain text characters. A example of GenBank file can be found here.

Filename problem

Although there are several metadata are available inside a GenBank record the name of the file are not always in accordance with the content of the file. This is potentially a source of confusion to organize files and requires an additional effort to rename the files according to their content.

Approach using Biopython

The Biopython project is a mature open source international collaboration of volunteer developers, providing Python libraries for a wide range of bioinformatics problems. Among other tools, Biopython includes modules for reading and writing different sequence file formats including the GenBank’s record files.

Despite the fact that is possible to write a parser for GenBank’ files it would represent a redundant effort to develop and maintain such tool. Biopython can be delegated to perform parsing and focus the programming on renaming mechanism.

Biopython installation on Linux (Ubuntu 11.10) or Apple OS X (Lion)

For both Ubuntu 11.10 and OS X Lion, a modern version of Python already comes out of the box.

For Linux you just need to install the Biopython package. One method to install Biopython in a APT ready distribution as Ubuntu 11.10 (Oneiric Ocelot) is:

# apt-get install python-biopython

For an Apple OS X (Lion) you can install Biopython using easy_install, a popular package manager for the Python. Easy_install is bundled with Setuptools, a set of tools for Python.

To install the Setuptools download the .egg file for your python version (probably setuptools-0.6c11-py2.7.egg) and execute it as a Shell Script:

sudo sh setuptools-0.6c11-py2.7.egg

After this you already have easy_install in place and you can use it to install the Biopython library:

sudo easy_install -f biopython

For both operational systems you can test if you already have Biopython installed using the Python iterative terminal:

$ python
Python 2.7.2+ (default, Oct 4 2011, 20:03:08)
[GCC 4.6.1] on linux2
Type “help”, “copyright”, “credits” or “license” for more information.
>>> import Bio
>>> Bio.__version__

Automatic rename example through scripting

Below the Python source-code for a simple use of using Biopython to rename a Genbank file to it’s description after removing commas and spaces.

Using the the previous example of GenBank file, suppose you have a file called To rename this file to the GenBank description metadata inside it you can use the script.


And after this it will be called Hippopotamus_amphibius_mitochondrial_DNA_complete_genome.gbk.


There is plenty of room for improvement as:

  • Better command line parsing with optparse and parameterization of all possible configuration.
  • A graphical interface
  • Handle special cases such multiple sequences in a single GenBank file.