Python’s random module makes it extremely easy to generate random DNA bases.
import random dna = ["A","G","C","T"] #output a random base print(random.choice(dna))
Now to generate a specific number of random bases, all we have to do is use Python’s range function:
Here are several talks from PyCon 2014 I thought looked rather interesting from a research perspective:
Line two of the zen of Python reads “explicit is better than implicit” and until relatively recently I never truly appreciated the wisdom of those words. My change of heart stems from a series of Python scripts, where a large portion of my code dealt with automating and retrieving the results of a BLAST search using the fantastic BioPython toolkit. I was filtering my results based on an expect value of 0.04, which during my initial testing worked perfectly. However, as I wanted to make this value variable, I rewrote it into the program as a command-line argument. What I had not considered (but definitely should have) was how Python implicitly processes a command-line argument – as a string! I was never thrown an error – the program continued to work, so I assumed it was still doing the job just like in my tests. However, behind the scenes the filtering of my results had completely ceased to function.
The second (and in my opinion less obvious) issue of I have had with implicit design decisions relates to the qblast method from BioPython. As far as I could see, I was retrieving plenty of sequences, therefore the program must be working. However, my PI was rather suspicious of how so few sequences were being retrieved compared to the hundreds that were coming up when she would BLAST our sequence with the online interface. I searched numerous sites and went through the BioPython documentation but could find no mention of a sequence retrieval cut-off. Finally in desparation I went through the source code itself from the module that I was using and found this:
A default limit of 50 unless overruled! A few extra characters and the problem was fixed, but until this discovery I was having serious problems later in the pipeline that I could not understand.
[Edit] After a Twitter conversation with Peter from the BioPython Project, I’d like to add that this issue is due to the default settings of the online BLAST tool I was calling, as well as potentially the settings of the BioPython wrapper. A good lesson in understanding the defaults of the tools (BLAST) your tools (BioPython) are calling!
These are two of the most recent examples that I have seen of how aware developers need to be about the implicit default values and methods that are present in the language and library they are calling. I hope my mistakes and learning experience will be useful to others who may come across similar issues.
This was originally posted by myself on the Australian Bioinformatics Network.
This tutorial is a brief overview of what you can achieve using the Python BioPython module. Although I’m hoping to write up some more articles on this site for beginners when time permits, this post will assume that you have experience programming in Python and have a bit of an understanding of basic biological concepts such as DNA, restriction enzymes etc. If you’re still interested once you finish reading, feel free to consult the BioPython documentation, it will help give you a bit of an idea of how massive (and awesome) this module really is.
So to start I’ll show you how to install the BioPython module. While on Linux systems it can be as simple as typing ‘sudo apt-get install python-biopython’ or going to the Software Center, you can manually install a module by going to PyPI, downloading and extracting the file, opening the command-line or terminal and navigating into the root directory of the folder you just extracted and running the command ‘setup.py install’.
You will need to download two modules to install BioPython, each of which are hosted on their own site. The first is SciPy and the second is BioPython. Once you have installed these you’re ready to get into using BioPython.
Pyral (Python + Viral) was the name of a project I worked on in Dr Joanne Macdonald’s lab between September 2012 – January 2013 (although I am still providing tech support for the code and helping manage the server to this date). Throughout this time I wrote a lot of Perl and Python code to run on the university’s Linux server. The aim of these programs were as follows:
The main use I’ve found for the Python sys module is allowing command-line arguments to be made to a script. Here is an example of how it looks:
import sys
if len(sys.argv) == 2:
input_file = sys.argv[1]
else:
print "Please input a command-line argument specifying the file"
This script checks that 2 command-line arguments had been passed to the program before assigning the value sys.argv[1] to a variable. We check for two command-line arguments because the first one (sys.argv[0]) is the name of the Python script currently being executed.
You can use the Python os module to send commands out to the operating system. Through os you can do anything from changing the current working directory through to listing the contents of a directory.
import os
# print the current working directory
print os.getcwd()
# change the working directory
os.chdir("..")
# create a new directory
os.mkdir("new_folder")
# list the contents of a directory
os.listdir("new_folder")
# remove directory
os.rmdir("new_folder")
# remove a file
os.remove("image.jpg")
# rename a file/directory and where it will go
os.rename("/data/jack", "/james")
Almost anyone doing research at some point will have to input or extract data from an Excel spreadsheet. However, it is possible to export an Excel file as a .csv file (comma separated file) and then use Python to grab all the data instantly. The Python csv module makes all this incredibly simple:
import csv
column1 = []
column2 = []
column3 = []
openfile = csv.reader(open('data.csv'))
for row in openfile:
first, second, third = row
column1.append(first)
column2.append(second)
column3.append(third)
This script will open your .csv file, and then use the ‘.next()’ method to create an array of all the lines in your file, which you can parse through with the for loop.
Before I made the switch to developing on a Linux machine, I noticed that the Python module for calling R (RPy2) seemed to be having some problems on Windows. This gave me an excuse to play around with writing my own Python script to create and run an R script. As you’ll see in the code below, I’ve used the subprocess module to execute the R script I created and then pipe the results back into the terminal.
import subprocess
# rscript is the content of the R file we'll create
rscript = '''#!/usr/bin/Rscript
cat('This is a simple program') # same as c()
NumOfIterations for (i in 1:NumOfIterations) { # 1:10 is range
cat(i, 'Hello world!')
cat(' ')
}
'''
r_file = open("example.r", "w")
r_file.write(rscript)
r_file.close()
r_file_name = "example.r"
ppath = r'C:Program FilesRR-2.15.2bini386Rscript.exe'
proc = subprocess.Popen("%s %s" % (ppath, r_file_name), stdout=subprocess.PIPE)
output = proc.stdout.read()
print output