BioPython

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Python Programming
in bioinformatics
Why Python?
 Python can be installed and used on different platforms,
including Windows, Mac, and Linux.
 Python has several built-in features that make it well-
suited for bioinformatics applications.
 Python‟s dynamic and modular nature allows researchers
to reuse and share code, reducing development time and
increasing productivity.
 Python has a relatively simple syntax, making it easy to
learn and use.
 Python is a high-level language that offers advanced data
structures and functions that make it easy to work with
complex biological data.
Tools for Python Programming in
Bioinformatics

1. Biopython
 One of the most widely used bioinformatics packages for Python. Biopython is an
open-source collection of Python modules that provides a set of powerful and easy-
to-use tools for performing biological computations.
 Biopython requires very less code and comes up with the following advantages −

 Some of the tasks of Biopython are:

 Biopython provides tools for working with DNA, RNA, and protein sequences,
including sequence alignment, motif and pattern matching, and translation between
nucleotide and protein sequences.
 Biopython includes tools for working with protein structures, such as parsing and
manipulating PDB files and performing structure comparisons.
 Biopython supports file formats commonly used in bioinformatics, such as FASTA,
GenBank, and BLAST.
 Biopython includes tools for visualizing biological data, such as sequence alignment
plots and phylogenetic trees.
 BioSQL − Standard set of SQL tables for storing sequences plus features and
annotations.
2. PyMOL
 PyMOL is a free and open-source molecular
visualization software used in bioinformatics. It creates
high-quality images and animations of molecular
structures, which can be useful in a variety of applications
including drug discovery, protein engineering, and
molecular biology research.
 PyMOL is written in Python and can easily integrate with
other Python-based tools and libraries.
3. Scikit-learn
 Scikit-learn is a Python library that provides tools for machine
learning. It is a powerful and flexible tool for machine learning
applications in bioinformatics which provides a wide range of
algorithms and tools that can be used to analyze complex
biological datasets and make predictions about biological
systems.
 Some uses of Scikit-learn in bioinformatics are:
 It can be used to classify biological samples based on gene
expression data or proteomics data.
 It can be used to cluster biological samples or reduce the
dimensionality of large datasets.
 It can be used to develop machine learning models to predict
the structure of proteins and protein-protein interactions
based on their amino acid sequences.
4. NumPy (Numerical Python)
 NumPy is a Python library that is used for working with
numerical data in Python. It is extensively used in Pandas,
SciPy, Matplotlib, Scikit-learn, and many other scientific
Python packages. NumPy provides a multidimensional
array object called „ndarray‟ and can be used to perform a
wide range of mathematical operations on arrays.
To install and import Biopython:
What are the input data types for Biopython?
 Text file:
 1. Sequence file (sequence.txt)

 2. Cell Microarray
What are the input data types for Biopython?

 CSV file:

 FASTA File:
What are the input data types for Biopython?

 Other files format like:

 Blast output
 GenBank
 PubMed and Medline
 SCOP, including „dom‟ and „lin‟ files
 UniGene
 SwissProt
Overview on

Some key notes in Python

 Data Types: Number Types

int, float, complex

1. Integer 2. Real 3. Complex

numbers: numbers: numbers:
>>> type(4) >>> type(4.5) >>> type(3+2j)

<type 'int’>
<type ’float’>
<type ’complex'>

>>> (2+1j)**2
>>> 17/5
(3+4j)

3
Data Types: Strings

 Single quote:
>>> ’atg’
’atg’

 Double quote:
>>> ”atg”
’atg’
>>> ’This is a codon, isn’t it?’
Invalid Syntax

>>> ” This is a codon, isn’t it?” # Or >>> ’This is a codon, isn\’t it?’

This is a codon, isn’t it?

String Operators
 Escape character: Backslash „\‟ , gives special meaning for the
following character.
 To produce more readable outputs: print()
 String Operators: Construct Meaning
 Concatenate + \n Newline
 Copy or replicate * \t Tab
 Checks if first IS in second string in \\ Backslash
 Checks if first IS NOT in second string not in \” Double Qoute
>>> ’atg’ + ’gcc’
’atggcc’
>>> ’atg’ * 3
’atgatgatg’
>>> ’tg’ in ’atgatgatg’
True
>>> ’tc’ in ’atgatgatg’
False
Variables
 Variables are containers that store numbers, strings,
and other data types and structures.
 Variables are names given to values that can be changed.
 Variables are assigned values using the equal sign (=).
>>> codon = ’tag'
>>> dna_sequence = "gtcgcctaaccgtatatttttcccgt"
 A variable cannot be used if not assigned a
value, an error occurs.
>>> dna

NameError: name 'dna' is not defined

Variables
 Naming
 Select meaningful names: dnaSequence, is better than s.
 Follow naming rules:
 Case-sensitive :
 DnaSequence = 1
 DNASEQUENCE = 2
 Dnasequence = 3
 Consists of letters and numbers combinations, and
underscore.
 Dna1, dna_1, dnaSeq.
 Numbers should not be the first letter.
 Invalid: 1dna
 No special characters.
 dna#, dna@1
String Operators
 [i] : returns the character in index i in a string. (index)
 [i:j] : returns the substring between index i and index j in a string. (slice)
>>> dna="gatcccccgatattatttgc”
>>> dna[0]
'g’ - The first position in a string is position 0
>>> dna[-1]
'c’ - Counting from the right using negative
indices, begins with -1
>>> dna[-2]
'g’
>>> dna[0:3]
'gat’ - In slices: Start index included, end index
excluded
>>> dna[:3]
‘gat’ - Ommiting start index means use default, 0
>>> dna[2:]
‘tcccccgatattatttgc’ - Ommiting end index means use default, end
of string
Strings as Objects
• String variables are objects that can perform specific
actions using built-in methods:
>>> dna="gatcccccgatattatttgc
>>> len(dna)
20
>>> dna.count(‟t') - Count characthers
7
>>> dna.count(‟ga') - Count substrings
2
Strings Functions
>>> dna="gatcccccgatattatttgc”
>>> dna.upper() - Convert all to upper case, lower(): Lower
case
GATCCCCCGATATTATTTGC
>>> dna.find(‟ga') - Returns the first occurrence of „ga‟, -1 if not
found
0
>>> dna.find(‟at‟,5) - Returns the first occurrence of „ga‟ starting
from index 5
9
>>> dna.rfind(„ga‟) - Returns the last occurrence of „ga‟, -1 if not
8
>>> dna.islower() - True if all is lower case
True
>>> dna.isupper()
False
>>> dna.replace('a','A') - Replaces all ‟a‟ with ‟A‟
Inputs

>>> dna = input("Enter a DNA sequence, please:")

Enter a DNA sequence, please: agtagcatgaggagggacttc
>>> dna
agtagcatgaggagggacttc
Examples:
 Create a random DNA sequence of length 10
import random
alphabet = "AGCT"
sequence = ""
for i in range(10):
index = random.randint(0, 3)
sequence = sequence + alphabet[index]
 Read from a text file
readlines(). read().

•readlines(x); read up to x bytes. If you read(x); read up to x bytes in a file. If

don’t supply a size, it reads all the data you don’t supply the size, it reads the
until it reaches a newline (\n) or the end entire file.
of a paragraph. The output is displayed as strings only
once.
Write a text file
 Notes about file modes

What is + means in open()?

• The + adds either reading or writing to an existing open mode (update mode).
• The r means reading file; r+ means reading and writing the file.
• The w means writing file; w+ means reading and writing the file.
• The a means writing file, append mode; a+ means reading and writing file, append mode.
Examples:
 Difference between r and r+ in open()

with open('file.txt„, „r‟) as f: with open('file.txt', 'r+') as f:

print(f.read()) f.write("new line \n")
Output Output
On Terminal On Terminal
new line
welcome to python 1
welcome to python 1
welcome to python 2
welcome to python 2
welcome to python 3
welcome to python 3
welcome to python 4
welcome to python 4

with open('file.txt', 'r') as f:

f.write("test \n")
io.UnsupportedOperation: not writable
Examples:
 Difference between w and w+ in open()
with open('file.txt', 'w+') as f: with open('file.txt', 'w+') as f:
f.write("test 1\n") f.write("test 1\n")
f.write("test 2\n") f.write("test 2\n")
f.write("test 3\n") f.write("test 3\n")
Output f.seek(0)
file.txt lines = f.read()
test 1
test 2
print(lines)
test 3 Output
Terminal
test 1
test 2
test 3

Note: f. seek(0)  move the file pointer to begining

Examples:
 Difference between a and a+ in open()
with open('file.txt', 'a') as f: with open('file.txt', 'a+') as f:
f.write(“3") f.seek(0)
Output lines = f.readlines()
file.txt f.write("\n" + str(len(lines)))
welcome to python 1
welcome to python 2 Output
welcome to python 3 file.txt
welcome to python 4 welcome to python 1
3 welcome to python 2
welcome to python 3
welcome to python 4
4
Assignment
 Apply all the discussed functions on a text file produced
by your self