Bioinformatics is a rapidly growing field that integrates biological science and computer science for the development and application of computational tools in order to analyze and interpret biological data. Programming languages are the most fundamental and versatile tool that has become essential in bioinformatics. There are various languages that are used in the field of bioinformatics. Python and R programming are the two most commonly used programming languages in bioinformatics.
What is Python Programming?
Python is a popular programming language widely used in many fields due to its versatility and ease of use. It is a high-level programming language that is easy to learn and use. It is widely used in bioinformatics for building software tools and applications, data manipulation and visualization, genome analysis, literature searches, and many other applications.
Advantages of Python Programming in bioinformatics
Some of the advantages of using Python in bioinformatics are:
- 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
There are several Python libraries and tools available for bioinformatics applications. Some of these tools and libraries include:
Biopython is 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 provides tools that can be used for a wide range of bioinformatics tasks, such as sequence analysis, structure analysis, and data manipulation.
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.
Python packages are not available in python by default. We have to install and import them. We can also import specific functions of a package.
# install package pip install biopython
# import package and specific function import Bio from Bio.Seq import Seq
# reverse complement a nucleotide sequence my_seq = Seq("AGTACACTGGT") print(my_seq) AGTACACTGGT
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. PyMOL can be extended using Python-based plugins, which can add new features and functionalities to the software. There are many Python-based plugins available for PyMOL, including plugins for sequence analysis, ligand docking, protein-protein interaction analysis, and more.
Biskit is a modular, object-oriented python library for structural bioinformatics. It provides a wide range of tools for analyzing and modeling macromolecular structures, including protein-ligand docking, molecular dynamics simulations, and protein structure prediction.
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.
5. 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:
pip install numpy
import numpy as np
Matplotlib is a Python visualization package. It is used for creating high-quality visualizations such as line plots, scatter plots, histograms, and heat maps. It can be used in bioinformatics for visualizing various types of data, including DNA and protein sequences and structures.
To install and import Biopython:
pip install matplotlib
import matplotlib.pyplot as plt
Some uses of Matplotlib in bioinformatics are:
- It can be used to visualize gene expression data that can help identify patterns and relationships in gene expression data.
- It can be used to visualize DNA and protein sequences that can be used to identify sequence variations and features that are important for understanding sequence function.
- It can be used to visualize phylogenetic trees and identify evolutionary relationships between different species or groups of organisms.
Applications of Python Programming in Bioinformatics
Python programming is used in a variety of bioinformatics applications, including:
- Python programming is used in genome analysis. It is used to align DNA and protein sequences, identify genetic variations, and perform gene expression analysis. Biopython is widely used for this purpose.
- Python is used in the analysis and visualization of protein structures. PyMOL is widely used for this purpose.
- Python programming is used in machine learning to classify genes, predict protein structures, and more. Scikit-learn is widely used for building predictive models using biological data.
- Python programming is used to create plots for visualizing data in bioinformatics. Python offers several packages for data visualization, including Matplotlib and Seaborn, which are widely used for visualizing biological data.
What is R Programming?
R is an open-source programming language specifically used for statistical computing and graphics. It is one of the widely used programming languages in bioinformatics. It is able to manipulate and analyze large datasets quickly and easily. It provides an extensive library of statistical and graphical methods, making it easy to visualize data and present it in a clear and concise way. R also provides a wide range of tools and techniques for analyzing biological data.
Advantages of R Programming in bioinformatics
Some of the advantages of using R programming in bioinformatics include the following:
- R is an open-source language. It is an accessible option for everyone, including bioinformatics researchers.
- R has a wide range of statistical tools and packages that can be used to analyze bioinformatics data.
- R has a large and active community of users and developers constantly creating new tools and packages specific to bioinformatics research needs.
- R can function on various operating systems, making it a cross-platform language.
Tools for R Programming in Bioinformatics
R provides many packages that are designed specifically for working with genomic data. Some of these tools include:
Bioconductor is an open-source and open-development software project for computation biology. It is a collection of R packages for bioinformatics, which includes tools for data visualization, statistical analysis, and genomic data analysis.
To install Bioconductor:
For installing specific packages like Biostrings and GenomicRanges:
biocLite(c( "Biostrings", "GenomicRanges"))
Some of the major Bioconductor packages used in bioinformatics are:
- GenomicRanges is a Bioconductor package that provides tools for storing, manipulating, and analyzing genomic intervals.
- DESeq2 is a package for differential gene expression analysis, commonly used in RNA-seq data analysis.
- Biostrings package provides efficient data structures and algorithms for working with biological sequences, including DNA, RNA, and protein sequences. This package is particularly useful for analyzing high-throughput sequencing data, such as whole-genome sequencing or transcriptome sequencing.
Here is a simple example of a code for performing pairwise sequence alignment using Biostrings:
library(Biostrings) seq1 <- DNAString("ATGGTGACCTGACGTCGAGGTAGCCAGCTGACTAGGACGTAGGCT") seq2 <- DNAString("ATGGTGACCTGACGTCGAGCTAGCCAGCTGACTAGGACGTAGGCT") alignment <- pairwiseAlignment(seq1, seq2) print(alignment)
ggplot2 is a popular R package for data visualization, which can be downloaded from the Comprehensive R Archive Network (CRAN). It provides a set of functions that allow users to easily create a wide range of graphs to explore and visualize their data. It is able to create aesthetically pleasing and informative graphs.
The following basic template is used to create a ggplot:
ggplot(data = <DATA>, mapping = aes(<MAPPINGS>)) + <GEOM_FUNCTION>()
For example: To creates a line plot of gene expression over time, with the time on the x-axis and the expression levels on the y-axis:
library(ggplot2) ggplot(gene_expression, aes(x=time, y=expression)) + geom_line()
Here, ‘gene_expression’ is the data, ‘aes’ (aesthetic) function is used for mapping and the ‘geom_line’ function is used to create line plot.
Shiny is an R package widely used in bioinformatics for creating web-based tools and applications that allow users to interact with and visualize genomic data. It can visualize genomic data, perform statistical analysis, and create interactive reports.
dplyr is an R package for data manipulation with functions for filtering, selecting, summarizing, and arranging data.
For example: To select and filter required data:
To select ‘gene’, ‘sample’ and ‘organism’ columns from a dataframe ‘rna’, the ‘select’ function is used:
library(dplyr) select(rna, gene, sample, organism
The ‘filter’ function can be used to select only the rows of the data frame where the sex column is equal to “Male”:
filter(rna, sex == "Male")
Applications of R Programming in Bioinformatics
R programming is used in bioinformatics for various applications, from data visualization and statistical analysis to genomics and machine learning. Some of the applications of R programming in bioinformatics are:
- R programming can be used to create graphs and charts, essential for exploring and interpreting complex biological data. Some of the popular visualization packages in R include ggplot2 and shiny.
- R programming provides various statistical tools and techniques for analyzing biological data.
- R programming provides tools for data manipulation, which are essential for working with large biological datasets. The dplyr package tools can clean and preprocess data, making it easier to analyze and interpret.
- R programming provides many packages specifically designed for working with genomic data, such as Bioconductor and the GenomicRanges package.
- DeLano, W.L. The PyMOL Molecular Graphics System (2002) DeLano Scientific, San Carlos, CA, USA. http://www.pymol.org
- Ekmekci, B., McAnany, C. E., & Mura, C. (2016). An Introduction to Programming for Bioscientists: A Python-Based Primer. PLoS Computational Biology, 12(6). https://doi.org/10.1371/journal.pcbi.1004867
- Giorgi, F. M., Ceraolo, C., & Mercatelli, D. (2022). The R Language: An Engine for Bioinformatics and Data Science. Life, 12(5). https://doi.org/10.3390/life12050648
- Grunberg, R., Nilges, M., & Leckner, J. (2007). Biskit A software platform for structural bioinformatics. Bioinformatics, 23(6), 769–770. https://doi.org/10.1093/bioinformatics/btl655
- Rosignoli, S., & Paiardini, A. (2022). Boosting the Full Potential of PyMOL with Structural Biology Plugins. Biomolecules, 12(12). https://doi.org/10.3390/biom12121764