Bioinformatics is still a relatively new field, meaning that biology graduates aren’t necessarily trained in using the programming languages that help in performing data-intensive research: developing and using the algorithms that allow us to decode complex living systems. Often bioinformaticians are learning on the job, especially Ph.D. students or early career postdoctoral scientists, which is one of the reasons we offer such a range of scripting courses suited to various needs.
Everyday Bioinformatics analysis involves the extensive study and analysis of huge biological datasets. The major part of bioinformatics is connecting together different processing steps into a single pipeline and then applying that pipeline to many other files repeatedly, which often involves massive and tedious data processing. Along with Linux, programming languages like Python and R have made it easy to perform analysis on huge biological data sets.
Python for Bioinformatics
The increasing demand to process big data and develop algorithms in all fields of science indicates that programming is becoming an essential skill for scientists, with Python the language of choice for most bioinformaticians. Python is a great general-purpose scripting language that’s becoming the programming language of choice for most bioinformaticians.
Python has become a staple in data science and Bioinformatics, allowing bioinformatics data analysts and other professionals to use the language to conduct complex statistical calculations, create genomic data visualizations, build machine learning algorithms, manipulate and analyze genomic data, and complete other biological data-related tasks.
The simple syntax and high-level data structures of Python, make it easier for nonprofessional programmers such as computational biologists to develop programming skills, enabling them to interact with data programmatically and eventually develop code on their own. Biopython is a set of freely available tools for biological computation written in Python language. It provides various modules and functions for the study and analysis of huge biological datasets.
Python Tools Used in Bioinformatics
Python-based implementation efficiently deals with biological datasets of more than one million cells. Various different Python tools and package are available to manipulate biological data.
Scanpy is a scalable toolkit for analyzing single-cell gene expression data built jointly with anndata. It includes preprocessing, visualization, clustering, trajectory inference and differential expression testing.
Opfi is a Python package for identifying gene clusters in large genomics and metagenomics data sets
Pysam is a python module for reading, manipulating and writing genomic data sets.
GATK stands for GenomeAnalysisToolkit. It is a collection of command-line tools for analyzing high-throughput sequencing data with a primary focus on variant discovery.
HTSeq is a Python package that provides infrastructure to process data from high-throughput sequencing assays.
Biopython is a set of freely available tools for biological computation written in Python by an international team of developers. include parsers for various Bioinformatics file formats (BLAST, Clustalw, FASTA, Genbank), access to online services (NCBI, Expasy), interfaces to common and not-so-common programs (Clustalw, DSSP, MSMS), a standard sequence class, various clustering modules, a KD tree data structure etc.
R for Bioinformatics
R is one of the most widely-used and powerful programming languages in bioinformatics. R is a programming language and software environment for statistical biological analysis, graphics representation, and reporting.
R especially shines where a variety of statistical tools are required (e.g. RNA-Seq, population genomics, etc.) and in the generation of publication-quality graphs and figures. Biological data visualization is also an important aspect of bioinformatics which involves the graphical representation of unstructured or structured data to display information hidden in the plots/graphs.
Data visualization is the process of graphical representation of unstructured or structured data to display information hidden in the plots/graphs. The approach not merely used visualization tools to present data in the form of graphs but also looked at the world from a graphical point of view.
R Tools Used in Bioinformatics
R provides various different packages to analyze biological datasets; ggplot2 is a package used for biological data visualization. DEseq2, Ballgown, and EdgeR are used for differential gene expression analysis. topGO and enrichR packages are used for functional enrichment analysis. Similarly, genefilter is used for filtering genes from high-throughput experiments.
Linux for Bioinformatics
Unfortunately, Bioinformatics tool options are very limited in Windows. Most standard Bioinformatics software, such as software for genome assembly, structural annotation, variant calling, and phylogenetic tree construction are all written exclusively for Linux. Although the transition to the command line can seem difficult at first, it is well worth the effort if you plan on working with large biological data sets, such as those arising from high-throughput sequencing projects.
Linux is a free, open-source operating system, that anyone can install, study, modify and even redistribute. Because all Linux distributions are open source, the development and advancement of Linux distributions come from the community and for the community. Linux has built-in programming languages and biological data processing is also comparatively faster in Linux.
The major part of bioinformatics is connecting together different processing steps into a single pipeline and then applying that pipeline to many other files repeatedly, which often involves massive and tedious data processing. A good practice bioinformaticians do is automating their pipeline. A common way for bioinformaticians to encapsulate all these processes is by listing them in a Bash script.
Linux Tools Used in Bioinformatics
Linux helps in performing various different analysis for instance variant calling, metagenomics, single-cell sequencing etc. Different tools are available which you can install on Linux to perform your biological data analysis.
Different tools are installed in Linux that are used in performing analysis for instance FastQC & Fastp are used for quality control, BWA & Bowtie2 are used for mapping, BamUtil for base quality recalibration, Freebayes for variant calling, SAMtools for filtration, EEF, SIFT, & VEP for annotation and many more.
Similarly another tool called EMBOSS (The European Molecular Biology Open Software Suite) contains several powerful bioinformatics programs for performing tasks such as sequence alignment, PCR primer design, and protein property prediction. Clustalw is a powerful sequence alignment program that can be used to generate large multiple alignments. The clustalw program offers several command-line options for controlling the se- quence alignment process.
BioCode’s Advanced Bioinformatics Scripting Course
In BioCode’s Advanced Bioinformatics Scripting Course you’ll learn from the very basics of biological programming in Python, BioPython & R to an advanced level understanding of Bioinformatics Scripting, even if you lack prior knowledge. Understand various concepts related to how to write scripts for MicroArray Gene Expression Analysis, ggplot2 biological data visualization & sequence retrieval, alignment, BLAST database searching & phylogenetic analysis in BioPython. You’ll also be learning complete end-to-end Linux (BASH) for Bioinformatics.
Joining and learning from the Advanced Bioinformatics Scripting in Python, BioPython, R & BioConductor can strengthen your biological programming career by learning through various useful & informative pre-recorded lectures on various biological programming/scripting languages.
Major course contents include:
-Introduction to Python, BioPython, R, Linux & BioConductor
-BLAST Database Searching, Parsing, and Extraction
-Sequence Analysis, Sequence Data Parsing, Sequence Retrieval, and Alignment
-Processing and Analysis of Biological Datasets
-Data Visualization: ggplot2
-Bioinformatics File Parsing and Writing
-Gene Enrichment Analysis
-MicroArray Analysis: BioConductor