Bioinformatics is the interdisciplinary branch of science that uses statistics, computer science, and biology to analyze the biological data.
As more biological data are being produced daily, there is a great need to have strong tools and languages for managing or rather processing it.
Among such languages, R has become quite well-known in the sphere of bioinformatics.
What is R Language?
R is an environment and a programming language that can be obtained for free. It is designed especially for conducting statistical calculations and graphic displays.
Released by Ross Ihaka and Robert Gentleman from University of Auckland, New Zealand in early 1990s, R has currently grown and becomes a powerful tool of data analysis, machine learning and statistical modeling.
Huge and multilayered, it encompasses thousands of packages by developers from around the world and can be applied to various domains starting with finance and terminating at healthcare and social sciences.
Why is R Language Important in Bioinformatics?
Currently, R language serves as a highly valuable instrument in the field of bioinformatics owing to widespread availability of packages and libraries that are devoted to biological data analysis.
Here are a few reasons why R language is important in bioinformatics:-
Data Manipulation and Analysis
R has many tools and libraries it can use in data manipulation and analysis functions.
It enables bioinformaticians to load, clean and preprocess the data in a rather fast manner and as mentioned above; preprocessed datasets can be shared and reused hence saving time.
Some of the features that are present in R that permits the users to filter, combine, convert, and manipulate data in a way that makes it easier to prepare for further analyses.
Statistical Analysis
R language contains a large number of statistical methods for the analysis of biological samples.
Some of the areas known to benefit from the package include hypothesis testing, regression analysis, the survival technique and many other packages.
R can be used by a bioinformatician to actually do specific statistical tests and also determine the results from the data gathered.
Data Visualization
R has excellent graphing utilities; bioinformaticians can use this ability to present the obtained data visually at a high level.
Data visualization is important in bioinformatics because it helps the researcher to analyze the information by pointing out regularities, tendencies and abnormalities in the data.
Thus, R offers the number of plotting functions and libraries for creating informative and rather nice graphics.
Integration with Other Tools
R can be incorporated with other bioinformatics utility and databases and therefore did not require extra effort to integrate with other tools.
It has packages that enable it to work with other common software such as Bioconductor which is a collection of tools that works in bioinformatics and genomics.
This integration makes it possible for the user, in this case, bioinformaticians, to harness the power of the tools and perform the complex analysis in the best way possible.
Popular R Packages for Bioinformatics
Bioinformatics is clearly one of the primary areas of applications for R since there exist thousands of packages specifically intended for bioinformatics.
Here are a few popular R packages used in bioinformatics:
Bioconductor
Bioconductor is an open source bioinformatics project for the analysis and comprehension of genomic data.
It has modules for processing of high throughput genomics data including DNA sequencing, microarray and proteomics data.
Bioconductor currently enjoys immense popularity among bioinformaticians and contains packages for most of the analytic functions.
DESeq2
DESeq2 is an R package for differential analysis and for estimating genome wide findings. This application has been employed extensively in RNA-Seq data analysis and enables the researcher to determine which genes are up or down regulated between different experimental sets.
ggplot2
R has a package ggplot2 for data visualization in their rich and vast library.
It has an undetermined grammar of graphics, which enables users to design modes and forms of their graphics plots.
One of the R packages commonly used in the analysis of sequenced data is ggplot2 to generate publication-ready graphic designs.
GenomicRanges
GenomicRanges is an appropriate R/Bioconductor package for representing, manipulating, as well as analyzing genomic intervals and ranges.
It has functions for such purposes as finding intersection of two genomic regions or extracting sequences and dealing with genomic annotations.
Conclusion
R language is now one of the most used tools in bioinformatics because of the vast number of possible operations in terms of data manipulation, statistical analysis, and data graphical representations.
The compatibility of this species of software with other bioinformatics tools and the accessibility of particular packages make it the favourite of many ‘bioinformaticists’.
Thus, understanding the role of the R language in bioinformatics, its significance will only grow in the future as the field actively develops.
Therefore, anyone who is bioinformatician or planning to be one should consider learning the R language since it would help in data analysis and research.
Thanks for this information.
Thank you for this well-written and informative post. The way you explained the details was very helpful and made the topic much more approachable. I appreciate the effort you put into creating such valuable content.