Ever since the development in the field of Data science, many techniques and methods of Statistics have been applied in the field of Biology. This created the field like Biostatistics, that uses and implements the statistical methods to the biological data.
Bioinformatics, in addition to Statistics relies on programming and mathematics. Bioinformatics uses statistical and computational approaches; that makes it worthy enough to use the power of statistics against the problems of biology and this power is channeled through R.
R and python are the most popular and frequent languages to retrieve, annotate and organize the biological data in the world of Data science. Both languages have their own properties and characteristics. R is the main leading language in this regard to assist the statistical computing regarding the biological datasets; heavily used in analyzing both structured and unstructured Data.
With the advent of R, that uses statistical computing to solve a problem provides a wide variety of statistical techniques such as linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering for the network analysis, gene annotation, differential gene expression, overlap analysis and predictive modeling. It has multiple strengths but one of its best strengths is that it provides ease with the production of well-crafted and publication quality graphs and plots and in addition to this, mathematical symbols and formulas can be added.
R has built-in libraries to get the best out of the Biological datasets. This provides the integrated environment to Genomic Sequence Retrieval of the biomolecules such as RNA, DNA or protein residues. And this doesn’t end here, R helps in performing RNA-seq and analysis in population genomics and much more, let’s have a look at some of R’s plethora of libraries.
R comes with loaded packages like CROME, which is an R package for combined analysis of gene regulators, ontologies, and microarray expression profiles; CADMIM, which is an R package for analyzing microarray data, and Limma has been the dominant package of choice for differential gene expression analysis, in microarrays. These all packages are used for the Differential Gene Expression. Bioconductor uses the R statistical programming language, and is open source and open development. Bioconductor provides tools for the analysis and comprehension of high-throughput genomic data.
Bioinformatics allows to develop analytical skills rather than just formulating theories and R provides the suitable environment in effective data handling and storage, a suite of operators for calculations on arrays and particular matrices, a large, coherent, integrated collection of intermediate tools for data analysis and many more. And including all these facilities, R is available as a free software in the source code form for the OS platforms like a wide variety of UNIX platforms and similar systems (including FreeBSD and Linux), Windows and MacOS.
In short, R is the mjolnir of Bioinformatics that makes it worthy to use the strength and precision of statistics to solve the biological problems and experimentation.