Bioinformatics - as the name suggests, involves the extensive study and analysis of huge biological datasets, such as Sequences Alignment and Analysis, Genome Analysis, Proteome Analysis, Phylogenetic Analysis, and much more. So, for everyday Bioinformatics tasks, it often involves massive and tedious data processing. Bioinformaticians require to run a single command on a dozen (and sometimes more than 100) of files. Hence, the main part of Bioinformatics is bridging together different processing steps into a single pipeline (script), and then applying that pipeline to many other files repeatedly.
The word “Bioinformatics” is intrinsically ambiguous. There are three quite different kinds of activities that fall within this term’s wide scope. Both the nature of the work performed and the educational backgrounds and technical talents of the people who perform these various activities differ significantly. The three main areas of bioinformatics are:
Computational Biology - Concerned with the development of algorithms for mining biological data and modeling biological phenomena.
Software Development - Focused on writing software to implement computational biology algorithms, visualize complex data, and support research and development activity, with particular attention to the challenges of organizing, searching, and manipulating enormous quantities of biological data.
Life Science Research and Development - Focused on the application of the tools and results provided by the other two areas to probe the processes of life.
Importance of Biological Programming and Coding/Scripting in Bioinformatics
Just like Biology, Bioinformatics is also a complicated and wide discipline. As different organisms, different systems & different conditions, all behave differently, therefore, experiments at each bench require a variety of approaches – from tested protocols to trial-and-error.
Bioinformatics is also an experimental science, consequently we’ve to utilize various programs and scripts to analyze the biological data computationally, otherwise we could use the same software and same parameters for every genome assembly. Learning to code opens up the full possibilities of computing, especially given that most bioinformatics tools exist only at the command line.
For better comprehension, If one could only do molecular biology experiments using a single kit, they could probably accomplish a fair amount of results. But, if a person doesn't understand the biochemistry of the kit, how would they troubleshoot? How would they perform experiments for which there are no kits available?
Robustness & Reproducibility
Writing these scripts repeatedly, is a day-to-day burden for a bioinformatician, therefore, it is essential that the scripts are written to be reproducible and robust.
Importance of robustness in scripting
The scripts must be robust for the problems that might occur during the data processing. This is due to the reason that when we directly process a dataset by executing a set of precise commands on it, typically we can see if something goes wrong like if the program exits with an error or if the resulting (output) files are empty while they should contain the data. Therefore, errors are more likely to occur when applying automated processing over more datasets by utilizing more steps for each command. That’s why it's critical to construct robust scripts for day-to-day analysis of huge biological datasets.
Importance of reproducibility in scripting
Similarly, the scripts must be reproducible. A well-crafted script or pipeline represents a perfect record of exactly how data has been processed. In ideal situations, any researcher or bioinformatician could download your processing scripts and data, thereby, can easily replicate your exact steps. However, it’s unfortunately quite easier to write obscure or unsystematic scripts that hinder reproducibility.
Perl in Bioinformatics
In Bioinformatics, the ability to quickly develop new scripts for scanning and transforming biological data into logical information, is a very important skill. In this regard, Perl is an excellent scripting language because of its syntax, which is quite compact, broader functional array, and orientation of datasets. BioProjects such as the Human Genome Project (HGP) yield such a huge amount of textual data, hence, it made the data near to impossible to analyze and extract meaningful information. In the early stages of HGP, it faced issues with data interchange between groups that were developing software, and Lincoln Stein has noted how Perl came to the rescue. Perl is certainly not the only language that possesses these positive features – many of them are found, as well, in other scripting languages such as Python.
In bioinformatics, a DNA sequence might be represented as an object inheriting from a more general implementation that covers the properties of all biological sequences. OOP would code this object by describing its properties such as length, checksum, and certainly the string of letters that comprises the sequence itself. Then one would implement accessor methods to retrieve or set these properties, and also more complex functions such as transcribe() that would take as an argument an organism-specific codon matrix and transform the DNA object to an RNA object.
Perl is the language that supports both the procedural and object-oriented programming approaches and runs on virtually all versions of Unix and Linux. In addition, ActivePerl from ActiveState allows Perl to run even on Windows computers, and MacPerl on Apple computers running System 9 and lower.
Moreover, there is a Perl, known as BioPerl, which contains built-in functions and modules for generating small scripts for run-time computational analysis of biological datasets.
Modularity which makes it easier to write programs as libraries, called modules.
Perl provides powerful ways to match and manipulate strings through the use of regular expressions. Changing file formats from one to another is a matter of contorting strings as required.
Dynamic loaders of Perl helps to extend Perl with programs written in C as well as create compiled libraries that can be interpreted by the Perl interpreter.
It is a good prototyping language and is easy to code. New algorithms can be easily tested in Perl before using a rigorous language.
Calls and pipes, the system of Perl, can be used to incorporate external programs.
Perl provides support for object-oriented program development.
Perl is excellent for writing CGI scripts to interface with the Web.
Flexible, with a global repository (CPAN), so it is trivial to install new modules. It has Bioperl, one of the first biological module repositories that increase the usability from, for example, change formats to do phylogenetic analysis. The web-based genome browser, known as GBrowse, is also based on Perl.
Python in Bioinformatics
With the emergence of Bioinformatics, through various experimentations, we’ve discovered huge amounts of DNA and protein sequence information. Traditionally, Bioinformatics involves the extraction of scientific information by studying the biological sequences, which is usually in large amounts, hence, must be analyzed computationally. The informatics of biological systems these days includes the study of molecular structures, including their dynamics and interactions, enzymatic activity, medical and pharmacological statistics, metabolic profiles, system-wide modelling and the organisation of experimental procedures.
Hysterically, the most famous programming language for being utilized with Bioinformatics was Perl/BioPerl. The reason for this is its ability to manipulate sequences, particularly when stored as letters within formatted text. Another reason is that it contains a library of modules to perform various bioinformatics tasks on the biological datasets.
Python is an object-oriented and interpreter-based programming language and can be installed on any OS (Windows/Linux/macOS). There is a Python, which can perform every task which can be performed by the BioPerl, named BioPython, and unsurprisingly at this time the uptake of Python within the bioinformatics community is growing, given our belief that it is an easier but more powerful language to work with. Following is a list the functions one can perform using BioPython/Python modules:
Pairwise sequence alignments (Sequence alignment, Calculating an alignment score, and Optimizing pairwise alignment).
Multiple sequence alignments (Multiple alignments, Alignment consensus & profiling, Generating simple multiple alignments and Infrastructuring multiple alignment programs).
Sequence variation and evolution (Similarity measures and Phylogenetic tree building).
Macromolecular structure (Using Python for 3D structures of macromolecules and Coordinate superimposition).
Array data (Multiplexed experiments & Array analysis).
High-throughput sequence analyses (High-throughput sequencing, Mapping sequences to a genome, and Using the HTSeq library).
Images (Biological images, Basic image operations, Adjustments and filters, and Feature detection)
Signal processing (Signals, Fast Fourier transform, and Peaks).
Probability (Random variables, and Markov chains).
Statistics (Statistical analyses, Simple statistical parameters, Statistical tests, and Correlation and covariance).
Clustering and discrimination (Separating and grouping data, Clustering methods, and Data discrimination).
Machine learning (k-nearest neighbours, Self-organising maps, Feed-forward artificial neural networks, and Support vector machines).
Databases and Networking
Molecular evolution to Phylogenetics
Systems biology and Structural Biology
Many of the tasks that a researcher performs with his or her computer are repetitive: Collect data from a Web page, convert files from one format to another, execute or interpret 10 or hundreds of BLAST results, first design, look for restriction enzymes, etc. In many cases it is evident that these are tasks that can be performed with a computer, with less effort on our part and without the possibility of errors caused by tiredness or distractions. 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.
R language in Bioinformatics:
R is a free open source software program used for programming statistics and graphics. Statisticians, scientists, analysts, data miners, and mathematicians use R programming to make calculations, conduct polls and surveys. It is a highly powerful and extensible language with a programmable environment with command-line scripting. This makes it easier for other users to verify facts and errors, for example evaluating complicated formulas in a spreadsheet. It helps with extracting important statistical data out of data set out of graphics and then making it easier to analyze. R is considered a data analysis tool, a programming language, a statistics analyzer, an open source software, and collaborative mathematical application for statisticians and computer scientists.
Categorization of R Language
Data analysis tool – It is a tool used for analyzing statistics, data visualization, and creating data models.
Programming language – It is used to write scripts and functions. Objects, functions, and operators are used to process, create and calculate data. Only a few lines of code are required to complete a complex calculation.
Statistics analyzer – Functions are used daily to create graphics, data models, and data. Methods are readily available to perform on-demand statistical research and modeling.
Open source software – Users can download and use the language for free, as well as use and modify the source code. This means that anyone can use the methods and algorithms with other applications and systems.
Collaborative mathematical application – It allows mathematicians, statisticians, computer scientists, and others to collaborate online. Users from various skill-sets and backgrounds can collaborate and communicate with each other on projects.
It provides effective tools for handling and storing data.
R is a very effective language for developing methods that require interactive data analysis.
It contains a well-organized collection of tools for analyzing data.
It provides graphical features for analyzing data and displaying it on the computer or physically.
It also contains S programming features, such as conditionals, user defined functions, and loops.
It supports matrix arithmetic and procedural programming with functions.
It contains data structures that include vectors, matrices, arrays, data frames, and lists.
It includes objects, such as regression models, time series, and geo-spatial coordinates.
R is one of the most widely-used and powerful programming languages in bioinformatics. R especially stands out in the areas of research 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. Both R and Python are of equal importance for bioinformatics scripting.
JAVA in Bioinformatics
Java programming language serves as a platform for your work in biomedical informatics, and for that, it will open you up to the possibility of using a wide range of software objects in use throughout the large software engineering and computer science communities.
Indeed, Java is not the only object-oriented platform that is appropriate for bioinformatics. Perl is very well established, and are python, C++ and many others. The lessons that you can learn in Java are transferable to any object-oriented system, and Java is proving to be a solid platform for work throughout the informatics community.
Java has emerged as a powerful programming language for developing secure, scalable and robust web-enabled applications and is particularly well suited for building the many interrelated components of the geographically dispersed biomedical research and business engine.
Java is one of the most commonly utilized languages for Bioinformatics scripting and software development. Just like C++, Python, and Perl, it is also an object-oriented language and easier to learn. There are various web-based applications available including BLAST hosted by NCBI.
Bash in Bioinformatics
Compared to other scripting languages, such as Python, R, Perl, & Java, Bash lacks several nice features useful for data-processing scripts including better numeric type support, useful data structures, better string processing, refined option parsing, availability of a large number of libraries, and powerful functions that help with structuring your programs.
However, there’s more overhead when calling command-line programs from a Python script compared to Bash. Bash is often the best and quickest “glue” solution. Most Bash scripts in bioinformatics are simply commands organized into a re-runnable script with some features to check that files exist and ensuring any error causes the script to abort. For using the Bash scripts effectively in bioinformatics, the trick is to know when to use them and when not to.
Unlike Python, Bash is not a general-purpose language. Bash is explicitly designed to make running and interfacing command-line programs as simple as possible. For these reasons, Bash often takes the role as the duct tape language of bioinformatics (also referred to as a glue language), as it’s used to tape many commands together into a cohesive workflow.
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