Network analysis seeks to understand the relationships within biological networks such as metabolic or protein–protein interaction networks. Although biological networks can be constructed from a single type of molecule or entity (such as genes), network biology often attempts to integrate many different data types, such as proteins, small molecules, gene expression data, and others, which are all connected physically, functionally, or both.
Systems biology involves the use of computer simulations of cellular subsystems (such as the networks of metabolites and enzymes that comprise metabolism, signal transduction pathways and gene regulatory networks) to both analyze and visualize the complex connections of these cellular processes. Artificial life or virtual evolution attempts to understand evolutionary processes via the computer simulation of simple (artificial) life forms.
Tens of thousands of three-dimensional protein structures have been determined by X-ray crystallography and protein nuclear magnetic resonance spectroscopy (protein NMR) and a central question in structural bioinformatics is whether it is practical to predict possible protein–protein interactions only based on these 3D shapes, without performing protein–protein interaction experiments. A variety of methods have been developed to tackle the protein–protein docking problem, though it seems that there is still much work to be done in this field.
Other interactions encountered in the field include Protein–ligand (including drug) and protein–peptide. Molecular dynamic simulation of movement of atoms about rotatable bonds is the fundamental principle behind computational algorithms, termed docking algorithms, for studying molecular interactions.
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Software and tools
Software tools for bioinformatics include simple command-line tools, more complex graphical programs, and standalone web-services. They are made by bioinformatics companies or by public institutions.
Open-source bioinformatics software
Many free and open-source software tools have existed and continued to grow since the 1980s. The combination of a continued need for new algorithms for the analysis of emerging types of biological readouts, the potential for innovative in silico experiments, and freely available open code bases have created opportunities for research groups to contribute to both bioinformatics regardless of funding. The open source tools often act as incubators of ideas, or community-supported plug-ins in commercial applications. They may also provide de facto standards and shared object models for assisting with the challenge of bioinformation integration.
Open-source bioinformatics software includes Bioconductor, BioPerl, Biopython, BioJava, BioJS, BioRuby, Bioclipse, EMBOSS,.NET Bio, Orange with its bioinformatics add-on, Apache Taverna, UGENE and GenoCAD.
The non-profit Open Bioinformatics Foundation and the annual Bioinformatics Open Source Conference promote open-source bioinformatics software.
Web services in bioinformatics
SOAP- and REST-based interfaces have been developed to allow client computers to use algorithms, data and computing resources from servers in other parts of the world. The main advantage are that end users do not have to deal with software and database maintenance overheads.
Basic bioinformatics services are classified by the EBI into three categories: SSS (Sequence Search Services), MSA (Multiple Sequence Alignment), and BSA (Biological Sequence Analysis). The availability of these service-oriented bioinformatics resources demonstrate the applicability of web-based bioinformatics solutions, and range from a collection of standalone tools with a common data format under a single web-based interface, to integrative, distributed and extensible bioinformatics workflow management systems.
Bioinformatics workflow management systems
A bioinformatics workflow management system is a specialized form of a workflow management system designed specifically to compose and execute a series of computational or data manipulation steps, or a workflow, in a Bioinformatics application. Such systems are designed to
provide an easy-to-use environment for individual application scientists themselves to create their own workflows,
provide interactive tools for the scientists enabling them to execute their workflows and view their results in real-time,
simplify the process of sharing and reusing workflows between the scientists, and
enable scientists to track the provenance of the workflow execution results and the workflow creation steps.
Some of the platforms giving this service: Galaxy, Kepler, Taverna, UGENE, Anduril, HIVE.
BioCompute and BioCompute Objects
In 2014, the US Food and Drug Administration sponsored a conference held at the National Institutes of Health Bethesda Campus to discuss reproducibility in bioinformatics. Over the next three years, a consortium of stakeholders met regularly to discuss what would become BioCompute paradigm. These stakeholders included representatives from government, industry, and academic entities. Session leaders represented numerous branches of the FDA and NIH Institutes and Centers, non-profit entities including the Human Variome Project and the European Federation for Medical Informatics, and research institutions including Stanford, the New York Genome Center, and the George Washington University.
It was decided that the BioCompute paradigm would be in the form of digital 'lab notebooks' which allow for the reproducibility, replication, review, and reuse, of bioinformatics protocols. This was proposed to enable greater continuity within a research group over the course of normal personnel flux while furthering the exchange of ideas between groups. The US FDA funded this work so that information on pipelines would be more transparent and accessible to their regulatory staff.
In 2016, the group reconvened at the NIH in Bethesda and discussed the potential for a BioCompute Object, an instance of the BioCompute paradigm. This work was copied as both a "standard trial use" document and a preprint paper uploaded to bioRxiv. The BioCompute object allows for the JSON-ized record to be shared among employees, collaborators, and regulators.
Education platforms
While bioinformatics is taught as an in-person master's degree at many universities, there are many other methods and technologies available to learn and obtain certification in the subject. The computational nature of bioinformatics lends it to computer-aided and online learning. Software platforms designed to teach bioinformatics concepts and methods include Rosalind and online courses offered through the Swiss Institute of Bioinformatics Training Portal. The Canadian Bioinformatics Workshops provides videos and slides from training workshops on their website under a Creative Commons license. The 4273π project or 4273pi project also offers open source educational materials for free. The course runs on low cost Raspberry Pi computers and has been used to teach adults and school pupils. 4273 is actively developed by a consortium of academics and research staff who have run research level bioinformatics using Raspberry Pi computers and the 4273π operating system.
MOOC platforms also provide online certifications in bioinformatics and related disciplines, including Coursera's Bioinformatics Specialization at the University of California, San Diego, Genomic Data Science Specialization at Johns Hopkins University, and EdX's Data Analysis for Life Sciences XSeries at Harvard University.
