Bioinformatics

Bioinformatics (

Bioinformatics includes biological studies that use computer programming as part of their methodology, as well as specific analysis "pipelines" that are repeatedly used, particularly in the field of genomics. Common uses of bioinformatics include the identification of candidates genes and single nucleotide polymorphisms (SNPs). Often, such identification is made with the aim to better understand the genetic basis of disease, unique adaptations, desirable properties (esp. in agricultural species), or differences between populations. In a less formal way, bioinformatics also tries to understand the organizational principles within nucleic acid and protein sequences, called proteomics.^[1]

Image and signal processing allow extraction of useful results from large amounts of raw data. In the field of genetics, it aids in sequencing and annotating genomes and their observed mutations. It plays a role in the text mining of biological literature and the development of biological and gene ontologies to organize and query biological data. It also plays a role in the analysis of gene and protein expression and regulation. Bioinformatics tools aid in comparing, analyzing and interpreting genetic and genomic data and more generally in the understanding of evolutionary aspects of molecular biology. At a more integrative level, it helps analyze and catalogue the biological pathways and networks that are an important part of systems biology. In structural biology, it aids in the simulation and modeling of DNA,^[2] RNA,^[2][3] proteins^[4] as well as biomolecular interactions.^[5][6][7][8]

History

Historically, the term bioinformatics did not mean what it means today. Paulien Hogeweg and Ben Hesper coined it in 1970 to refer to the study of information processes in biotic systems.^{[9][10][11][12][13]} This definition placed bioinformatics as a field parallel to biochemistry (the study of chemical processes in biological systems).^[10]

Sequences

There has been a tremendous advance in speed and cost reduction since the completion of the Human Genome Project, with some labs able to sequence over 100,000 billion bases each year, and a full genome can be sequenced for a thousand dollars or less.

Goals

To study how normal cellular activities are altered in different disease states, the biological data must be combined to form a comprehensive picture of these activities. Therefore, the field of bioinformatics has evolved such that the most pressing task now involves the analysis and interpretation of various types of data. This also includes nucleotide and

• Development and implementation of computer programs that enable efficient access to, management, and use of, various types of information.
• Development of new algorithms (mathematical formulas) and statistical measures that assess relationships among members of large data sets. For example, there are methods to locate a gene within a sequence, to predict protein structure and/or function, and to cluster protein sequences into families of related sequences.

The primary goal of bioinformatics is to increase the understanding of biological processes. What sets it apart from other approaches, however, is its focus on developing and applying computationally intensive techniques to achieve this goal. Examples include:

Common activities in bioinformatics include mapping and analyzing DNA and protein sequences, aligning DNA and protein sequences to compare them, and creating and viewing 3-D models of protein structures.

Relation to other fields

Bioinformatics is a science field that is similar to but distinct from

Since the

DNA sequencing

Before sequences can be analyzed they have to be obtained from the data storage bank example Genbank. DNA sequencing is still a non-trivial problem as the raw data may be noisy or affected by weak signals. Algorithms have been developed for base calling for the various experimental approaches to DNA sequencing.

Sequence assembly

Most DNA sequencing techniques produce short fragments of sequence that need to be assembled to obtain complete gene or genome sequences. The so-called

In the context of genomics, annotation is the process of marking the genes and other biological features in a DNA sequence. This process needs to be automated because most genomes are too large to annotate by hand, not to mention the desire to annotate as many genomes as possible, as the rate of sequencing has ceased to pose a bottleneck. Annotation is made possible by the fact that genes have recognisable start and stop regions, although the exact sequence found in these regions can vary between genes.

Genome annotation can be classified into three levels: the nucleotide, protein, and process levels.

Gene finding is a chief aspect of nucleotide-level annotation. For complex genomes, the most successful methods use a combination of ab initio gene prediction and sequence comparison with expressed sequence databases and other organisms. Nucleotide-level annotation also allows the integration of genome sequence with other genetic and physical maps of the genome.

The principal aim of protein-level annotation is to assign function to the products of the genome. Databases of protein sequences and functional domains and motifs are powerful resources for this type of annotation. Nevertheless, half of the predicted proteins in a new genome sequence tend to have no obvious function.

Understanding the function of genes and their products in the context of cellular and organismal physiology is the goal of process-level annotation. One of the obstacles to this level of annotation has been the inconsistency of terms used by different model systems. The Gene Ontology Consortium is helping to solve this problem.^[25]

The first description of a comprehensive genome annotation system was published in 1995

Following the goals that the Human Genome Project left to achieve after its closure in 2003, a new project developed by the National Human Genome Research Institute in the U.S appeared. The so-called ENCODE project is a collaborative data collection of the functional elements of the human genome that uses next-generation DNA-sequencing technologies and genomic tiling arrays, technologies able to automatically generate large amounts of data at a dramatically reduced per-base cost but with the same accuracy (base call error) and fidelity (assembly error).

Gene function prediction

While genome annotation is primarily based on sequence similarity (and thus homology), other properties of sequences can be used to predict the function of genes. In fact, most gene function prediction methods focus on protein sequences as they are more informative and more feature-rich. For instance, the distribution of hydrophobic amino acids predicts transmembrane segments in proteins. However, protein function prediction can also use external information such as gene (or protein) expression data, protein structure, or protein-protein interactions.^[26]

Computational evolutionary biology

• trace the evolution of a large number of organisms by measuring changes in their DNA, rather than through physical taxonomy or physiological observations alone,
• compare entire
  genomes, which permits the study of more complex evolutionary events, such as gene duplication, horizontal gene transfer, and the prediction of factors important in bacterial speciation
  ,
• build complex computational population genetics models to predict the outcome of the system over time^[27]
• track and share information on an increasingly large number of species and organisms

Future work endeavours to reconstruct the now more complex

The area of research within computer science that uses genetic algorithms is sometimes confused with computational evolutionary biology, but the two areas are not necessarily related.

Comparative genomics

The core of comparative genome analysis is the establishment of the correspondence between

Many of these studies are based on the detection of sequence homology to assign sequences to protein families.^[29]

Pan genomics

Pan genomics is a concept introduced in 2005 by Tettelin and Medini which eventually took root in bioinformatics. Pan genome is the complete gene repertoire of a particular taxonomic group: although initially applied to closely related strains of a species, it can be applied to a larger context like genus, phylum, etc. It is divided in two parts- The Core genome: Set of genes common to all the genomes under study (These are often housekeeping genes vital for survival) and The Dispensable/Flexible Genome: Set of genes not present in all but one or some genomes under study. A bioinformatics tool BPGA can be used to characterize the Pan Genome of bacterial species.^[30]

Genetics of disease

With the advent of next-generation sequencing we are obtaining enough sequence data to map the genes of complex diseases including infertility,^[31] breast cancer^[32] or Alzheimer's disease.^[33] Genome-wide association studies are a useful approach to pinpoint the mutations responsible for such complex diseases.^[34] Through these studies, thousands of DNA variants have been identified that are associated with similar diseases and traits.^[35] Furthermore, the possibility for genes to be used at prognosis, diagnosis or treatment is one of the most essential applications. Many studies are discussing both the promising ways to choose the genes to be used and the problems and pitfalls of using genes to predict disease presence or prognosis.^[36]

Genome-wide association studies have successfully identified thousands of common genetic variants for complex diseases and traits; however, these common variants only explain a small fraction of heritability.

In

Two important principles can be used in the analysis of cancer genomes bioinformatically pertaining to the identification of mutations in the exome. First, cancer is a disease of accumulated somatic mutations in genes. Second cancer contains driver mutations which need to be distinguished from passengers.^[43]

With the breakthroughs that this next-generation sequencing technology is providing to the field of Bioinformatics, cancer genomics could drastically change. These new methods and software allow bioinformaticians to sequence many cancer genomes quickly and affordably. This could create a more flexible process for classifying types of cancer by analysis of cancer driven mutations in the genome. Furthermore, tracking of patients while the disease progresses may be possible in the future with the sequence of cancer samples.^[44]

Another type of data that requires novel informatics development is the analysis of lesions found to be recurrent among many tumors.

Gene and protein expression

Analysis of gene expression

The

Analysis of protein expression

Protein microarrays and high throughput (HT) mass spectrometry (MS) can provide a snapshot of the proteins present in a biological sample. Bioinformatics is very much involved in making sense of protein microarray and HT MS data; the former approach faces similar problems as with microarrays targeted at mRNA, the latter involves the problem of matching large amounts of mass data against predicted masses from protein sequence databases, and the complicated statistical analysis of samples where multiple, but incomplete peptides from each protein are detected. Cellular protein localization in a tissue context can be achieved through affinity proteomics displayed as spatial data based on immunohistochemistry and tissue microarrays.^[46]

Analysis of regulation

Gene regulation is the complex orchestration of events by which a signal, potentially an extracellular signal such as a hormone, eventually leads to an increase or decrease in the activity of one or more proteins. Bioinformatics techniques have been applied to explore various steps in this process.

For example, gene expression can be regulated by nearby elements in the genome. Promoter analysis involves the identification and study of sequence motifs in the DNA surrounding the coding region of a gene. These motifs influence the extent to which that region is transcribed into mRNA. Enhancer elements far away from the promoter can also regulate gene expression, through three-dimensional looping interactions. These interactions can be determined by bioinformatic analysis of chromosome conformation capture experiments.

Expression data can be used to infer gene regulation: one might compare

Microscopic pictures allow us to locate both organelles as well as molecules. It may also help us to distinguish between normal and abnormal cells, e.g. in cancer.

Protein localization

The localization of proteins helps us to evaluate the role of a protein. For instance, if a protein is found in the nucleus it may be involved in gene regulation or splicing. By contrast, if a protein is found in mitochondria, it may be involved in respiration or other metabolic processes. Protein localization is thus an important component of protein function prediction. There are well developed protein subcellular localization prediction resources available, including protein subcellular location databases, and prediction tools.^[47][48]

Nuclear organization of chromatin

Data from high-throughput

Protein structure prediction is another important application of bioinformatics. The

One of the key ideas in bioinformatics is the notion of homology. In the genomic branch of bioinformatics, homology is used to predict the function of a gene: if the sequence of gene A, whose function is known, is homologous to the sequence of gene B, whose function is unknown, one could infer that B may share A's function. In the structural branch of bioinformatics, homology is used to determine which parts of a protein are important in structure formation and interaction with other proteins. In a technique called homology modeling, this information is used to predict the structure of a protein once the structure of a homologous protein is known. Until recently, this remained the only way to predict protein structures reliably. However, a game-changing breakthrough occurred with the release of new deep-learning algorithms-based software called AlphaFold, developed by a bioinformatics team within Google's A.I. research department DeepMind.^[50] AlphaFold, during the 14th Critical Assessment of protein Structure Prediction (CASP14) computational protein structure prediction software competition, became the first contender ever to deliver prediction submissions with accuracy competitive with experimental structures in a majority of cases and greatly outperforming all other prediction software methods up to that point.^[51] AlphaFold has since released the predicted structures for hundreds of millions of proteins.^[52]

One example of this is hemoglobin in humans and the hemoglobin in legumes (leghemoglobin), which are distant relatives from the same protein superfamily. Both serve the same purpose of transporting oxygen in the organism. Although both of these proteins have completely different amino acid sequences, their protein structures are virtually identical, which reflects their near identical purposes and shared ancestor.^[53]

Other techniques for predicting protein structure include protein threading and de novo (from scratch) physics-based modeling.

Another aspect of structural bioinformatics include the use of protein structures for

Network and systems biology

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.

Molecular interaction networks

Tens of thousands of three-dimensional protein structures have been determined by

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.

Others

Literature analysis

The growth in the number of published literature makes it virtually impossible to read every paper, resulting in disjointed sub-fields of research. Literature analysis aims to employ computational and statistical linguistics to mine this growing library of text resources. For example:

• Abbreviation recognition – identify the long-form and abbreviation of biological terms
• Named-entity recognition – recognizing biological terms such as gene names
• Protein–protein interaction – identify which proteins interact with which proteins from text

The area of research draws from statistics and computational linguistics.

High-throughput image analysis

Computational technologies are used to accelerate or fully automate the processing, quantification and analysis of large amounts of high-information-content

• high-throughput and high-fidelity quantification and sub-cellular localization (high-content screening, cytohistopathology, Bioimage informatics)
• morphometrics
• clinical image analysis and visualization
• determining the real-time air-flow patterns in breathing lungs of living animals
• quantifying occlusion size in real-time imagery from the development of and recovery during arterial injury
• making behavioral observations from extended video recordings of laboratory animals
• infrared measurements for metabolic activity determination
• inferring clone overlaps in DNA mapping, e.g. the Sulston score

High-throughput single cell data analysis

Computational techniques are used to analyse high-throughput, low-measurement single cell data, such as that obtained from flow cytometry. These methods typically involve finding populations of cells that are relevant to a particular disease state or experimental condition.

Biodiversity informatics

Biodiversity informatics deals with the collection and analysis of

Biological ontologies are directed acyclic graphs of controlled vocabularies. They are designed to capture biological concepts and descriptions in a way that can be easily categorised and analysed with computers. When categorised in this way, it is possible to gain added value from holistic and integrated analysis.

The

Databases

Databases are essential for bioinformatics research and applications. Many databases exist, covering various information types: for example, DNA and protein sequences, molecular structures, phenotypes and biodiversity. Databases may contain empirical data (obtained directly from experiments), predicted data (obtained from analysis), or, most commonly, both. They may be specific to a particular organism, pathway or molecule of interest. Alternatively, they can incorporate data compiled from multiple other databases. These databases vary in their format, access mechanism, and whether they are public or not.

Some of the most commonly used databases are listed below. For a more comprehensive list, please check the link at the beginning of the subsection.

• Used in biological sequence analysis:
  Genbank, UniProt
• Used in structure analysis: Protein Data Bank (PDB)
• Used in finding Protein Families and Motif Finding: InterPro, Pfam
• Used for Next Generation Sequencing: Sequence Read Archive
• Used in Network Analysis: Metabolic Pathway Databases (KEGG, BioCyc), Interaction Analysis Databases, Functional Networks
• Used in design of synthetic genetic circuits: GenoCAD

Software and tools

Software tools for bioinformatics range from simple command-line tools, to more complex graphical programs and standalone web-services available from various bioinformatics companies or public institutions.

Open-source bioinformatics software

Many

The range of open-source software packages includes titles such as Bioconductor, BioPerl, Biopython, BioJava, BioJS, BioRuby, Bioclipse, EMBOSS, .NET Bio, Orange with its bioinformatics add-on, Apache Taverna, UGENE and GenoCAD. To maintain this tradition and create further opportunities, the non-profit Open Bioinformatics Foundation^[55] have supported the annual Bioinformatics Open Source Conference (BOSC) since 2000.^[56]

Web services in bioinformatics

SOAP- and REST-based interfaces have been developed for a wide variety of bioinformatics applications allowing an application running on one computer in one part of the world to use algorithms, data and computing resources on servers in other parts of the world. The main advantages derive from the fact that end users do not have to deal with software and database maintenance overheads.

Basic bioinformatics services are classified by the

Bioinformatics workflow management systems

A

• 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.^[58] Over the next three years, a consortium of stakeholders met regularly to discuss what would become BioCompute paradigm.^[59] 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.^[60]

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.^[61][62]

Education platforms

As well as in-person Masters degree courses being taught at many universities, the computational nature of bioinformatics lends it to computer-aided and online learning.^[63][64] 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^[65] 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.^[66][67] 4273π 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.^[68][69]

MOOC platforms also provide online certifications in bioinformatics and related disciplines, including Coursera's Bioinformatics Specialization (UC San Diego) and Genomic Data Science Specialization (Johns Hopkins) as well as EdX's Data Analysis for Life Sciences XSeries (Harvard).

Conferences

There are several large conferences that are concerned with bioinformatics. Some of the most notable examples are Intelligent Systems for Molecular Biology (ISMB), European Conference on Computational Biology (ECCB), and Research in Computational Molecular Biology (RECOMB).

See also

References

Further reading

• Sehgal et al. : Structural, phylogenetic and docking studies of D-amino acid oxidase activator(DAOA ), a candidate schizophrenia gene. Theoretical Biology and Medical Modelling 2013 10 :3.
• Raul Isea The Present-Day Meaning Of The Word Bioinformatics, Global Journal of Advanced Research, 2015
• Achuthsankar S Nair Computational Biology & Bioinformatics – A gentle Overview, Communications of Computer Society of India, January 2007
• ISBN 1-58488-406-1
  (Chapman & Hall/Crc Computer and Information Science Series)
• Baldi, P and Brunak, S, Bioinformatics: The Machine Learning Approach, 2nd edition. MIT Press, 2001.
  ISBN 0-262-02506-X
• Barnes, M.R. and Gray, I.C., eds., Bioinformatics for Geneticists, first edition. Wiley, 2003.
  ISBN 0-470-84394-2
• Baxevanis, A.D. and Ouellette, B.F.F., eds., Bioinformatics: A Practical Guide to the Analysis of Genes and Proteins, third edition. Wiley, 2005.
  ISBN 0-471-47878-4
• Baxevanis, A.D., Petsko, G.A., Stein, L.D., and Stormo, G.D., eds.,
  ISBN 0-471-25093-7
• Cristianini, N. and Hahn, M. Introduction to Computational Genomics, Cambridge University Press, 2006. (
  ISBN 0-521-67191-4
  )
• Durbin, R., S. Eddy, A. Krogh and G. Mitchison, Biological sequence analysis. Cambridge University Press, 1998.
  ISBN 0-521-62971-3
• Gilbert D (September 2004). "Bioinformatics software resources". Briefings in Bioinformatics. 5 (3): 300–4.
  PMID 15383216
  .
• Keedwell, E., Intelligent Bioinformatics: The Application of Artificial Intelligence Techniques to Bioinformatics Problems. Wiley, 2005.
  ISBN 0-470-02175-6
• Kohane, et al. Microarrays for an Integrative Genomics. The MIT Press, 2002.
  ISBN 0-262-11271-X
• Lund, O. et al. Immunological Bioinformatics. The MIT Press, 2005.
  ISBN 0-262-12280-4
• ISBN 0-521-85700-7
• Pevzner, Pavel A. Computational Molecular Biology: An Algorithmic Approach The MIT Press, 2000.
  ISBN 0-262-16197-4
• Soinov, L. Bioinformatics and Pattern Recognition Come Together Journal of Pattern Recognition Research (JPRR), Vol 1 (1) 2006 p. 37–41
• Stevens, Hallam, Life Out of Sequence: A Data-Driven History of Bioinformatics, Chicago: The University of Chicago Press, 2013,
  ISBN 9780226080208
• Tisdall, James. "Beginning Perl for Bioinformatics" O'Reilly, 2001.
  ISBN 0-596-00080-4
• Catalyzing Inquiry at the Interface of Computing and Biology (2005) CSTB report
• Calculating the Secrets of Life: Contributions of the Mathematical Sciences and computing to Molecular Biology (1995)
• Foundations of Computational and Systems Biology MIT Course
• Computational Biology: Genomes, Networks, Evolution Free MIT Course

External links

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• The dictionary definition of bioinformatics at Wiktionary
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