# Bioinformatics

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Computational analysis of large, complex sets of biological data

For the journal, see [Bioinformatics (journal)](/source/Bioinformatics_(journal)).

Not to be confused with [Biological computation](/source/Biological_computation) or [Genetic algorithm](/source/Genetic_algorithm).

Early bioinformatics—computational alignment of experimentally determined sequences of a class of related proteins; see [§ Sequence analysis](#Sequence_analysis) for further information.

Map of the human X chromosome (from the [National Center for Biotechnology Information](/source/National_Center_for_Biotechnology_Information) (NCBI) website)

**Bioinformatics** ([/ˌbaɪ.oʊˌɪnfərˈmætɪks/](https://en.wikipedia.org/wiki/Help:IPA/English) [ⓘ](https://en.wikipedia.org/wiki/File:En-us-bioinformatics.ogg)) is an [interdisciplinary](/source/Interdisciplinary) field of [science](/source/Science) that develops computational methods and [software tools](/source/Bioinformatics_software) for understanding [biological](/source/Biological) data, especially when the data sets are large and complex. Bioinformatics integrates principles from [biology](/source/Biology), [chemistry](/source/Chemistry), [physics](/source/Physics), [computer science](/source/Computer_science), [data science](/source/Data_science), [computer programming](/source/Computer_programming), [information engineering](/source/Information_engineering), [mathematics](/source/Mathematics), and [statistics](/source/Statistics) to analyze and interpret [biological data](/source/Biological_data).[1] This process can sometimes be referred to as [computational biology](/source/Computational_biology); however, the distinction between the two terms is often disputed. The term *[computational biology](/source/Computational_biology)* can refer to building and using models of [biological systems](/source/Biological_system).

Computational, statistical, and computer programming techniques have been used for [computer simulation](/source/In_silico) analyses of biological queries. They include reused specific analysis "pipelines", particularly in the field of [genomics](/source/Genomics), such as by the identification of [genes](/source/Gene) and single [nucleotide](/source/Nucleotide) polymorphisms ([SNPs](/source/SNPs)). These pipelines are used to better understand the genetic basis of disease, unique adaptations, desirable properties (especially in agricultural species), or differences between populations. Bioinformatics also includes [proteomics](/source/Proteomics), which aims to understand the organizational principles within [nucleic acid](/source/Nucleic_acid) and [protein](/source/Protein) sequences.[2]

Image and [signal processing](/source/Signal_processing) allow the extraction of useful results from large amounts of raw data. It aids in sequencing and annotating genomes and their observed [mutations](/source/Mutation). Bioinformatics includes [text mining](/source/Text_mining) of biological literature and the development of biological and gene [ontologies](/source/Ontologies) to organize and query biological data. It also plays a role in the analysis of gene and protein expression and regulation. Bioinformatic tools aid in comparing, analyzing, and interpreting genetic and genomic data and 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](/source/Systems_biology). In [structural biology](/source/Structural_biology), it aids in the simulation and modeling of DNA,[3] RNA,[3][1] proteins[4] as well as biomolecular interactions.[5][6][7][8]

## History

The first definition of the term *bioinformatics* was coined by [Paulien Hogeweg](/source/Paulien_Hogeweg) and [Ben Hesper](https://en.wikipedia.org/w/index.php?title=Ben_Hesper&action=edit&redlink=1) 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](/source/Biochemistry) (the study of chemical processes in biological systems).[10]

Bioinformatics and computational biology involved the analysis of biological data, particularly DNA, RNA, and protein sequences. The field of bioinformatics experienced explosive growth starting in the mid-1990s, driven largely by the [Human Genome Project](/source/Human_Genome_Project) and by rapid advances in DNA sequencing technology.[*[citation needed](https://en.wikipedia.org/wiki/Wikipedia:Citation_needed)*]

Analyzing biological data to produce meaningful information involves writing and running software programs that use [algorithms](/source/Algorithm) from [graph theory](/source/Graph_theory), [artificial intelligence](/source/Artificial_intelligence), [soft computing](/source/Soft_computing), [data mining](/source/Data_mining), [image processing](/source/Image_processing), and [computer simulation](/source/Computer_simulation). The algorithms in turn depend on theoretical foundations such as [discrete mathematics](/source/Discrete_mathematics), [control theory](/source/Control_theory), [system theory](/source/System_theory), [information theory](/source/Information_theory), and [statistics](/source/Statistics).[*[citation needed](https://en.wikipedia.org/wiki/Wikipedia:Citation_needed)*]

### Sequences

Sequences of genetic material are frequently used in bioinformatics and are easier to manage using computers than manually.

These are sequences being compared in a MUSCLE multiple sequence alignment (MSA). Each sequence name (leftmost column) is from various louse species, while the sequences themselves are in the second column.

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](/source/DNA_sequencing) over 100,000 billion bases each year, and a full genome can be sequenced for $1,000 or less.[14]

Computers became essential in molecular biology when [protein sequences](/source/Protein_sequences) became available after [Frederick Sanger](/source/Frederick_Sanger) determined the sequence of [insulin](/source/Insulin) in the early 1950s.[15][16] Comparing multiple sequences manually turned out to be impractical. [Margaret Oakley Dayhoff](/source/Margaret_Oakley_Dayhoff), a pioneer in the field,[17] compiled one of the first protein sequence databases, initially published as books[18] as well as methods of sequence alignment and [molecular evolution](/source/Molecular_evolution).[19] Another early contributor to bioinformatics was [Elvin A. Kabat](/source/Elvin_A._Kabat), who pioneered biological sequence analysis in 1970 with his comprehensive volumes of antibody sequences released online with Tai Te Wu between 1980 and 1991.[20]

In the 1970s, new techniques for sequencing DNA were applied to bacteriophage MS2 and øX174, and the extended nucleotide sequences were then parsed with informational and statistical algorithms. These studies illustrated that well known features, such as the coding segments and the triplet code, are revealed in straightforward statistical analyses and were the proof of the concept that bioinformatics would be insightful.[21][22]

## Goals

In order to study how normal cellular activities are altered in different disease states, raw biological data must be combined to form a comprehensive picture of these activities. Therefore[*[when?](https://en.wikipedia.org/wiki/Wikipedia:Manual_of_Style/Dates_and_numbers#Chronological_items)*], 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 [amino acid sequences](/source/Amino_acid_sequence), [protein domains](/source/Protein_domain), and [protein structures](/source/Protein_structure).[23]

Important sub-disciplines within bioinformatics and [computational biology](/source/Computational_biology) include:

- Development and implementation of computer programs to efficiently access, manage, and use various types of information.

- Development of new mathematical algorithms and statistical measures to assess relationships among members of large data sets. For example, there are methods to locate a [gene](/source/Gene) within a sequence, to predict protein structure and/or function, and to [cluster](/source/Cluster_analysis) 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 is its focus on developing and applying computationally intensive techniques to achieve this goal. Examples include: [pattern recognition](/source/Pattern_recognition), [data mining](/source/Data_mining), [machine learning algorithms](/source/Machine_learning_in_bioinformatics), and [visualization](/source/Biological_Data_Visualization). Major research efforts in the field include [sequence alignment](/source/Sequence_alignment), [gene finding](/source/Gene_finding), [genome assembly](/source/Genome_assembly), [drug design](/source/Drug_design), [drug discovery](/source/Drug_discovery), [protein structure alignment](/source/Protein_structural_alignment), [protein structure prediction](/source/Protein_structure_prediction), prediction of [gene expression](/source/Gene_expression) and [protein–protein interactions](/source/Protein%E2%80%93protein_interaction), [genome-wide association studies](/source/Genome-wide_association_studies), the modeling of [evolution](/source/Evolution) and [cell division/mitosis.](/source/Cellular_model)

Bioinformatics entails the creation and advancement of databases, algorithms, computational and statistical techniques, and theory to solve formal and practical problems arising from the management and analysis of biological data.

Over the past few decades, rapid developments in genomic and other molecular research technologies and developments in [information technologies](/source/Information_technologies) have combined to produce a tremendous amount of information related to molecular biology. Bioinformatics is the name given to these mathematical and computing approaches used to glean understanding of biological processes.

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

## Sequence analysis

Main articles: [Sequence alignment](/source/Sequence_alignment), [Sequence database](/source/Sequence_database), and [Alignment-free sequence analysis](/source/Alignment-free_sequence_analysis)

Since the bacteriophage [Phage Φ-X174](/source/Phi_X_174) was [sequenced](/source/Sequenced) in 1977,[24] the [DNA sequences](/source/DNA_sequence) of thousands of organisms have been decoded and stored in databases. This sequence information is analyzed to determine genes that encode [proteins](/source/Protein), RNA genes, regulatory sequences, structural motifs, and repetitive sequences. A comparison of genes within a [species](/source/Species) or between different species can show similarities between protein functions, or relations between species (the use of [molecular systematics](/source/Molecular_systematics) to construct [phylogenetic trees](/source/Phylogenetic_tree)). With the growing amount of data, it long ago became impractical to analyze DNA sequences manually. [Computer programs](/source/Computer_program) such as [BLAST](/source/BLAST_(biotechnology)) are used routinely to search sequences—as of 2008, from more than 260,000 organisms, containing over 190 billion [nucleotides](/source/Nucleotide).[25]

### DNA sequencing

Main article: [DNA sequencing](/source/DNA_sequencing)

Before sequences can be analyzed, they are obtained from a data storage bank, such as GenBank. [DNA sequencing](/source/DNA_sequencing) is still a non-trivial problem as the raw data may be noisy or affected by weak signals. [Algorithms](/source/Algorithm) have been developed for [base calling](/source/Base_calling) for the various experimental approaches to DNA sequencing.

Image: 450 pixels Sequencing analysis steps

### Sequence assembly

Main article: [Sequence assembly](/source/Sequence_assembly)

Most DNA sequencing techniques produce short fragments of sequence that need to be assembled to obtain complete gene or genome sequences. The [shotgun sequencing](/source/Shotgun_sequencing) technique (used by [The Institute for Genomic Research](/source/The_Institute_for_Genomic_Research) (TIGR) to sequence the first bacterial genome, *[Haemophilus influenzae](/source/Haemophilus_influenzae)*)[26] generates the sequences of many thousands of small DNA fragments (ranging from 35 to 900 nucleotides long, depending on the sequencing technology). The ends of these fragments overlap and, when aligned properly by a genome assembly program, can be used to reconstruct the complete genome. Shotgun sequencing yields sequence data quickly, but the task of assembling the fragments can be quite complicated for larger genomes. For a genome as large as the [human genome](/source/Human_genome), it may take many days of CPU time on large-memory, multiprocessor computers to assemble the fragments, and the resulting assembly usually contains numerous gaps that must be filled in later. Shotgun sequencing is the method of choice for virtually all genomes sequenced (rather than chain-termination or chemical degradation methods), and genome assembly algorithms are a critical area of bioinformatics research.

See also: [sequence analysis](/source/Sequence_analysis), [sequence mining](/source/Sequence_mining), [sequence profiling tool](/source/Sequence_profiling_tool), and [sequence motif](/source/Sequence_motif)

### Genome annotation

Main article: [Gene prediction](/source/Gene_prediction)

In [genomics](/source/Genomics), [annotation](/source/Genome_project#Genome_annotation) refers to the process of marking the stop and start regions of genes and other biological features in a sequenced DNA sequence. Many genomes are too large to be annotated by hand. As the rate of [sequencing](/source/DNA_sequencing) exceeds the rate of genome annotation, genome annotation has become the new bottleneck in bioinformatics.[*[when?](https://en.wikipedia.org/wiki/Wikipedia:Manual_of_Style/Dates_and_numbers#Chronological_items)*]

Genome annotation can be classified into three levels: the [nucleotide](/source/Nucleotide), protein, and process levels.

Gene finding is a chief aspect of nucleotide-level annotation. For complex genomes, a combination of [ab initio](/source/Ab_initio) gene prediction and sequence comparison with expressed sequence databases and other organisms can be successful. 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 [protein](/source/Protein) products of the genome. Databases of protein sequences and functional domains and motifs are used for this type of annotation. About 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. An obstacle of process-level annotation has been the inconsistency of terms used by different model systems. The Gene Ontology Consortium is helping to solve this problem.[27]

The first description of a comprehensive annotation system was published in 1995[26] by [The Institute for Genomic Research](/source/The_Institute_for_Genomic_Research), which performed the first complete sequencing and analysis of the genome of a free-living (non-[symbiotic](/source/Symbiotic)) organism, the bacterium *[Haemophilus influenzae](/source/Haemophilus_influenzae)*.[26] The system identifies the genes encoding all proteins, transfer RNAs, ribosomal RNAs, in order to make initial functional assignments. The [GeneMark](/source/GeneMark) program trained to find protein-coding genes in *[Haemophilus influenzae](/source/Haemophilus_influenzae)* is constantly changing and improving.

Following the goals that the Human Genome Project left to achieve after its closure in 2003, the [ENCODE](/source/ENCODE) project was developed by the [National Human Genome Research Institute](/source/National_Human_Genome_Research_Institute). This 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](/source/Homology_(biology))), 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](/source/Amino_acid) predicts [transmembrane segments](/source/Transmembrane_domain) in proteins. However, protein function prediction can also use external information such as gene (or protein) [expression](/source/Gene_expression) data, [protein structure](/source/Protein_structure), or [protein–protein interactions](/source/Protein%E2%80%93protein_interaction).[28]

### Computational evolutionary biology

Further information: [Computational phylogenetics](/source/Computational_phylogenetics)

[Evolutionary biology](/source/Evolutionary_biology) is the study of the origin and descent of [species](/source/Species), as well as their change over time. [Informatics](/source/Informatics) has assisted evolutionary biologists by enabling researchers to:

- trace the evolution of a large number of organisms by measuring changes in their [DNA](/source/DNA), rather than through physical taxonomy or physiological observations alone,

- compare entire [genomes](/source/Genomes), which permits the study of more complex evolutionary events, such as [gene duplication](/source/Gene_duplication), [horizontal gene transfer](/source/Horizontal_gene_transfer), and the prediction of factors important in bacterial [speciation](/source/Speciation),

- build complex computational [population genetics](/source/Population_genetics) models to predict the outcome of the system over time[29]

- track and share information on an increasingly large number of species and organisms

### Comparative genomics

Main article: [Comparative genomics](/source/Comparative_genomics)

The core of comparative genome analysis is the establishment of the correspondence between [genes](/source/Genes) ([orthology](/source/Homology_(biology)#Orthology) analysis) or other genomic features in different organisms. Intergenomic maps are made to trace the evolutionary processes responsible for the divergence of two genomes. A multitude of evolutionary events acting at various organizational levels shape genome evolution. At the lowest level, point mutations affect individual nucleotides. At a higher level, large chromosomal segments undergo duplication, lateral transfer, inversion, transposition, deletion and insertion.[30] Entire genomes are involved in processes of hybridization, polyploidization and [endosymbiosis](/source/Endosymbiosis) that lead to rapid speciation. The complexity of genome evolution poses many exciting challenges to developers of mathematical models and algorithms, who have recourse to a spectrum of algorithmic, statistical and mathematical techniques, ranging from exact, [heuristics](/source/Heuristics), fixed parameter and [approximation algorithms](/source/Approximation_algorithms) for problems based on parsimony models to [Markov chain Monte Carlo](/source/Markov_chain_Monte_Carlo) algorithms for [Bayesian analysis](/source/Bayesian_analysis) of problems based on probabilistic models.

Many of these studies are based on the detection of [sequence homology](/source/Sequence_homology) to assign sequences to [protein families](/source/Protein_families).[31]

### Pan genomics

Main article: [Pan-genome](/source/Pan-genome)

Pan genomics is a concept introduced in 2005 by Tettelin and Medini. Pan genome is the complete gene repertoire of a particular [monophyletic](/source/Monophyletic) 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, a set of genes common to all the genomes under study (often housekeeping genes vital for survival), and the Dispensable/Flexible genome: a 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.[32]

### Genetics of disease

Main article: [Genome-wide association studies](/source/Genome-wide_association_studies)

As of 2013, the existence of efficient high-throughput next-generation sequencing technology allows for the identification of cause many different human disorders. Simple [Mendelian inheritance](/source/Mendelian_inheritance) has been observed for over 3,000 disorders that have been identified at the [Online Mendelian Inheritance in Man](/source/Online_Mendelian_Inheritance_in_Man) database, but complex diseases are more difficult. Association studies have found many individual genetic regions that individually are weakly associated with complex diseases (such as [infertility](/source/Infertility),[33] [breast cancer](/source/Breast_cancer)[34] and [Alzheimer's disease](/source/Alzheimer's_disease)[35]), rather than a single cause.[36][37] There are currently many challenges to using genes for diagnosis and treatment, such as how we don't know which genes are important, or how stable the choices an algorithm provides.[38]

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.[39] [Rare variants](/source/Rare_functional_variant) may account for some of the [missing heritability](/source/Missing_heritability).[40] Large-scale [whole genome sequencing](/source/Whole_genome_sequencing) studies have rapidly sequenced millions of whole genomes, and such studies have identified hundreds of millions of [rare variants](/source/Rare_functional_variant).[41] [Functional annotations](/source/SNP_annotation) predict the effect or function of a genetic variant and help to prioritize rare functional variants, and incorporating these annotations can effectively boost the power of genetic association of rare variants analysis of whole genome sequencing studies.[42] Some tools have been developed to provide all-in-one rare variant association analysis for whole-genome sequencing data, including integration of genotype data and their functional annotations, association analysis, result summary and visualization.[43][44] Meta-analysis of whole genome sequencing studies provides an attractive solution to the problem of collecting large sample sizes for discovering rare variants associated with complex phenotypes.[45]

### Analysis of mutations in cancer

Main article: [Oncogenomics](/source/Oncogenomics)

In [cancer](/source/Cancer), the genomes of affected cells are rearranged in complex or unpredictable ways. In addition to [single-nucleotide polymorphism](/source/Single-nucleotide_polymorphism) arrays identifying [point mutations](/source/Point_mutation) that cause cancer, [oligonucleotide](/source/Oligonucleotide) microarrays can be used to identify chromosomal gains and losses (called [comparative genomic hybridization](/source/Comparative_genomic_hybridization)). These detection methods generate [terabytes](/source/Terabyte) of data per experiment.[46] The data is often found to contain considerable variability, or [noise](/source/Noise), and thus [Hidden Markov model](/source/Hidden_Markov_model) and change-point analysis methods are being developed to infer real [copy number](/source/Copy_number) changes.[47]

Two important principles can be used to identify cancer by mutations in the [exome](/source/Exome). First, cancer is a disease of accumulated somatic mutations in genes. Second, cancer contains driver mutations which need to be distinguished from passengers.[48]

Further improvements in bioinformatics could allow 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. Another type of data that requires novel informatics development is the analysis of [lesions](/source/Lesion) found to be recurrent among many tumors.[49]

## Gene and protein expression

### Analysis of gene expression

The [expression](/source/Gene_expression) of many genes can be determined by measuring [mRNA](/source/MRNA) levels with multiple techniques including [microarrays](/source/DNA_microarray), [expressed cDNA sequence tag](/source/Expressed_sequence_tag) (EST) sequencing, [serial analysis of gene expression](/source/Serial_analysis_of_gene_expression) (SAGE) tag sequencing, [massively parallel signature sequencing](/source/Massively_parallel_signature_sequencing) (MPSS), [RNA-Seq](/source/RNA-Seq), also known as "Whole Transcriptome Shotgun Sequencing" (WTSS), or various applications of multiplexed in-situ hybridization. All of these techniques are extremely noise-prone and/or subject to bias in the biological measurement, and a major research area in computational biology involves developing statistical tools to separate [signal](/source/Signal) from [noise](/source/Noise) in high-throughput gene expression studies.[50] Such studies are often used to determine the genes implicated in a disorder: one might compare microarray data from cancerous [epithelial](/source/Epithelial) cells to data from non-cancerous cells to determine the transcripts that are up-regulated and down-regulated in a particular population of cancer cells.

MIcroarray vs RNA-Seq

### Analysis of protein expression

[Protein microarrays](/source/Protein_microarray) and high throughput (HT) [mass spectrometry](/source/Mass_spectrometry) (MS) can provide a snapshot of the proteins present in a biological sample. 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 when multiple incomplete peptides from each protein are detected. Cellular protein localization in a tissue context can be achieved through affinity [proteomics](/source/Proteomics) displayed as spatial data based on [immunohistochemistry](/source/Immunohistochemistry) and [tissue microarrays](/source/Tissue_microarray).[51]

### Analysis of regulation

[Gene regulation](/source/Gene_regulation) is a complex process where a signal, such as an extracellular signal such as a [hormone](/source/Hormone), eventually leads to an increase or decrease in the activity of one or more [proteins](/source/Protein). 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](/source/Sequence_motif) in the DNA surrounding the [protein-coding region](/source/Protein-coding_region) of a gene. These motifs influence the extent to which that region is transcribed into mRNA. [Enhancer](/source/Enhancer_(genetics)) 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](/source/Chromosome_conformation_capture) experiments.

Expression data can be used to infer gene regulation: one might compare [microarray](/source/Microarray) data from a wide variety of states of an organism to form hypotheses about the genes involved in each state. In a single-cell organism, one might compare stages of the [cell cycle](/source/Cell_cycle), along with various stress conditions (heat shock, starvation, etc.). [Clustering algorithms](/source/Cluster_analysis) can be then applied to expression data to determine which genes are co-expressed. For example, the upstream regions (promoters) of co-expressed genes can be searched for over-represented [regulatory elements](/source/Regulatory_elements). Examples of clustering algorithms applied in gene clustering are [k-means clustering](/source/K-means_clustering), [self-organizing maps](/source/Self-organizing_map) (SOMs), [hierarchical clustering](/source/Hierarchical_clustering), and [consensus clustering](/source/Consensus_clustering) methods.

## Analysis of cellular organization

Several approaches have been developed to analyze the location of organelles, genes, proteins, and other components within cells. A [gene ontology](/source/Gene_ontology) category, *cellular component*, has been devised to capture subcellular localization in many [biological databases](/source/Biological_database).

### Microscopy and image analysis

Microscopic pictures allow for the location of [organelles](/source/Organelle) as well as molecules, which may be the source of abnormalities in diseases.

### Protein localization

Finding the location of proteins allows us to predict what they do. This is called [protein function prediction](/source/Protein_function_prediction). For instance, if a protein is found in the [nucleus](/source/Cell_nucleus) it may be involved in [gene regulation](/source/Gene_regulation) or [splicing](/source/RNA_splicing). By contrast, if a protein is found in [mitochondria](/source/Mitochondria), it may be involved in [respiration](/source/Cellular_respiration) or other [metabolic processes](/source/Metabolism). There are well developed [protein subcellular localization prediction](/source/Protein_subcellular_localization_prediction) resources available, including protein subcellular location databases, and prediction tools.[52][53]

### Nuclear organization of chromatin

Main article: [Nuclear organization](/source/Nuclear_organization)

Data from high-throughput [chromosome conformation capture](/source/Chromosome_conformation_capture) experiments, such as [Hi-C (experiment)](/source/Hi-C_(experiment)) and [ChIA-PET](/source/ChIA-PET), can provide information on the three-dimensional structure and [nuclear organization](/source/Nuclear_organization) of [chromatin](/source/Chromatin). Bioinformatic challenges in this field include partitioning the genome into domains, such as [Topologically Associating Domains](/source/Topologically_Associating_Domain) (TADs), that are organised together in three-dimensional space.[54]

## Structural bioinformatics

Main articles: [Structural bioinformatics](/source/Structural_bioinformatics) and [Protein structure prediction](/source/Protein_structure_prediction)

See also: [Structural motif](/source/Structural_motif) and [Structural domain](/source/Structural_domain)

3-dimensional protein structures such as this one are common subjects in bioinformatic analyses.

Finding the structure of proteins is an important application of bioinformatics. The Critical Assessment of Protein Structure Prediction (CASP) is an open competition where worldwide research groups submit protein models for evaluating unknown protein models.[55][56]

### Amino acid sequence

The linear [amino acid](/source/Amino_acid) sequence of a protein is called the [primary structure](/source/Primary_structure). The primary structure can be easily determined from the sequence of [codons](/source/Codons) on the DNA gene that codes for it. In most proteins, the primary structure uniquely determines the 3-dimensional structure of a protein in its native environment. An exception is the misfolded [prion](/source/Prion) protein involved in [bovine spongiform encephalopathy](/source/Bovine_spongiform_encephalopathy). This structure is linked to the function of the protein. Additional structural information includes the *[secondary](/source/Secondary_structure)*, *[tertiary](/source/Tertiary_structure)* and *[quaternary](/source/Quaternary_structure)* structure. A viable general solution to the prediction of the function of a protein remains an open problem. Most efforts have so far been directed towards heuristics that work most of the time.[*[citation needed](https://en.wikipedia.org/wiki/Wikipedia:Citation_needed)*]

### 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 structural bioinformatics, homology is used to determine which parts of a protein are important in structure formation and interaction with other proteins. [Homology modeling](/source/Homology_modeling) is used to predict the structure of an unknown protein from existing homologous proteins.

One example of this is hemoglobin in humans and the hemoglobin in legumes ([leghemoglobin](/source/Leghemoglobin)), which are distant relatives from the same [protein superfamily](/source/Protein_superfamily). Both serve the same purpose of transporting oxygen in the organism. Although both of these proteins have very different amino acid sequences, their protein structures are very similar, reflecting their shared function and shared ancestor.[57]

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 [Virtual Screening](/source/Virtual_screening) models such as [Quantitative Structure-Activity Relationship](/source/Quantitative_Structure-Activity_Relationship) models and proteochemometric models (PCM). Furthermore, a protein's crystal structure can be used in simulation of for example ligand-binding studies and *in silico* mutagenesis studies.

A 2021 [deep-learning](/source/Deep-learning) algorithms-based software called [AlphaFold](/source/AlphaFold), developed by Google's [DeepMind](/source/DeepMind), greatly outperforms all other prediction software methods,[58][*[how?](https://en.wikipedia.org/wiki/Wikipedia:Please_clarify)*] and has released predicted structures for hundreds of millions of proteins in the AlphaFold protein structure database.[59]

## Network and systems biology

Main articles: [Computational systems biology](/source/Computational_systems_biology), [Biological network](/source/Biological_network), and [Interactome](/source/Interactome)

*Network analysis* seeks to understand the relationships within [biological networks](/source/Biological_network) such as [metabolic](/source/Metabolic_network) or [protein–protein interaction networks](/source/Interactome). 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](/source/Computer_simulation) of [cellular](/source/Cell_(biology)) subsystems (such as the [networks of metabolites](/source/Metabolic_network) and [enzymes](/source/Enzyme) that comprise [metabolism](/source/Metabolism), [signal transduction](/source/Signal_transduction) pathways and [gene regulatory networks](/source/Gene_regulatory_network)) to both analyze and visualize the complex connections of these cellular processes. [Artificial life](/source/Artificial_life) or virtual evolution attempts to understand evolutionary processes via the computer simulation of simple (artificial) life forms.

### Molecular interaction networks

Main articles: [Protein–protein interaction prediction](/source/Protein%E2%80%93protein_interaction_prediction) and [interactome](/source/Interactome)

Interactions between proteins are frequently visualized and analyzed using networks. This network is made up of protein–protein interactions from *[Treponema pallidum](/source/Treponema_pallidum)*, the causative agent of [syphilis](/source/Syphilis) and other diseases.[60]

Tens of thousands of three-dimensional protein structures have been determined by [X-ray crystallography](/source/X-ray_crystallography) and [protein nuclear magnetic resonance spectroscopy](/source/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](/source/Protein%E2%80%93protein_interaction) experiments. A variety of methods have been developed to tackle the [protein–protein docking](/source/Protein%E2%80%93protein_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](https://en.wikipedia.org/w/index.php?title=Protein%E2%80%93peptide&action=edit&redlink=1). Molecular dynamic simulation of movement of atoms about rotatable bonds is the fundamental principle behind computational [algorithms](/source/Algorithm), termed docking algorithms, for studying [molecular interactions](/source/Interactome).

## Biodiversity informatics

Main article: [Biodiversity informatics](/source/Biodiversity_informatics)

Biodiversity informatics deals with the collection and analysis of [biodiversity](/source/Biodiversity) data, such as [taxonomic databases](/source/Taxonomic_database), or [microbiome](/source/Microbiome) data. Examples of such analyses include [phylogenetics](/source/Phylogenetics), [niche modelling](/source/Niche_modelling), [species richness](/source/Species_richness) mapping, [DNA barcoding](/source/DNA_barcoding), or [species](/source/Speciesism) identification tools. A growing area is also [macro-ecology](/source/Macroecology), i.e. the study of how biodiversity is connected to [ecology](/source/Ecology) and human impact, such as [climate change](/source/Climate_change).

## Others

### Literature analysis

Main articles: [Text mining](/source/Text_mining) and [Biomedical text mining](/source/Biomedical_text_mining)

The enormous number of published literature makes it virtually impossible for individuals 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](/source/Named-entity_recognition) – recognizing biological terms such as gene names

- Protein–protein interaction – identify which [proteins](/source/Protein) interact with which proteins from text

The area of research draws from [statistics](/source/Statistics) and [computational linguistics](/source/Computational_linguistics).

### High-throughput image analysis

Computational technologies are used to automate the processing, quantification and analysis of large amounts of high-information-content [biomedical imagery](/source/Medical_imaging). Modern [image analysis](/source/Image_analysis) systems can improve an observer's [accuracy](/source/Accuracy), [objectivity](/source/Objectivity_(science)), or speed. Image analysis is important for both [diagnostics](/source/Diagnostics) and research. Some examples are:

- high-throughput and high-fidelity quantification and sub-cellular localization ([high-content screening](/source/High-content_screening), cytohistopathology, [Bioimage informatics](/source/Bioimage_informatics))

- [morphometrics](/source/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](/source/Gene_mapping), e.g. the [Sulston score](/source/Sulston_score)

### High-throughput single cell data analysis

Main article: [Flow cytometry bioinformatics](/source/Flow_cytometry_bioinformatics)

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

### Ontologies and data integration

Biological ontologies are [directed acyclic graphs](/source/Directed_acyclic_graph) of [controlled vocabularies](/source/Controlled_vocabularies). They create categories for biological concepts and descriptions so they can be easily analyzed with computers. When categorised in this way, it is possible to gain added value from holistic and integrated analysis.[61]

The [OBO Foundry](/source/OBO_Foundry) was an effort to standardise certain ontologies. One of the most widespread is the [Gene ontology](/source/Gene_ontology) which describes gene function. There are also ontologies which describe phenotypes.

## Databases

Main articles: [List of biological databases](/source/List_of_biological_databases) and [Biological database](/source/Biological_database)

Databases are essential for bioinformatics research and applications. Databases exist for many different information types, including DNA and protein sequences, molecular structures, phenotypes and biodiversity. Databases can contain both empirical data (obtained directly from experiments) and predicted data (obtained from analysis of existing data). They may be specific to a particular organism, pathway or molecule of interest. Alternatively, they can incorporate data compiled from multiple other databases. Databases can have different formats, access mechanisms, and be public or private.

Some of the most commonly used databases are listed below:

- Used in biological sequence analysis: [Genbank](/source/Genbank), [UniProt](/source/UniProt)

- Used in structure analysis: [Protein Data Bank](/source/Protein_Data_Bank) (PDB)

- Used in finding Protein Families and [Motif](/source/Sequence_motif) Finding: [InterPro](/source/InterPro), [Pfam](/source/Pfam)

- Used for Next Generation Sequencing: [Sequence Read Archive](/source/Sequence_Read_Archive)

- Used in Network Analysis: Metabolic Pathway Databases ([KEGG](/source/KEGG), [BioCyc](/source/BioCyc)), Interaction Analysis Databases, Functional Networks

- Used in design of synthetic genetic circuits: [GenoCAD](/source/GenoCAD)[*[citation needed](https://en.wikipedia.org/wiki/Wikipedia:Citation_needed)*]

## Software and tools

[Software tools for bioinformatics](/source/List_of_bioinformatics_software) include simple command-line tools, more complex graphical programs, and standalone web-services. They are made by [bioinformatics companies](/source/Bioinformatics_companies) or by public institutions.

### Open-source bioinformatics software

Main article: [List of open-source bioinformatics software](/source/List_of_open-source_bioinformatics_software)

See also: [List of bioinformatics software](/source/List_of_bioinformatics_software)

Many [free and open-source software](/source/Free_and_open-source_software) tools have existed and continued to grow since the 1980s.[62] The combination of a continued need for new [algorithms](/source/Algorithm) for the analysis of emerging types of biological readouts, the potential for innovative *[in silico](/source/In_silico)* experiments, and freely available [open code](/source/Open_code) bases have created opportunities for research groups to contribute to both bioinformatics regardless of [funding](/source/Funding_of_science). The open source tools often act as incubators of ideas, or community-supported [plug-ins](/source/Plug-in_(computing)) in commercial applications. They may also provide *[de facto](/source/De_facto)* standards and shared object models for assisting with the challenge of bioinformation integration.

Open-source bioinformatics software includes [Bioconductor](/source/Bioconductor), [BioPerl](/source/BioPerl), [Biopython](/source/Biopython), [BioJava](/source/BioJava), [BioJS](/source/BioJS), [BioRuby](/source/BioRuby), [Bioclipse](/source/Bioclipse), [EMBOSS](/source/EMBOSS), .NET Bio, [Orange](/source/Orange_(software)) with its bioinformatics add-on, [Apache Taverna](/source/Apache_Taverna), [UGENE](/source/UGENE) and [GenoCAD](/source/GenoCAD).

The non-profit [Open Bioinformatics Foundation](/source/Open_Bioinformatics_Foundation)[62] and the annual [Bioinformatics Open Source Conference](/source/Bioinformatics_Open_Source_Conference) promote open-source bioinformatics software.[63]

### Web services in bioinformatics

[SOAP](/source/SOAP)- and [REST](/source/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](/source/European_Bioinformatics_Institute) into three categories: [SSS](/source/Sequence_alignment_software) (Sequence Search Services), [MSA](/source/Multiple_sequence_alignment) (Multiple Sequence Alignment), and [BSA](#Sequence_analysis) (Biological Sequence Analysis).[64] The availability of these [service-oriented](/source/Service-orientation) 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](/source/Bioinformatics_workflow_management_systems).

#### Bioinformatics workflow management systems

Main article: [Bioinformatics workflow management systems](/source/Bioinformatics_workflow_management_systems)

A [bioinformatics workflow management system](/source/Bioinformatics_workflow_management_system) is a specialized form of a [workflow management system](/source/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](/source/Provenance) of the workflow execution results and the workflow creation steps.

Some of the platforms giving this service: [Galaxy](/source/Galaxy_(computational_biology)), [Kepler](/source/Kepler_scientific_workflow_system), [Taverna](/source/Apache_Taverna), [UGENE](/source/UGENE), [Anduril](/source/Anduril_(workflow_engine)), [HIVE](/source/High-performance_Integrated_Virtual_Environment).

### BioCompute and BioCompute Objects

In 2014, the [US Food and Drug Administration](/source/US_Food_and_Drug_Administration) sponsored a conference held at the [National Institutes of Health](/source/National_Institutes_of_Health) Bethesda Campus to discuss reproducibility in bioinformatics.[65] Over the next three years, a consortium of stakeholders met regularly to discuss what would become BioCompute paradigm.[66] 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](/source/Human_Variome_Project) and the [European Federation for Medical Informatics](/source/European_Federation_for_Medical_Informatics), and research institutions including [Stanford](/source/Stanford), the [New York Genome Center](/source/New_York_Genome_Center), and the [George Washington University](/source/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.[67]

In 2016, the group reconvened at the NIH in Bethesda and discussed the potential for a [BioCompute Object](/source/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.[68][69]

## Education platforms

While bioinformatics is taught as an in-person [master's degree](/source/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](/source/Educational_technology).[70][71] Software platforms designed to teach bioinformatics concepts and methods include [Rosalind](/source/Rosalind_(education_platform)) and online courses offered through the [Swiss Institute of Bioinformatics](/source/Swiss_Institute_of_Bioinformatics) Training Portal. The [Canadian Bioinformatics Workshops](/source/Canadian_Bioinformatics_Workshops) provides videos and slides from training workshops on their website under a [Creative Commons](/source/Creative_Commons) license. The 4273π project or 4273pi project[72] also offers open source educational materials for free. The course runs on low cost [Raspberry Pi](/source/Raspberry_Pi) computers and has been used to teach adults and school pupils.[73][74] 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.[75][76]

[MOOC](/source/MOOC) platforms also provide online certifications in bioinformatics and related disciplines, including [Coursera](/source/Coursera)'s Bioinformatics Specialization at the [University of California, San Diego](/source/University_of_California%2C_San_Diego), Genomic Data Science Specialization at [Johns Hopkins University](/source/Johns_Hopkins_University), and [EdX](/source/EdX)'s Data Analysis for Life Sciences XSeries at [Harvard University](/source/Harvard_University).

## Conferences

There are several large conferences that are concerned with bioinformatics. Some of the most notable examples are [European Conference on Computational Biology](/source/European_Conference_on_Computational_Biology) (ECCB), [Intelligent Systems for Molecular Biology](/source/Intelligent_Systems_for_Molecular_Biology) (ISMB), [Pacific Symposium on Biocomputing](https://psb.stanford.edu) (PSB), and [Research in Computational Molecular Biology](/source/Research_in_Computational_Molecular_Biology) (RECOMB).

## See also

- [Biology portal](https://en.wikipedia.org/wiki/Portal:Biology)
- [Evolutionary biology portal](https://en.wikipedia.org/wiki/Portal:Evolutionary_biology)

- [Biodiversity informatics](/source/Biodiversity_informatics)

- [Bioinformatics companies](/source/Bioinformatics_companies)

- [Computational biology](/source/Computational_biology)

- [Computational biomodeling](/source/Computational_biomodeling)

- [Computational genomics](/source/Computational_genomics)

- [Cyberbiosecurity](/source/Cyberbiosecurity)

- [Earth BioGenome Project](/source/Earth_BioGenome_Project)

- [Functional genomics](/source/Functional_genomics)

- [Gene Disease Database](/source/Gene_Disease_Database)

- [Health informatics](/source/Health_informatics)

- [International Society for Computational Biology](/source/International_Society_for_Computational_Biology)

- [Jumping library](/source/Jumping_library)

- [List of bioinformatics institutions](/source/List_of_bioinformatics_institutions)

- [List of open-source bioinformatics software](/source/List_of_open-source_bioinformatics_software)

- [List of bioinformatics journals](/source/List_of_bioinformatics_journals)

- [Metabolomics](/source/Metabolomics)

- [MitoMap](/source/MitoMap)

- [Nucleic acid sequence](/source/Nucleic_acid_sequence)

- [Phylogenetics](/source/Phylogenetics)

- [Proteomics](/source/Proteomics)

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1. **[^](#cite_ref-60)** Titz B, Rajagopala SV, Goll J, Häuser R, McKevitt MT, Palzkill T, et al. (May 2008). Hall N (ed.). ["The binary protein interactome of Treponema pallidum--the syphilis spirochete"](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2386257). *PLOS ONE*. **3** (5) e2292. [Bibcode](/source/Bibcode_(identifier)):[2008PLoSO...3.2292T](https://ui.adsabs.harvard.edu/abs/2008PLoSO...3.2292T). [doi](/source/Doi_(identifier)):[10.1371/journal.pone.0002292](https://doi.org/10.1371%2Fjournal.pone.0002292). [PMC](/source/PMC_(identifier)) [2386257](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2386257). [PMID](/source/PMID_(identifier)) [18509523](https://pubmed.ncbi.nlm.nih.gov/18509523).

1. **[^](#cite_ref-61)** Groß A, Pruski C, Rahm E (1 January 2016). ["Evolution of biomedical ontologies and mappings: Overview of recent approaches"](https://www.sciencedirect.com/science/article/pii/S2001037016300319). *Computational and Structural Biotechnology Journal*. **14**: 333–340. [doi](/source/Doi_(identifier)):[10.1016/j.csbj.2016.08.002](https://doi.org/10.1016%2Fj.csbj.2016.08.002). [ISSN](/source/ISSN_(identifier)) [2001-0370](https://search.worldcat.org/issn/2001-0370). [PMC](/source/PMC_(identifier)) [5018063](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5018063).

1. ^ [***a***](#cite_ref-obf-main_62-0) [***b***](#cite_ref-obf-main_62-1) ["Open Bioinformatics Foundation: About us"](http://www.open-bio.org/wiki/Main_Page). *Official website*. [Open Bioinformatics Foundation](/source/Open_Bioinformatics_Foundation). [Archived](https://web.archive.org/web/20110512022059/http://open-bio.org/wiki/Main_Page) from the original on 12 May 2011. Retrieved 10 May 2011.

1. **[^](#cite_ref-obf-bosc_63-0)** ["Open Bioinformatics Foundation: BOSC"](http://www.open-bio.org/wiki/BOSC). *Official website*. [Open Bioinformatics Foundation](/source/Open_Bioinformatics_Foundation). [Archived](https://web.archive.org/web/20110718175922/http://www.open-bio.org/wiki/BOSC) from the original on 18 July 2011. Retrieved 10 May 2011.

1. **[^](#cite_ref-64)** Nisbet R, Elder IV J, Miner G (2009). ["Bioinformatics"](https://books.google.com/books?id=U5np34a5fmQC&q=bioinformatics%20service%20categories%20EBI&pg=PA328). *Handbook of Statistical Analysis and Data Mining Applications*. Academic Press. p. 328. [ISBN](/source/ISBN_(identifier)) [978-0-08-091203-5](https://en.wikipedia.org/wiki/Special:BookSources/978-0-08-091203-5).

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1. **[^](#cite_ref-66)** Simonyan V, Goecks J, Mazumder R (2017). ["Biocompute Objects-A Step towards Evaluation and Validation of Biomedical Scientific Computations"](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5510742). *PDA Journal of Pharmaceutical Science and Technology*. **71** (2): 136–146. [doi](/source/Doi_(identifier)):[10.5731/pdajpst.2016.006734](https://doi.org/10.5731%2Fpdajpst.2016.006734). [PMC](/source/PMC_(identifier)) [5510742](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5510742). [PMID](/source/PMID_(identifier)) [27974626](https://pubmed.ncbi.nlm.nih.gov/27974626).

1. **[^](#cite_ref-67)** Office of the Commissioner. ["Advancing Regulatory Science – Community-based development of HTS standards for validating data and computation and encouraging interoperability"](https://web.archive.org/web/20180126133504/https://www.fda.gov/ScienceResearch/SpecialTopics/RegulatoryScience/ucm491893.htm). *www.fda.gov*. Archived from [the original](https://www.fda.gov/ScienceResearch/SpecialTopics/RegulatoryScience/ucm491893.htm) on 26 January 2018. Retrieved 30 November 2017.

1. **[^](#cite_ref-68)** Alterovitz G, Dean D, Goble C, Crusoe MR, Soiland-Reyes S, Bell A, et al. (December 2018). ["Enabling precision medicine via standard communication of HTS provenance, analysis, and results"](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6338479). *PLOS Biology*. **16** (12) e3000099. [doi](/source/Doi_(identifier)):[10.1371/journal.pbio.3000099](https://doi.org/10.1371%2Fjournal.pbio.3000099). [PMC](/source/PMC_(identifier)) [6338479](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6338479). [PMID](/source/PMID_(identifier)) [30596645](https://pubmed.ncbi.nlm.nih.gov/30596645).

1. **[^](#cite_ref-69)** [*BioCompute Object (BCO) project is a collaborative and community-driven framework to standardize HTS computational data. 1. BCO Specification Document: user manual for understanding and creating B.*](https://github.com/biocompute-objects/HTS-CSRS), biocompute-objects, 3 September 2017, [archived](https://web.archive.org/web/20180627081221/https://github.com/biocompute-objects/HTS-CSRS) from the original on 27 June 2018, retrieved 30 November 2017

1. **[^](#cite_ref-70)** Campbell AM (1 June 2003). ["Public Access for Teaching Genomics, Proteomics, and Bioinformatics"](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC162192). *Cell Biology Education*. **2** (2): 98–111. [doi](/source/Doi_(identifier)):[10.1187/cbe.03-02-0007](https://doi.org/10.1187%2Fcbe.03-02-0007). [PMC](/source/PMC_(identifier)) [162192](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC162192). [PMID](/source/PMID_(identifier)) [12888845](https://pubmed.ncbi.nlm.nih.gov/12888845).

1. **[^](#cite_ref-71)** Arenas M (September 2021). ["General considerations for online teaching practices in bioinformatics in the time of COVID -19"](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8426940). *Biochemistry and Molecular Biology Education*. **49** (5): 683–684. [doi](/source/Doi_(identifier)):[10.1002/bmb.21558](https://doi.org/10.1002%2Fbmb.21558). [ISSN](/source/ISSN_(identifier)) [1470-8175](https://search.worldcat.org/issn/1470-8175). [PMC](/source/PMC_(identifier)) [8426940](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8426940). [PMID](/source/PMID_(identifier)) [34231941](https://pubmed.ncbi.nlm.nih.gov/34231941).

1. **[^](#cite_ref-72)** Barker D, Ferrier DE, Holland PW, Mitchell JB, Plaisier H, Ritchie MG, et al. (August 2013). ["4273π: bioinformatics education on low cost ARM hardware"](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3751261). *BMC Bioinformatics*. **13** 243: 522. [doi](/source/Doi_(identifier)):[10.1186/1471-2105-14-243](https://doi.org/10.1186%2F1471-2105-14-243). [PMC](/source/PMC_(identifier)) [3751261](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3751261). [PMID](/source/PMID_(identifier)) [23937194](https://pubmed.ncbi.nlm.nih.gov/23937194).

1. **[^](#cite_ref-73)** Barker D, Alderson RG, McDonagh JL, Plaisier H, Comrie MM, Duncan L, et al. (2015). ["University-level practical activities in bioinformatics benefit voluntary groups of pupils in the last 2 years of school"](https://doi.org/10.1186%2Fs40594-015-0030-z). *International Journal of STEM Education*. **2** (17) 17. [doi](/source/Doi_(identifier)):[10.1186/s40594-015-0030-z](https://doi.org/10.1186%2Fs40594-015-0030-z). [hdl](/source/Hdl_(identifier)):[10023/7704](https://hdl.handle.net/10023%2F7704). [S2CID](/source/S2CID_(identifier)) [256396656](https://api.semanticscholar.org/CorpusID:256396656).

1. **[^](#cite_ref-74)** McDonagh JL, Barker D, Alderson RG (2016). ["Bringing computational science to the public"](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4775721). *SpringerPlus*. **5** (259) 259. [doi](/source/Doi_(identifier)):[10.1186/s40064-016-1856-7](https://doi.org/10.1186%2Fs40064-016-1856-7). [PMC](/source/PMC_(identifier)) [4775721](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4775721). [PMID](/source/PMID_(identifier)) [27006868](https://pubmed.ncbi.nlm.nih.gov/27006868).

1. **[^](#cite_ref-75)** Robson JF, Barker D (October 2015). ["Comparison of the protein-coding gene content of Chlamydia trachomatis and Protochlamydia amoebophila using a Raspberry Pi computer"](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4604092). *BMC Research Notes*. **8** (561) 561. [doi](/source/Doi_(identifier)):[10.1186/s13104-015-1476-2](https://doi.org/10.1186%2Fs13104-015-1476-2). [PMC](/source/PMC_(identifier)) [4604092](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4604092). [PMID](/source/PMID_(identifier)) [26462790](https://pubmed.ncbi.nlm.nih.gov/26462790).

1. **[^](#cite_ref-76)** Wreggelsworth KM, Barker D (October 2015). ["A comparison of the protein-coding genomes of two green sulphur bacteria, Chlorobium tepidum TLS and Pelodictyon phaeoclathratiforme BU-1"](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4606965). *BMC Research Notes*. **8** (565) 565. [doi](/source/Doi_(identifier)):[10.1186/s13104-015-1535-8](https://doi.org/10.1186%2Fs13104-015-1535-8). [PMC](/source/PMC_(identifier)) [4606965](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4606965). [PMID](/source/PMID_(identifier)) [26467441](https://pubmed.ncbi.nlm.nih.gov/26467441).

## Further reading

[Library resources](https://en.wikipedia.org/wiki/Wikipedia:The_Wikipedia_Library) about
 **Bioinformatics**

- [Resources in your library](https://ftl.toolforge.org/cgi-bin/ftl?st=wp&su=Bioinformatics)

- [Resources in other libraries](https://ftl.toolforge.org/cgi-bin/ftl?st=wp&su=Bioinformatics&library=0CHOOSE0)

- 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.

- Achuthsankar S Nair [Computational Biology & Bioinformatics – A gentle Overview](http://print.achuth.googlepages.com/BINFTutorialV5.0CSI07.pdf) [Archived](https://web.archive.org/web/20081216212125/http://print.achuth.googlepages.com/BINFTutorialV5.0CSI07.pdf) 16 December 2008 at the [Wayback Machine](/source/Wayback_Machine), Communications of Computer Society of India, January 2007

- [Aluru, Srinivas](/source/Srinivas_Aluru), ed. *Handbook of Computational Molecular Biology*. Chapman & Hall/Crc, 2006. [ISBN](/source/ISBN_(identifier)) [1-58488-406-1](https://en.wikipedia.org/wiki/Special:BookSources/1-58488-406-1) (Chapman & Hall/Crc Computer and Information Science Series)

- Baldi, P and [Brunak](/source/S%C3%B8ren_Brunak), S, *Bioinformatics: The Machine Learning Approach*, 2nd edition. MIT Press, 2001. [ISBN](/source/ISBN_(identifier)) [0-262-02506-X](https://en.wikipedia.org/wiki/Special:BookSources/0-262-02506-X)

- Barnes, M.R. and Gray, I.C., eds., *Bioinformatics for Geneticists*, first edition. Wiley, 2003. [ISBN](/source/ISBN_(identifier)) [0-470-84394-2](https://en.wikipedia.org/wiki/Special:BookSources/0-470-84394-2)

- Baxevanis, A.D. and [Ouellette](https://en.wikipedia.org/w/index.php?title=Francis_Ouellette&action=edit&redlink=1), B.F.F., eds., *Bioinformatics: A Practical Guide to the Analysis of Genes and Proteins*, third edition. Wiley, 2005. [ISBN](/source/ISBN_(identifier)) [0-471-47878-4](https://en.wikipedia.org/wiki/Special:BookSources/0-471-47878-4)

- Baxevanis, A.D., Petsko, G.A., Stein, L.D., and Stormo, G.D., eds., *[Current Protocols](/source/Current_Protocols) in Bioinformatics*. Wiley, 2007. [ISBN](/source/ISBN_(identifier)) [0-471-25093-7](https://en.wikipedia.org/wiki/Special:BookSources/0-471-25093-7)

- Cristianini, N. and Hahn, M. [*Introduction to Computational Genomics*](http://www.computational-genomics.net/) [Archived](https://web.archive.org/web/20090104042023/http://www.computational-genomics.net/) 4 January 2009 at the [Wayback Machine](/source/Wayback_Machine), Cambridge University Press, 2006. ([ISBN](/source/ISBN_(identifier)) [9780521671910](https://en.wikipedia.org/wiki/Special:BookSources/9780521671910) |[ISBN](/source/ISBN_(identifier)) [0-521-67191-4](https://en.wikipedia.org/wiki/Special:BookSources/0-521-67191-4))

- Durbin, R., S. Eddy, A. Krogh and G. Mitchison, *Biological sequence analysis*. Cambridge University Press, 1998. [ISBN](/source/ISBN_(identifier)) [0-521-62971-3](https://en.wikipedia.org/wiki/Special:BookSources/0-521-62971-3)

- Gilbert D (September 2004). ["Bioinformatics software resources"](https://doi.org/10.1093%2Fbib%2F5.3.300). *Briefings in Bioinformatics*. **5** (3): 300–4. [doi](/source/Doi_(identifier)):[10.1093/bib/5.3.300](https://doi.org/10.1093%2Fbib%2F5.3.300). [PMID](/source/PMID_(identifier)) [15383216](https://pubmed.ncbi.nlm.nih.gov/15383216).

- Keedwell, E., *Intelligent Bioinformatics: The Application of Artificial Intelligence Techniques to Bioinformatics Problems*. Wiley, 2005. [ISBN](/source/ISBN_(identifier)) [0-470-02175-6](https://en.wikipedia.org/wiki/Special:BookSources/0-470-02175-6)

- Kohane, et al. *Microarrays for an Integrative Genomics.* The MIT Press, 2002. [ISBN](/source/ISBN_(identifier)) [0-262-11271-X](https://en.wikipedia.org/wiki/Special:BookSources/0-262-11271-X)

- Lund, O. et al. *Immunological Bioinformatics.* The MIT Press, 2005. [ISBN](/source/ISBN_(identifier)) [0-262-12280-4](https://en.wikipedia.org/wiki/Special:BookSources/0-262-12280-4)

- [Pachter, Lior](/source/Lior_Pachter) and [Sturmfels, Bernd](/source/Bernd_Sturmfels). "Algebraic Statistics for Computational Biology" Cambridge University Press, 2005. [ISBN](/source/ISBN_(identifier)) [0-521-85700-7](https://en.wikipedia.org/wiki/Special:BookSources/0-521-85700-7)

- Pevzner, Pavel A. *Computational Molecular Biology: An Algorithmic Approach* The MIT Press, 2000. [ISBN](/source/ISBN_(identifier)) [0-262-16197-4](https://en.wikipedia.org/wiki/Special:BookSources/0-262-16197-4)

- Soinov, L. [Bioinformatics and Pattern Recognition Come Together](http://jprr.org/index.php/jprr/article/view/8/5) [Archived](https://web.archive.org/web/20130510213503/http://jprr.org/index.php/jprr/article/view/8/5) 10 May 2013 at the [Wayback Machine](/source/Wayback_Machine) Journal of Pattern Recognition Research ([JPRR](http://www.jprr.org) [Archived](https://web.archive.org/web/20080908110041/http://www.jprr.org/) 8 September 2008 at the [Wayback Machine](/source/Wayback_Machine)), 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](/source/ISBN_(identifier)) [9780226080208](https://en.wikipedia.org/wiki/Special:BookSources/9780226080208)

- Tisdall, James. "Beginning Perl for Bioinformatics" O'Reilly, 2001. [ISBN](/source/ISBN_(identifier)) [0-596-00080-4](https://en.wikipedia.org/wiki/Special:BookSources/0-596-00080-4)

- [Catalyzing Inquiry at the Interface of Computing and Biology (2005) CSTB report](https://www.nap.edu/catalog/11480.html) [Archived](https://web.archive.org/web/20070128222920/http://www.nap.edu/catalog/11480.html) 28 January 2007 at the [Wayback Machine](/source/Wayback_Machine)

- [Calculating the Secrets of Life: Contributions of the Mathematical Sciences and computing to Molecular Biology (1995)](https://www.nap.edu/catalog/2121.html) [Archived](https://web.archive.org/web/20080706035211/http://www.nap.edu/catalog/2121.html) 6 July 2008 at the [Wayback Machine](/source/Wayback_Machine)

- [Foundations of Computational and Systems Biology MIT Course](https://web.archive.org/web/20071222091912/http://ocw.mit.edu/OcwWeb/Biology/7-91JSpring2004/LectureNotes/index.htm)

- [Computational Biology: Genomes, Networks, Evolution Free MIT Course](https://compbio.mit.edu/6.047/) [Archived](https://web.archive.org/web/20130408034631/http://compbio.mit.edu/6.047/) 8 April 2013 at the [Wayback Machine](/source/Wayback_Machine)

## External links

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v t e Bioinformatics Databases Sequence databases: GenBank, European Nucleotide Archive, DNA Data Bank of Japan and China National GeneBank Secondary databases: UniProt, database of protein sequences grouping together Swiss-Prot, TrEMBL and Protein Information Resource Other databases: BioNumbers, Protein Data Bank, Ensembl, InterPro, KEGG, and Gene Ontology Specialised genomic databases: BOLD, Saccharomyces Genome Database, FlyBase, VectorBase, WormBase, Rat Genome Database, PHI-base, Arabidopsis Information Resource, GISAID and Zebrafish Information Network Software BLAST Bowtie Clustal EMBOSS HMMER MUSCLE PANGOLIN SAMtools SOAP suite TopHat Other Server: ExPASy Rosalind (education platform) Institutions Broad Institute Computational Biology Department (CBD) Microsoft Research - University of Trento Centre for Computational and Systems Biology (COSBI) Database Center for Life Science (DBCLS) DNA Data Bank of Japan (DDBJ) European Bioinformatics Institute (EMBL-EBI) European Molecular Biology Laboratory (EMBL) Flatiron Institute J. Craig Venter Institute (JCVI) Joint Genome Institute (JGI) Max Planck Institute for Molecular Genetics (MPIMG) Max Planck Institute of Molecular Cell Biology and Genetics (MPI-CBG) US National Center for Biotechnology Information (NCBI) Japanese Institute of Genetics Netherlands Bioinformatics Centre (NBIC) Philippine Genome Center (PGC) Scripps Research Swiss Institute of Bioinformatics (SIB) Wellcome Sanger Institute Whitehead Institute Organizations African Society for Bioinformatics and Computational Biology (ASBCB) Australia Bioinformatics Resource (EMBL-AR) European Molecular Biology network (EMBnet) International Nucleotide Sequence Database Collaboration (INSDC) International Society for Biocuration (ISB) International Society for Computational Biology (ISCB) Student Council (ISCB-SC) Institute of Genomics and Integrative Biology (CSIR-IGIB) Japanese Society for Bioinformatics (JSBi) Meetings Basel Computational Biology Conference‎ ([BC2]) European Conference on Computational Biology (ECCB) Intelligent Systems for Molecular Biology (ISMB) International Conference on Bioinformatics (InCoB) International Conference on Computational Intelligence Methods for Bioinformatics and Biostatistics (CIBB) ISCB Africa ASBCB Conference on Bioinformatics Pacific Symposium on Biocomputing (PSB) Research in Computational Molecular Biology (RECOMB) File formats CRAM format FASTA format FASTQ format NeXML format Nexus format Pileup format SAM format Stockholm format VCF format GFF format GTF format Related topics Computational biology List of biobanks List of biological databases Molecular phylogenetics Sequencing Sequence database Sequence alignment Category Commons

v t e Information processing Information processes information processes by function perception attention influence operating communication reasoning learning storing decision-making information processing abstractions event processing sign processesing signal processing data processing stream processing agent processing state processing Information processors natural nature as information processing humans as information processing systems society as information processing system mixed mixed reality brain–computer interface physical computing human–computer interaction artificial processors and processes bio-inspired computing ubiquitous computing artificial brain and mind uploading virtual reality virtual world Information processing theories and concepts in biology computational and systems biology genetic informatics and cellular computing computational neuroscience and neurocomputing in cognitive psychology information processing theory mind and intelligence cognitive informatics and neuroinformatics behavior informatics in computer science neural computation computation theory algorithms and information structures computational circuits artificial intelligence in philosophy computational theory of mind philosophy of information philosophy of artificial intelligence interdisciplinary information theory decision theory systems theory other infosphere inforg Decoding the Universe information overload

v t e Genomics Fields Cognitive genomics Computational genomics Comparative genomics Functional genomics Genome project Human Genome Project Metagenomics Human Microbiome Project Oncogenomics Pangenomics Personal genomics Population genomics Sociogenomics Structural genomics Bioinformatics Biochip Cheminformatics Chemogenomics Connectomics Human Connectome Project Epigenomics Human Epigenome Project Epitranscriptome Glycomics Immunomics Lipidomics Metabolomics Human Metabolome Database Microbiomics Nutrigenomics Paleopolyploidy Pharmacogenetics Pharmacogenomics Systems biology Multiomics Toxicogenomics Transcriptomics technologies Structural biology Proteomics Human proteome project Call-map proteomics Structure-based drug design Expression proteomics Research tools 2-D electrophoresis Mass spectrometer Electrospray ionization Matrix-assisted laser desorption ionization Matrix-assisted laser desorption ionization-time of flight mass spectrometer Microfluidic-based tools Isotope affinity tags Chromosome conformation capture Organizations DNA Data Bank of Japan (JP) European Molecular Biology Laboratory (EU) National Institutes of Health (USA) Wellcome Sanger Institute (UK) List Category

v t e Biology Introduction (Genetics, Evolution) Outline History Timeline Index Overview Science Life Properties (Adaptation, Energy processing, Growth, Order, Regulation, Reproduction (Self-replication), Response to environment) Hierarchy of life (Atom > Molecule > Organelle > Cell > Tissue > Organ > Organ system > Organism > Population > Community > Ecosystem > Biosphere) Reductionist Emergent property Mechanist Scientific method Taxonomic rank Theory Law Peer review Biology journals Common name Chemical basis Atoms Amino acids Carbohydrates Chemical bond Chemical element Lipids Matter Quantum Molecules Monomer Nucleic acids Organic compounds pH Polymer Proteins Water Cells ATP Cell cycle Cell theory Cell signaling Cellular respiration Energy transformation Enzyme Eukaryote Fermentation Metabolism Meiosis Mitosis Photosynthesis Prokaryote Genetics DNA Epigenetics Evolutionary developmental biology Gene expression Gene regulation Genomes Mendelian inheritance Post-transcriptional modification Evolution Adaptation Earliest known life forms Function Genetic drift Gene flow History of life Macroevolution Microevolution Mutation Natural selection Phylogenetics Speciation Taxonomy Diversity Archaea Bacteria Eukaryote Alga Animal Fungus Plant Protist Virus Plant form, function Epidermis (botany) Flower Ground tissue Leaf Phloem Plant stem Root Shoot Vascular plant Vascular tissue Xylem Animal form, function Breathing Circulatory system Endocrine system Digestive system Homeostasis Immune system Internal environment Muscular system Nervous system Reproductive system Respiratory system Ecology Biogeochemical cycle Biological interaction Biomass Biomes Biosphere Climate Climate change Community Conservation Ecosystem Habitat niche Microbiome Population dynamics Resources Biology portal Commons WikiProject Category

v t e Branches of biology Abiogenesis Aerobiology Agronomy Agrostology Anatomy Anthropogeny Anthropology Astrobiology Bacteriology Biochemistry Biogeography Biogeology Bioinformatics Biological engineering Biomathematics Biomechanics Biophysics Biosemiotics Biostatistics Biotechnology Botany Cell biology Cellular microbiology Chemical biology Chronobiology Cognitive biology Comparative biology Computational biology Conservation biology Cryobiology Cytogenetics Dendrology Developmental biology Ecological genetics Ecology Embryology Epidemiology Epigenetics Evolutionary biology Freshwater biology Generative biology Genetics Genomics Geobiology Gerontology Herpetology Histology Human biology Ichthyology Immunology Lipidology Mammalogy Marine biology Microbiology Molecular biology Mycology Neontology Neuroscience Nutrition Ornithology Osteology Paleontology Parasitology Pathology Pharmacology Photobiology Phycology Phylogenetics Physiology Pomology Primatology Proteomics Protistology Quantum biology Relational biology Reproductive biology Sociobiology Spatial biology Structural biology Synthetic biology Systematics Systems biology Taxonomy Teratology Toxicology Virology Virophysics Welfare biology Xenobiology Zoology See also History of biology Nobel Prize in Physiology or Medicine Timeline of biology and organic chemistry

v t e Computer science This template follows roughly the 2012 ACM Computing Classification System Hardware Printed circuit board Peripheral Integrated circuit Very-large-scale integration System on a chip (SoC) Energy consumption (green computing) Electronic design automation Hardware acceleration Processor Size – Form Systems organization Computer architecture Computational complexity Dependability Embedded system Real-time computing Cyber-physical system Fault tolerance Wireless sensor network Networks Network architecture Network protocol Network components Network scheduler Network performance evaluation Network service Software organization Interpreter Middleware Virtual machine Operating system Software quality Software notations, tools Programming paradigm Programming language Compiler Domain-specific language Modeling language Software framework Integrated development environment Software configuration management Software library Software repository Software development Control flow Software development process Requirements analysis Software design Software construction Software deployment Software engineering Software maintenance Programming team Open source model Theory of computing Model of computation Stochastic Formal language Automata theory Computability theory Computational complexity theory Logic Semantics Algorithms Algorithm design Analysis of algorithms Algorithmic efficiency Randomized algorithm Computational geometry Mathematics of computing Discrete mathematics Probability Statistics Mathematical software Information theory Mathematical analysis Numerical analysis Theoretical computer science Computational problem Information systems Database management Information storage Enterprise information Social information Geographic information Decision support Process control Multimedia information Data mining Digital library Computing platform Digital marketing World Wide Web Information retrieval Security Cryptography Formal methods Security hacker Security services Intrusion detection system Hardware security Network security Information security Application security Human- centered computing Accessibility Extended reality augmented virtual Human–computer interaction Interaction design Mobile computing Social computing Ubiquitous computing Visualization Concurrency Concurrent computing Parallel computing Distributed computing Multithreading Multiprocessing Artificial intelligence Computational intelligence Natural language processing Knowledge representation and reasoning Computer vision Automated planning and scheduling Search methodology Control method Philosophy of Distributed Machine learning Supervised Unsupervised Reinforcement Multi-task Cross-validation Graphics Animation Rendering Photograph manipulation Graphics processing unit Image compression Solid modeling Applied computing Quantum computing E-commerce Enterprise software Computational mathematics Computational physics Computational chemistry Computational biology Computational social science Computational engineering Differentiable computing Computational healthcare Digital art Electronic publishing Cyberwarfare Electronic voting Video games Word processing Operations research Educational technology Document management Outline Glossaries Category

v t e Health informatics Subdisciplines Medical image computing and imaging informatics Artificial intelligence in healthcare Neuroinformatics in healthcare Behavior informatics in healthcare Computational biology in healthcare Translational bioinformatics Translational medicine health information technology Telemedicine Public health informatics Health information management Consumer health informatics Medical classification Continuity of Care Record HRHIS ICD ISO 27799 LOINC Professional organizations American Association for Medical Systems and Informatics American Medical Informatics Association Australian Society / Australasian College (to merge from 2020) Brazilian Society European Federation Indian Association International Association American College of Medical Informatics Other concepts Electronic health record Health Level 7 Remote manipulator Personalized medicine / precision medicine List of medical and health informatics journals openEHR

Authority control databases International GND National United States France BnF data Japan Czech Republic Israel Other Yale LUX

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Adapted from the Wikipedia article [Bioinformatics](https://en.wikipedia.org/wiki/Bioinformatics) by Wikipedia contributors ([contributor history](https://en.wikipedia.org/wiki/Bioinformatics?action=history)). Available under [Creative Commons Attribution-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-sa/4.0/). Changes may have been made.