bioinformatics bioinformatics bioinformatics (/ˌbaɪ.oʊˌɪnfərˈmætɪks/ (listen)) is an interdisciplinary field that develops methods and software tools for understanding biological data, in particular when the data sets are large and complex. as an interdisciplinary field of science, bioinformatics combines biology, chemistry, physics, computer science, information engineering, mathematics and statistics to analyze and interpret the biological data. bioinformatics has been used for in silico analyses of biological queries using computational and statistical techniques. early bioinformatics—computational alignment of experimentally determined sequences of a class of related proteins; see § sequence analysis for further information. map of the human x chromosome (from the national center for biotechnology information website) 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. 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, rna, proteins as well as biomolecular interactions. 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. this definition placed bioinformatics as a field parallel to biochemistry (the study of chemical processes in biological systems). sequences sequences of genetic material are frequently used in bioinformatics and are easier to manage using computers than manually. 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. computers became essential in molecular biology when protein sequences became available after frederick sanger determined the sequence of insulin in the early 1950s. comparing multiple sequences manually turned out to be impractical. a pioneer in the field was margaret oakley dayhoff. she compiled one of the first protein sequence databases, initially published as books and pioneered methods of sequence alignment and molecular evolution. another early contributor to bioinformatics was elvin a. kabat, who pioneered biological sequence analysis in 1970 with his comprehensive volumes of antibody sequences released with tai te wu between 1980 and 1991. 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 thus proof of the concept that bioinformatics would be insightful. 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. 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 amino acid sequences, protein domains, and protein structures. the actual process of analyzing and interpreting data is referred to as computational biology. important sub-disciplines within bioinformatics and computational biology include: 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: pattern recognition, data mining, machine learning algorithms, and visualization. major research efforts in the field include sequence alignment, gene finding, genome assembly, drug design, drug discovery, protein structure alignment, protein structure prediction, prediction of gene expression and protein–protein interactions, genome-wide association studies, the modeling of evolution and cell division/mitosis. bioinformatics now 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 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 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 biological computation, while it is often considered synonymous to computational biology. biological computation uses bioengineering and biology to build biological computers, whereas bioinformatics uses computation to better understand biology. bioinformatics and computational biology involve 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 and by rapid advances in dna sequencing technology. analyzing biological data to produce meaningful information involves writing and running software programs that use algorithms from graph theory, artificial intelligence, soft computing, data mining, image processing, and computer simulation. the algorithms in turn depend on theoretical foundations such as discrete mathematics, control theory, system theory, information theory, and statistics. sequence analysis since the phage φ-x174 was sequenced in 1977, the dna sequences of thousands of organisms have been decoded and stored in databases. this sequence information is analyzed to determine genes that encode proteins, rna genes, regulatory sequences, structural motifs, and repetitive sequences. a comparison of genes within a species or between different species can show similarities between protein functions, or relations between species (the use of molecular systematics to construct phylogenetic trees). with the growing amount of data, it long ago became impractical to analyze dna sequences manually. computer programs such as blast are used routinely to search sequences—as of 2008, from more than 260,000 organisms, containing over 190 billion nucleotides. image: 450 pixels sequencing analysis steps dna sequencing before sequences can be analyzed they have to be obtained from a data storage bank, for 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 shotgun sequencing technique (which was used, for example, by the institute for genomic research (tigr) to sequence the first bacterial genome, haemophilus influenzae) 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, 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. genome annotation 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. the first description of a comprehensive genome annotation system was published in 1995 by the team at the institute for genomic research that performed the first complete sequencing and analysis of the genome of a free-living organism, the bacterium haemophilus influenzae. owen white designed and built a software system to identify the genes encoding all proteins, transfer rnas, ribosomal rnas (and other sites) and to make initial functional assignments. most current genome annotation systems work similarly, but the programs available for analysis of genomic dna, such as the genemark program trained and used to find protein-coding genes in haemophilus influenzae, are constantly changing and improving. 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. computational evolutionary biology evolutionary biology is the study of the origin and descent of species, as well as their change over time. informatics has assisted evolutionary biologists by enabling researchers to: trace the evolution of a large number of organisms by measuring changes in their dna, rather