Informatics Educational Institutions & Programs
Bioinformatics is the application of computer science and information technology to the field of biology, with a primary goal of understanding biological processes. What sets it apart from other approaches, however, is its focus on developing and applying computationally intensive techniques (e.g., pattern recognition, data mining, machine learning algorithms, and visualization) to achieve this goal. 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, and the modeling of evolution.
The term "bioinformatics" was coined by Paulien Hogeweg and Ben Hesper in 1978 for "the study of informatic processes in biotic systems." Its primary use since at least the late 1980s has been in genomics and genetics, particularly in those areas of genomics involving large-scale DNA sequencing. However, rapid developments in genomic, molecular research, and information technologies have combined to produce a tremendous amount of information related to molecular and other types of biology. 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.
Common activities in bioinformatics include mapping and analyzing DNA and protein sequences, aligning different DNA and protein sequences to compare them, and creating and viewing 3-D models of protein structures.
- 1 History
- 2 Bioinformatics vs. computational biology
- 3 Major research areas
- 4 Structural bioinformatic approaches
- 5 Software and tools
- 6 Further reading
- 7 External links
- 8 Notes
- 9 References
Arguably one of the first "bioinformatics" projects—though the concept didn't yet exist—involved the 1965 creation and maintenance of a protein sequence database called the Atlas of Protein Sequence and Structure by Margaret O. Dayhoff, Richard V. Eck, and Robert S. Ledley. The work grew out of their "biochemical investigation of the relations between the structures and function of proteins and the theoretical attempt to decipher the genetic code." Six years later, the Brookhaven National Laboratory and the Cambridge Crystallographic Data Centre jointly created the Protein Data Bank, intended as a public database of three-dimensional protein structures.
The work at Brookhaven would go on to influence others in the field to contribute, with 23 structures contributed in 1976, breaking 5,000 by the end of 1996 and 40,000 in 2006. The significant growth in contributions was fueled by several events, including: Peter Y. Chou and Gerald D. Fasman's 1974 creation (and later, refinement) of a protein structure prediction algorithm; David J. Lipman and William R. Pearson's 1985 development (and later, refinement) of FASTP (later FASTA) as well as Stephen Altschul and company's 1990 development and refinement of BLAST, both database sequence searching algorithms and programs; and the formal start of the Human Genome Project in 1990.
A flurry of genome studies went on to produce unprecedented amounts of biological data, creating a sudden demand for rapid and efficient computational tools to manage and analyze the data. "The development of these computational tools depended on knowledge generated from a wide range of disciplines including mathematics, statistics, computer science, information technology, and molecular biology." The merger of these disciplines largely went on to form what is now known as bioinformatics.
Bioinformatics vs. computational biology
In order to study how normal cellular activities are altered in different disease states, 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, including nucleotide and amino acid sequences, protein domains, and protein structures. However, the related field of computational biology differs slightly from bioinformatics. Jin Xiong, author of Essential Bioinformatics, describes the differences between the two as such:
Bioinformatics is limited to sequence, structural, and functional analysis of genes and genomes and their corresponding products and is often considered computational molecular biology. However, computational biology encompasses all biological areas that involve computation. For example, mathematical modeling of ecosystems, population dynamics, application of the game theory in behavioral studies, and phylogenetic construction using fossil records all employ computational tools, but do not necessarily involve biological macromolecules.
Major research areas
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 polypeptides (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. Today, computer programs such as BLAST are used daily to search sequences from more than 260,000 organisms, containing over 190 billion nucleotides. These programs can compensate for mutations (exchanged, deleted, or inserted bases) in the DNA sequence, to identify sequences that are related, but not identical. A variant of this sequence alignment is used in the sequencing process itself. The so-called shotgun sequencing technique—which was used, for example, by The Institute for Genomic Research to sequence the first bacterial genome, Haemophilus influenzae—does not produce entire chromosomes, but instead 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 will usually contain numerous gaps that have to be filled in later. Shotgun sequencing is the method of choice for virtually all genomes sequenced today, and genome assembly algorithms are a critical area of bioinformatics research.
Another aspect of bioinformatics in sequence analysis is annotation, which involves computational gene finding to search for protein-coding genes, RNA genes, and other functional sequences within a genome. Not all of the nucleotides within a genome are part of genes. Within the genome of higher organisms, large parts of the DNA do not serve any obvious purpose. This so-called junk DNA may, however, contain unrecognized functional elements. Bioinformatics helps to bridge the gap between genome and proteome projects, as in the use of DNA sequences for protein identification.
Gene expression analysis
The expression of many genes can be determined by measuring mRNA levels with multiple techniques including microarrays, expressed cDNA sequence tag (EST) sequencing, serial analysis of gene expression (SAGE) tag sequencing, massively parallel signature sequencing (MPSS), 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 from noise in high-throughput gene expression studies. Such studies are often used to determine the genes implicated in a disorder: one might compare microarray data from cancerous 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.
Regulation is the complex orchestration of events starting with an extracellular signal such as a hormone and leading to an increase or decrease in the activity of one or more proteins. Bioinformatics techniques have been applied to explore various steps in this process. For example, promoter analysis involves the identification and study of sequence motifs in the DNA surrounding the coding region of a gene. These motifs influence the extent to which that region is transcribed into mRNA. Expression data can be used to infer gene regulation: one might compare 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, along with various stress conditions (heat shock, starvation, etc.). One can then apply clustering algorithms to that 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.
Protein expression analysis
Protein microarrays and high-throughput mass spectrometry can provide a snapshot of the proteins present in a biological sample. Bioinformatics is very much involved in making sense of protein microarray and mass spectrometry data; the former approach faces similar problems as with microarrays targeted at mRNA, the latter involves the problem of matching large amounts of mass data against predicted masses from protein sequence databases, and the complicated statistical analysis of samples where multiple, but incomplete peptides from each protein are detected.
Cancer mutation analysis
In cancer, the genomes of affected cells are rearranged in complex or even unpredictable ways. Massive sequencing efforts are used to identify previously unknown point mutations in a variety of genes in cancer. Bioinformaticians continue to produce specialized automated systems to manage the sheer volume of sequence data produced, and they create new algorithms and software to compare the sequencing results to the growing collection of human genome sequences and germline polymorphisms. New physical detection technologies are employed, such as oligonucleotide microarrays to identify chromosomal gains and losses (called comparative genomic hybridization), and single-nucleotide polymorphism arrays to detect known point mutations. These detection methods simultaneously measure several hundred thousand sites throughout the genome, and when used in high-throughput to measure thousands of samples, generate terabytes of data per experiment. The data is often found to contain considerable variability, or noise, and thus hidden Markov model and change-point analysis methods are being developed to infer real copy number changes.
In the context of genomics, annotation is the process of marking the genes and other biological features in a DNA sequence. The first genome annotation software system was designed in 1995 by Dr. Owen White, who was part of the team at The Institute for Genomic Research that sequenced and analyzed the first genome of a free-living organism to be decoded, the bacterium Haemophilus influenzae. Dr. White built a software system to find the genes (places in the DNA sequence that encode a protein), the transfer RNA, and other features, and to make initial assignments of function to those genes. Most current genome annotation systems work similarly, but the programs available for analysis of genomic DNA are constantly changing and improving.
Comparative and computational genomics
The core of comparative genome analysis is the establishment of the correspondence between genes (orthology analysis) or other genomic features in different organisms. It is these intergenomic maps that make it possible 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. Ultimately, whole genomes are involved in processes of hybridization, polyploidization and endosymbiosis, often leading to rapid speciation. The complexity of genome evolution poses many exciting challenges to developers of mathematical models and algorithms, who have recourse to a spectra of algorithmic, statistical, and mathematical techniques. Examples range from exact, heuristics, fixed-parameter, and approximation algorithms for problems based on parsimony models to Markov Chain Monte Carlo algorithms for Bayesian analysis of problems based on probabilistic models.
Biological systems modeling
Systems biology involves the use of computer simulations of cellular subsystems (such as the networks of metabolites and enzymes which comprise metabolism, signal transduction pathways, and gene regulatory networks) to both analyze and visualize the complex connections of these cellular processes. Artificial life or virtual evolution attempts to understand evolutionary processes via the computer simulation of simple (artificial) life forms.
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 in several key ways, enabling researchers to:
- trace the evolution of a large number of organisms by measuring changes in their DNA, rather than through physical taxonomy or physiological observations alone.
- compare entire genomes, which permits the study of more complex evolutionary events, such as gene duplication, horizontal gene transfer, and the prediction of factors important in bacterial speciation.
- build complex computational models of populations to predict the outcome of the system over time.
- track and share information on an increasingly large number of species and organisms.
The area of research within computer science that uses genetic algorithms is sometimes confused with computational evolutionary biology, but the two areas are not necessarily related.
The sheer amount of published literature makes it virtually impossible to read every paper, resulting in disjointed subfields of research. Literature analysis aims to employ computational and statistical linguistics to mine this growing library of text resources. For example:
- abbreviation recognition - identify the long-form and abbreviation of biological terms
- named entity recognition - recognizing biological terms such as gene names
- protein-protein interaction - identify which proteins interact with which proteins from text
The area of research uses statistics and computational linguistics, and is substantially influenced by them.
Structural bioinformatic approaches
Prediction of protein structure
Protein structure prediction is another important application of bioinformatics. The amino acid sequence of a protein, the so-called primary structure, can be easily determined from the sequence on the gene that codes for it. In the vast majority of cases, this primary structure uniquely determines a structure in its native environment. (Of course, there are exceptions, such as the bovine spongiform encephalopathy (a.k.a. Mad Cow Disease) prion.) Knowledge of this structure is vital in understanding the function of the protein. For lack of better terms, structural information is usually classified as one of secondary, tertiary, and quaternary structure. A viable general solution to such predictions remains an open problem. As of now, most efforts have been directed towards heuristics that work most of the time.
One of the key ideas in bioinformatics is the notion of homology. In the genomic branch of bioinformatics, homology is used to predict the function of a gene: if the sequence of gene A, whose function is known, is homologous to the sequence of gene B, whose function is unknown, one could infer that B may share A's function. In the structural branch of bioinformatics, homology is used to determine which parts of a protein are important in structure formation and interaction with other proteins. In a technique called homology modeling, this information is used to predict the structure of a protein once the structure of a homologous protein is known. This currently remains the only way to predict protein structures reliably.
One example of this is the similar protein homology between hemoglobin in humans and the hemoglobin in legumes (leghemoglobin). Both serve the same purpose of transporting oxygen in the organism. Though both of these proteins have completely different amino acid sequences, their protein structures are virtually identical, which reflects their near identical purposes.
Efficient software is available today for studying interactions among proteins, ligands, and peptides. Types of interactions most often encountered in the field include protein–ligand (including drug), protein–protein and protein–peptide.
Molecular dynamic simulation of movement of atoms about rotatable bonds is the fundamental principle behind computational algorithms, termed docking algorithms for studying molecular interactions.
In the last two decades, tens of thousands of protein three-dimensional structures have been determined by X-ray crystallography and protein nuclear magnetic resonance spectroscopy (protein NMR). One central question for the biological scientist is whether it is practical to predict possible protein–protein interactions only based on these 3D shapes, without doing protein–protein interaction experiments. A variety of methods have been developed to tackle the protein–protein docking problem, though it seems that there is still much work to be done in this field.
Software and tools
Software tools for bioinformatics range from simple command-line tools to more complex graphical programs and standalone web-services available from various bioinformatics companies or public institutions.
Open source bioinformatics software
Many free and open-source bioinformatics software tools have existed since the 1980s. The combination of a continued need for new algorithms for the analysis of emerging types of biological readouts, the potential for innovative in silico experiments, and freely available open code bases have helped to create opportunities for all research groups to contribute to both bioinformatics and the range of open-source software available, regardless of their funding arrangements. In order to maintain this tradition and create further opportunities, the non-profit Open Bioinformatics Foundation have supported the annual Bioinformatics Open Source Conference (BOSC) since 2000.
Web services in bioinformatics
SOAP and REST-based interfaces have been developed for a wide variety of bioinformatics applications, allowing an application running on one computer in one part of the world to use algorithms, data, and computing resources on servers in other parts of the world. The main advantages derive from the fact that end users do not have to deal with software and database maintenance overheads.
Basic bioinformatics services are classified by the European Bioinformatics Institute (EBI) into numerous categories, including ontologies, structures, gene expression, proteins, etc. The availability of these service-oriented bioinformatics resources demonstrate the applicability of web-based bioinformatics solutions, and range from a collection of standalone tools with a common data format under a single, standalone, or web-based interface, to integrative, distributed, and extensible bioinformatics workflow management systems.
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Some elements of this article are reused from the Wikipedia article.
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