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Biological: Behavioural genetics · Evolutionary psychology · Neuroanatomy · Neurochemistry · Neuroendocrinology · Neuroscience · Psychoneuroimmunology · Physiological Psychology · Psychopharmacology (Index, Outline)
Bioinformatics and computational biology involve the use of techniques from applied mathematics, informatics, statistics, and computer science to solve biological problems. Research in computational biology often overlaps with systems biology. Major research efforts in the field include sequence alignment, gene finding, genome assembly, protein structure alignment, protein structure prediction, prediction of gene expression and protein-protein interactions, and the modeling of evolution.
The terms bioinformatics and computational biology are often used interchangeably. However bioinformatics more properly refers to the creation and advancement of algorithms, computational and statistical techniques, and theory to solve formal and practical problems posed by or inspired from the management and analysis of biological data. Computational biology, on the other hand, refers to hypothesis-driven investigation of a specific biological problem using computers, carried out with experimental and simulated data, with the primary goal of discovery and the advancement of biological knowledge. A similar distinction is made by National Institutes of Health in their working definitions of Bioinformatics and Computational Biology, where it is further emphasized that there is a tight coupling of developments and knowledge between the more hypothesis-driven research in computational biology and technique-driven research in bioinformatics. Computational biology also includes lesser known but equally important subdisciplines such as computational biochemistry and computational biophysics.
A common thread in projects in bioinformatics and computational biology is the use of mathematical tools to extract useful information from noisy data produced by high-throughput biological techniques such as genomics (The field of data mining overlaps with computational biology in this regard). A representative problem in bioinformatics is the assembly of high-quality DNA sequences from fragmentary "shotgun" DNA sequencing, while in computational biology, a representative problem might be statistical testing of a hypothesis of common gene regulation using data from mRNA microarrays or mass spectrometry.
Major Research AreasEdit
- Main article: Sequence alignment
Since the Phage Φ-X174 was sequenced in 1977, the DNA sequences of more and more organisms have been decoded and stored in electronic databases. This data is analyzed to determine genes that code for proteins, as well as regulatory 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 are used to search the genome of thousands of organisms, containing billions of nucleotides. These programs can compensate for mutations (exchanged, deleted or inserted bases) in the DNA sequence, in order 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 influenza) does not give a sequential list of nucleotides, but instead the sequences of thousands of small DNA fragments (each about 600-800 nucleotides long). The ends of these fragments overlap and, when aligned in the right way, make up the complete genome. Shotgun sequencing yields sequence data quickly, but the task of assembling the fragments can be quite complicated for larger genomes. In the case of the Human Genome Project, it took several months of CPU time (on a circa-2000 vintage DEC Alpha computer) to assemble the fragments. 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 the automatic search for genes and regulatory sequences within a genome. Not all of the nucleotides within a genome are 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--for example, in the use of DNA sequences for protein identification.
- Main article: Gene finding
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 Owen White, who was part of the team 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.
Computational evolutionary biologyEdit
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; it has enabled 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,
- more recently, compare entire genomes, which permits the study of more complex evolutionary events, such as gene duplication, lateral gene transfer, and the prediction of bacterial speciation factors,
- 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
Future work endeavours to reconstruct the now more complex tree of life.
The area of research within computer science that uses genetic algorithms is sometimes confused with computational evolutionary biology. Work in this area involves using specialized computer software to improve equations, algorithms, or integrated circuit designs. It is inspired by evolutionary principles such as replication, diversification through recombination or mutation, fitness, survival through selection or culling, and iteration, collectively called a Darwinian machine or Darwinian ratchet.
Biodiversity of an ecosystem might be defined as the total genomic complement of a particular environment, from all of the species present, whether it is a biofilm in an abandoned mine, a drop of sea water, a scoop of soil, or the entire biosphere of the planet Earth. Databases are used to collect the species names, descriptions, distributions, genetic information, status and size of populations, habitat needs, and how each organism interacts with other species. Specialized software programs are used to find, visualize, and analyze the information, and most importantly, communicate it to other people. Computer simulations model such things as population dynamics, or calculate the cumulative genetic health of a breeding pool (in agriculture) or endangered population (in conservation). One very exciting potential of this field is that entire DNA sequences, or genomes of endangered species can be preserved, allowing the results of Nature's genetic experiment to be remembered in silico, and possibly reused in the future, even if that species is eventually lost.
Important Projects: Species 2000 project.
Analysis of gene expressionEdit
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), 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.
Analysis of regulationEdit
Regulation is the complex orchestration of events starting with an extra-cellular signal and ultimately leading to an increase or decrease in the activity of one or more protein molecules. Bioinformatics techniques have been applied to explore various steps in this process. For example, promoter analysis involves the elucidation and study of sequence motifs in the genomic region 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. Further analysis could take a variety of directions: one 2004 study analyzed the promoter sequences of co-expressed (clustered together) genes to find common regulatory elements and used machine learning techniques to identify the promoter elements involved in regulating each cluster.
Analysis of protein expressionEdit
Protein microarrays and high throughput (HT) mass spectrometry (MS) can provide a snapshot of the proteins present in a biological sample. Bioinformatics is very much involved in making sense of protein microarray and HT MS data; the former approach faces similar problems as with microarrays targeted at mRNA, the latter involves the problem of matching large amounts of mass data against predicted masses from protein sequence databases, and the complicated statistical analysis of samples where multiple, but incomplete peptides from each protein are detected.
Analysis of mutations in cancerEdit
Massive sequencing efforts are currently underway to identify point mutations in a variety of genes in cancer. The sheer volume of data produced requires automated systems to read sequence data, and to compare the sequencing results to the known sequence of the human genome, including known germline polymorphisms.
Oligonucleotide microarrays, including comparative genomic hybridization and single nucleotide polymorphism arrays, able to probe simultaneously up to several hundred thousand sites throughout the genome are being used to identify chromosomal gains and losses in cancer. Hidden Markov model and change-point analysis methods are being developed to infer real copy number changes from often noisy data. Further informatics approaches are being developed to understand the implications of lesions found to be recurrent across many tumors.
Some modern tools (e.g. Quantum 3.1 ) provide tool for changing the protein sequence at specific sites through alterations to its amino acids and predict changes in the bioactivity after mutations.
Prediction of protein structureEdit
- Main article: Protein structure prediction
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 - aka 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 modelling, 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 haemoglobin in humans and the haemoglobin 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.
Other techniques for predicting protein structure include protein threading and de novo (from scratch) physics-based modeling.
Comparative genomics Edit
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, ranging 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.
Many of these studies are based on the homology detection and protein families computation.
Modeling biological systemsEdit
- Main article: Systems biology
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.
High-throughput image analysisEdit
Computational technologies are used to accelerate or fully automate the processing, quantification and analysis of large amounts of high-information-content biomedical imagery. Modern image analysis systems augment an observer's ability to make measurements from a large or complex set of images, by improving accuracy, objectivity, or speed. A fully developed analysis system may completely replace the observer. Although these systems are not unique to biomedical imagery, biomedical imaging is becoming more important for both diagnostics and research. Some examples are:
- high-throughput and high-fidelity quantification and sub-cellular localization (high-content screening, cytohistopathology)
- 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 behavioural observations from extended video recordings of laboratory animals
- infrared measurements for metabolic activity determination
The computational biology tool best-known among biologists is probably BLAST, an algorithm for searching large databases of protein or DNA sequences. NCBI provides a popular implementation that searches their massive sequence databases. Bioinformatic meta search engines (Entrez, Bioinformatic Harvester) help finding relevant information from several databases. There are also free Web-based software designed for structural bioinformatics such as  STING.
Computer scripting languages such as Perl and Python are often used to interface with biological databases and parse output from bioinformatics programs. Communities of bioinformatics programmers have set up free/open source projects such as EMBOSS, Bioconductor, BioPerl, BioLinux, BioPython, BioRuby, and BioJava which develop and distribute shared programming tools and objects (as program modules) that make bioinformatics easier.
An integrated software workbench consisting of many free/open source tools described above and many others is known as VigyaanCD. Taverna an open-source bioinformatics workbench that utilises a workflow model of experimental design. Taverna is included as part of the myGRID package of e-science software. Quantum 3.1 is an example of the bioinformatics post-QSAR technology applying quantum and molecular physics instead of statistical methods. Genevestigator is an example of how large-scale gene expression microarray data is used to predict gene function based on contextual information.
More recently, SOAP-based interfaces have been developed for a wide variety of bioinformatics applications such as blast, fasta, EMBOSS, clustalw, t-coffee, MUSCLE and many others. These are available from the EBI at EBI Web Services.
See also Edit
- Biomedical informatics
- Biologically-inspired computing
- Computational biomodeling
- Computational genomics
- List of Bioinformatics Software Projects
- List of Bioinformatics Research Groups
- List of publications in bioinformatics
- Molecular modelling
- Metabolic network modelling
- Natural computation
- Protein-protein interaction prediction
- Biosynthetic phylogeny
Related fields Edit
- applied mathematics — biology — computer science — informatics — mathematical biology — theoretical biology — Scientific computing — cheminformatics — neuroinformatics — computational science — Systems biology — statistics
- Wikiomics.org: bioinformatics wiki for users and developers of bioinformatics worldwide. Focused on practical questions and pointers towards both academic publications and software resources (opened November 2005).
- Major Societies
- The International Society for Computational Biology
- Major Organizations
- Bioinformatics Organization (Bioinformatics.Org): The Open-Access Institute
- EMBnet is a science-based group of collaborating nodes throughout Europe and a number of nodes outside Europe
- UCSC Center for Biomolecular Science and Engineering
- Laurence H. Baker Center for Bioinformatics and Biological Statistics at Iowa State University
- Swiss Institute of Bioinformatics
- European Bioinformatics Institute
- European Molecular Biology Laboratory
- Genetic Information Research Institute
- National Center for Biotechnology Information
- Open Bioinformatics Foundation: umbrella non-profit organization supporting certain open-source projects in bioinformatics
- National Center for Biomedical Ontology
- South African National Bioinformatics Institute
- US Department of Energy Joint Genome Institute
- Commercial vendors
- Comprehensive, Reviewed, Third-Party Course Lists
- Major Journals
- Statistical Applications in Genetic and Molecular Biology
- The International Journal of Biostatistics
- PLoS Computational Biology
- Evolutionary Bioinformatics Online
- Nature Molecular Systems Biology
- Bioinformatics journal
- BMC Bioinformatics journal
- International Journal of Biological Sciences
- Cancer Informatics Open Access journal
- Online Journal of Bioinformatics
- EMBnet.News Online Journal
- Genome Research
- Other Important Sites
Notes & referencesEdit
- ↑ Beer MA, Tavazoie S. "Predicting gene expression from sequence." In Cell. 2004 Apr 16;117(2):185-98.
- Baldi, P and Brunak, S, Bioinformatics: The Machine Learning Approach, 2nd edition. MIT Press, 2001. ISBN 0-262-02506-X
- Barnes, M.R. and Gray, I.C., eds., Bioinformatics for Geneticists, first edition. Wiley, 2003. ISBN 0470843942
- Baxevanis, A.D. and Ouellette, B.F.F., eds., Bioinformatics: A Practical Guide to the Analysis of Genes and Proteins, third edition. Wiley, 2005. ISBN 0471478784
- Claverie, J.M. and C. Notredame, Bioinformatics for Dummies. Wiley, 2003. ISBN 0764516965
- Durbin, R., S. Eddy, A. Krogh and G. Mitchison, Biological sequence analysis. Cambridge University Press, 1998. ISBN 0521629713
- Kohane, et al. Microarrays for an Integrative Genomics. The MIT Press, 2002. ISBN 026211271X
- Lund, O. et al. Immunological Bioinformatics. The MIT Press, 2005. ISBN 0-262-12280-4
- Michael S. Waterman, Introduction to Computational Biology: Sequences, Maps and Genomes. CRC Press, 1995. ISBN 0412993910
- Mount, David W. Bioinformatics: Sequence and Genome Analysis Spring Harbor Press, May 2002. ISBN 0879696087
- Pachter, Lior and Sturmfels, Bernd. "Algebraic Statistics for Computational Biology" Cambridge University Press, 2005. ISBN 0521857007
- Pevzner, Pavel A. Computational Molecular Biology: An Algorithmic Approach The MIT Press, 2000. ISBN 0262161974
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