The use of computer science, mathematics, and information theory to model and analyze biological systems, especially systems involving genetic material.
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n. (used with a sing. verb)
Computer Desktop Encyclopedia:
bioinformatics
Using computers in biological research to analyze or predict the composition of molecules (nucleic acids, proteins, etc.) and model biologic systems. Bioinformatics is most prominent in the Human Genome Project, which has recorded the three billion chemical base pairs that make up the human DNA system. See Human Genome Project.
Genetics Encyclopedia:
Bioinformatics
Bioinformatics is the use of mathematical, statistical and computer methods to analyze biological, biochemical, and biophysical data. Because bioinformatics is a young, rapidly evolving field, however, it also has a number of other credible definitions. It can also be defined as the science and technology of learning, managing, and processing biological information. Bioinformatics is often focused on obtaining biologically oriented data, organizing this information into databases, developing methods to get useful information from such databases, and devising methods to integrate related data from disparate sources. The computer databases and algorithms are developed to speed up and enhance biological research.
Bioinformatics can help answer such questions as whether a newly analyzed gene is similar to any previously known gene, whether a protein's sequence can suggest how the protein functions, and whether the genes turned on in a cancer cell are different from those turned on in a healthy cell.
Databases and Analysis Programs
A good deal of the early work in bioinformatics focused on processing and analyzing gene and protein sequences catalogued in databases such as GenBank, EMBL, and SWISS-PROT. Such databases were developed in academia or by government-sponsored groups and served as repositories where scientists could store and share their sequence data with other researchers. With the start of the Human Genome Project in 1990, efforts in bioinformatics intensified, rising to the challenge of handling the large amounts of DNA sequence data being generated at an unprecedented rate. By the midto late-1990s, much of the efforts in bioinformatics centered around genomic data, generated by the Human Genome Project and by private companies, and around proteomic data.
Early analysis of sequence information focused on looking for similarities between genes and between proteins. Algorithms were developed to help researchers rapidly identify similar gene or protein sequences. Such tools were extremely useful for determining whether a newly sequenced piece of DNA was at all similar to sequences already entered in a database. To determine how multiple sequences align and to view their similarities, multiplealignment programs were developed. Such programs helped scientists compare the sequences of closely related genes or compare the sequence of a particular gene or protein as it appears in several species.
To better understand the functional roles of new nucleotide and amino acid sequences, researchers developed algorithms to look for particular sequence "domains." Domains are regions where a particular sequence of nucleotides or amino acids is indicative of function in the protein. For example, a protein may have a domain that binds to ATP or GTP, two important protein regulators.
In addition, these algorithms can detect sequences that denote a region involved in particular types of post-translational modifications, such as tyrosine phosphorylation. Tools such as prosite, blocks, prints, and Pfam can be used to detect and predict such protein domains in sequence data.
Structure is central to protein function, and another set of tools, including SWISS-MODEL, allows researchers to use gene and protein sequence data to predict a protein's three-dimensional structure. Such tools can help predict how mutations in a gene sequence could alter the three-dimensional structure of the corresponding protein. They accomplish such molecular modeling by comparing a novel sequence to the sequences of genes whose protein structures are known.
The majority of tools were developed as academic freeware distributed on the Internet. In the early-to mid-1990s, commercial companies began to develop their own proprietary algorithms and tools, as well as their own proprietary databases. Those databases were then marketed to pharmaceutical and biotech companies as well as to academic research groups. The most commercially viable and profitable businesses focused on the production and sale of proprietary DNA-and gene-sequence databases in the mid-to late-1990s. These databases primarily contained genetic information that were not in the public domain databases, such as GenBank, and they thus offered potential competitive advantages to the drug discovery groups of large pharmaceutical and biotech companies.
Applications of Bioinformatics to Drug Discovery
The application of bioinformatics to genomics data could be a huge potential boon for the discovery of new drugs. During the 1990s many pharmaceutical companies and biotech companies became convinced that they could speed up their drug-discovery pipelines by taking advantage of the data from the Human Genome Project as well as by funding their own internal genomics programs and by collaborating with third-party genomics companies.
The goal in such practical applications is to use such data as DNA sequence information and gene expression levels to help discover new drug targets. The vast majority of drugs target proteins, but there are a handful of drugs, such as some chemotherapeutic agents, that bind to DNA. In cases where the target is a protein, the drugs themselves are primarily small chemical molecules or, in some cases, small proteins, such as hormones, that bind to a larger protein in the body. Some drugs are therapeutic proteins delivered to the site of the disease.
The extent to which genomics will actually be able to help identify validated drug targets is uncertain. Genomics and bioinformatics are still young areas, and the drug development cycle can take up to ten years. As of 2001 relatively few of the drugs on the market or in the late stages of clinical trials were discovered via genomics or bioinformatics programs.
Specialists
Bioinformatics is applied to at least five major types of activities: data acquisition, database development, data analysis, data integration, and analysis of integrated data.
Data Acquisition
Data acquisition is primarily concerned with accessing and storing data generated directly off of laboratory instruments. Many of these instruments are either automated or semi-automated high-throughput instruments that generate large volumes of data. The Human Genome Project utilized hundreds of DNA sequencers, producing enormous amounts of data. The data had to be captured in the appropriate format, and it had to be capable of being linked to all the information related to the DNA samples, such as the species, tissue type, and quality parameters used in the experiments. This area of bioinformatics primarily relates to the use of "laboratory information management systems," which are the computer systems used to manage the information needs of a particular laboratory.
Database Development
Many laboratories generate large volumes of such data as DNA sequences, gene expression information, three-dimensional molecular structure, and high-throughput screening. Consequently, they must develop effective databases for storing and quickly accessing data. For each type of data, it is likely that a different database organization must be used. A database must be designed to allow efficient storage, search, and analysis of the data it contains. Designing a high-quality database is complicated by the fact that there are several formats for many types of data and a wide variety of ways in which scientists may want to use the data. Many of these databases are best built using a relational database architecture, often based on Oracle or Sybase.
A strong background in relational databases is a fundamental requirement for working in database development. Having some background in the molecular biology techniques used to generate the data is also important. Most critical for the bioinformatics specialist is to have a strong working relationship with the researchers who will be using the database and the ability to understand and interpret their needs into functional database capabilities.
Data Analysis
Being able to analyze data efficiently requires having a good database design, allowing researchers to query the database effectively and letting them quickly obtain the types of information they need to begin their data analysis. If queries cannot be performed, or if performance is tediously slow, the whole system breaks down, since scientists will not be inclined to use the database. Once data is obtained from the database, the user must be able to easily transform it into the format appropriate for the desired analysis tools.
This can be challenging, since researchers often use a combination of publicly available tools, tools developed in-house, and third-party commercial tools. Each tool may have different input and output formats. Starting in the late 1990s, there have been both commercial and in-house efforts at pharmaceutical and biotech companies to reduce the formatting complexities. Such simplification efforts focus on building analysis systems with a number of tools integrated within them such that the transfer of data between tools appears seamless to the end user.
Bioinformatics analysts have a broad range of opportunities. They may write specific algorithms to analyze data, or they may be expert users of analysis tools, helping scientists understand how the tools analyze the data and how to interpret results. A knowledge of various programming languages, such as Java, PERL, C, C++, and Visual Basic, is very useful, if not required, for those working in this area.
Data Integration
Once information has been analyzed, a researcher often needs to associate or integrate it with related data from other databases. For example, a scientist may run a series of gene expression analysis experiments and observe that a particular set of 100 genes is more highly expressed in cancerous lung tissue than in normal lung tissue. The scientist might wonder which of the genes is most likely to be truly related to the disease. To answer the question, the researcher might try to find out more information about those 100 genes, including any associated gene sequence, protein, enzyme, disease, metabolic pathways, or signal transduction pathway data.
Such information will help the researcher narrow the list down to a smaller set of genes. Finding this information, however, requires connections or links between the different databases and a good way to present and store the information. An understanding of database architectures and the relationship between the various biological concepts in the databases is key to doing effective data integration.
Analysis of Integrated Data
Once various types of data are integrated, users need a good way to present these various pieces of data so they can be interpreted and analyzed. The information should be capable of being stored and retrieved so that, over time, various pieces of information can be combined to form a "knowledge base" that can be extended as more experiments are run and additional data are integrated from other sources. This type of work requires skills related to database design and architecture. It also requires specific programming skills in various computer languages, as well as expertise in developing interfaces between a computer and its user.
Bibliography
Howard, Ken. "The Bioinformatics Gold Rush." Scientific American 283, no. 1 (2000): 58-64.
Internet Resources
"EID V3 N3: Host Genes and HIV." Centers for Disease Control and Prevention. http://www.cdc.gov/ncidod/eid/vol3no3/smith.htm.
EMBL Nucleotide Sequence Database. Release 69. December 2001. European Bioinformatics Institute. http://www.ebi.ac.uk/.
GenBank. National Center for Biotechnology Information. http://www.ncbi.nlm.nih.gov/.
SWISS-PROT. Swiss Institute of Bioinformatics. http://www.expasy.org/sprot/.
—Anthony J. Recupero
Wikipedia:
bioinformatics
Bioinformatics and computational biology involve the use of techniques including applied mathematics, informatics, statistics, computer science, artificial intelligence, chemistry, and biochemistry to solve biological problems usually on the molecular level. 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.
Introduction
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 arising 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 or simulated data, with the primary goal of discovery and the advancement of biological knowledge. Put more simply, bioinformatics is concerned with the information while computational biology is concerned with the hypotheses. 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. Bioinformatics is also often specified as an applied subfield of the more general discipline of Biomedical informatics.
A common thread in projects in bioinformatics and computational biology is the use of mathematical tools to extract useful
information from data produced by high-throughput biological techniques such as genome
sequencing. A representative problem in bioinformatics is the assembly of high-quality genome sequences from fragmentary
"shotgun" DNA sequencing. Other common problems include the study of gene regulation using data from
Major research areas
Sequence analysis
Since the Phage Φ-X174 was sequenced in 1977, the DNA sequences of hundreds of organisms have been decoded and stored in databases. The information is analyzed to determine genes that encode polypeptides, 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 would 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 influenzae) 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.
See also: sequence analysis, sequence profiling tool, sequence motif.
Genome annotation
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 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 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; 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 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
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, but the two areas are unrelated.
Measuring biodiversity
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; uBio Project.
Analysis of gene expression
The expression of many genes can be determined by measuring mRNA levels with multiple techniques including
Analysis of regulation
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
Analysis of protein expression
Protein
Analysis of mutations in cancer
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 technology 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. Again the massive amounts and new types of data generate new opportunities for bioinformaticians. 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.
Another type of data that requires novel informatics development is the analysis of lesions found to be recurrent across many tumors .
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 - 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 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.
Other techniques for predicting protein structure include protein threading and de novo (from scratch) physics-based modeling.
See also structural motif and structural domain.
Comparative 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, 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.
See also comparative genomics, bayesian network and protein family.
Modeling biological systems
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 analysis
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
- high-throughput and high-fidelity quantification and sub-cellular localization (high-content screening, cytohistopathology)
- 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
Protein-protein docking
In the last two decades, tens of thousands of protein three-dimensional structures are 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 place to work on in this field.
Software tools
Software tools for bioinformatics range from simple command-line tools, to more complex graphical programs and standalone
web-services. The computational biology tool best-known among biologists is probably BLAST, an
algorithm for determining the similarity of arbitrary sequences against other sequences, possibly from curated databases of
protein or DNA sequences. The NCBI provides a popular
web-based implementation that searches their databases.
SOAP-based (Service Oriented Architecture Protocol) 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 availability of these SOAP-based bioinformatics web services through systems such as the BioMoby service register demonstrate the applicability of web based bioinformatics solutions. These tools range from a collection of standalone tools with a common data format under a single, standalone or web-based interface, to integrative and extensible bioinformatics workflow management systems.
See also
Related topics
Related fields
References
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This article or section includes a list of references or a list of external
links, but its sources remain unclear because it lacks in-text citations. You can improve this article by introducing more precise citations. |
- Aluru, Srinivas, ed. Handbook of Computational Molecular Biology. Chapman & Hall/Crc, 2006. ISBN 1584884061 (Chapman & Hall/Crc Computer and Information Science Series)
- Baldi, P and Brunak, S, Bioinformatics: The Machine Learning Approach, 2nd edition. MIT Press, 2001. ISBN 0-262-02506-X
- Barnes, M.R. and Gray, I.C., eds., Bioinformatics for Geneticists, first edition. Wiley, 2003. ISBN 0-470-84394-2
- Baxevanis, A.D. and Ouellette, B.F.F., eds., Bioinformatics: A Practical Guide to the Analysis of Genes and Proteins, third edition. Wiley, 2005. ISBN 0-471-47878-4
- Baxevanis, A.D., Petsko, G.A., Stein, L.D., and Stormo, G.D., eds., Current Protocols in Bioinformatics. Wiley, 2007. ISBN 0-471-25093-7
- Claverie, J.M. and C. Notredame, Bioinformatics for Dummies. Wiley, 2003. ISBN 0-7645-1696-5
- Cristianini, N. and Hahn, M. Introduction to Computational Genomics, Cambridge University Press, 2006. (ISBN 9780521671910 | ISBN 0521671914)
- Durbin, R., S. Eddy, A. Krogh and G. Mitchison, Biological sequence analysis. Cambridge University Press, 1998. ISBN 0-521-62971-3
- Gilbert, D. Bioinformatics software resources. Briefings in Bioinformatics, Briefings in Bioinformatics, 2004 5(3):300-304.
- Keedwell, E., Intelligent Bioinformatics: The Application of Artificial Intelligence Techniques to Bioinformatics Problems. Wiley, 2005. ISBN 0-470-02175-6
- Kohane, et al. Microarrays for an Integrative Genomics. The MIT Press, 2002. ISBN 0-262-11271-X
- Lund, O. et al. Immunological Bioinformatics. The MIT Press, 2005. ISBN 0-262-12280-4
- Michael S. Waterman, Introduction to Computational Biology: Sequences, Maps and Genomes. CRC Press, 1995. ISBN 0-412-99391-0
- Mount, David W. Bioinformatics: Sequence and Genome Analysis Spring Harbor Press, May 2002. ISBN 0-87969-608-7
- Pachter, Lior and Sturmfels, Bernd. "Algebraic Statistics for Computational Biology" Cambridge University Press, 2005. ISBN 0-521-85700-7
- Pevzner, Pavel A. Computational Molecular Biology: An Algorithmic Approach The MIT Press, 2000. ISBN 0-262-16197-4
External links
At Wikiversity you can learn more and teach others about
Bioinformatics at:
- Major Organizations
- Bioinformatics Organization (Bioinformatics.Org): The Open-Access Institute
- EMBnet
- European Bioinformatics Institute
- European Molecular Biology Laboratory
- The International Society for Computational Biology
- National Center for Biotechnology Information
- National Institutes of Health homepage
- Open Bioinformatics Foundation: umbrella non-profit organization supporting certain open-source projects in bioinformatics
- Swiss Institute of Bioinformatics
- Wellcome Trust Sanger Institute
- Major Journals
- Algorithms in Molecular Biology
- Bioinformatics
- BMC Bioinformatics
- Briefings in Bioinformatics
- Evolutionary Bioinformatics
- Genome Research
- The International Journal of Biostatistics
- Journal of Computational Biology
- Molecular Systems Biology
- PLoS Computational Biology
- Statistical Applications in Genetic and Molecular Biology
- International Journal of Bioinformatics Research and Applications
- Other sites
| Genomics topics |
| Genome project | Paleopolyploidy | Glycomics | Human Genome Project | Proteomics |
| Chemogenomics | Structural genomics | Pharmacogenetics | Pharmacogenomics | Toxicogenomics | Computational genomics |
| Bioinformatics | Cheminformatics | Systems biology |
| Major subtopics of biology |
|---|
| Anatomy - Astrobiology - Biochemistry - Bioinformatics - Botany - Cell biology - Ecology - Developmental biology - Evolutionary biology - Genetics - Genomics - Marine biology - Human biology - Microbiology - Molecular biology - Origin of life - Paleontology - Parasitology - Pathology - Physiology - Taxonomy - Zoology |
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