ontologies and standards have emerged in bioscience research during the past decade in response to the fast accumulation of biology data , largely because of the advancement of bioscience research technologies . the main purposes of the ontologies and standards are to ( 1 ) organize the data in a controlled and structured format ; ( 2 ) enable scientists from different research disciplines to share the data and collaborate ; and ( 3 ) allow different computation tools and software to access and interpret the data in an unambiguous way . after 10 years of development , the concept of applying ontologies and standards in bioscience research has been well accepted , especially in the field of bioinformatics , systems biology , and computational biology . although it has gained significant momentum in wet - lab research fields , especially those using high - throughput technologies , it still remains a challenge to be adopted by a wider research community . it is a study of properties , events , processes , and relations of existence ( smith , 2003 ) . the information representations built by different groups and communities often used a diverse variety of terms and concepts . the same concept could be represented with different nomenclatures by different groups , while identical labels might be used for different meanings . when the information needed to be shared and transferred among different groups or accessed by different platforms , a controlled and structured expression of the information became essential . as a result , ontology was used by computer and information scientists to describe these controlled terms , concepts , and descriptions of the information . this ontology differs from the classical philosophical ontology in that it is expressed in a machine - readable format , and that it assesses in terms of usefulness rather than truth . gene ontology ( go ) was the first ontology designed as the formal representation of biological knowledge ( ashburner et al . biology research used to be conducted within small research groups or communities that had common interests in a particular organism or a small group of molecules . molecular biology required wider collaboration among researchers of diverse disciplines , such as geneticists , biochemists , and molecular biologists . a geneticist could name a gene differently from a molecular biologist , while scientists working on different organisms might use different naming conventions to name the same  or strictly speaking , evolutionarily related or orthologous  genes in the organism of their interest . for example , drosophila geneticists first identified an allele that was important to potassium ion conductance on cell membrane . this allele was named shaker based on the phenotype when it mutated ( salkoff and wyman , 1981 ) . subsequently , a number of drosophila shaker homologs and their orthologs in mammalians were cloned , and a variety of nomenclatures was used in naming these genes ( e.g. , shaw , shab , rck1 , drk1 ; perney and kaczmarek , 1991 ; pongs , 1992 ) . the urge to provide a more unified nomenclature system to facilitate communication and collaboration prompted the initial proposal of a naming standard for these potassium channels based on phylogenetic relationships ( chandy , 1991 ; chandy and gutman , 1993 ) . the system was later refined and adopted by the community ( gutman et al . this case - by - case approach was quite common within various research communities until the genome - sequencing era . near the end of the last century , a huge amount of molecular sequence data was produced because of the advancement of dna sequencing technology . the conservation of sequences and functions of proteins across different organisms lets scientists believe that a unification of nomenclature should be given to characterize genes across all organisms . this led to the founding of the gene ontology consortium and the creation of go ( ashburner et al . , 2000 ) . gene ontology covers three basic domains of biology knowledge : molecular function , biological process , and cellular component . any given gene can be classified by one or more terms from any of the three domains . significant progress has been made in go annotation , mainly by major model organism databases ( hong et al . , 2010 ) and reference genome project ( reference genome group of the gene ontology consortium , 2009 ) . it is also supported by a large number of software tools . because of the success of this pioneer work , a number of ontologies were subsequently proposed and developed to cover other domains of biology , such as anatomy , structure , disease , phenotype , and pathways . thus , open biological and biomedical ontologies ( obo ) foundry was formed to provide a collaborative environment for coordinated expansion of ontologies that are interoperable and logically well formed ( smith et al . , 2007 ) . most ontologies in obo foundry are driven by the biologists trying to understand the properties and functions of biological entities . examples are , in addition to go , ontology for chemical entities ( chebi ; natale et al . , 2007 ; , 2008 ) , protein ontology ( pro ; natale et al . , 2007 ) , ontology for phenotype quality ( pato ; sprague et al . , 2008 ) , and anatomy ontologies . the foundry also includes a number of candidate ontologies that cover an even wider spectrum of biological knowledge . another intricate aspect of biology is that in the biological system , molecules often interact with each other . dynamics and dependencies of various molecules through such interactions result in the formation of pathways and networks that regulate functions of individual molecules and , by doing so , impact the biological system . scientists have been studying the interactions of biological entities and pathways in laboratories for decades . most activities of this work , however , are isolated and are conducted in different conditions / cell types / organisms . it is often difficult to compose a comprehensive and intuitive overall pathway without great human labor and effort . during the past decade around 2000 , systems biology emerged , spurred on by the completion of various genome - sequencing projects , together with the advancement in high - throughput experimental technology that resulted in a large increase in data from genomics and proteomics . researchers in the fields have developed software and tools having the ability to integrate and analyze large amounts of complex data from various sources , and to build and model large pathway networks . around the same time came the pathway databases , where large pathway maps have been created through careful curation by expert scientists . according to pathguide , a public pathway resource website , 325 different pathway databases are currently available . with the rapid growth of these fields , it is crucial for scientists to share the data and allow software and tools to access , interpret , and analyze the data . when a controlled data representation of pathway knowledge became essential , several pathway standards emerged to address this need . in the field of systems biology in order to better share , evaluate , and develop these models in a collaborative way  the community of systems biologists developed an information standard called the systems biology markup language ( sbml ; hucka et al . , 2003 ) . sbml is a machine - readable format , written in extensible markup language ( xml ) form , for representing pathway models . by supporting sbml as a format for reading and writing models , different software tools ( including programs for building and editing models , simulation programs , databases , and other systems ) can directly communicate and store the same computable representation of those models . biopax is another standard through collaborative effort to create a data - exchange format for biological pathway data . its main purpose is to facilitate data access , sharing , and integration from multiple pathway databases . pathway data that support biopax are stored in the web ontology language ( owl ) , and can be viewed using software that supports owl , such as protg ( noy et al . , while sbml is concentrated on mathematical modeling , biopax is more focused on qualitative pathway knowledge ; therefore , these two standards complement each other . the development of ontologies and standards in bioscience is still in its infant stage . in order for them to succeed , it is crucial that the ontologies and standards are accepted and adopted by the entire bioscience research community . the ontologies and standards in bioscience should be designed to be not only readable by computers , but also scientifically accurate and intuitive to human biologists . experiments are subsequently performed , and data are collected and analyzed by scientists , often with the assistance of tools and software . finally , the conclusions are drawn , and new hypotheses are proposed to start a new round of research . human involvement is essential at each of these steps , while computer software and tools will greatly facilitate the process by better storing , representing , and analyzing the data . at the current stage , if these scientists adopt the ontologies and standards , produce the data , and organize the data in compliance with the standards , it will enable computation biologists and systems biologists to directly analyze the data with computation tools and software . the results produced by the software and tools can be interpreted unambiguously by human scientists . human involvement is essential at each of the steps illustrated in this schematic diagram , while computer software and tools will greatly facilitate the process by better storing , representing , and analyzing the data . thus , it is crucial to include ontologies and standards at each of these steps for unambiguous interpretation of data by both human and computers . it is not just a list of controlled vocabulary , but rather a knowledge representation with well - defined structure and relationship . therefore , it is usually expressed in terms that are not conventionally used by biologists . for example , homeobox protein is a common term used by biologists to describe a family of transcription factors that bind to a particular domain of dna ( homeobox ) and usually are involved in the development ( morphogenesis ) of an organism . because homeobox domains are found in genes involved in development , the term homeobox also has functional implications when used by biologists . in go , homeobox is considered as a sequence structure , and therefore , it can not be used to describe the molecular function of a protein . go molecular function simply classify these proteins as sequence - specific dna binding and transcription factory activity . in fact , the term homeobox can not be found in any of the three ontologies in go . first , the ontologies and standards must be designed to accurately represent the biological knowledge . involvement of biologists and domain experts is necessary because they can ensure the accuracy and integrity of the knowledge , and ultimately they are the ones who will produce and interpret the data . second , efforts must be spent to enable biologists to appreciate how biological knowledge is captured and represented by ontologies and standards . this would involve educating the biologists by providing documentation , hosting tutorials , including the biologists in the process of development , and encouraging them in the curation effort . last , tools and software must be developed to facilitate the use of the ontology by biologists . gene ontology is the best accepted and most widely used standard , but with a price . the project has been well funded for the past 10 years , and is supported by a number of well - funded model organism databases , including saccharomyces genome database ( sgd ; hong et al . , 2008 ) , flybase ( tweedie et al . , 2009 ) , wormbase ( harris et al . , 2010 ) , and mouse genome database ( mgd ; bult et al . , 2010 ) . from the very beginning of the go development , scientists recognized the importance of reaching out to biologists . various efforts have been focused on this goal , such as the involvement of biologists in the development , hosting go - user meetings to discuss issues in the ontology , building tools to enable end users to utilize go , and  most important  conducting large - scale genome curation using the ontology . besides model organism databases that were actively participating in the genome curation effort using go , other gene or protein databases also joined forces , such as uniprot ( camon et al . , 2004 ) and panther classification system ( thomas et al . , 2003b ) . this opened a window to the biologists for quick access to the ontologies . during this process , developers of the panther classification system , a protein evolution database , also recognized the gap between the conventional terms used by the biologists and the ontology terms . they built an index of controlled vocabulary , called panther index , to capture protein functions and biological processes in more conventional terms , but also mapped the index to all go terms ( thomas et al . by doing so , users of the panther system , mostly biologists , had a chance of getting used to the go terms through the more familiar panther index terms , thus appreciating the rigorousness of the ontology . this became part of the education process to the biologists about the usefulness of the ontology . because of the wide acceptance of go , panther retired its panther index in 2009 and developed go - slim for its annotation ( mi et al . , 2010 ) . in the field of pathway standards , things are a little different . both sbml and biopax are designed more for data exchange and are in machine - readable formats . the purpose of sbml is to provide a standard data format for systems biologists ( modelers ) to exchange pathway models so that they can be accessed by different software and tools . biopax provides a more rigorous data format for pathway data exchange among different pathway databases . neither standard is intuitive to biologists ; therefore , specially trained experts ( curators ) are required to translate biological data into the standards using highly specialized tools . a diagram is an intuitive way to illustrate biological knowledge , and it is the most powerful method of communication among humans . to provide an unambiguous graphical representation of pathways that enables better pathway data sharing among scientists of different backgrounds and among different software programs and other tools , a consortium of molecular biologists , systems biologists , computational biologists , and software developers recently have proposed a graphical notation standard , called systems biology graphical notation ( sbgn ; le novre et al . , 2009 ) . by developing tools that support sbgn , biologists can easily draw pathway diagrams in a format with which they are familiar , and the tools can save those diagrams to either sbml or biopax . at the same time , the modelers can simulate pathway networks , and output the models in graphs that can be viewed and interpreted by a human . database developers can easily illustrate pathway diagrams that accurately reflect the data stored in the database . thus , the sbgn standard serves as an interface to the human biologists to read and write pathways that are compliant with community standards for modeling , exchanging , and storing . it is not possible to capture and represent the entire biology with one standard or ontology . collaboration and subsequent integration will be necessary for the understanding of biology as a whole . for example , the pathway exchange standard biopax captures pathway data where the entities are characterized by go terms . also , when analyzing data from high - throughput experiments  such as mass spectrometry , microarray , or genotyping analysis these sources may provide clinical , genomic , proteomic , and expression data . therefore , integration of phenotype ontology , pathway standards , gene ontology , and protein ontology will be essential to facilitate such analysis . one such example is a recent study that utilizes an integrated ontology network by combining phenotype ontology , gene ontology , and pathway standards for a large genetic association study of nicotine addiction and treatment that includes epidemiological , clinical , and gene association data ( thomas et al . , 2009 ) . this is another reason why ontologies and standards should be intuitive to human biologists , so that scientists from different domains of expertise can understand them and utilize them for their research . computer software and tools have been employed for better storage , representation , and analysis of ever - growing data knowledge . ontologies and standards have been introduced to the field to facilitate data sharing among different platforms and software , and among scientists across different research fields . the fundamental research paradigm remains the same ; it always starts with a hypothesis , followed by experiment design , data collection and careful analysis , and completed by drawing a conclusion . this paradigm relies heavily on human involvement to observe , interpret , and analyze the data . therefore , when ontologies and standards are designed and developed , one must always keep in mind that ontologies and standards are not just for machine , but for human also . the authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest .
ontologies and standards are very important parts of today 's bioscience research . with the rapid increase of biological knowledge , 
 they provide mechanisms to better store and represent data in a controlled and structured way , so that scientists can share the data , and utilize a wide variety of software and tools to manage and analyze the data . 
 most of these standards are initially designed for computers to access large amounts of data that are difficult for human biologists to handle , and it is important to keep in mind that ultimately biologists are going to produce and interpret the data . 
 while ontologies and standards must follow strict semantic rules that may not be familiar to biologists , effort must be spent to lower the learning barrier by involving biologists in the process of development , and by providing software and tool support . 
 a standard will not succeed without support from the wider bioscience research community . 
 thus , it is crucial that these standards be designed not only for machines to read , but also to be scientifically accurate and intuitive to human biologists .