An organized collection of data is referred to as database that aims to collect schemes, tables, queries, reports, images, and other objects.
The DBMS allows users to access all of the data contained in the databases. It has general functions for data definition, entry, storage, update, administration, and retrieval of large quantities of information in an organized way that requires modeling hierarchical and network models , clustering, query languages and query optimization, and visualization algorithms [ 1 , 2 , ]. Development of databases, therefore, is significantly dependent on bioinformatics tools, advances, research, and applications.
There is a large number of different types of databases available, which cover all aspects of biological data storage and organization. There are meta-databases that incorporate data compiled from multiple other databases such as Entrez, mGen, Metascape, etc. Some others are specialized databases such as those specific to an organism, for example, TAIR, the p53 Knowledgebase p53 , the plant alternative splicing database PASD ; the plant secretome, and subcellular proteome knowledgebase PlantSecKB [ ].
All databases vary in their data definition, usage, format, and access types. In this book, the chapter by Kadam et al. As mentioned above, astronomical accumulation of genomic and proteomic as well as metabolomic data, and their expression profiles and annotation, storage, organization, systematization, and integration into biological networks as well as database systems and their wide utilization by the science research community a priori required computer programming algorithms, analysis tools, services, and workflow systems.
Therefore, software and analysis tools, and bioinformatics services and workflow have been the main fields and core targets of bioinformatics since its emergence. Because of the contributions of various bioinformatics companies or public institutions, bioinformatics software, and tools started to exist as simple command-line tools, but later improved to more complex graphical programs standalone packages, and web services.
Since development of the first bioinformatics software and analysis tools for molecular sequence evaluations in the early s, many free and open-source software tools have been developed and continue to grow and improve with the advancement made in genomics sciences [ 2 , ]. The main driving forces for the current and future development of bioinformatics software and tools have been made on the past-decade advances of genome decoding technologies, accumulation of large volume biological data, consequent need for their analyses, as well as advancements of computer technologies, graphics, visualization, and molecular modeling and networking techniques.
Development of sharing models and web access tools is also an important bioinformatics objective that allows users to utilize and access bioinformatics tools over the internet and from their computer systems to the main computing resources via servers in other parts of the world. SOAP is a standard-based web service access protocol, originally developed by Microsoft. Both tools share similarities over the HTTP protocol and have its own issues and challenges, differ in messaging patterns, rules, architecture style, and flexibility.
The main advantages derive from the fact that end users do not have to deal with software and database maintenance overheads [ ]. These web service-based bioinformatics analysis resources represent a collection of standalone or web-based interface data analysis tools as well as integrative, distributed, and extensible bioinformatics workflow management systems BWMS.
The BWMSs are designed specifically to compose and execute a series of interactive computational or data manipulation steps i. Such systems provide interactive analysis of biological data, build the specific workflows for the analysis, enable the visualization of the analysis outputs in real time, and simplify the process of sharing and reusing workflows between scientists.
Several chapters of this book cover bioinformatics software, web-based analysis tools, and bioinformatics services for membrane analysis see Leong et al. Part of objectives in bioinformatics research and application is the utilization of computational algorithms and bioinformatics tools to collect, organize, and structure the growing body of biomedical literature allowing scientists to query, mine, read, and synthesize the specific literature and published articles of their research interest [ 2 — 4 , 7 , , ].
Biomedical literature and text mining, therefore, are very important for scientific development, innovations, and integration and application of discoveries to society through extracting information EI and assessing the relationships of publications [ 3 , 4 ]. Pattern recognition and matching such as the recognition of biological abbreviations, terms, and interactions are important methods in text mining [ 2 — 4 ].
Bioinformatics training and education aim to create, collect, deliver, and share educational and training materials and techniques as well as develop university degree-program curricula on bioinformatics. This is a great bottleneck and critical need of current life sciences and bioinformatics field, especially in all developing countries, for example, analyzed by some recent reports for African [ 82 ] and Central American [ ] countries.
To address this, bioinformatics research community has put specific efforts to develop local and global platforms for bioinformatics training and education. As an outcome of European 7th Framework grant, BTN targeted to develop and share educational materials, short courses, and training delivery methods as well as discuss the challenge, issues, and needed requirements for bioinformatics training [ ].
Furthermore, GOBLET continues similar efforts beyond Europe, aiming to coordinate efforts at the global scales with concentrated strategy and within the frame of single, dedicated foundation although it requires much time, focused strategic efforts, and modern innovative approaches [ ].
As public bioinformatics databases, the MediaWiki engine with the WikiOpener extension, extensively referenced in this chapter, also contributes for training and education of bioinformatics through gathering research materials and descriptions of tools that can be accessed and updated by all experts in the field [ ].
With the specific objectives to develop bioinformatics research and application, its integration to genomics research, and training and education as well as to prepare well-qualified new generation scientists to life sciences, we established a dedicated organization—Center of Genomics and Bioinformatics in the developing country Uzbekistan [ ].
As in other developing countries, there are many challenges and limitations in funding and in accessing to sophisticated bioinformatics tools and computer operating systems as well as lack of sufficient experience to carry bioinformatics research and resource development. However, our first step goal is to integrate genomics and bioinformatics curricula to the higher education system of Uzbekistan, develop training and educational materials, provide basic training and research practices to the university students and biology field specialists, and establish international collaborations on this direction.
The long-term objective is to efficiently and broadly apply genomics and bioinformatics approaches to all areas of life sciences in national and regional levels that would contribute the development of biological sciences in Central Asia. Some efforts are ongoing regarding the establishment of international collaborations [ ] and providing training and education in both national and regional levels.
Community resources and a globally coordinated foundation of bioinformatics training and education platforms as well as research conferences, workshops, short online training, and web-based educational courses and materials are available to accomplish toward this goal. However, there is an urgent need for the development of bioinformatics education and training, in particular in developing countries, which requires innovative platforms, training techniques, better funding, web and network access, and high-performance computing systems.
In the research side, bioinformatics tools need to be improved for analysis of the growing body of high-throughput pangenomics, metagenomics, proteomics, and metabolomics data. These efforts also help to improve orthologous gene identification tools that currently need attention [ ]. There is a great need for sampling and handling diverse strains in pangenomic analysis, integration of prokaryotic genome-organization frameworks GOFs as well as integration of non-coding RNAs, pseudogenes, and epigenetics elements into the bioinformatics annotation and ontology tools and software [ ].
There is a need to make sequenced genome data more functional and integrated through the construction of more organized, user friendly, cell-wide biological networks, and metabolic pathways [ ] with better visualization effects, graphics outputs [ ], and knowledge base construction KB [ ]. I also thank Prof. Gilbert S. Mirzakamol Ayubov and Mr. Muhammad Mirzahmedov, Center of Genomics and Bioinformatics, Uzbekistan, for their technical assistance while preparing this chapter material.
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Account Information. That said, if you feel your contribution isn't being adequately acknowledged -- find new collaborators. There is a bit of a confounding problem here. For bioinformatics to truly succeed, you need all the data you can get.
Biologists do not provide that. Just try to look up the raw data for any biology paper. You will only get to see whatever supports to story. That makes it hard for bioinformatics to prove itself: it's success is just rate limited by lack of Open Data in the life sciences. It's a bit like those cartoons where they depict your living room as if there was no plastic.
Now, visualize for yourself what the latest Nature issue would look like without bioinformatics. Is anyone aware of any open notebook science projects with a major bioinformatic part?
In such a project one might get a true view on where the efforts were - and this could be used to "make the case". Signal sequences are N-terminal extensions that direct proteins destined for protein translocation. The pioneric work of Gunnar von Heijne started in the eighties of the last century has attributed significantly to the understanding of signal sequences general structure, differences of signal sequences in different translocation systems etc.
His work was initially purely theoretical, still his work and of co-workers has helped the field of protein translocation significantly further. So yes bioinformatics is a proper research area and can attibute to our understanding in life sciences. Login before adding your answer. Use of this site constitutes acceptance of our User Agreement and Privacy Policy. Log In. Log In Sign Up About. Entering edit mode.
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