Bioinformatics Ã, ( listen ) is an interdisciplinary field that develops methods and software for understanding biological data. As an interdisciplinary field of science, bioinformatics combines biology, computer science, mathematics and statistics to analyze and interpret biological data. Bioinformatics has been used for in silico analysis of biological questions using mathematical and statistical techniques.
Bioinformatics is a general term for biological study bodies that use computer programming as part of their methodology, as well as references to "pipes" of specialized analysis pipes that are repeatedly used, especially in the genomic field. Common uses of bioinformatics include identification of candidate genes and single nucleotide polymorphisms (SNPs). Often, such identification is made with the aim of better understanding the genetic basis of disease, unique adaptation, desirable nature (especially in agricultural species), or differences between populations. In less formal ways, bioinformatics also tries to understand organizational principles in the sequence of nucleic acids and proteins, called proteomics.
Video Bioinformatics
Introduction
Bioinformatics has become an important part of many areas of biology. In experimental molecular biology, bioinformatics techniques such as image and signal processing allow the extraction of useful results from large amounts of raw data. In the field of genetics and genomics, it helps sequence and annotate the observed genomes and mutations. It plays a role in mining the texts of biological literature and the development of biological ontologies and genes for organizing and querying biological data. It also plays a role in the analysis of gene and protein expression and regulation. The bioinformatics tool helps compare genetic and genomic data and is more common in understanding the evolutionary aspects of molecular biology. At a more integrative level, it helps analyze and catalog paths and biological networks that are an essential part of biological systems. In structural biology, it assists in the simulation and modeling of DNA, RNA, proteins as well as biomolecular interactions.
History
Historically, the term bioinformatics does not mean what it means today. Paulien Hogeweg and Ben Hesper created it in 1970 to refer to the study of information processes in biotic systems. This definition places bioinformatics as a field parallel to biophysics (the study of physical processes in biological systems) or biochemistry (the study of chemical processes in biological systems).
Order
Computers became important in molecular biology when protein sequences became available after Frederick Sanger determined the order of insulin in the early 1950s. Comparing some sequences manually is not practical. A pioneer in the field is Margaret Oakley Dayhoff, who has been praised by David Lipman, director of the National Biotechnology Information Center, as "mother and father of bioinformatics." Dayhoff compiled one of the first protein sequence databases, originally published as a book and pioneered sequence alignment methods and molecular evolution. Another early contributor to bioinformatics was Elvin A. Kabat, who pioneered the analysis of biological sequences in 1970 with a comprehensive volume of antibody sequences released with Tai Te Wu between 1980 and 1991.
Goal
To study how normal cellular activity is altered in different disease states, biological data should be combined to form a comprehensive picture of this activity. Therefore, the field of bioinformatics has developed in such a way that the most urgent task now involves the analysis and interpretation of various types of data. These include the sequence of nucleotides and amino acids, protein domains, and protein structures. The actual process of analyzing and interpreting data is referred to as computational biology. Important sub-disciplines in bioinformatics and computational biology include:
- Development and implementation of computer programs that enable efficient access, use and management, various types of information
- Development of new algorithms (mathematical formulas) and statistical measures that assess relationships among members of large data sets. For example, there are methods to find genes in a sequence, to predict the structure and/or function of proteins, and to group the sequence of proteins into families of related sequences.
The primary goal of bioinformatics is to enhance the understanding of biological processes. What sets it apart from other approaches, however, is its focus on developing and applying computational intensive techniques to achieve this goal. Examples include: pattern recognition, data mining, machine learning algorithms, and visualization. Key research efforts in the field include sequence alignment, gene discovery, genome assembly, drug design, drug discovery, protein structure flattening, protein structure prediction, gene expression prediction and protein-protein interactions, genome association studies, evolution modeling and cell division/mitosis.
Bioinformatics now includes the creation and development of databases, algorithms, computational and statistical techniques, and theories for solving formal and practical problems arising from the management and analysis of biological data.
Over the last few decades, the rapid development in genomics and other molecular research technologies and the development of information technology has been combined to produce vast amounts of information related to molecular biology. Bioinformatics is the name given to mathematical and computational approaches used to gather an understanding of biological processes.
Common activities in bioinformatics include mapping and analysis of DNA and protein sequences, aligning DNA sequences and proteins to compare them, and creating and viewing protein structures of 3-D models.
Relation to other fields
Bioinformatics is a field of science similar to but different from biological calculations, while it is often considered identical to computational biology. Biological computing uses biotechnology and biology to build biological computers, whereas bioinformatics uses computation to better understand biology. Bioinformatics and computational biology involve the analysis of biological data, especially DNA, RNA, and protein sequences. The bioinformatics field experienced explosive growth beginning in the mid-1990s, largely driven by the Human Genome Project and by rapid advances in DNA sequencing technology.
Analyzing biological data to produce meaningful information involves writing and running software programs that use algorithms from graph theory, artificial intelligence, soft computing, data mining, image processing, and computer simulations. The algorithm in turn depends on theoretical foundations such as discrete mathematics, control theory, system theory, information theory, and statistics.
Maps Bioinformatics
Sequence analysis
Since Phage? -X174 was sequenced in 1977, DNA sequences from thousands of organisms have been decoded and stored in the database. The sequence information was analyzed to determine the genes that encode proteins, RNA genes, regulatory sequences, structural motives, and repetitive sequences. Comparison of genes in a species or between different species may indicate similarities between protein functions, or relationships between species (the use of molecular systematics to construct phylogenetic trees). With the growing amount of data, it has long been impractical to analyze DNA sequences manually. Currently, computer programs such as BLAST are used daily to find sequences from more than 260,000 organisms, which contain more than 190 billion nucleotides. These programs can compensate mutations (exchanging, deleted or inserted bases) in a DNA sequence, to identify related sequences, but not identical. Variants of this sequence alignment are used in the sequencing process itself.
DNA sequencing
Before the sequence can be analyzed, they must be obtained. DNA sequencing is still a problem that is not trivial because raw data may be noisy or struck by weak signals. Algorithms have been developed for basic calls for various experimental approaches to DNA sequencing.
Order sort
Most DNA sorting techniques produce short sequence fragments that need to be assembled to gain complete gene sequences or genomes. The so-called shotgun sequence technique (used, for example, by The Institute for Genomic Research (TIGR) for the first sequence of bacterial genomes, Haemophilus influenzae produces a sequence of thousands of tiny DNA fragments (ranging from 35 to 900 long nucleotides , depending on the sequencing technology). The edges of this fragment overlap and, when properly aligned by the genome assembly program, can be used to reconstruct the complete genome. Shotgun sequencing generates quick sequence data, but the task of assembling fragments can be very complicated for larger genomes. For genomes of the human genome, it may take long CPU time in large multiprocessor computers to collect fragments, and the resulting assembly usually contains many loopholes that must be filled in later. Shotgun sequencing is the preferred method for almost all genomes sorted today, and the genome assembly algorithm is an important field in bioinformatics research.
Genome Annotations
In the genomic context, annotations are the process of marking genes and other biological features in DNA sequences. This process needs to be automated because most genomes are too large to annotate by hand, not to mention the desire to make as many annotations as possible of the genome, since the sorting rate has stopped causing congestion. Annotations are made possible by the fact that genes have recognized the start and stop regions, although the exact sequence found in these regions may vary between genes.
The first description of a comprehensive genome annotation system was published in 1995 by a team at The Institute for Genomic Research that performed the first complete sequence and analysis of the free-living organism of the organism, the bacterium Haemophilus influenzae . Owen White designed and built a software system to identify genes that encode all proteins, transfer RNA, ribosome RNA (and other sites) and to create initial functional tasks. Most genome annotation systems are currently working together, but the programs available for genomic DNA analysis, such as the GeneMark program trained and used to find the protein-coding genes in Haemophilus influenzae, are constantly changing and improving.
Following the goals the Human Genome Project left behind after its closure in 2003, a new project developed by the US National Human Genome Research Institute emerged. The so-called ENCODE project is the collaborative data collection of functional elements of the human genome using next-generation DNA sequencing technology and the arrangement of genomic tiles, a technology that can automatically generate large amounts of data at a dramatically reduced per-basis cost but with accuracy (base call error) and fidelity (assembly error).
Computational evolutionary biology
Evolutionary biology is the study of the origin and descendants of species, as well as their changes over time. Informatics has assisted evolutionary biologists by allowing researchers to:
- trace the evolution of large numbers of organisms by measuring changes in their DNA, rather than through physical taxonomy or physiological observations only,
- recently, compare the entire genome, allowing the study of more complex evolutionary events, such as gene duplication, horizontal gene transfer, and prediction of important factors in bacterial speciation,
- builds complex computational population genetic models to predict system results over time
- track and share information about the growing number of species and organisms
Future work seeks to reconstruct the now more complicated tree of life.
The field of research in computer science that uses genetic algorithms is sometimes confused with the biology of computational evolution, but the two fields are not always related.
Comparative genomics
The essence of the comparative genome analysis is the formation of correspondence between genes (orthological analysis) or other genomic features in different organisms. It is an intergenomic map that makes it possible to trace the evolutionary process responsible for the differences between the two genomes. Many evolutionary events that act at different levels of organization shape the evolution of the genome. At the lowest level, mutation points affect individual nucleotides. At higher levels, large chromosome segments are duplicated, lateral transfers, inversions, transpositions, deletions and insertions. In the end, the whole genome is involved in the process of hybridization, polyploidization and endosimbiosis, often leading to rapid speciation. The complexity of the evolution of the genome poses many interesting challenges for model developers and mathematical algorithms, which have other avenues to the spectrum of algorithmic, statistical and mathematical techniques, ranging from precise, heuristic, fixed parameters, and approximate algorithms to problems based on parsimonial models to Markov chain Monte Carlo algorithms for Bayesian analysis of problems based on probabilistic models.
Many of these studies are based on homology detection and protein family calculations.
Genomic pan
Pan genomics is a concept introduced in 2005 by Tettelin and Medini that are finally rooted in bioinformatics. Pan genome is a complete gene repertoire of certain taxonomic groups: although originally applied to closely related species strains, these genes can be applied to larger contexts such as genus, phylum etc. It is divided into two parts- Core Genome: A set of genes common to all the genomes studied (This is an important household genes for survival) and Dispensable/Flexible Genomes: The gene set is absent in all but one or more of the genomes studied. The BPin bioinformatics tool can be used to characterize the Pan Genome bacterial species.
Genetics of disease
With the emergence of new generation sequencing, we obtain enough sequence data to map complex disease genes such as diabetes, infertility, breast cancer or Alzheimer's Disease. The study of genome associations is a useful approach to determine the mutations responsible for the complex disease. Through this research, thousands of DNA variants have been identified that are associated with similar diseases and traits. In addition, the possibility of genes to be used in prognosis, diagnosis or treatment is one of the most important applications. Many studies have discussed both promising ways to select the genes to be used and the problems and traps of using genes to predict the presence or prognosis of the disease.
Analysis of mutations in cancer
In cancer, the affected cell genome is altered in a complicated or even unpredictable way. Massive sequencing efforts were used to identify previously unknown point mutations in various genes in cancer. Bioinformaticians continue to produce specialized automated systems to manage the volume of sequence data generated, and they create new algorithms and software to compare sequencing results with collection of human genome collections and germline polymorphisms. Newly used physical detection technologies, such as microarray oligonucleotides to identify the advantages and disadvantages of chromosomes (called comparative genomic hybridisations), and the arrangement of single nucleotide polymorphisms to detect known point mutations. This detection method simultaneously measures several hundred thousand sites throughout the genome, and when used in high throughput to measure thousands of samples, yields terabytes of data per experiment. Once again large numbers and new data types generate new opportunities for bioinformatics. Data are often found to contain considerable variability, or noise, and thus Hidden Markov models and point change analysis methods are being developed to infer real-time copy number changes.
Two important principles can be used in bioinformatic cancer genome analysis related to the identification of mutations in the exome. First, cancer is an accumulation of somatic mutations in genes. The second cancer contains a driver mutation that needs to be distinguished from passengers.
With the breakthroughs made by the next generation of sequencing technology into the field of Bioinformatics, the cancer genome can change drastically. These new methods and software allow bioinformatics to sequence many cancer genomes quickly and affordably. This could create a more flexible process for classifying types of cancer with cancer-driven mutation analysis in the genome. Furthermore, patient tracking while the disease progresses may be possible in the future with a sequence of cancer samples.
Another type of data that requires the development of new informatics is the analysis of lesions found recurrently among many tumors.
Gene and protein expression
Analysis of gene expression
The expression of many genes can be determined by measuring mRNA levels by several techniques including microarrays, testing of cDNA tag sequences, serial tag gene sequence analysis (SAGE) tags, massive parallel signature series (MPSS), RNA-Seq, also known as " Whole Transcriptome Shotgun Sequencing "(WTSS), or various in-situ multiplex hybridization applications. All of these techniques are particularly vulnerable to noise and/or bias subjects in biological measurements, and the main research area in computational biology involves the development of statistical tools for separating signals from noise in studies of high throughput gene expression. Such research is often used to determine the genes involved in the disorder: one can compare microarray data from cancer epithelial cells to data from non-cancer cells to determine regulated and down-regulated transcripts in specific populations of cancer cells..
Analysis of protein expression
Microarray proteins and high throughput (HT) mass spectrometry (MS) can provide an overview of proteins present in biological samples. Bioinformatics is heavily involved in the manufacture of microarray proteins and HT MS data; previous approaches face the same problems as with microarrays targeted at mRNAs, the latter involves the problem of matching large amounts of mass data to the predicted masses of protein sequence databases, and the elaborate statistical analysis of samples in which the peptides are double, but incomplete from each other, each protein is detected. The localization of cellular proteins in the context of tissue can be achieved through the affinity proteomics shown as spatial data based on immunohistochemistry and tissue microarrays.
Regulatory analysis
Regulation is a complex orchestration of events where signals, potentially extracellular signals such as hormones, ultimately lead to an increase or decrease in activity of one or more proteins. The bioinformatics technique has been applied to explore the various steps in this process.
For example, gene expression can be regulated by nearby elements in the genome. Promotional analysis involves the identification and study of sequence motifs in the DNA surrounding the encoding region of a gene. These motifs influence the extent to which the region is transcribed into mRNA. Remote enhancement elements of the promoter can also regulate gene expression, through three-dimensional repetition interactions. This interaction can be determined by bioinformatic analysis of chromosomal conformational capture experiments.
The expression data can be used to infer gene regulation: one can compare microarray data from different organism states to form hypotheses about genes involved in each country. In a single cell organism, one can compare the stages of the cell cycle, along with various stress conditions (heat shock, hunger, etc.). One can then apply the grouping algorithm to the expression data to determine which genes are expressed. For example, upstream areas (promoters) of genes expressed simultaneously can be searched for over-represented representation elements. Examples of grouping algorithms applied in gene grouping are k-means clustering, self-organizing maps (SOMs), hierarchical clustering, and clustering consensus methods.
Analysis of mobile organizations
Several approaches have been developed to analyze the location of organelles, genes, proteins, and other components within the cell. This is relevant because the location of these components affects events within the cell and thus helps us to predict the behavior of biological systems. A category of gene ontologies, cellular compartments , have been designed to capture subcellular localization across many biological databases.
Microscope and image analysis
Microscopic images allow us to discover both organelles and molecules. It can also help us distinguish between normal and abnormal cells, e.g. on cancer.
Localization of protein
Localization of proteins helps us evaluate the role of proteins. For example, if proteins found in the nucleus may be involved in regulation or splicing genes. Conversely, if a protein is found in the mitochondria, it may be involved in respiration or other metabolic processes. Localization of proteins is an important component of protein function prediction. There are well-developed sources of predicted localization of subcellular proteins, including subbases of protein location data, and predictors.
Chromatin nuclear organization
Data from high-throughput conformation intake experiments, such as Hi-C (experimental) and ChIA-PET, can provide information about the spatial closeness of the DNA locus. This experimental analysis can determine the three-dimensional structure and nuclear organization of chromatin. The challenges of bioinformatics in this area include partitioning the genome into domains, such as Topologically Associating Domains (TADs), which are organized together in three-dimensional space.
Structural bioinformatics
Predicted protein structure is another important bioinformatics application. The amino acid sequence of a protein, called the primary structure, can be easily determined from the sequence of genes encoding it. In most cases, this primary structure uniquely determines the structure in its original environment. (Of course, there are exceptions, such as spongiform encephalopathy bovine - a.k.a. Mad Cow disease - prions.) Knowledge of these structures is essential in understanding protein function. Structural information is usually classified as one of the secondary structures , tertiary and quaternary . Decent general solutions to these predictions remain an open issue. Much of the effort so far has been directed to the heuristics that work most of the time.
One of the key ideas in bioinformatics is the idea of ​​homology. In the genomic branch of bioinformatics, homology is used to predict gene function: if the gene sequence A , whose function is known, is homologous to the sequence of the B, gene whose function is unknown, it can be concluded that B can share function A. In the structural branch of bioinformatics, homology is used to determine which part of the protein is important in the formation of structures and interactions with other proteins. In a technique called homology modeling, this information is used to predict protein structure after homologous protein structure is known. This is currently the only way to predict protein structure reliably.
One such example is the homology of similar proteins between hemoglobin in humans and hemoglobin in legumes (leghemoglobin). Both serve the same purpose for transporting oxygen in the organism. Although these two proteins have an entirely different amino acid sequence, their protein structure is almost identical, reflecting their nearly identical goals.
Other techniques to predict protein structure include protein threading and de novo (from the beginning) physics-based modeling.
Other aspects of Structural bioinformatics include the use of protein structures for the Virtual Screening model such as the Quantitative Relationship-Aactivity model and the proteochemometric model (PCM). Furthermore, the protein crystal structure can be used in simulations such as ligand-binding studies and in silico study of mutagenesis.
Network and system biology
Network analysis seeks to understand relationships in biological tissues such as metabolic tissue interactions or proteins. Although biological networks can be constructed from one type of molecule or entity (such as genes), biological networks often try to integrate different types of data, such as proteins, small molecules, gene expression data, and so on, all of which are physically connected. , functionally, or both.
System biology involves the use of computer simulations of cellular subsystems (such as metabolite and enzyme networks comprising metabolism, signal transduction pathways and gene regulatory networks) to analyze and visualize the complex connections of these cells. process. Artificial life or virtual evolution tries to understand the process of evolution through computer simulations of simple (artificial) life forms.
Molecular interaction network
Tens of thousands of three-dimensional protein structures have been determined by X-ray crystallography and nuclear magnetic resonance spectroscopy proteins (NMR proteins) and the central question in structural bioinformatics is whether it is practical to predict the likelihood of protein-protein interactions based solely on these 3D forms, without experimenting interactions proteins. Various methods have been developed to address the problem of docking proteins, although there seems to be much work to do in this field.
Other interactions encountered in the field include Protein-ligands (including drugs) and protein-peptides. The dynamic molecular dynamics of atomic motions about rotatable bonds are fundamental principles behind computational algorithms, called docking algorithms, to study molecular interactions.
More
Literature analysis
The growing number of published literature makes it almost impossible to read every paper, so the sub-fields of study are disjointed. The literature analysis aims to use computational linguistics and statistics to mine this growing library of text resources. As an example:
- Abbreviation Introduction - identification of long form and abbreviated biological terms
- Named entity recognition - recognize biological terms like the gene name
- Interactions of proteins - identify which proteins interact with which proteins from the text
The field of research is taken from statistics and computational linguistics.
High throughput image analysis
Computational technology is used to accelerate or completely automate the processing, quantification, and analysis of a large number of high-information content biomedical images. Modern image analysis systems increase the ability of the observer to take measurements of a large or complex set of images, by increasing accuracy, objectivity, or speed. A fully developed analysis system can completely replace the observer. Although these systems are not unique to biomedical imagery, biomedical imaging becomes more important for diagnostics and research. Some examples are:
- high-throughput and quantification of high-fidelity and sub-cell localization (high-content screening, cytohistopathology, Bioimage informatics)
- morfometric
- analysis and visualization of clinical images
- determine the pattern of real-time airflow in lung breathing of living animals
- measures the real-time occlusion size of development and recovery during arterial injury
- makes a behavioral observation of the expanded video recordings of laboratory animals
- infrared measurements for metabolic activity determination
- conclude overlapping clones in DNA mapping, e.g. Sulston score
Single high cell data analysis
Computational techniques are used to analyze single-cell, high-density, low-measurement data, such as those obtained from flow cytometry. These methods usually involve searching for cell populations relevant to a particular disease state or experimental condition.
Informatics biodata
Biodiversity informatics deals with the collection and analysis of biodiversity data, such as taxonomic databases, or microbiome data. Examples of such analyzes include phylogenetics, niche modeling, species richness mapping, DNA barcodes, or species identification tools.
Ontology and data integration
Ontology biology is directed to the acyclic graph of controlled vocabulary. They are designed to capture biological concepts and descriptions in ways that can be easily categorized and analyzed by computer. When categorized in this way, it is possible to get added value from holistic and integrated analysis.
The OBO Foundry is an attempt to standardize certain ontologies. One of the most extensive is the Gen ontology that describes the function of genes. There is also an ontology that describes the phenotype.
Database
Databases are essential for bioinformatics research and applications. Many databases exist, covering various types of information: for example, DNA and protein sequences, molecular structures, phenotypes and biodiversity. The database may contain empirical data (obtained directly from the experiment), predictive data (obtained from analysis), or, most commonly, both. They may be specific to certain organisms, pathways or interesting molecules. Alternatively, they can combine data collected from several other databases. These databases vary in format, access mechanism, and whether they are public or not.
Some of the most commonly used databases are listed below. For a more complete list, please check the links at the beginning of the subsection.
- Used in biological sequence analysis: Genbank, UniProt
- Used in structural analysis: Protein Data Bank (GDP)
- Used to find Family Protein and Motion Imaging: InterPro, Pfam
- Used for Next Generation Sequence: Archive Reading Order
- Used in Network Analysis: Metabolic Pathway Databases (KEGG, BioCyc), Database Interaction Analysis, Functional Network
- Used in the design of a synthetic genetic circuit: GenoCAD
Software and tools
Software tools for bioinformatics ranging from simple command line tools, to more complex graphics programs and self-service web services available from various bioinformatics companies or public agencies.
Open source bioinformatics
A lot of free and open source software has been around and has grown since the 1980s. The combination of the ongoing need for new algorithms for the analysis of emerging biological readings, the potential for innovative experiments in silico , and freely available open codes has helped create opportunities for all research groups to contribute to bioinformatics and various open-source software available, regardless of their funding arrangements. Open source tools often act as idea incubators, or community supported plug-ins in commercial applications. They can also provide a de facto standard and a shared object model to help challenge the integration of bioinformation.
The range of open-source software packages includes titles such as Bioconductor, BioPerl, Biopython, BioJava, BioJS, BioRuby, Bioclipse, EMBOSS,.NET Bio, Orange with add-on bioinformatics, Apache Taverna, UGENE and GenoCAD. To maintain this tradition and create further opportunities, the Nonprofit Open Bioinformatics Foundation has supported the Bioinformatics Open Source Conference (BOSC) since 2000.
An alternative method to build public bioinformatics database is to use MediaWiki engine with the extension WikiOpener . This system allows the database to be accessed and updated by all experts in the field.
Web services in bioinformatics
The SOAP and REST-based interface has been developed for various bioinformatics applications that allow applications to run 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 advantage comes from the fact that end users do not have to deal with software and database maintenance costs.
Basic bioinformatics services are classified by EBI into three categories: SSS (Sequence Search Services), MSA (Multiple Sequence Alignment), and BSA (Biological Sequence Analysis). The availability of these service-oriented bioinformatics resources demonstrates the adoption of a web-based bioinformatics solution, and ranges from a collection of self-contained tools with common data formats under a single, stand-alone or web-based interface, to integrative, distributed and expandable bioinformatics. workflow management system.
Workflow management system Bioinformatics
The bioinformatics workflow management system is a special form of workflow management system designed specifically for composing and executing a series of computational or data manipulation steps, or workflow, in a Bioinformatics application. Such a system is designed for
- provides an easy-to-use environment for individual application scientists themselves to create their own workflows,
- provides interactive tools for scientists that enable them to execute their workflow and see the results in real-time,
- simplifies sharing and reusing workflows between scientists, and
- allows scientists to track the origin of workflow execution and workflow creation steps.
Beberapa platform memberikan layanan ini: Galaxy, Kepler, Taverna, UGENE, Anduril, HIVE.
BioCompute dan BioCompute Objects
In 2014, the US Food and Drug Administration is sponsoring a conference held at the National Institutes of Health on Bethesda Campus to discuss bioinformatics reproducibility. Over the next three years, the stakeholder consortium meets regularly to discuss what will become the BioCompute paradigm. These stakeholders include representatives from government, industry, and academic entities. Session leaders represent the various branches of the FDA and NIH Institutes and Centers, non-profit entities including the Human Various Project and the European Federation for Medical Informatics, and research institutes including Stanford, the New York Genome Center, and George Washington University.
It was decided that the BioCompute paradigm would be in the form of a digital 'notebook labor' that made it possible for reproducibility, replication, review, and reuse, the bioinformatics protocol. It is proposed to allow for greater continuity in the research group during normal personnel fluctuations while continuing the exchange of ideas between groups. The US FDA is funding this work so information on pipelines will be more transparent and accessible to its regulatory staff.
In 2016, the group reunited at NIH in Bethesda and discussed the potential for the BioCompute Object, an example of the BioCompute paradigm. This work was copied as a "standard experiment use" document and a printed paper uploaded to bioRxiv. The BioCompute object allows for JSON-ized notes to be shared among employees, collaborators, and regulators.
Education platform
A software platform designed to teach bioinformatics concepts and methods including Rosalind and online courses offered through the Swiss Institute of Bioinformatics Training Portal. Canadian Bioinformatics Workshop provides videos and slides from training workshops on their website under a Creative Commons license. The 4273? projects or project projects also offer free open source educational materials. This course runs on low cost Raspberry Pi computers and has been used to teach adults and schoolchildren. 4273? is actively developed by a consortium of academics and research staff who have run a research-grade bioinformatics using Raspberry Pi and 4273 computers? operating system.
The MOOC Platform also provides online certification in bioinformatics and related disciplines, including Coursera's Bioinformatics Specialization (UC San Diego) and Genomic Data Science Specialization (Johns Hopkins) as well as EdX Data Analysis for Life Science XSeries (Harvard). The University of Southern California offers a Masters In Translational Bioinformatics that focuses on biomedical applications.
Conference
There are several major conferences related to bioinformatics. Some of the most notable examples are Intelligent Systems for Molecular Biology (ISMB), European Conference on Computational Biology (ECCB), and Research in Computational Molecular Biology (RECOMB).
See also
References
Further reading
External links
- Definition of bioinformatics dictionary in Wiktionary
- Learning materials related to Bioinformatics at Wikiversity
- Media related to Bioinformatics on Wikimedia Commons
- Bioinformatics Resource Portal (SIB)
Source of the article : Wikipedia
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