World Health Statistics data visualizations dashboard
Use the interactive database systems to find state level nutrition, physical activity, and obesity, data, trends and legislation information. Even in relatively mild cases, it can cause high fever, chills, flu-like symptoms, and severe anemia. Please search for a U. The Washington Post employed visual data analysis to create a rare. Chronic Disease State Tracking System The Chronic Disease State Tracking System is a searchable database that provides legislation information pertaining to nutrition, physical activity and obesity. Fire conditions may change in a particular area as fire season progresses.
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At a higher level, large chromosomal segments undergo duplication, lateral transfer, inversion, transposition, deletion and insertion. The complexity of genome evolution poses many exciting challenges to developers of mathematical models and algorithms, who have recourse to a spectrum 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. Pan genomics is a concept introduced in by Tettelin and Medini which eventually took root in bioinformatics.
Pan genome is the complete gene repertoire of a particular taxonomic group: It is divided in two parts- The Core genome: Set of genes not present in all but one or some genomes under study. With the advent of next-generation sequencing we are obtaining enough sequence data to map the genes of complex diseases such as diabetes ,  infertility ,  breast cancer  or Alzheimer's Disease.
Many studies are discussing both the promising ways to choose the genes to be used and the problems and pitfalls of using genes to predict disease presence or prognosis. 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 technologies 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.
Two important principles can be used in the analysis of cancer genomes bioinformatically pertaining to the identification of mutations in the exome. First, cancer is a disease of accumulated somatic mutations in genes. Second cancer contains driver mutations which need to be distinguished from passengers.
With the breakthroughs that this next-generation sequencing technology is providing to the field of Bioinformatics, cancer genomics could drastically change. These new methods and software allow bioinformaticians to sequence many cancer genomes quickly and affordably.
This could create a more flexible process for classifying types of cancer by analysis of cancer driven mutations in the genome. Furthermore, tracking of patients while the disease progresses may be possible in the future with the sequence of cancer samples. Another type of data that requires novel informatics development is the analysis of lesions found to be recurrent among many tumors. Protein microarrays and high throughput HT mass spectrometry MS can provide a snapshot of the proteins present in a biological sample.
Bioinformatics is very much involved in making sense of protein microarray and HT MS data; the former approach faces similar problems as with microarrays targeted at mRNA, the latter involves the problem of matching large amounts of mass data against predicted masses from protein sequence databases, and the complicated statistical analysis of samples where multiple, but incomplete peptides from each protein are detected.
Cellular protein localization in a tissue context can be achieved through affinity proteomics displayed as spatial data based on immunohistochemistry and tissue microarrays. Regulation is the complex orchestration of events by which a signal, potentially an extracellular signal such as a hormone , eventually leads 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, gene expression can be regulated by nearby elements in the genome. 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. Enhancer elements far away from the promoter can also regulate gene expression, through three-dimensional looping interactions.
These interactions can be determined by bioinformatic analysis of chromosome conformation capture experiments. Expression data can be used to infer gene regulation: In a single-cell organism, one might compare stages of the cell cycle , along with various stress conditions heat shock, starvation, etc.
One can then apply clustering algorithms to that expression data to determine which genes are co-expressed. For example, the upstream regions promoters of co-expressed genes can be searched for over-represented regulatory elements. Examples of clustering algorithms applied in gene clustering are k-means clustering , self-organizing maps SOMs , hierarchical clustering , and consensus clustering methods.
Several approaches have been developed to analyze the location of organelles, genes, proteins, and other components within cells. This is relevant as the location of these components affects the events within a cell and thus helps us to predict the behavior of biological systems. A gene ontology category, cellular compartment , has been devised to capture subcellular localization in many biological databases.
Microscopic pictures allow us to locate both organelles as well as molecules. It may also help us to distinguish between normal and abnormal cells, e. The localization of proteins helps us to evaluate the role of a protein.
For instance, if a protein is found in the nucleus it may be involved in gene regulation or splicing. By contrast, if a protein is found in mitochondria , it may be involved in respiration or other metabolic processes. Protein localization is thus an important component of protein function prediction. There are well developed protein subcellular localization prediction resources available, including protein subcellualr location databases, and prediction tools. Analysis of these experiments can determine the three-dimensional structure and nuclear organization of chromatin.
Bioinformatic challenges in this field include partitioning the genome into domains, such as Topologically Associating Domains TADs , that are organised together in three-dimensional space. 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 — a. Mad Cow Disease — prion.
Knowledge of this structure is vital in understanding the function of the protein. Structural information is usually classified as one of secondary , tertiary and quaternary structure.
A viable general solution to such predictions remains an open problem. Most efforts have so far 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: 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. Although 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. Another aspect of Structural bioinformatics include the use of protein structures for Virtual Screening models such as Quantitative Structure-Aactivity Relationship models and proteochemometric models PCM.
Furthermore, a protein's crystal structure can be used in simulation of for example ligand-binding studies and In silico mutagenesis studies. Network analysis seeks to understand the relationships within biological networks such as metabolic or protein—protein interaction networks.
Although biological networks can be constructed from a single type of molecule or entity such as genes , network biology often attempts to integrate many different data types, such as proteins, small molecules, gene expression data, and others, which are all connected physically, functionally, or both. Systems biology involves the use of computer simulations of cellular subsystems such as the networks of metabolites and enzymes that 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. Tens of thousands of three-dimensional protein structures have been determined by X-ray crystallography and protein nuclear magnetic resonance spectroscopy protein NMR and a central question in structural bioinformatics is whether it is practical to predict possible protein—protein interactions only based on these 3D shapes, without performing 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 work to be done in this field. Other interactions encountered in the field include Protein—ligand including drug and protein—peptide. Molecular dynamic simulation of movement of atoms about rotatable bonds is the fundamental principle behind computational algorithms , termed docking algorithms, for studying molecular interactions.
The growth in the number of published literature makes it virtually impossible to read every paper, resulting in disjointed sub-fields of research. Literature analysis aims to employ computational and statistical linguistics to mine this growing library of text resources. The area of research draws from statistics and computational linguistics. Computational technologies are used to accelerate or fully automate the processing, quantification and analysis of large amounts of high-information-content biomedical imagery.
Modern image analysis systems augment an observer's ability to make measurements from a large or complex set of images, by improving accuracy , objectivity , or speed. A fully developed analysis system may completely replace the observer. Although these systems are not unique to biomedical imagery, biomedical imaging is becoming more important for both diagnostics and research.
Computational techniques are used to analyse high-throughput, low-measurement single cell data, such as that obtained from flow cytometry. These methods typically involve finding populations of cells that are relevant to a particular disease state or experimental condition. Biodiversity informatics deals with the collection and analysis of biodiversity data, such as taxonomic databases , or microbiome data.
Examples of such analyses include phylogenetics , niche modelling , species richness mapping, DNA barcoding , or species identification tools. Biological ontologies are directed acyclic graphs of controlled vocabularies. They are designed to capture biological concepts and descriptions in a way that can be easily categorised and analysed with computers. When categorised in this way, it is possible to gain added value from holistic and integrated analysis.
The OBO Foundry was an effort to standardise certain ontologies. One of the most widespread is the Gene ontology which describes gene function. There are also ontologies which describe phenotypes. Databases are essential for bioinformatics research and applications. Many databases exist, covering various information types: Databases may contain empirical data obtained directly from experiments , predicted data obtained from analysis , or, most commonly, both.
They may be specific to a particular organism, pathway or molecule of interest. Alternatively, they can incorporate data compiled from multiple other databases. These databases vary in their format, access mechanism, and whether they are public or not.
Some of the most commonly used databases are listed below. For a more comprehensive list, please check the link at the beginning of the subsection.
Software tools for bioinformatics range from simple command-line tools, to more complex graphical programs and standalone web-services available from various bioinformatics companies or public institutions.
Many free and open-source software tools have existed and continued to grow since the s. The open source tools often act as incubators of ideas, or community-supported plug-ins in commercial applications.
They may also provide de facto standards and shared object models for assisting with the challenge of bioinformation integration. An alternative method to build public bioinformatics databases is to use the MediaWiki engine with the WikiOpener extension.
This system allows the database to be accessed and updated by all experts in the field. SOAP - and REST -based 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 main advantages derive from the fact that end users do not have to deal with software and database maintenance overheads. Basic bioinformatics services are classified by the EBI into three categories: A bioinformatics workflow management system is a specialized form of a workflow management system designed specifically to compose and execute a series of computational or data manipulation steps, or a workflow, in a Bioinformatics application.
Such systems are designed to. Some of the platforms giving this service: This was proposed to enable greater continuity within a research group over the course of normal personnel flux while it furthering the exchange of ideas between groups. The US FDA funded this work so that information on pipelines would be more transparent and accessible to their regulatory staff.
Software platforms designed to teach bioinformatics concepts and methods include Rosalind and online courses offered through the Swiss Institute of Bioinformatics Training Portal. The Canadian Bioinformatics Workshops provides videos and slides from training workshops on their website under a Creative Commons license. The course runs on low cost Raspberry Pi computers and has been used to teach adults and school pupils. University of Southern California offers a Masters In Translational Bioinformatics focusing on biomedical applications.
There are several large conferences that are concerned with bioinformatics. From Wikipedia, the free encyclopedia. For the journal, see Bioinformatics journal. Introduction to evolution Evidence of evolution Common descent. History of evolutionary theory. Applications of evolution Biosocial criminology Ecological genetics Evolutionary aesthetics Evolutionary anthropology Evolutionary computation Evolutionary ecology Evolutionary economics Evolutionary epistemology Evolutionary ethics Evolutionary game theory Evolutionary linguistics Evolutionary medicine Evolutionary neuroscience Evolutionary physiology Evolutionary psychology Experimental evolution Phylogenetics Paleontology Selective breeding Speciation experiments Sociobiology Systematics Universal Darwinism.
Evolution as fact and theory Social effects Creation—evolution controversy Objections to evolution Level of support. Sequence alignment and Sequence database. Structural bioinformatics and Protein structure prediction. Structural motif and Structural domain. Computational systems biology , Biological network , and Interactome. Protein—protein interaction prediction and interactome. Eradication of malaria is biologically and technically feasible, with sufficient global commitment and major investments in transformative new tools and delivery strategies.
Malaria occurs in nearly countries worldwide, exacting a huge toll on human health and imposing a heavy social and economic burden in developing countries, particularly in Sub-Saharan Africa and South Asia.
An estimated million people suffered from the disease in , and about , died. About 90 percent of the deaths were in Sub-Saharan Africa, and 77 percent were among children under age 5. Malaria is caused by parasites transmitted by mosquitoes. Even in relatively mild cases, it can cause high fever, chills, flu-like symptoms, and severe anemia.
These symptoms can be especially dangerous for pregnant women and young children who are experiencing the disease for the first time. In the past dozen years, malaria funding has increased nearly fold and major gains have been made in controlling the disease in developing nations.
The number of new cases has declined by 25 percent globally, and deaths from malaria have fallen by 42 percent. These gains have been made through a combination of interventions, including timely diagnosis and treatment using reliable diagnostic tests and effective drugs; indoor spraying with safe, long-lasting insecticides; and the use of bed nets treated with long-lasting insecticide to protect people from mosquito bites at night. Current tools and treatments are insufficient, however, to achieve elimination in many countries.
And the cost of maintaining these interventions has reached several billion dollars a year. The malaria parasite has begun to develop resistance to currently available insecticides and drugs, and these resistant strains will spread.
Infected individuals who are asymptomatic—the majority of those infected—remain an ongoing source of transmission. Given sufficient global commitment, major investments in research and development, and transformative new tools and delivery strategies, the ambitious goal of malaria eradication can be met.
Without an immediate, coordinated worldwide effort to eradicate malaria, this window of opportunity could close indefinitely and the progress already achieved will remain at risk. Malaria is preventable and treatable, and history shows that it can be eliminated. Less than a century ago, it was prevalent across the world, including Europe and North America.
Malaria was eliminated in most of Western Europe by the mids; the United States achieved elimination of the disease in We have the opportunity to accelerate progress toward elimination in all countries by improving the delivery of existing interventions as well as developing new tools and new strategies that target not just malaria-transmitting mosquitoes but also the parasite itself, which can survive in humans for more than 10 years.
By mobilizing the required commitment and resources, we can achieve global eradication and save many millions of lives. Our new multi-year Malaria strategy, Accelerate to Zero, adopted in late , addresses the areas in which we believe the foundation is best positioned, among a broad spectrum of partners, to develop groundbreaking approaches to reducing the burden of malaria and accelerating progress toward eradication of the disease.
Our Malaria strategy is based on a core set of foundational principles that support our evolving strategic choices. We concentrate our resources in areas where we can identify significant leverage points, and we assume risks that are more challenging for others to take.
An entomologist examines dead mosquitoes extracted from a window trap in a village. Potentially transformative measures that could accelerate malaria eradication include single-dose treatments that are safe and well tolerated, highly sensitive diagnostic tests, and vaccines that prevent infection or block transmission. Because emerging resistance to insecticides and drugs is the most important biologic threat to the goal of eradication, we are investing in the development of new tools and strategies to prevent or delay resistance.
We also advocate for sustained and increased funding of malaria-related efforts by donor governments and endemic countries. We are generating evidence that malaria can be eliminated in diverse geopolitical and transmission zones, including Southern Africa and the Greater Mekong Sub-region. This includes improving the delivery of existing vector-control tools, diagnostics, and drugs; investigating the potential of existing drugs to achieve complete cure at the individual and population levels, including the asymptomatic reservoir; and refining strategies for reaching special populations, such as pregnant women and children under age 5.
Because current tools are not sufficient to achieve global eradication, we are investing in a range of new interventions that have greater impact. We are working to develop transmission-blocking vaccines as well as a single, fixed-dose combination drug for complete cure and prevention. We invest in high-sensitivity diagnostic tools and real-time data transfer methods to better understand epidemiological patterns of infection. This can help lead to better surveillance strategies and more efficient and effective elimination campaigns.
We are exploring new vector-control tools that address increasing resistance to insecticides that kill mosquitoes or prevent them from biting people. Rural residents of Pailin Province, Cambodia, attend a course on malaria prevention.