Bioinformatics and Molecular Evolution
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More questions? Visit the Learner Help Center. Browse Chevron Right. Health Chevron Right. Health Informatics. Molecular Evolution Bioinformatics IV. Offered By. About this Course 5, recent views. Course 1 of 1 in the Bioinformatics Specialization. Flexible deadlines.
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Bioinformatics and Molecular Evolution by Paul G. Higgs
Transforming Distance Matrices into Evolutionary Trees 6m. Additive Phylogeny 9m. Reading 2 readings. Course Details 10m. Week 1 FAQs Optional. Quiz 1 practice exercise. Week 1 Quiz 15m. Recently, large amount of sequence data of gene is available by high throughput sequencer.
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Therefore, we examined the robustness of the method to construct the phylogenetic tree using large amount of genes by simulation tests. Laboratory of Molecular Evolution and Bioinformatics Tokumasa Horiike Original Site Our laboratory studies molecular evolution and information biology.
What is the study of molecular evolution? The origin of the orphan genes Orphan genes are detected by homology search as genes which do not have any homologs in other organisms. Convergent evolution of proteins Convergent evolution of the body shape has often occurred through the adaptation of organisms to the environment.
Influence of gene-gain and gene-loss event on the evolution of a lineage We are developing a software to detect gene gain and gene loss event on a lineage, and estimate the functions of the gene. Inferring phylogenetic trees using large amount of genes Recently, large amount of sequence data of gene is available by high throughput sequencer. 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-Activity 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. For example:. The area of research draws from statistics and computational linguistics.
- Bioinformatics and Molecular Evolution.
- Molecular Evolution & Phylogeny: What, When, Why & How?.
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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. Some examples are:. 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: for example, DNA and protein sequences, molecular structures, phenotypes and biodiversity. 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. 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. This was proposed to enable greater continuity within a research group over the course of normal personnel flux while 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. This is the latest accepted revision , reviewed on 22 June Software tools for understanding biological data. For the journal, see Bioinformatics journal.
Darwin's finches by John Gould. Key topics. Introduction to evolution Evidence of evolution Common descent Evidence of common descent. Processes and outcomes. Natural history. History of evolutionary theory. Fields and applications. 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.
Social implications. Evolution as fact and theory Social effects Creation—evolution controversy Objections to evolution Level of support.
Molecular Evolution & Phylogeny: What, When, Why & How?
Index Outline. Main articles: Sequence alignment , Sequence database , and Alignment-free sequence analysis. Main article: DNA sequencing. Main article: Sequence assembly. See also: sequence analysis , sequence mining , sequence profiling tool , and sequence motif. Main article: Gene prediction. Further information: Computational phylogenetics. Main article: Comparative genomics. Main article: Pan-genome. Main article: Genome-wide association studies. Main article: Oncogenomics. Main article: Nuclear organization. Main articles: Structural bioinformatics and Protein structure prediction.
See also: Structural motif and Structural domain.
Main articles: Computational systems biology , Biological network , and Interactome. Main articles: Protein—protein interaction prediction and interactome. Main articles: Text mining and Biomedical text mining. Main article: Flow cytometry bioinformatics. Main article: Biodiversity informatics. Main articles: List of biological databases and Biological database. Main article: Bioinformatics workflow management systems. Biodiversity informatics Bioinformatics companies Computational biology Computational biomodeling Computational genomics Functional genomics Health informatics International Society for Computational Biology Jumping library List of bioinformatics institutions List of open-source bioinformatics software List of bioinformatics journals Metabolomics Nucleic acid sequence Phylogenetics Proteomics Structural bioinformatics Gene Disease Database.
Retrieved 17 April Current Opinion in Structural Biology. Chemical Reviews. Computational Biology and Bioinformatics: Gene Regulation. Briefings in Functional Genomics. In Karabencheva-Christova, T. Biomolecular Modelling and Simulations. Advances in Protein Chemistry and Structural Biology.
Academic Press. Drug Discovery Today. Searls, David B. PLoS Computational Biology. Kameleon: 28— National Biomedical Research Foundation, pp. Bibcode : Sci Nucleic Acids Res. Bioinformatics - Trends and Methodologies. Bioinformatics — Trends and Methodologies. Retrieved 8 January Bibcode : Natur.