Network components and biological network construction methods
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This document outlines methods for constructing biological networks. It discusses the basic components and features of networks, including nodes, edges, degree, distance, and types of biological networks. It describes approaches for building networks from genome sequences, "omics" data, and literature. These include predicting interactions from gene neighbor/order, co-evolution, co-expression analysis, and integrating data sources to build meta-networks representing entire genomes or phenotypes.
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3) For short protein sequences like "QKGSYPVRSTC" and "QKGSGPVRSTC", the optimal alignment implies one single mutation occurred since the sequences diverged from a common ancestor.
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This document provides an overview and introduction to bioinformatics. It discusses the large amounts of biological sequence data that have been generated and how bioinformatics is needed to analyze this data computationally. The document outlines topics that will be covered, including databases, sequence alignment tools like BLAST, gene finding, and protein analysis. Practical workshops are described that will involve database searching, multiple sequence alignments, and interpreting results to understand molecular biology and solve biomedical problems. Questions are welcomed throughout the workshops.
Introduction to Apollo for i5k
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The i5K, an initiative to sequence the genomes of 5,000 insect and related arthropod species, is a broad and inclusive effort that seeks to involve scientists from around the world in their genome curation process, and Apollo is serving as the platform to empower this community.
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Apollo Introduction for the Chestnut Research Community
This document provides an introduction and overview of manual genome annotation using the Apollo genome annotation tool. It begins with an outline of the webinar topics, which include an introduction to manual annotation and its necessity, an overview of the Apollo tool and its functionality for collaborative curation, and examples and demonstrations. The document then covers key concepts for manual annotation such as the definition of a gene, genome curation steps, transcription and translation including reading frames, splice sites, and phase. The goal of the webinar is to help participants better understand genome curation and manual annotation using Apollo to identify and modify gene models.
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A Workshop at the Stowers Institute for Medical Research.
Bioinformatics introduction
Bioinformatics is the use of computers for storage, retrieval, manipulation, and distribution of information related to biological macromolecules such as DNA, RNA, and proteins. It involves developing computational tools and databases to analyze biological data. Key areas include sequence analysis, structural analysis, functional analysis, biological databases, sequence alignment, protein structure prediction, molecular phylogenetics, and genomics. The goals are to better understand living systems at the molecular level through computational analysis of biological data.
Qi liu 08.08.2014
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Introduction to Bioinformatics-1.pdf
Bioinformatics is the application of computational tools and techniques to analyze and interpret biological data. It involves the development of these tools and databases, as well as their application to better understand biological systems and functions at the molecular level through analysis of genetic sequences, protein structures, and more. The goal is to gain a global understanding of cellular functions by analyzing genetic data as dictated by the central dogma of biology, and relating sequence information to protein functions and cellular processes.
Final Acb All Hands 26 11 07.Key
The BioMANTA Network project aims to develop a Semantic Web infrastructure for computational modelling and analysis of large-scale protein-protein interaction and compound activity networks. This will involve creating a Semantic Interactome Model using an OWL-based ontology to represent public interaction data. The goals are to perform network inference and knowledge discovery through network meta-analysis and global network inference. Results will be visualized using COBALT software and high quality ontologies and data sets will be produced.
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This document provides an overview of statistical analyses that can be performed in PRISM. It discusses how to perform common statistical tests like t-tests, ANOVA, linear regression, and summarizes the appropriate tests to address different research questions. Examples are given of how to analyze pre-post treatment data using paired t-tests and compare groups using independent t-tests or ANOVA. Guidance is also provided on interpreting results and checking assumptions.
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2) The document reviews various features of JMP including importing data, organizing data tables, performing statistical analyses through platforms like distribution and fit model, and creating graphs and reports.
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This document discusses methods for analyzing categorical data and response variables, including contingency tables, chi-square tests, Fisher's exact test, odds ratios, logistic regression, and generalized linear models. Contingency tables are used to display relationships between categorical variables and tests of independence. Fisher's exact test and chi-square tests determine if a relationship is statistically significant. Odds ratios and relative risk indicate the magnitude of relationships. Logistic regression models relationships between continuous predictors and categorical responses. Generalized linear models extend these methods.
Better graphics in R
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Automating biostatistics workflows using R-based webtools
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The document provides an overview of statistical testing, including:
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- When large sample tests or exact tests are needed
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It discusses key concepts like null and alternative hypotheses, test statistics, p-values, and type I and II errors. Examples of the Student's t-test and Wilcoxon rank sum test are provided.GraphPad Prism: Curve fitting
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Appendix: Crash course in R and BioConductor
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This document provides a training manual for using R and BioConductor. It introduces R as a powerful open source software for statistical analysis and data visualization that also includes a scripting language. BioConductor is a related open source project that provides tools for analyzing genomic data using R packages. The manual then covers downloading and installing R and BioConductor, describes different interfaces for using R, and provides tutorials on basic R functions for data manipulation, graphics, statistics, and scripting.
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Current Ms word generated power point presentation covers major details about the micronuclei test. It's significance and assays to conduct it. It is used to detect the micronuclei formation inside the cells of nearly every multicellular organism. It's formation takes place during chromosomal sepration at metaphase.
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Network components and biological network construction methods
- 1. August 15th, 2013 Vijayaraj Nagarajan PhD Computational Biologist BCBB/OCICIB/NIAID, National Institutes of Health
- 2. Outline § Network Components – Basic components of a network – Basic features of a network – Types of biological networks § Biological Network Construc9on Methods – Methods (Logic and concept) – Genome Sequences – “omics” data – Literature mining – Integra9on – Meta-‐networks
- 3. § Nodes § DNA/RNA/Protein/Metabolite/Ontology § Edges Directed § Dis9nc9on between source and target • Ac9va9on (direct/indirect) • Repression (direct/indirect) Undirected § No dis9nc9on between source and target • Co-‐expression (indirect) • Binding (direct) • Similarity/strength Basic Components
- 4. Basic Features § Degree – Number of connec9ons that a node has § Distance – Number of connec9ons between two nodes, in a shortest path § Path – A sequence of connec9ons – Is there a path (reachability) – Mean Shortest Path distance (closeness) – In how many shortest paths (betweenness)
- 5. § Size of a network (Number of nodes) § Density of a network (Propor9on of the connec9ons) § Mo9fs/Cliques/Clusters/Sub-‐networks Loops Chains Parallels Multi-input Single input Basic Features
- 6. Types of Biological Networks § DNA-‐Protein • Transcrip9onal regulatory networks • Methyla9on networks § RNA-‐RNA • miRNA regulatory networks § RNA-‐Protein • Splicing regulatory networks § Protein-‐Protein • Co-‐expression networks • Co-‐localiza9on networks • Co-‐evolu9on networks • Structure networks • Pathway networks • Protease regulatory networks • Signal transduc9on networks • Gene Ontology networks meta-networks
- 7. Single gene § Regulators/Co-‐regulators § Upstream/Downstream elements in the network § Global connec9vity/interconnec9vity § Func9onal features § Differen9ally expressed subnetworks § One gene – one disease : bunch of genes – pathways § Nextgen sequencing data § Meta-‐analysis List of genes Why Build/Analyze Biological Networks ?
- 8. Outline § Network Components – Basic components of a network – Basic features of a network – Types of biological networks § Biological Network Construc9on Methods – Methods (Logic and concept) – Genome Sequences – “omics” data – Literature mining – Integra9on – Meta-‐networks
- 9. How to Build Biological Networks ? § Search/Retrieve from knowledge bases § Predict from genome sequences § Predict from “omics” data § Predict from literature § Integrate and analyze § Meta-‐networks from genome/phenome scale data analysis
- 10. Protein Engineering, Vol. 14, No. 9, 609-614, September 2001 PredicCon from genome sequences § Gene neighbor (gene cluster, gene order) § Gene fusion (RoseWa stone) § Phylogene9c profiling § Co-‐evolu9on § Mirror tree
- 11. PredicCon from “omics” data § Co-‐expression (Correla9on, Mutual Informa9on)
- 12. PPI PredicCon Using Microarray Data § Co-‐expression concept – Correla9on Coefficient • SIMoNE (Sta9s9cal Inference for Modular Networks) -‐ R – Mutual Informa9on • Reference Networks • ARACNE (Algorithm for Reconstruc9on of Accurate Cellular Networks) – R, geWorkbench • CLR (Context Likelihood of Relatedness) – R • MRNET (Maximum Relevance/Minimum Redundancy) – R • MONET (Modularized NETwork Learning) -‐ Cytoscape – Bayesian Network
- 14. Predicted PPI Network § Could form a complex § Could be func9onally associated § Could be involved in a same metabolic pathway § Could be involved in a specific signal transduc9on path § False posi9ve
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