Bioinformatics Toolbox User's Guide

Description

Getting Started Bioinformatics Toolbox Product Description Key Features Product Overview Features Expected Users Data Formats and Databases Sequence Alignments Sequence Utilities and Statistics Protein Property Analysis Phylogenetic Analysis Microarray Data Analysis Tools Microarray Data Storage Mass Spectrometry Data Analysis Graph Theory Functions Statistical Learning and Visualization Prototyping and Development Environment Data Visualization Exchange Bioinformatics Data Between Excel and MATLAB Using Excel and MATLAB Together About the Example Before Running the Example Running the Example for the Entire Data Set Editing Formulas to Run the Example on a Subset of the Data Using the Spreadsheet Link product to Interact With the Data in MATLAB Working with Whole Genome Data Comparing Whole Genomes High-Throughput Sequence Analysis Work with Next-Generation Sequencing Data Overview What Files Can You Access? Before You Begin Create a BioIndexedFile Object to Access Your Source File Determine the Number of Entries Indexed By a BioIndexedFile Object Retrieve Entries from Your Source File Read Entries from Your Source File Manage Sequence Read Data in Objects Overview Represent Sequence and Quality Data in a BioRead Object Represent Sequence, Quality, and Alignment/Mapping Data in a BioMap Object Retrieve Information from a BioRead or BioMap Object Set Information in a BioRead or BioMap Object Determine Coverage of a Reference Sequence Construct Sequence Alignments to a Reference Sequence Filter Read Sequences Using SAM Flags Store and Manage Feature Annotations in Objects Represent Feature Annotations in a GFFAnnotation or GTFAnnotation Object Construct an Annotation Object Retrieve General Information from an Annotation Object Access Data in an Annotation Object Use Feature Annotations with Sequence Read Data Bioinformatics Toolbox Software Support Packages Install Support Package Available Support Packages Count Features from NGS Reads Identifying Differentially Expressed Genes from RNA-Seq Data Visualize NGS Data Using Genomics Viewer App Open the App Add Tracks by Importing Data Visualize Single Nucleotide Variation in Cytochrome P450 Exploring Genome-Wide Differences in DNA Methylation Profiles Exploring Protein-DNA Binding Sites from Paired-End ChIP-Seq Data Working with Illumina/Solexa Next-Generation Sequencing Data Sequence Analysis Exploring a Nucleotide Sequence Using Command Line Overview of Example Searching the Web for Sequence Information Reading Sequence Information from the Web Determining Nucleotide Composition Determining Codon Composition Open Reading Frames Amino Acid Conversion and Composition Exploring a Nucleotide Sequence Using the Sequence Viewer App Overview of the Sequence Viewer Importing a Sequence into the Sequence Viewer Viewing Nucleotide Sequence Information Searching for Words Exploring Open Reading Frames Closing the Sequence Viewer Explore a Protein Sequence Using the Sequence Viewer App Overview of the Sequence Viewer Viewing Amino Acid Sequence Statistics Closing the Sequence Viewer References Compare Sequences Using Sequence Alignment Algorithms View and Align Multiple Sequences Overview of the Sequence Alignment App Visualize Multiple Sequence Alignment Adjust Sequence Alignments Manually Rearrange Rows Generate Phylogenetic Tree from Aligned Sequences Analyzing Synonymous and Nonsynonymous Substitution Rates Investigating the Bird Flu Virus Exploring Primer Design Identifying Over-Represented Regulatory Motifs Predicting and Visualizing the Secondary Structure of RNA Sequences Using HMMs for Profile Analysis of a Protein Family Predicting Protein Secondary Structure Using a Neural Network Visualizing the Three-Dimensional Structure of a Molecule Calculating and Visualizing Sequence Statistics Aligning Pairs of Sequences Assessing the Significance of an Alignment Using Scoring Matrices to Measure Evolutionary Distance Calling Bioperl Functions from MATLAB Accessing NCBI Entrez Databases with E-Utilities Microarray Analysis Managing Gene Expression Data in Objects Representing Expression Data Values in DataMatrix Objects Overview of DataMatrix Objects Constructing DataMatrix Objects Getting and Setting Properties of a DataMatrix Object Accessing Data in DataMatrix Objects Representing Expression Data Values in ExptData Objects Overview of ExptData Objects Constructing ExptData Objects Using Properties of an ExptData Object Using Methods of an ExptData Object References Representing Sample and Feature Metadata in MetaData Objects Overview of MetaData Objects Constructing MetaData Objects Using Properties of a MetaData Object Using Methods of a MetaData Object Representing Experiment Information in a MIAME Object Overview of MIAME Objects Constructing MIAME Objects Using Properties of a MIAME Object Using Methods of a MIAME Object Representing All Data in an ExpressionSet Object Overview of ExpressionSet Objects Constructing ExpressionSet Objects Using Properties of an ExpressionSet Object Using Methods of an ExpressionSet Object Analyzing Illumina Bead Summary Gene Expression Data Detecting DNA Copy Number Alteration in Array-Based CGH Data Analyzing Array-Based CGH Data Using Bayesian Hidden Markov Modeling Visualizing Microarray Data Gene Expression Profile Analysis Working with Affymetrix Data Preprocessing Affymetrix Microarray Data at the Probe Level Analyzing Affymetrix SNP Arrays for DNA Copy Number Variants Working with GEO Series Data Identifying Biomolecular Subgroups Using Attractor Metagenes Working with the Clustergram Function Working with Objects for Microarray Experiment Data Phylogenetic Analysis Using the Phylogenetic Tree App Overview of the Phylogenetic Tree App Opening the Phylogenetic Tree App File Menu Tools Menu Window Menu Help Menu Building a Phylogenetic Tree for the Hominidae Species Analyzing the Origin of the Human Immunodeficiency Virus Bootstrapping Phylogenetic Trees Mass Spectrometry and Bioanalytics Preprocessing Raw Mass Spectrometry Data Visualizing and Preprocessing Hyphenated Mass Spectrometry Data Sets for Metabolite and Protein/Peptide Profiling Identifying Significant Features and Classifying Protein Profiles Differential Analysis of Complex Protein and Metabolite Mixtures using Liquid Chromatography/Mass Spectrometry (LC/MS) Genetic Algorithm Search for Features in Mass Spectrometry Data Batch Processing of Spectra Using Sequential and Parallel Computing