دانلود کتاب Bioinformatics Toolbox User's Guide

فهرست مطالب :

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