Bioinformatics: A Practical Guide to the Analysis of Genes and Proteins

Cover
Title Page
Copyright
Contents
Foreword
Preface
Contributors
About the Companion Website
Chapter 1 Biological Sequence Databases
	Introduction
	Nucleotide Sequence Databases
	Nucleotide Sequence Flatfiles: A Dissection
		The Header
		The Feature Table
		Graphical Interfaces
		RefSeq
	Protein Sequence Databases
		The NCBI Protein Database
		UniProt
	Summary
	Acknowledgments
	Internet Resources
	Further Reading
	References
Chapter 2 Information Retrieval from Biological Databases
	Introduction
	Integrated Information Retrieval: The Entrez System
		Relationships Between Database Entries: Neighboring
			BLAST
			VAST
			Weighted Key Terms
		Hard Links
		The Entrez Discovery Pathway
	Medical Databases
	Organismal Sequence Databases Beyond NCBI
	Summary
		Internet Resources
	Further Reading
	References
Chapter 3 Assessing Pairwise Sequence Similarity: BLAST and FASTA
	Introduction
	Global Versus Local Sequence Alignments
	Scoring Matrices
		PAM Matrices
		BLOSUM Matrices
		Which Matrices Should be Used When?
		Nucleotide Scoring Matrices
		Gaps and Gap Penalties
	BLAST
		The Algorithm
		Performing a BLAST Search
		Understanding the BLAST Output
		Suggested BLAST Cut‐Offs
	BLAST 2 Sequences
	MegaBLAST
	PSI‐BLAST
		The Method
		Performing a PSI‐BLAST Search
	BLAT
	FASTA
		The Method
		Running a FASTA Search
		Statistical Significance of Results
		Comparing FASTA and BLAST
	Summary
		Internet Resources
	Further Reading
	References
Chapter 4 Genome Browsers
	Introduction
	The UCSC Genome Browser
	UCSC Table Browser
	ENSEMBL Genome Browser
	Ensembl Biomart
	JBrowse
	Summary
		Internet Resources
	Further Reading
	References
Chapter 5 Genome Annotation
	Introduction
	Gene Prediction Methods
	Ab Initio Gene Prediction in Prokaryotic Genomes
	Ab Initio Gene Prediction in Eukaryotic Genomes
		Predicting Exon‐Defining Signals
		Predicting and Scoring Exons
		Exon Assembly
	How Well Do Gene Predictors Work?
	Assessing Prokaryotic Gene Predictors
	Assessing Eukaryotic Gene Predictors
	Evidence Generation for Genome Annotation
		Gene Annotation and Evidence Generation Using RNA‐seq Data
		Gene Annotation and Evidence Generation Using Protein Sequence Databases
	Gene Annotation and Evidence Generation using Comparative Gene Prediction
		Evidence Generation for Non‐Protein‐Coding, Non‐Coding, or Foreign Genes
		tRNA and rRNA Gene Finding
		Prophage Finding in Prokaryotes
		Repetitive Sequence Finding/Masking in Eukaryotes
		Finding and Removing Pseudogenes in Eukaryotes
	Genome Annotation Pipelines
		Prokaryotic Genome Annotation Pipelines
		Eukaryotic Genome Annotation Pipelines
		Visualization and Quality Control
	Summary
	Acknowledgments
	Internet Resources
	Further Reading
	References
Chapter 6 Predictive Methods Using RNA Sequences
	Introduction
	Overview of RNA Secondary Structure Prediction Using Thermodynamics
	Dynamic Programming
	Accuracy of RNA Secondary Structure Prediction
		Experimental Methods to Refine Secondary Structure Prediction
	Predicting the Secondary Structure Common to Multiple RNA Sequences
		Algorithms That Are Constrained by an Initial Alignment
		Algorithms That Are Not Constrained by the Initial Alignment
	Practical Introduction to Single‐Sequence Methods
		Using the Mfold Web Server
		Using the RNAstructure Web Server
	Practical Introduction to Multiple Sequence Methods
		Using the RNAstructure Web Server to Predict a Common Structure for Multiple Sequences
	Other Computational Methods to Study RNA Structure
	Comparison of Methods
	Predicting RNA Tertiary Structure
	Summary
		Internet Resources
	Further Reading
	References
Chapter 7 Predictive Methods Using Protein Sequences
	Introduction
	One‐Dimensional Prediction of Protein Structure
		Synopsis
		Secondary Structure and Solvent Accessibility
			Background
			Methods
		Performance Assessment of Secondary Structure Prediction
		Transmembrane Alpha Helices and Beta Strands
			Background
			Methods
			Performance
		Disordered Regions
			Background
			Methods
			Performance
	Predicting Protein Function
		Synopsis
		Motifs and Domains
			Background
			Databases
			Methods
			Performance
		Gene Function Prediction Based on the Gene Ontology
			Background
			Methods
			Performance
		Subcellular Localization
			Background
			Methods
			Performance
		Protein Interaction Sites
			Background
			Methods
			Performance
		Effect of Sequence Variants
			Background
			Methods
			Performance
	Summary
		Internet Resources
	Further Reading
	References
Chapter 8 Multiple Sequence Alignments
	Introduction
	Measuring Multiple Alignment Quality
	Making an Alignment: Practical Issues
	Commonly Used Alignment Packages
		Clustal Omega
			Iteration
			Benchmarking Clustal Omega
		ClustalW2
		DIALIGN
		Kalign
		MAFFT
			Default MAFFT
			L‐INS‐i
			PartTree
		MUSCLE
		PASTA
		PRANK
		T‐Coffee
	Viewing a Multiple Alignment
		Clustal X
		Jalview
		SeaView
		ProViz
	Summary
		Internet Resources
	References
Chapter 9 Molecular Evolution and Phylogenetic Analysis
	Introduction
	Early Classification Schemes
	Sequences As Molecular Clocks
	Background Terminology and the Basics
	How to Construct a Tree
		Multiple Sequence Alignment and Alignment Editing
		Determining the Substitution Model
		Tree Building
		Tree Visualization
	Marker‐Based Evolution Studies
	Phylogenetic Analysis and Data Integration
	Future Challenges
		Internet Resources
	References
Chapter 10 Expression Analysis
	Introduction
	Step 0: Choose an Expression Analysis Technology
		DNA Microarrays
		RNA‐seq
		The Choice is Yours
	Step 1: Design the Experiment
	Step 2: Collect and Manage the Data – and Metadata
	Step 3: Data Pre‐Processing
	Step 4: Quality Control
		Quality Control Tools
		Screening for Misidentified Samples: PCA on Y Chromosome Expression
	Step 5: Normalization and Batch Effects
		The Importance of Normalizing and Batch‐Correcting Data
		FPKM and Count Data
		Sample and Quantile Normalization
		Additional Methods of Sample Normalization
			Counts per Million
			Upper Quantile Normalization
			Relative Log Expression
			Trimmed Mean of M Values
		Batch Correction
	Step 6: Exploratory Data Analysis
		Hierarchical Clustering
		Principal Component Analysis
		Non‐Negative Matrix Factorization
	Step 7: Differential Expression Analysis
		Student's t‐Test: The Father of Them All
		Limma
		Voom
		Negative Binomial Models
		Fold‐Change
		Correcting for Multiple Testing
			Family‐Wise Error Rate
			False Discovery Rate
	Step 8: Exploring Mechanisms Through Functional Enrichment Analysis
		List‐Based Methods
		Rank‐Based Methods
	Step 9: Developing a Classifier
		Measuring Classifier Performance
		Feature Selection
			Differential Expression Testing
			Minimum Redundancy Maximum Relevance
			Significance Analysis of Prognostic Signatures
		Classification Methods
		Validation of Predictive Models
			Validation of Population‐Level Predictions Using Independent Test Sets
			Validation of Population‐Level Predictions Using Cross‐Validation
			Validation of Individual‐Level Assignment Robustness Using Independent Training Sets
	Single‐Cell Sequencing
	Summary
		Internet Resources
	Further Reading
	References
Chapter 11 Proteomics and Protein Identification by Mass Spectrometry
	Introduction
		What Is a Proteome?
		Why Study Proteomes?
	Mass Spectrometry
		Ionization
		Mass Analyzers
		Ion Detectors
	Tandem Mass Spectrometry for Peptide Identification
	Sample Preparation
	Bioinformatics Analysis for MS‐based Proteomics
	Proteomics Strategies
	Peptide Mass Fingerprinting
	PMF on the Web
		Mascot
	Proteomics and Tandem MS
		Peptide Spectral Matching
		De Novo Peptide Sequencing
		Spectral Library Searching
		Hybrid Search
		Top‐Down (Intact Protein) MS
		Database Search Models
	PSM Software
		SEQUEST
		X! Tandem
		MaxQuant (Andromeda)
	PSM on the Web
	Reporting Standards
		Proteomics XML Formats
	Proteomics Data Repositories
		ProteomeXchange
		PRIDE
		PeptideAtlas
		Global Proteome Machine + GPMdb
	Protein/Proteomics Databases
		UniProt
		PTM Databases
	Selected Applications of Proteomics
		Differential Proteomics
		Functional Proteomics
		Structural Proteomics
	Summary
	Acknowledgments
	Internet Resources
	Further Reading
	References
Chapter 12 Protein Structure Prediction and Analysis
	Introduction to Protein Structures
	How Protein Structures are Determined
	How Protein Structures are Described
	Protein Structure Databases
		Other Structure Databases
		MMDB
		Proteopedia
	Visualizing Proteins
	Protein Structure Prediction
		Homology Modeling
		Threading
		Ab Initio Structure Prediction
	Protein Structure Evaluation
	Protein Structure Comparison
	Summary
		Internet Resources
	Further Reading
	References
Chapter 13 Biological Networks and Pathways
	Introduction
	Pathway and Molecular Interaction Mapping: Experiments and Predictions
	Pathway and Molecular Interaction Databases: An Overview
		Representing Biological Pathways and Interaction Networks in a Computer
		Considerations for Pathway and Interaction Data Representation
	Pathway Databases
		Reactome
		EcoCyc
		KEGG
	Molecular Interaction Databases
		BioGRID
		IntAct
	Functional Interaction Databases
		STRING
		GeneMANIA
	Strategies for Navigating Pathway and Interaction Databases
	Standard Data Formats for Pathways and Molecular Interactions
		BioPAX
		PSI‐MI
		SBML
	Pathway Visualization and Analysis
	Network Visualization and Analysis
		Network Visualization
		Network Analysis
	Summary
	Acknowledgments
	Internet Resources
	Further Reading
	References
Chapter 14 Metabolomics
	Introduction
	Data Formats
		Chemical Representation and Exchange Formats
		Spectral Representation and Exchange Formats
		Molecular Editors
		Spectral Viewers
	Databases
		Chemical Compound Databases
		Spectral Databases
		Metabolic Pathway Databases
		Organism‐Specific Metabolomic Databases
	Bioinformatics for Metabolite Identification
		Levels of Metabolite Identification
		NMR‐Based Compound Identification
		GC‐MS‐Based Compound Identification
		LC‐MS‐Based Compound Identification
	Multivariate Statistics
		Principal Component Analysis
		Partial Least Squares Discriminant Analysis
	Bioinformatics for Metabolite Interpretation
	Summary
		Internet Resources
	Further Reading
	References
Chapter 15 Population Genetics
	Introduction
	Evolutionary Processes and Genetic Variation
	Allele Frequencies and Population Variation
	Display Methods
	Demographic History Inference
	Admixture and Ancestry Estimation
	Detection of Natural Selection
	Other Applications
	Summary
		Internet Resources
	References
Chapter 16 Metagenomics and Microbial Community Analysis
	Introduction
	Why Study the Microbiome?
	The Origins of Microbiome Analysis
	Metagenomic Workflow
	General Considerations in Marker‐Gene and Metagenomic Data Analysis
	Marker Genes
		Quality Control
		Grouping of Similar Sequences
		Taxonomic Assignment
		Calculating and Comparing Diversity
		Associations with Metadata
	Metagenomic Data Analysis
		Predicting Functional Information from Marker‐Gene Data
		Metagenomic Analysis Protocol
		Quality Control and Merging of Paired‐End Reads
		Assembly
		Gene Annotation and Homology Searching
		Taxonomic Assignment and Profiling
		Functional Predictions
		Statistical Associations
	Other Techniques to Characterize the Microbiome
	Summary
		Internet Resources
	Further Reading
	References
Chapter 17 Translational Bioinformatics
	Introduction
	Databases Describing the Genetics of Human Health
	Prediction and Characterization of Impactful Genetic Variants from Sequence
		Characterizing Genetic Variants at the Protein Level
		Characterizing Genetic Variants at the Genomic or Transcriptomic Level
		Using Informatics to Prioritize Disease‐Causing Genes
		Translating Model Organism Data to Humans
	Computing with Patient Phenotype Using Data in Electronic Health Records
		Introduction to Electronic Health Records
		Structured Clinical Data with Biomedical Ontologies
		Common Data Models
		Much of Electronic Health Record Data are Plaintext
	Informatics and Precision Medicine
		Describing Patient Phenotype
		Drug Repurposing
		Clinical Marker Development from ‐omics Data
		Integration of Heterogeneous Data Sources
		Precision Medicine Initiatives
		Community Challenges Solve Innovative Problems Collaboratively
		Electronic Health Record Systems can be Customized
		Informatics for Prevention Policy
	Ethical, Legal, and Social Implications of Translational Medicine
		Protecting Patient Privacy
	Summary
		Internet Resources
	References
Chapter 18 Statistical Methods for Biologists
	Introduction
	Descriptive Representations of Data
		Data vs. Information vs. Knowledge
		Datasets and Data Schemas
		Descriptive Statistics
		The Right Graph Is the Most Descriptive Representation of a Dataset
		Frequency and Probability Distributions
	Statistical Inference and Statistical Hypothesis Testing
		Statistical Inference
		Statistical Hypothesis Testing
		Type I and II Errors that Arise from Statistical Hypothesis Testing
		Statistical Significance
		Testing the Null Hypothesis with a Two‐Sample t‐Test
		Statistical Power
		Correcting for False Discovery due to Multiple Testing
		The Global Problem with the Use of p Values
		Common Statistical Tests Used in a Typical Statistical Inference Process
	Summary
	Acknowledgments
	Internet Resources
	Further Reading
	References
Appendices
	1.1 Example of a Flatfile Header in ENA Format
	1.2 Example of a Flatfile Header in DDBJ/GenBank Format
	1.3 Example of a Feature Table in ENA Format
	1.4 Example of a Feature Table in GenBank/DDBJ Format
	6.1 Dynamic Programming
	Reference
Glossary
Index
EULA