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5.2 BLAST and FASTA Facilitate the Development of Drugs Against Human PathogensAppendix: Being Colorful Is Not Enough-How Are Stop Codons Decoded?; Chapter 2: Sequence Alignment; 1 Introduction; 2 Dynamic Programming for Pairwise Alignment; 2.1 Pairwise Alignment with Constant Gap Penalty; 2.1.1 Global Alignment; 2.1.2 Local Alignment; 2.2 Match-Mismatch Matrix; 2.2.1 Match-Mismatch Matrix for Nucleotide Sequences; 2.2.1.1 PAM Matrix for Nucleotide Sequences; 2.2.1.2 BLOSUM Matrix for Nucleotide Sequences; 2.2.2 Match-Mismatch Matrix for Proteins
2.3 Pairwise Alignment with Gap Penalty Specified by the Affine Function2.3.1 Scoring Matrices; 2.3.2 Backtrack Matrices; 2.3.3 Obtain Sequence Alignment from Backtrack Matrices; 3 Multiple Sequence Alignment (MSA); 3.1 Dynamic Programming for Profile Alignment; 3.2 Replacing a Sequence Profile by a Reconstructed Ancestral Sequence; 3.3 Multiple Alignment with a Guide Tree; 3.4 The Inconsistency Problem with Pairwise Alignments; 4 Sequence Alignment with Secondary Structure; 5 Align Nucleotide Sequences Against Amino Acid Sequences; Postscript; Chapter 3: Position weight matrix and Perceptron
1 Introduction2 Position Weight Matrix (PWM); 2.1 Basic Concepts of PWM; 2.2 Specification of the Background Frequencies; 2.3 Specification of Pseudocounts; 2.4 Statistical Significance Tests for PWM; 2.4.1 Statistical Significance Tests for Individual Sites; 2.4.2 Evaluating Statistical Significance of PWM When Pseudocounts Are Used; 2.4.3 Statistical Significance of PWMS; 2.5 Using PWM to Refine Multiple Sequence Alignment; 3 Perceptron; 3.1 Perceptron Algorithm; 3.2 Perceptron and XOR Problem; Postscript; Chapter 4: Gibbs sampler; 1 Introduction
1.1 Gibbs Sampler and Its Biological Applications1.2 What Is De Novo Motif Discovery?; 1.3 What Are the Estimated Parameters When Applying Gibbs Sampler in Motif Discovery?; 2 Computational Details of Gibbs Sampler; 2.1 Initialization; 2.2 Predictive Update; 2.3 Motif Sampler; Postscript; Chapter 5: Transcriptomics and RNA-Seq Data Analysis; 1 Introduction; 2 Reduce File Size Without Losing Sequence Information; 3 Assigning Sequence Reads to Paralogous Genes; 3.1 Allocating Reads to a Two-Member Paralogous Gene Family; 3.2 Allocating Sequence Reads in Gene Family with More Than Two Members
2.3 Pairwise Alignment with Gap Penalty Specified by the Affine Function2.3.1 Scoring Matrices; 2.3.2 Backtrack Matrices; 2.3.3 Obtain Sequence Alignment from Backtrack Matrices; 3 Multiple Sequence Alignment (MSA); 3.1 Dynamic Programming for Profile Alignment; 3.2 Replacing a Sequence Profile by a Reconstructed Ancestral Sequence; 3.3 Multiple Alignment with a Guide Tree; 3.4 The Inconsistency Problem with Pairwise Alignments; 4 Sequence Alignment with Secondary Structure; 5 Align Nucleotide Sequences Against Amino Acid Sequences; Postscript; Chapter 3: Position weight matrix and Perceptron
1 Introduction2 Position Weight Matrix (PWM); 2.1 Basic Concepts of PWM; 2.2 Specification of the Background Frequencies; 2.3 Specification of Pseudocounts; 2.4 Statistical Significance Tests for PWM; 2.4.1 Statistical Significance Tests for Individual Sites; 2.4.2 Evaluating Statistical Significance of PWM When Pseudocounts Are Used; 2.4.3 Statistical Significance of PWMS; 2.5 Using PWM to Refine Multiple Sequence Alignment; 3 Perceptron; 3.1 Perceptron Algorithm; 3.2 Perceptron and XOR Problem; Postscript; Chapter 4: Gibbs sampler; 1 Introduction
1.1 Gibbs Sampler and Its Biological Applications1.2 What Is De Novo Motif Discovery?; 1.3 What Are the Estimated Parameters When Applying Gibbs Sampler in Motif Discovery?; 2 Computational Details of Gibbs Sampler; 2.1 Initialization; 2.2 Predictive Update; 2.3 Motif Sampler; Postscript; Chapter 5: Transcriptomics and RNA-Seq Data Analysis; 1 Introduction; 2 Reduce File Size Without Losing Sequence Information; 3 Assigning Sequence Reads to Paralogous Genes; 3.1 Allocating Reads to a Two-Member Paralogous Gene Family; 3.2 Allocating Sequence Reads in Gene Family with More Than Two Members