Coverart for item
The Resource Bioinformatics and the cell : modern computational approaches in genomics, proteomics, and transcriptomics, Xuhua Xia

Bioinformatics and the cell : modern computational approaches in genomics, proteomics, and transcriptomics, Xuhua Xia

Label
Bioinformatics and the cell : modern computational approaches in genomics, proteomics, and transcriptomics
Title
Bioinformatics and the cell
Title remainder
modern computational approaches in genomics, proteomics, and transcriptomics
Statement of responsibility
Xuhua Xia
Creator
Subject
Language
eng
http://library.link/vocab/creatorDate
1959-
http://library.link/vocab/creatorName
Xia, Xuhua
Dewey number
572.80285
Illustrations
illustrations
Index
index present
Literary form
non fiction
Nature of contents
  • dictionaries
  • bibliography
http://library.link/vocab/subjectName
  • Bioinformatics
  • Genomics
  • Proteomics
Label
Bioinformatics and the cell : modern computational approaches in genomics, proteomics, and transcriptomics, Xuhua Xia
Instantiates
Publication
Antecedent source
unknown
Bibliography note
Includes bibliographical references and index
Color
multicolored
Contents
  • 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
Control code
on1043830952
Dimensions
unknown
Edition
Second edition
Extent
1 online resource (xiii, 489 pages)
File format
unknown
Form of item
online
Isbn
9783319906843
Level of compression
unknown
Note
SpringerLink
Quality assurance targets
not applicable
Reformatting quality
unknown
Sound
unknown sound
Specific material designation
remote
System control number
(OCoLC)1043830952
Label
Bioinformatics and the cell : modern computational approaches in genomics, proteomics, and transcriptomics, Xuhua Xia
Publication
Antecedent source
unknown
Bibliography note
Includes bibliographical references and index
Color
multicolored
Contents
  • 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
Control code
on1043830952
Dimensions
unknown
Edition
Second edition
Extent
1 online resource (xiii, 489 pages)
File format
unknown
Form of item
online
Isbn
9783319906843
Level of compression
unknown
Note
SpringerLink
Quality assurance targets
not applicable
Reformatting quality
unknown
Sound
unknown sound
Specific material designation
remote
System control number
(OCoLC)1043830952

Library Locations

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