Bioinformatics and the cell: modern computational approaches in genomics, proteomics and transcriptomics
Gespeichert in:
| 1. Verfasser: | |
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| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Cham
Springer
[2018]
|
| Ausgabe: | Second edition |
| Schlagworte: | |
| Online-Zugang: | Inhaltstext http://www.springer.com/ Inhaltsverzeichnis |
| Beschreibung: | xiii, 489 Seiten Illustrationen, Diagramme 23.5 cm x 15.5 cm |
| ISBN: | 9783319906829 3319906828 |
Internformat
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Datensatz im Suchindex
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Contents
4)
-
?
»•■ •
#;■ ■
1 String Mathematics, BLAST, and FASTA 1
1 Introduction 1
2 Mathematics of String Matching 4
2 1 Basic Concepts 4
„ 2 2 BLAST Parameters X, K, and H 10
,, 3 String-Matching Algorithms in FASTA and BLAST 15
3 1 FASTA Algorithm 16
3 2 BLAST Algorithm 20
; 4 BLAST and FASTA in Genomics, Transcriptomics,
t and Proteomics 23
41 Genomics 23
4 2 Transcriptomics 25
4 3 Proteomics 26
5 BLAST and FASTA in Biomedical Research
and Drug Discovery 26
5 1 Human Genetic Disorders 26
5 2 BLAST and FASTA Facilitate the Development
of Drugs Against Human Pathogens 27
Appendix: Being Colorful Is Not Enough—How Are Stop
Codons Decoded? 29
2 Sequence Alignment 33
1 Introduction 33
2 Dynamic Programming for Pairwise Alignment 35
2 1 Pairwise Alignment with Constant Gap Penalty 36
2 2 Match-Mismatch Matrix 40
2 3 Pairwise Alignment with Gap Penalty Specified
by the Affine Function 55
Vll
Contents
3 Multiple Sequence Alignment (MSA) 62
3 1 Dynamic Programming for Profile Alignment 63
3 2 Replacing a Sequence Profile by a Reconstmcted
Ancestral Sequence 66
3 3 Multiple Alignment with a Guide Tree 68
3 4 The Inconsistency Problem with Pairwise Alignments 70
4 Sequence Alignment with Secondary Structure 71
5 Align Nucleotide Sequences Against Amino Acid Sequences 72
Postscript 75
3 Position weight matrix and Perceptron 77
1 Introduction 77
2 Position Weight Matrix (PWM) 79
2 1 Basic Concepts of PWM 79
2 2 Specification of the Background Frequencies 82
2 3 Specification of Pseudocounts 83
2 4 Statistical Significance Tests for PWM 84
2 5 Using PWM to Refine Multiple Sequence Alignment 90
3 Perceptron 93
3 1 Perceptron Algorithm 93
3 2 Perceptron and XOR Problem 97
Postscript 98
4 Gibbs sampler 99
1 Introduction 99
1 1 Gibbs Sampler and Its Biological Applications 99
1 2 What Is De Novo Motif Discovery? 100
1 3 What Are the Estimated Parameters When Applying
Gibbs Sampler in Motif Discovery? 101
2 Computational Details of Gibbs Sampler 102
2 1 Initialization 103
2 2 Predictive Update 104
2 3 Motif Sampler 110
Postscript Ill
5 Transcriptomics and RNA-Seq Data Analysis 113
1 Introduction 113
2 Reduce File Size Without Losing Sequence Information 115
3 Assigning Sequence Reads to Paralogous Genes 118
3 1 Allocating Reads to a Two-Member
Paralogous Gene Family 118
3 2 Allocating Sequence Reads in Gene Family with More
Than Two Members 119
Contents
ix
4 Comparison Between ARSDA and Cufflinks in Characterizing
Gene Expression 121
5 Use Transcriptomic Data to Refine Genome Annotation 124
6 Other Applications Using RNA-Seq Data 126
Postscript 127
6 Self-Organizing Map and Other Clustering Methods in
Transcriptomics 129
1 Introduction 129
2 Similarity and Distance Indices 131
3 Clustering Algorithms 133
3 1 UPGMA: A Hierarchical Clustering Algorithm 133
3 2 Self-Organizing Map (SOM): A Nonhierarchical
Cluster Algorithm 137
Postscript 144
7 Hidden Markov Models and Protein Secondary
Structure Prediction 145
1 Introduction 145
2 Markov Models 146
3 Hidden Markov Models 151
3 1 The Essential Elements in a Hidden Markov Model 151
3 2 Training HMM for Predicting Protein
Secondary Structure 153
3 3 The Viterbi Algorithm 157
3 4 Forward Algorithm 164
3 5 HMM and Gene Prediction 169
Postscript 171
8 Bioinformatics and Translation Initiation 173
1 Introduction 173
2 Translation Initiation in Bacteria 175
2 1 Linear Structure of a Bacterial Gene and Its mRNA 175
22A Model of SD/aSD Interaction and Other Factors
Contributing to Translation Initiation 176
2 3 Dtostait Is Constrained in a Narrow Range 179
2 4 Genes with High AoStait Tend to Have Better Codon
Adaptation 180
2 5 Secondary Structure and Translation Initiation Efficiency 181
2 6 Translation Initiation and Phage Host Specificity 183
3 Translation Initiation in Eukaryotes 184
3 1 Models of Eukaryotic Translation Initiation 184
3 2 Effect of Poly(A) in 5 UTR on Translation Initiation
in Saccharomyces cerevisiae 186
x Contents
3 3 Internal Ribosome Entry Site Activity and Stability
of Secondary Structure 190
3 4 Two Alternative Hypotheses on Kozak Consensus 193
Postscript 194
9 Bioinformatics and Translation Elongation 197
1 Introduction 197
1 1 Basic Notations, Definitions, and Abbreviations 197
1 2 Elongation Efficiency Depends on Amino Acid
and Codon Usage 204
1 3 Empirical Illustration of Codon-Anticodon Adaptation 204
1 4 Effect of Biased Mutation on Codon Usage and Some
Misconceptions 208
1 5 Two Hypotheses on Translation Elongation Efficiency 210
1 6 Wobble Hypothesis and Its Extensions 211
2 Commonly Used Codon Usage Indices 214
2 1 RSCU (Relative Synonymous Codon Usage) 216
2 2 CAI (Codon Adaptation Index) 221
2 3 /te (Index of Translation Elongation) 227
3 Translation Elongation Efficiency and Accuracy 232
4 Amino Acid Usage and Translation Elongation Efficiency 234
4 1 Factors Related to Selection for Translation
Elongation Efficiency 234
4 2 Number of Synonymous Codons 235
4 3 Genomic Mutation Bias 236
Postscript 236
10 Bioinformatics and Translation Termination in Bacteria 239
1 Introduction 239
1 1 The Release Factors 239
1 2 Stop Codons as Part of the Stop Signal 244
2 Three Key Factors Affecting Stop Codon Usage 245
2 1 Genomic Mutation Bias and Codon Usage 246
2 2 Differential Abundance of Release Factors
and Stop Codon Usage 247
2 3 Near-Cognate tRNA and Stop Codon Usage 249
2 4 Why UAG Is Rarely Used as Stop Codons? 251
3 Quantification of Translation Termination Efficiency 253
Postscript 254
11 Genomic Features: Content Sensors, Nucleotide Skew Plot,
Strand Asymmetry, and DNA Methylation 255
1 Introduction 255
2 Genomic Strand Asymmetry and GC Skew 256
Contents
xi
3 Content Sensors Related to DNA Methylation and Spontaneous
Deamination 258
3 1 Indices of DNA Methylation 258
3 2 Are These Indices Useful in Discriminating Between
Coding and Noncoding Sequences? 260
4 Do Mycoplasma Genomes Challenge the Association Between
DNA Methylation and CpG Deficiency? 261
5 Epigenetics and New Perspectives on Human Diseases 263
5 1 DNA Methylation and Conventional View of Methylated
Gene Silencing 263
5 2 Epigenetics and New Perspective on Methylation-Mediated
Gene Interaction 264
5 3 Bioinformatics and Epigenetics 265
Postscript 267
12 Nucleotide Substitution Models and Evolutionary Distances 269
1 Introduction 269
2 Three Methods to Obtain Transition Probabilities 272
2 1 Probability Reasoning to Obtain Transition Probabilities
and Evolutionary Distances 272
2 2 Obtaining Transition Probabilities by Solving
Differential Equations 291
2 3 Obtain Transition Probabilities from the Rate Matrix
by Using Matrix Exponential 294
3 How Far Can We Trace Back the Evolutionary History? 309
4 Selecting the Best-Fitting Substitution Model 311
4 1 Likelihood Ratio Test for Alternative
Substitution Models 312
4 2 Information-Theoretic Indices for Substitution
Model Selection 314
Postscript 314
13 Protein Substitution Model and Evolutionary Distance 315
1 Introduction 315
2 Deriving P and Q Matrices for Computing
Evolutionary Distances 317
14 Maximum Parsimony Method in Phylogenetics 327
1 Introduction 327
2 The Sankoff Algorithm 329
3 The Uphill Search and Branch-and-Bound Search Algorithms 334
4 The Long-Branch Attraction Problem 335
5 Bootstrapping and Delete-Half Jackknifing 337
Contents
6 Statistical Tests of Alternative MP Topologies 338
6 1 What Sites Are Relevant to the Test? 338
6 2 What OTUs to Include? 339
6 3 Include Sites as a Random Effect in a Mixed Model 340
Postscript 341
15 Distance-Based Phylogenetic Methods 343
1 Introduction 343
2 Simultaneously Estimated Distances and Paralinear/LogDet
Distances 344
2 1 Simultaneously Estimated Distances 344
2 2 Paralinear and LogDet Distances 347
2 3 Other Distances Not from Aligned Sequences 349
3 Distance-Based Phylogenetic Algorithms 350
3 1 Least-Squares (LS) Methods and Minimum
Evolution (ME) Criterion 350
3 2 Neighbor-Joining (NJ) Method 352
4 Dating Speciation and Gene Duplication Events 356
4 1 The Least-Squares Method for Conventional Internal
Node-Calibrated Dating 356
4 2 Tip-Dating for Rapidly Evolving Viruses 367
4 3 Obtaining Confidence Intervals by Using Bootstrapping
or Jackknifing 369
4 4 Application of the Dating Methods 370
Postscript 379
16 Maximum Likelihood in Molecular Phylogenetics 381
1 Introduction 381
2 The Rationale of Maximum Likelihood Approach 382
3 Likelihood for a Phylogenetic Tree 385
3 1 The Brute-Force Approach 385
3 2 The Pruning Algorithm 387
4 Calculating Likelihood by Imposing a Molecular Clock 389
5 Handling of Missing Data with the Pruning Algorithm
(and the Potential Bias) 392
Postscript 395
17 Protein Isoelectric Point and Helicobacter pylori 397
1 What Is Protein Isoelectric Point and Why It Is Important
to Know Its Computation 397
2 Computation of Protein Isoelectric Point 399
3 Genomic pi Profiling 403
tents
Xlll
w4A Case Study with the Gastric Pathogen Helicobacter pylori 404
4 1 Two Hypothesized Mechanisms of Acid Resistance
Ji in H pylori 405
4 2 Where Do Positively Charged Proteins in H pylori
Come From? 407
4 3 Is the Basic Proteome in H pylori Really an Adaptation
j to the Acidic Environment? 409
4 4 Discriminate Between AAH and PAH 410
Postscript 411
Bioinformatics and In Silico 2D Gel Electrophoresis 413
1 Scientific Rationale Behind the 2D-SDS-PAGE 413
2 In Silico Gel with Expected Separation Pattern 414
3 Differences Between the In Silico and the Actual 2D Gel 418
Fundamentals of Proteomics 421
• 1 Introduction 421
2 Protein Mass Spectrometry 422
/ 3 Charge Deconvolution 424
4 Peptide Mass Fingerprinting 429
441 Peptide Digestion 429
*4 4 2 MS Determination of Peptide Mass 432
4» 4 3 Protein Database and In Silico Digestion 432
I4 4 4 Protein Identification 434
Postscript 435
Appendix 437
The Delta Method for Deriving Parameter Variance 437
V Variance of Allele Frequency for a Recessive Allele 438
^ Variance of JC69 Distance 439
* The Variance of K80 Distance 439
‘ Illustration of Expectation-Maximization (EM) Algorithm 440
References 443
Index 485
I
|
| any_adam_object | 1 |
| author | Xia, Xuhua 1959- |
| author_GND | (DE-588)1053079028 |
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| author_role | aut |
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| building | Verbundindex |
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| callnumber-first | Q - Science |
| callnumber-label | QH324 |
| callnumber-raw | QH324.2 |
| callnumber-search | QH324.2 |
| callnumber-sort | QH 3324.2 |
| callnumber-subject | QH - Natural History and Biology |
| classification_rvk | WC 7700 WE 2400 |
| classification_tum | CHE 828f BIO 180f BIO 110f |
| ctrlnum | (OCoLC)1048209293 (DE-599)DNB115510806X |
| dewey-full | 572.80285 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 572 - Biochemistry |
| dewey-raw | 572.80285 |
| dewey-search | 572.80285 |
| dewey-sort | 3572.80285 |
| dewey-tens | 570 - Biology |
| discipline | Biologie Chemie Informatik |
| edition | Second edition |
| format | Book |
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| genre | 1\p (DE-588)4143413-4 Aufsatzsammlung gnd-content |
| genre_facet | Aufsatzsammlung |
| id | DE-604.BV045011928 |
| illustrated | Illustrated |
| indexdate | 2024-07-10T08:06:56Z |
| institution | BVB |
| institution_GND | (DE-588)1064344704 |
| isbn | 9783319906829 3319906828 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-030403826 |
| oclc_num | 1048209293 |
| open_access_boolean | |
| owner | DE-355 DE-BY-UBR DE-19 DE-BY-UBM DE-11 DE-M49 DE-BY-TUM |
| owner_facet | DE-355 DE-BY-UBR DE-19 DE-BY-UBM DE-11 DE-M49 DE-BY-TUM |
| physical | xiii, 489 Seiten Illustrationen, Diagramme 23.5 cm x 15.5 cm |
| publishDate | 2018 |
| publishDateSearch | 2018 |
| publishDateSort | 2018 |
| publisher | Springer |
| record_format | marc |
| spelling | Xia, Xuhua 1959- Verfasser (DE-588)1053079028 aut Bioinformatics and the cell modern computational approaches in genomics, proteomics and transcriptomics Xuhua Xia Second edition Cham Springer [2018] © 2018 xiii, 489 Seiten Illustrationen, Diagramme 23.5 cm x 15.5 cm txt rdacontent n rdamedia nc rdacarrier Molekulare Bioinformatik (DE-588)4531334-9 gnd rswk-swf Molekularbiologie (DE-588)4039983-7 gnd rswk-swf Bioinformatik (DE-588)4611085-9 gnd rswk-swf Transkription Genetik (DE-588)4185906-6 gnd rswk-swf Zelle (DE-588)4067537-3 gnd rswk-swf Proteomanalyse (DE-588)4596545-6 gnd rswk-swf Genomik (DE-588)4776397-8 gnd rswk-swf PSD DNA replication Protein Separation Proteomics Sequence alignment interdisciplinary training translation 1\p (DE-588)4143413-4 Aufsatzsammlung gnd-content Bioinformatik (DE-588)4611085-9 s Genomik (DE-588)4776397-8 s Proteomanalyse (DE-588)4596545-6 s Transkription Genetik (DE-588)4185906-6 s DE-604 Molekularbiologie (DE-588)4039983-7 s DE-188 Zelle (DE-588)4067537-3 s Molekulare Bioinformatik (DE-588)4531334-9 s 2\p DE-604 Springer International Publishing (DE-588)1064344704 pbl Erscheint auch als Online-Ausgabe 978-3-319-90684-3 Vorangegangen ist 978-0-387-71336-6 X:MVB text/html http://deposit.dnb.de/cgi-bin/dokserv?id=3d22514c40be43d2b71972903f572b65&prov=M&dok_var=1&dok_ext=htm Inhaltstext X:MVB http://www.springer.com/ HEBIS Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=030403826&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 2\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Xia, Xuhua 1959- Bioinformatics and the cell modern computational approaches in genomics, proteomics and transcriptomics Molekulare Bioinformatik (DE-588)4531334-9 gnd Molekularbiologie (DE-588)4039983-7 gnd Bioinformatik (DE-588)4611085-9 gnd Transkription Genetik (DE-588)4185906-6 gnd Zelle (DE-588)4067537-3 gnd Proteomanalyse (DE-588)4596545-6 gnd Genomik (DE-588)4776397-8 gnd |
| subject_GND | (DE-588)4531334-9 (DE-588)4039983-7 (DE-588)4611085-9 (DE-588)4185906-6 (DE-588)4067537-3 (DE-588)4596545-6 (DE-588)4776397-8 (DE-588)4143413-4 |
| title | Bioinformatics and the cell modern computational approaches in genomics, proteomics and transcriptomics |
| title_auth | Bioinformatics and the cell modern computational approaches in genomics, proteomics and transcriptomics |
| title_exact_search | Bioinformatics and the cell modern computational approaches in genomics, proteomics and transcriptomics |
| title_full | Bioinformatics and the cell modern computational approaches in genomics, proteomics and transcriptomics Xuhua Xia |
| title_fullStr | Bioinformatics and the cell modern computational approaches in genomics, proteomics and transcriptomics Xuhua Xia |
| title_full_unstemmed | Bioinformatics and the cell modern computational approaches in genomics, proteomics and transcriptomics Xuhua Xia |
| title_short | Bioinformatics and the cell |
| title_sort | bioinformatics and the cell modern computational approaches in genomics proteomics and transcriptomics |
| title_sub | modern computational approaches in genomics, proteomics and transcriptomics |
| topic | Molekulare Bioinformatik (DE-588)4531334-9 gnd Molekularbiologie (DE-588)4039983-7 gnd Bioinformatik (DE-588)4611085-9 gnd Transkription Genetik (DE-588)4185906-6 gnd Zelle (DE-588)4067537-3 gnd Proteomanalyse (DE-588)4596545-6 gnd Genomik (DE-588)4776397-8 gnd |
| topic_facet | Molekulare Bioinformatik Molekularbiologie Bioinformatik Transkription Genetik Zelle Proteomanalyse Genomik Aufsatzsammlung |
| url | http://deposit.dnb.de/cgi-bin/dokserv?id=3d22514c40be43d2b71972903f572b65&prov=M&dok_var=1&dok_ext=htm http://www.springer.com/ http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=030403826&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT xiaxuhua bioinformaticsandthecellmoderncomputationalapproachesingenomicsproteomicsandtranscriptomics AT springerinternationalpublishing bioinformaticsandthecellmoderncomputationalapproachesingenomicsproteomicsandtranscriptomics |