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E-raamat: Bioinformatics: Methods and Applications

Edited by (Assistant Professor, Department of Biotechnology, Siddharth University, Siddharth Nagar, India), Edited by (Post-Doctoral Researcher at Chung-Ang University, Anseong, Gyeonggi-do, Republic of Korea)
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  • Ilmumisaeg: 21-Oct-2021
  • Kirjastus: Academic Press Inc
  • Keel: eng
  • ISBN-13: 9780323900058
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Bioinformatics: Methods and ApplicationsSuurem pilt
  • Formaat: PDF+DRM
  • Ilmumisaeg: 21-Oct-2021
  • Kirjastus: Academic Press Inc
  • Keel: eng
  • ISBN-13: 9780323900058
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Bioinformatics: Methods and Applications provides a thorough and detailed description of principles, methods, and applications of bioinformatics in different areas of life sciences. It presents a compendium of many important topics of current advanced research and basic principles/approaches easily applicable to diverse research settings. The content encompasses topics such as biological databases, sequence analysis, genome assembly, RNA sequence data analysis, drug design, and structural and functional analysis of proteins. In addition, it discusses computational approaches for vaccine design, systems biology and big data analysis, and machine learning in bioinformatics.

It is a valuable source for bioinformaticians, computer biologists, and members of biomedical field who needs to learn bioinformatics approaches to apply to their research and lab activities.

  • Covers basic and more advanced developments of bioinformatics with a diverse and interdisciplinary approach to fulfill the needs of readers from different backgrounds
  • Explains in a practical way how to decode complex biological problems using computational approaches and resources
  • Brings case studies, real-world examples and several protocols to guide the readers with a problem-solving approach
List of contributors
xvii
Preface xxi
1 Introduction to basics of bioinformatics
1(16)
Rajesh Kumar Pathak
Dev Bukhsh Singh
Rahul Singh
1.1 Introduction
1(4)
1.1.1 Concept behind bioinformatics, in silico biology, and computational biology
3(2)
1.1.2 Scientific discipline and support systems for bioinformatics
5(1)
1.1.3 Needs of bioinformatics
5(1)
1.2 Historical background of bioinformatics
5(2)
1.3 Aim of bioinformatics
7(1)
1.4 The recent development in the field of bioinformatics
7(1)
1.5 Challenges in bioinformatics
8(1)
1.6 Application of bioinformatics
8(3)
1.6.1 Sequence analysis
8(1)
1.6.2 Phylogenetic analysis
8(1)
1.6.3 Prediction of protein secondary structure
9(1)
1.6.4 Protein 3D structure prediction
9(1)
1.6.5 Evaluation and validation of predicted protein model
10(1)
1.6.6 Discovery and designing of small molecules leading to drugs/agrochemical development
10(1)
1.6.7 Next-generation sequencing data analysis
10(1)
1.6.8 SNP and SSR identification
10(1)
1.6.9 Pharmacogenomics
10(1)
1.6.10 Metabolomics and metabolic flux analysis
11(1)
1.6.11 Systems biology and omics data integration
11(1)
1.7 Future perspective
11(1)
1.8 Conclusion
11(6)
Conflict of interest
12(1)
References
12(5)
2 Biological databases and their application
17(16)
Parva Kumar Sharma
Inderjit Singh Yadav
2.1 Introduction
17(2)
2.1.1 Relational databases
17(1)
2.1.2 Object-oriented databases
17(2)
2.2 Sequence databases
19(2)
2.2.1 GenBank
19(1)
2.2.2 European Nucleotide Archive
19(1)
2.2.3 DNA Database of Japan
20(1)
2.2.4 GenPept
20(1)
2.2.5 Protein information resources
20(1)
2.3 Composite database
21(1)
2.3.1 Universal Protein Resource (UniProt)
21(1)
2.3.2 Nonredundant database
22(1)
2.4 Secondary database
22(1)
2.4.1 PROSITE
22(1)
2.4.2 PRINTS
22(1)
2.4.3 Pfam
23(1)
2.4.4 InterPro
23(1)
2.5 Structural databases
23(1)
2.5.1 Research collaboratory for structural bioinformatics protein data bank
23(1)
2.5.2 SCOP
24(1)
2.5.3 CATH/Gene3D
24(1)
2.6 Specialized database
24(4)
2.6.1 Clustering databases
24(2)
2.6.2 Bibliographic databases
26(1)
2.6.3 Expression databases
26(1)
2.6.4 Taxonomy databases
26(1)
2.6.5 Interaction databases
26(1)
2.6.6 Pathway databases
27(1)
2.6.7 Enzyme databases
27(1)
2.6.8 microRNA database
27(1)
2.6.9 Small molecule database
27(1)
2.6.10 Vaccine design database
28(1)
2.7 Database searching and annotation
28(1)
2.7.1 Entrez
28(1)
2.7.2 The sequence retrieval system
28(1)
2.7.3 Annotation
28(1)
2.8 Conclusions
29(4)
Conflict of interest
29(1)
References
29(4)
3 Biological sequence analysis
33(16)
Samvedna Shukla
Bhawana Mishra
Himanshu Avashthi
Muktesh Chandra
3.1 Introduction
33(1)
3.2 Sequence alignments: determining similarity and deducing homology
34(2)
3.2.1 Why construct sequence alignment?
34(1)
3.2.2 Similarity of sequences
34(1)
3.2.3 Homology of sequences
35(1)
3.2.4 Global sequence alignment
35(1)
3.2.5 Local sequence alignment
35(1)
3.2.6 Working of alignment algorithm
36(1)
3.3 Scoring matrices: construction and proper selection
36(2)
3.3.1 Scoring matrices
36(1)
3.3.2 PAM matrices
37(1)
3.3.3 BLOSUM matrices
37(1)
3.3.4 PAM versus BLOSUM
37(1)
3.4 Basic Local Alignment Search Tool
38(2)
3.4.1 Seeding step
39(1)
3.4.2 Ungapped extension step
39(1)
3.4.3 Gapped extension step
39(1)
3.4.4 Types of BLAST
39(1)
3.4.5 BLAT versus BLAST
39(1)
3.5 Multiple sequence alignment (MSA)
40(2)
3.5.1 Need for MSA
40(1)
3.5.2 Multiple sequence alignment algorithm
41(1)
3.5.3 ClustalW
41(1)
3.6 Phylogenetic analysis
42(2)
3.6.1 Types of trees
43(1)
3.6.2 Algorithms for phylogenetic analysis
43(1)
3.6.3 Terminology of phylogenetic tree
43(1)
3.7 Application of sequence alignment
44(1)
3.8 Conclusion
44(5)
Conflict of interest
45(1)
References
45(4)
4 Genome assembly and annotation
49(18)
Pallavi Mishra
Ranjeet Maurya
Himanshu Avashthi
Shikha Mittal
Muktesh Chandra
Pramod Wasudeo Ramteke
4.1 Introduction
49(2)
4.1.1 How do you reassemble a genome after sequencing?
50(1)
4.1.2 Assembler technology: historical landscape
50(1)
4.2 Genome assembly algorithms
51(1)
4.2.1 The overlap-layout-consensus/string graph assemblers
51(1)
4.2.2 De Bruijn graph assemblers
51(1)
4.3 Data preprocessing
52(1)
4.3.1 Quality control of raw sequencing data
52(1)
4.3.2 Trimming and filtering of raw reads
52(1)
4.4 Genome assembly approaches: types of assembly
52(4)
4.4.1 De novo assembly approach
52(2)
4.4.2 Reference-based assembly approach
54(1)
4.4.3 Hybrid assembly approach
54(1)
4.4.4 Meta-assembly approach
54(2)
4.5 Tools and software for genome assembly
56(2)
4.5.1 Genome finishing/polishing
56(2)
4.5.2 Assembly quality assessment and validation
58(1)
4.6 Pitfall in genome assemblies
58(2)
4.6.1 DNA quality
58(1)
4.6.2 Library preparation
59(1)
4.6.3 Data quality
59(1)
4.6.4 Repetitive DNA
59(1)
4.7 A mathematical calculation for depth / coverage
60(1)
4.8 Genome annotation
60(2)
4.8.1 Structural annotation
61(1)
4.8.2 Functional annotation
62(1)
4.9 Application and future prospects of genome assembly
62(1)
4.10 Conclusion
63(4)
Conflict of interest
63(1)
References
63(4)
5 Computational molecular phylogeny: concepts and applications
67(24)
Krishna Kumar Ojha
Swapnil Mishra
Vijay Kumar Singh
5.1 Introduction
67(1)
5.2 Convergent and divergent evolution
67(1)
5.3 Concept of cladistics and systematics
68(1)
5.4 Phylogenetic trees' terminology
69(4)
5.4.1 Phylogenetic tree
69(1)
5.4.2 Taxon
69(1)
5.4.3 Node
69(1)
5.4.4 Leaf
69(1)
5.4.5 Internal node
70(1)
5.4.6 Edge
70(1)
5.4.7 Topology
70(1)
5.4.8 Root
70(1)
5.4.9 Rooted tree
70(1)
5.4.10 Unrooted tree
70(1)
5.4.11 Binary tree
70(1)
5.4.12 Clade
71(1)
5.4.13 Monophyletic taxon
71(1)
5.4.14 Paraphyletic taxon
71(1)
5.4.15 Polyphyletic taxon
71(1)
5.4.16 Split
72(1)
5.4.17 Subtree
72(1)
5.4.18 Edge length
72(1)
5.4.19 Ultrametric tree
72(1)
5.4.20 Cladogram
73(1)
5.4.21 Phylogram
73(1)
5.4.22 Dendrogram
73(1)
5.5 Evolutionary inference of phylogenetic trees
73(1)
5.5.1 Importance of shared derived characters in phylogeny
74(1)
5.6 Tree construction methods
74(10)
5.6.1 UPGMA
74(2)
5.6.2 Neighbor-joining algorithm
76(4)
5.6.3 Maximum parsimony
80(2)
5.6.4 Maximum likelihood phylogeny
82(1)
5.6.5 Bayesian phylogeny
83(1)
5.7 Estimating reliability of phylogenetic tree
84(1)
5.7.1 Bootstrapping
85(1)
5.8 Phylogenetic tools
85(1)
5.9 Application of molecular phylogeny
86(1)
5.9.1 Classification
86(1)
5.9.2 Identifying the origin of pathogens
87(1)
5.9.3 Species conservation
87(1)
5.10 Conclusion
87(4)
Conflict of interest
87(1)
References
87(4)
6 Applications and challenges of microarray and RNA-sequencing
91(14)
Ankita Negi
Abhimati Shukla
Akanksha Jaiswar
Jatin Shrinet
Rahul Singh Jasrotia
6.1 Introduction
91(1)
6.2 Evolution of microarray
92(2)
6.2.1 Automated arrays and cDNA cloning to microarray technology
92(1)
6.2.2 Principle of microarray
92(2)
6.2.3 List of microarray tools and their utility
94(1)
6.3 DNA sequencing
94(2)
6.3.1 First generation
94(1)
6.3.2 Second generation
95(1)
6.3.3 Third generation
96(1)
6.4 RNA-sequencing
96(3)
6.4.1 Library preparation and sequencing
96(1)
6.4.2 Pipeline and usage of RNA-sequencing
97(2)
6.5 Biological databases for data submission
99(1)
6.6 Applications of microarray
99(1)
6.7 Applications of RNA-sequencing
100(1)
6.8 Advantages of transcriptome sequencing over microarray technology
100(1)
6.9 Limitations and future perspective of RNA-sequencing
100(1)
6.10 Conclusion
100(5)
Conflict of interest
101(1)
References
101(4)
7 RNA-seq for revealing the function of the transcriptome
105(26)
Kavita Goswami
Neeti Sanan-Mishra
7.1 Introduction
105(1)
7.2 Next-generation sequencing platforms and their technologies
105(4)
7.2.1 Illumina
106(1)
7.2.2 Roche 454
106(1)
7.2.3 ION torrent
107(1)
7.2.4 SoLidABI
107(1)
7.2.5 Illumina Tru-seq SLR technology
108(1)
7.2.6 Pacific Biosciences (PacBio) SMRT sequencing
108(1)
7.2.7 Oxford Nanopore
108(1)
7.3 Analyzing the RNA-seq data
109(2)
7.3.1 Quality and depth of raw sequencing data
109(1)
7.3.2 Adapter removal
110(1)
7.3.3 Level of alignment
110(1)
7.3.4 Redundancy rate of reads
111(1)
7.4 RNA-seq applications
111(10)
7.4.1 Transcriptome assembly
111(1)
7.4.2 Identification of novel protein-coding genes
112(1)
7.4.3 Identification of other classes of RNAs
113(2)
7.4.4 Profiling expression patterns
115(4)
7.4.5 Degradome sequencing
119(1)
7.4.6 Variants detection and allele-specific expression
120(1)
7.4.7 Expression quantitative trait loci
120(1)
7.5 Databases and software for small RNA analysis
121(1)
7.5.1 miRBase
121(1)
7.5.2 PMRD
121(1)
7.5.3 Armour
121(1)
7.5.4 UEA sRNA workbench
121(1)
7.5.5 sRNAtoolbox
122(1)
7.5.6 sRNAbench
122(1)
7.5.7 miRNAconsTarget
122(1)
7.5.8 Massively parallel signature sequencing database
122(1)
7.5.9 CLC Genomics Workbench
122(1)
7.6 Conclusion
122(9)
Conflict of interest
123(1)
References
123(8)
8 Analysis of SSR and SNP markers
131(14)
Ankita Mishra
Pramod Kumar Singh
Abhishek Bhandawat
Vinay Sharma
Vikas Sharma
Pradeep Singh
Joy Roy
Himanshu Sharma
8.1 Introduction
131(1)
8.2 Analysis of SSR markers
132(3)
8.2.1 Benefits and limitations of microsatellite markers
132(1)
8.2.2 SSR mining and primer designing
133(1)
8.2.3 Classification and genomic localization
133(1)
8.2.4 Functional annotations of SSR containing sequences
133(1)
8.2.5 SSR amplification and evaluation of polymorphic potential
133(1)
8.2.6 Cross-transferability of SSR markers
134(1)
8.2.7 Future perspective
135(1)
8.3 Analysis of SNP markers
135(5)
8.3.1 Approaches for sequence data generation
135(1)
8.3.2 Sample preparation
135(1)
8.3.3 Sequencing of complex genomes
135(1)
8.3.4 Assessment of sequence quality
136(1)
8.3.5 Reads assembly and mapping to reference genome
136(2)
8.3.6 Postprocessing of mapped reads
138(1)
8.3.7 Variant calling and filtration
138(1)
8.3.8 Functional annotation of variant
138(2)
8.4 Conclusion
140(5)
Acknowledgments
140(1)
Conflict of interest
140(1)
References
140(4)
Further reading
144(1)
9 Gene Ontology: application and importance in functional annotation of the genomic data
145(14)
Reshu Saxena
Ritika Bishnoi
Deepak Singla
9.1 Background
145(1)
9.2 Gene Ontology-based classification
146(1)
9.2.1 Cellular component
147(1)
9.2.2 Molecular function
147(1)
9.2.3 Biological process
147(1)
9.3 Annotation of unknown gene/genome
147(2)
9.3.1 Blast2GO
148(1)
9.3.2 IPRscan (InterProScan)
148(1)
9.3.3 Genome Assembly and Annotation Package
149(1)
9.3.4 GOnet
149(1)
9.3.5 DEEPred
149(1)
9.3.6 SDNGO
149(1)
9.3.7 AmiGO
149(1)
9.3.8 OBO-Edit
149(1)
9.3.9 Gene Ontology Functional Enrichment Annotation Tool
149(1)
9.4 GO enrichment analysis
149(4)
9.4.1 DAVID (Database for Annotation, Visualization, and Integrated Discovery)
150(1)
9.4.2 PANTHER (Protein ANalysis THrough Evolutionary Relationships)
150(1)
9.4.3 g:Profiler
150(1)
9.4.4 clusterProfiler
151(1)
9.4.5 Enrichr
151(1)
9.4.6 ToppGene
151(1)
9.4.7 QuickGo
152(1)
9.4.8 REVIGO (Reduce & Visualize Gene Ontology)
152(1)
9.4.9 WEGO (Web Gene Ontology)
152(1)
9.4.10 ShinyGO
152(1)
9.4.11 ViSEAGO
152(1)
9.4.12 PoGo (Prediction of Gene Ontology)
152(1)
9.4.13 GOrilla (Gene Ontology enRlchment anaLysis and visuaLizAtion tool)
152(1)
9.4.14 EasyGO
152(1)
9.4.15 GOEAST (Gene Ontology Enrichment Analysis Software Toolkit)
153(1)
9.4.16 GOAT (Gene Ontology Annotation Tool)
153(1)
9.4.17 GOLEM (Gene Ontology Local Exploration Map)
153(1)
9.4.18 GOssTo (Gene Ontology semantic similarity Tool)
153(1)
9.4.19 NaviGO
153(1)
9.5 Applications
153(1)
9.6 Future prospects
154(1)
9.7 Conclusion
154(5)
Acknowledgment
154(1)
Conflict of interest
155(1)
Author contributions
155(1)
References
155(4)
10 Metagenomics: the boon for microbial world knowledge and current challenges
159(18)
J.K. Choudhari
J. Choubey
M.K. Verma
T. Chatterjee
B.P. Sahariah
10.1 Introduction: an overview of metagenomics
159(2)
10.2 Resources in metagenomics
161(1)
10.3 Challenges in metagenomics
161(1)
10.4 The workflow in metagenome analysis
162(1)
10.5 Dataset acquire and processing
162(1)
10.6 Quality control analysis
162(2)
10.6.1 Base quality score
162(1)
10.6.2 Sequence quality score
162(1)
10.6.3 Overrepresented sequences
163(1)
10.6.4 Per base N content
163(1)
10.6.5 Duplicate sequences
163(1)
10.6.6 Read length distribution
163(1)
10.6.7 Per base sequence content
163(1)
10.6.8 Guanine-cytosine content
163(1)
10.6.9 Overrepresented k-mers
163(1)
10.6.10 Quality analysis and improving software tools
163(1)
10.7 Genome assembly tools in metagenomics
164(1)
10.8 Binning tools in metagenomics
164(2)
10.8.1 Statistical analysis
166(1)
10.9 Data storage and sharing
166(1)
10.10 Metagenomics analysis: a case study
166(1)
10.11 Material, methodology, and outcome
166(6)
10.11.1 Metagenome dataset
166(1)
10.11.2 Sequencing quality analysis
166(2)
10.11.3 Hits distribution of metagenome from the database sources
168(1)
10.11.4 Hits distribution of functional group
168(2)
10.11.5 Taxonomic hits distribution
170(1)
10.11.6 Rarefaction curve
170(1)
10.11.7 Alpha diversity
170(2)
10.12 Advantages of metagenomics study
172(1)
10.13 Limitations and future perspective
172(1)
10.14 Conclusion
172(5)
Conflict of interest
173(1)
References
173(4)
11 Protein structure prediction
177(12)
Shikha Agnihotry
Rajesh Kumar Pathak
Dev Bukhsh Singh
Apoorv Tiwari
Imran Hussain
11.1 Introduction
177(1)
11.2 Protein structure prediction
177(1)
11.3 Method of protein structure prediction
178(6)
11.3.1 Homology modeling (comparative modeling)
178(2)
11.3.2 Threading or fold recognition
180(2)
11.3.3 Ab initio approach
182(2)
11.4 Evaluation of predicted protein structure
184(1)
11.5 Applications of structure prediction
185(1)
11.5.1 Mutation studies
185(1)
11.5.2 Unfolding studies
185(1)
11.5.3 Binding site prediction
185(1)
11.5.4 Protein docking and virtual screening
185(1)
11.5.5 Understanding the dynamics of protein or protein--ligand complex
186(1)
11.5.6 Structure evolution analysis
186(1)
11.6 Conclusion
186(3)
Conflict of interest
186(1)
References
186(2)
Further reading
188(1)
12 Structural and functional analysis of protein
189(18)
Neetu Singh Yadav
Pawan Kumar
Indra Singh
12.1 Protein preliminaries
189(1)
12.2 Growth of the protein structural database
189(1)
12.3 Structural topology and fold classification scheme
190(1)
12.4 D-Structure quality assessment protocol
191(1)
12.5 Protein 3D structure prediction
191(6)
12.5.1 Energy functions
192(5)
12.6 Machine learning in PSP
197(3)
12.6.1 Feature engineering and representation
197(1)
12.6.2 Feature selection
198(1)
12.6.3 ML algorithms
198(1)
12.6.4 ML models' implementation and evaluation
199(1)
12.7 Conclusion
200(7)
Conflict of interest
201(1)
References
201(6)
13 Computational approaches in drug designing
207(12)
Anshul Tiwari
Sakshi Singh
13.1 Introduction
207(1)
13.2 Computer-aided drug designing
207(2)
13.2.1 Structure-based drug design
207(2)
13.2.2 Ligand-based drug design
209(1)
13.3 Computational approaches
209(5)
13.3.1 Molecular modeling
209(2)
13.3.2 Binding site and cavity prediction
211(1)
13.3.3 Computational ligand designing and searching
211(1)
13.3.4 Pharmacophore modeling
211(1)
13.3.5 Molecular docking
211(1)
13.3.6 Molecular dynamics simulation
212(1)
13.3.7 Quantitative structure--activity relationship
213(1)
13.3.8 Lead optimization
214(1)
13.4 Limitations
214(1)
13.5 Recent trends in drug designing
214(1)
13.6 Conclusion
215(4)
Conflict of interest
215(1)
References
215(4)
14 Structure-based drug designing
219(14)
Shubham Pant
Shivani Verma
Rajesh Kumar Pathak
Dev Bukhsh Singh
14.1 Introduction
219(1)
14.2 Background of structure-based drug design
220(1)
14.3 Process of SBDD
221(6)
14.3.1 Target identification
221(1)
14.3.2 Target structure determination/prediction
221(2)
14.3.3 Cavity/binding site prediction
223(1)
14.3.4 Ligand structure preparation / retrieval
223(1)
14.3.5 Molecular docking and virtual screening
224(2)
14.3.6 ADMET analysis
226(1)
14.3.7 Molecular dynamics simulation
226(1)
14.3.8 Binding-free-energy calculation-MM-PBSA
227(1)
14.4 Recent development in SBDD
227(1)
14.5 Challenges and limitations
228(1)
14.6 Future prospective
228(1)
14.7 Conclusion
229(4)
Conflict of interest
229(1)
References
229(4)
15 Ligand-based drug designing
233(20)
Suchitra M. Ajjarapu
Apoorv Tiwari
Pramod Wasudeo Ramteke
Dev Bukhsh Singh
Sundip Kumar
15.1 Introduction
233(1)
15.2 Pharmacophore
234(1)
15.2.1 Build pharmacophore hypothesis
234(1)
15.2.2 Alignment of molecules
235(1)
15.2.3 Similarity search methods
235(1)
15.2.4 2D finger prints
235(1)
15.3 3D fingerprints
235(1)
15.3.1 3D similarities depend on the alignment
236(1)
15.3.2 Conformational flexibility
236(1)
15.3.3 Scaffold hopping
236(1)
15.3.4 Fragment-based drug design
236(1)
15.4 Pharmacophore mapping
236(2)
15.4.1 Diverse conformation generation
236(1)
15.4.2 Generation of 3D pharmacophore
237(1)
15.4.3 Validation of the pharmacophore model
237(1)
15.5 Pharmacophore classifications
238(1)
15.5.1 Ligand-based pharmacophore modeling
238(1)
15.5.2 Structure-based pharmacophore modeling
239(1)
15.6 Application of pharmacophore in virtual screening and de novo design
239(1)
15.6.1 Virtual screening
239(1)
15.6.2 De novo pathway
240(1)
15.7 Advancement in exploring 3D pharmacophore principles over the above limitations
240(1)
15.8 Quantitative structure-activity relationship
240(8)
15.8.1 Designation of QSAR
241(1)
15.8.2 Backbone of chemical similarity
241(1)
15.8.3 QSAR data
242(1)
15.8.4 Classification of 3D QSAR approaches
243(1)
15.8.5 Molecular interactions and energies
243(2)
15.8.6 QSAR modeling
245(1)
15.8.7 Concept of applicability domain and QSAR approaches
246(2)
15.9 Development of new QSAR: HQSAR
248(1)
15.10 Application of QSAR/SAR
248(2)
15.10.1 Synthetic organic chemistry and QSAR
248(1)
15.10.2 Prediction of kinetic and thermodynamic parameters
249(1)
15.10.3 Drug development and other applications
249(1)
15.11 Conclusion
250(3)
Conflict of interest
250(1)
References
250(3)
16 Discovery and optimization of lead molecules in drug designing
253(16)
Shivani Verma
Rajesh Kumar Pathak
16.1 Introduction
253(1)
16.2 Principles of CADD
254(2)
16.2.1 Case study: inhibition of lipoxygenases
255(1)
16.3 Discovery of the lead molecule
256(1)
16.4 Types of lead molecules
256(3)
16.4.1 Natural lead compounds
256(1)
16.4.2 Synthetic lead molecules
257(2)
16.4.3 Semisynthetic drugs
259(1)
16.5 Lead optimization and strategies
259(4)
16.5.1 By using organic synthetic chemistry
259(2)
16.5.2 Structure simplification
261(1)
16.5.3 Structure modification
261(1)
16.5.4 Functional group interconversion
261(1)
16.5.5 Bonding strength and selectivity
262(1)
16.5.6 Using thermodynamic, pharmacodynamics, and pharmacokinetic parameters
262(1)
16.6 Computational lead optimization
263(1)
16.7 Advantages of computational lead designing
263(1)
16.8 Future perspectives
263(1)
16.9 Conclusion
264(5)
Conflict of interest
265(1)
References
265(4)
17 Pharmacophore modeling and its applications
269(22)
Rashmi Tyagi
Amisha Singh
Kamal Kumar Chaudhary
Manoj Kumar Yadav
17.1 Introduction
269(2)
17.2 Basics of pharmacophore modeling
271(2)
17.2.1 Division of initial data into diverse datasets
271(1)
17.2.2 Conformational analysis with three-dimensional structures
272(1)
17.3 Different methods of pharmacophore generation
273(3)
17.3.1 Ligand-based pharmacophore modeling
273(2)
17.3.2 Structure-based pharmacophore modeling
275(1)
17.4 Validation of pharmacophore models
276(2)
17.4.1 Receiver operating characteristic curve
276(1)
17.4.2 Structure-based pharmacophore modeling approach for the design of azaindole derivatives as DprEI inhibitors for tuberculosis: a case study
277(1)
17.5 Recent trends in pharmacophore generation
278(3)
17.5.1 Machine-learning models incorporated with pharmacophore descriptors
278(1)
17.5.2 Prediction of pharmacokinetic properties
279(1)
17.5.3 Target identification and de novo ligand design using pharmacophore approaches
279(1)
17.5.4 Protein functionality studies
279(1)
17.5.5 3D pharmacophore modeling using a web platform
279(1)
17.5.6 Pharmacophore methods in light of molecular dynamics simulations
280(1)
17.6 Applications of pharmacophore modeling
281(2)
17.6.1 Generation of e-pharmacophore for virtual screening of drug molecules
281(1)
17.6.2 ADME-Tox analysis
282(1)
17.6.3 Generation of a multitarget ligand
282(1)
17.6.4 Modulation of the immune system
282(1)
17.6.5 Pharmacophore-guided drug target identification
282(1)
17.7 Future perspectives of pharmacophore models
283(1)
17.7.1 Fragment-based drug design
283(1)
17.7.2 Protein--protein interaction inhibition
283(1)
17.7.3 A potential role in protein design
283(1)
17.8 Conclusion
284(7)
Conflict of interest
284(1)
References
284(7)
18 Molecular docking and molecular dynamics simulation
291(14)
Sakshi Singh
Qanita Bani Baker
Dev Bukhsh Singh
18.1 Introduction
291(1)
18.2 Molecular docking
292(3)
18.2.1 Algorithms
292(2)
18.2.2 Scoring functions
294(1)
18.2.3 Knowledge-based SFs
294(1)
18.3 Docking methodologies
295(2)
18.3.1 Flexible docking
295(1)
18.3.2 Semiflexible docking
295(1)
18.3.3 Virtual screening of high-throughput docking
295(1)
18.3.4 Fragment docking
296(1)
18.3.5 Machine learning in docking
296(1)
18.3.6 Docking tools and their features
297(1)
18.4 Molecular dynamics simulation
297(3)
18.4.1 Postdocking refinement
298(1)
18.4.2 Binding-free energy calculations: MM-GBSA/MM-PBSA
299(1)
18.5 Challenges in molecular docking and MD simulation techniques
300(1)
18.6 Conclusion
301(4)
Conflict of interest
301(1)
References
301(4)
19 Pharmacokinetics and pharmacodynamics analysis of drug candidates
305(12)
Satendra Singh
Dev Bukhsh Singh
Budhayash Gautam
Anamika Singh
Namrata Yadav
19.1 Introduction
305(1)
19.2 Postgenomic era and drug discovery
306(1)
19.3 Pharmacokinetics
307(4)
19.3.1 Drug absorption
308(1)
19.3.2 Drug distribution (binding / localization/storage)
308(1)
19.3.3 Drug metabolism
309(2)
19.4 Pharmacodynamics
311(1)
19.4.1 Drug toxicity
311(1)
19.5 Computational approaches for ADMET prediction
311(2)
19.6 Translational bioinformatics
313(1)
19.7 Drug repurposing
313(1)
19.7.1 Benefits of drug repurposing
313(1)
19.7.2 Computational drug repurposing
314(1)
19.8 Role of pharmacogenomics in precision medicine
314(1)
19.9 Chemical diversity of natural products: a source for computer-aided drug discovery
315(1)
19.10 Conclusion
315(2)
Conflict of interest
315(1)
References
315(1)
Further reading
316(1)
20 Computational approaches for vaccine designing
317(20)
Animesh Awasthi
Gaurav Sharma
Piyush Agrawal
20.1 Introduction
317(1)
20.2 Antigen selection and immunological databases
318(1)
20.2.1 Exogenous antigens
318(1)
20.2.2 Endogenous antigens
319(1)
20.2.3 Autoantigens
319(1)
20.3 In silico method for B-cell epitope prediction
319(3)
20.3.1 Prediction of conformational B-cell epitopes
320(1)
20.3.2 Prediction of linear B-cell epitopes
321(1)
20.4 In silico method for T-cell epitope prediction
322(3)
20.4.1 MHC class I binder prediction
323(1)
20.4.2 MHC class II binder prediction
324(1)
20.4.3 MHC gene diversity and its importance in T-cell epitope prediction
325(1)
20.5 Adjuvant and linker selection
325(1)
20.6 Building 3D model and validation of fusion vaccine construct
326(1)
20.7 Miscellaneous properties
326(2)
20.7.1 Peptide toxicity
326(1)
20.7.2 Half-life or stability
327(1)
20.7.3 Delivery methodology
327(1)
20.8 Role of next-generation sequencing technology in vaccine design
328(1)
20.9 Computer-aided vaccine development example
328(1)
20.10 Conclusion
329(8)
Acknowledgments
329(1)
Conflict of interest
330(1)
References
330(7)
21 Metabolomics and flux balance analysis
337(30)
Priyanka Narad
G. Naresh
Abhishek Sengupta
21.1 Introduction
337(1)
21.2 Definition of metabolomics
338(1)
21.3 MS- and NMR-based techniques in metabolomics
338(1)
21.4 Data processing in metabolomics
339(2)
21.4.1 Nuclear magnetic resonance spectroscopy
339(1)
21.4.2 Workflow of MS-based metabolomics
339(1)
21.4.3 Limitations of NMR and MS methods
340(1)
21.4.4 Recent advances in MS- and NMR-based metabolomics
341(1)
21.4.5 Challenges and affecting factors
341(1)
21.5 Applications of metabolomics
341(2)
21.5.1 Microbial science
341(1)
21.5.2 Plant science
341(1)
21.5.3 Animal science
342(1)
21.5.4 Medical science
342(1)
21.5.5 Food and herbal medicines
343(1)
21.6 Flux balance analysis
343(1)
21.7 Metabolic networks and model construction
344(8)
21.7.1 Metabolic model: construction and refinement
345(3)
21.7.2 Mass balance
348(1)
21.7.3 Steady state
349(1)
21.7.4 Types of constraints
349(1)
21.7.5 Optimization
350(1)
21.7.6 Steps in FBA
351(1)
21.8 Metabolic control analysis and isotopic steady state/carbon flux analysis
352(3)
21.8.1 Metabolic control analysis
352(1)
21.8.2 Carbon flux analysis
352(1)
21.8.3 Different types of flux balance analysis at different conditions
353(2)
21.9 Some important tools of flux balance analysis
355(3)
21.9.1 OptKnock
355(1)
21.9.2 OptGene
355(1)
21.9.3 OptStrain
356(1)
21.9.4 COBRA Toolbox
356(1)
21.9.5 MetaboAnalyst 4.0
356(1)
21.9.6 OptFlux
356(1)
21.9.7 OpenFlux
357(1)
21.9.8 CellNetAnalyzer
357(1)
21.9.9 SBRT
357(1)
21.9.10 Escher-FBA
357(1)
21.9.11 MetaFluxNet
357(1)
21.10 Applications, challenges, and future perspectives of FBA
358(3)
21.10.1 Applications
358(2)
21.10.2 Challenges and future perspectives
360(1)
21.11 Case study: applications of metabolomics and flux balance analysis in industrially important microorganisms
361(2)
21.11.1 Lactococcus lactis
361(1)
21.11.2 Saccharomyces cerevisiae
361(1)
21.11.3 Escherichia coli
362(1)
21.12 Conclusion
363(4)
References
363(4)
22 Topological parameters, patterns, and motifs in biological networks
367(14)
Arvind Kumar Yadav
Rohit Shukla
Tiratha Raj Singh
22.1 Introduction
367(1)
22.2 Biological networks
368(1)
22.2.1 Construction of biological networks
368(1)
22.3 Network motifs and patterns
369(1)
22.3.1 Motif discovery and counting
369(1)
22.4 Analysis of biological network
370(3)
22.4.1 Graph theory
371(2)
22.4.2 Adjacency matrices
373(1)
22.5 Topological parameters
373(2)
22.5.1 Node degree
374(1)
22.5.2 The average of shortest path length
374(1)
22.5.3 Clustering coefficient
375(1)
22.5.4 Betweenness centrality
375(1)
22.5.5 Statistical comparison
375(1)
22.6 Biological significance of network motifs
375(1)
22.7 Applications of network biology
376(1)
22.8 Limitations and challenges
376(1)
22.9 Conclusion
376(5)
Conflict of interest
377(1)
References
377(4)
23 Network biology and applications
381(28)
Neeru Redhu
Zoozeal Thakur
23.1 Introduction to biological networks
381(1)
23.2 Biological networks properties
381(1)
23.2.1 Path, average path length, and diameter
381(1)
23.2.2 Degree aka connectivity
382(1)
23.2.3 Scale free
382(1)
23.2.4 Small world network
382(1)
23.2.5 Date and party hub
382(1)
23.2.6 Network motifs
382(1)
23.3 Types of biological networks
382(5)
23.3.1 Ecological networks
383(1)
23.3.2 Gene (genetic) regulatory network
383(1)
23.3.3 Protein--protein interaction network
384(1)
23.3.4 Metabolic networks
385(1)
23.3.5 Cellular signaling network
385(2)
23.3.6 Gene coexpression network
387(1)
23.4 Experimental methods in network biology
387(6)
23.4.1 Microarray
387(1)
23.4.2 Deep mRNA sequencing
388(1)
23.4.3 Exome sequencing
388(1)
23.4.4 ChlP-seq
389(1)
23.4.5 Genome-wide bisulfite sequencing
389(1)
23.4.6 Yeast two-hybrid
390(1)
23.4.7 Mass spectrometry-based proteomics
391(1)
23.4.8 Flow and mass cytometry
392(1)
23.4.9 Live cell imaging
392(1)
23.5 Resources for biological network-based studies
393(1)
23.5.1 Kyoto Encyclopedia of Genes and Genomes
393(1)
23.5.2 BioCyc Database Collection
393(1)
23.5.3 ENZYME
393(1)
23.5.4 ExplorEnz: the enzyme database
394(1)
23.5.5 Biochemical Genetic and Genomic/Biochemical Genetic and Genomic models
394(1)
23.5.6 STRING
394(1)
23.5.7 metaTIGER
394(1)
23.6 Tools for network pathway analysis
394(2)
23.6.1 Pathway tools
395(1)
23.6.2 ERGO
395(1)
23.6.3 KEGGtranslator
395(1)
23.6.4 ModelSEED
395(1)
23.6.5 Network Analysis Tools
396(1)
23.6.6 BioNetStat
396(1)
23.6.7 OmicsNet
396(1)
23.7 Applications of network biology
396(3)
23.7.1 Applications in rare diseases
396(2)
23.7.2 Determination of protein function
398(1)
23.7.3 Pathway determination
398(1)
23.7.4 Essential protein identification
398(1)
23.7.5 Functional modules' identification
399(1)
23.8 Challenges and future perspective
399(1)
23.8.1 Pseudo temporal ordering
399(1)
23.8.2 Multiple data sources
400(1)
23.8.3 Combination of algorithms
400(1)
23.9 Conclusion
400(9)
Conflict of interest
401(1)
References
401(8)
24 Pathway modeling and simulation analysis
409(16)
Gitanjali Tandon
Sunita Yadav
Sukhdeep Kaur
24.1 Introduction
409(1)
24.2 Computational modeling of a pathway
409(2)
24.2.1 Type of modeling
410(1)
24.2.2 Approaches of modeling
410(1)
24.3 General diagram and language used in pathway modeling
411(2)
24.3.1 Systems Biology Graphical Notation
411(1)
24.3.2 Systems Biology Markup Language
412(1)
24.4 Pathway simulations analysis
413(2)
24.4.1 Ordinary differential equations
413(1)
24.4.2 Stochastic simulation
414(1)
24.5 Platforms used for modeling and simulations
415(3)
24.5.1 Pathway designing tools
415(1)
24.5.2 Pathway Tools
415(1)
24.5.3 Simulation tools
416(2)
24.6 Applications of pathway modeling and simulations
418(2)
24.6.1 Metabolic engineering
418(1)
24.6.2 Drug designing
418(1)
24.6.3 Study of phenomics
419(1)
24.6.4 Flux balance analysis
419(1)
24.7 Challenges
420(1)
24.7.1 Knowledge gaps between computationalists and experimentalists
420(1)
24.7.2 Theory development
420(1)
24.7.3 Miscellaneous computational challenges
420(1)
24.8 Conclusion
420(5)
Conflict of interest
421(1)
References
421(4)
25 Systems biology and big data analytics
425(18)
Rohit Shukla
Arvind Kumar Yadav
William O. Sote
Moacyr Comar Junior
Tiratha Raj Singh
25.1 Introduction
425(1)
25.2 Big data in general and in the context of biology
425(1)
25.3 Types of data in systems biology
426(3)
25.3.1 Biological sequences
428(1)
25.3.2 Molecular structure
428(1)
25.3.3 Gene expression
428(1)
25.3.4 Binding sites and domains
429(1)
25.3.5 Protein-protein interaction
429(1)
25.3.6 Mass spectroscopy
429(1)
25.3.7 Metabolic pathways
429(1)
25.4 Biological big data resources
429(3)
25.4.1 Genomics and transcriptomics resources
430(1)
25.4.2 Proteomics resources
430(1)
25.4.3 Cellular metabolome
430(1)
25.4.4 Protein-protein interaction databases
430(2)
25.4.5 Drug and chemical compound databases
432(1)
25.4.6 Different other databases
432(1)
25.5 Network generation and its analysis from various sources of data
432(2)
25.6 Big data in drug repurposing and systems pharmacology
434(1)
25.6.1 Network-based approaches for systems pharmacology
435(1)
25.7 Case study related to transcriptome data analysis
435(2)
25.8 Limitations in big data analysis
437(1)
25.9 Conclusion
438(5)
Acknowledgment
438(1)
Conflict of interest
438(1)
References
438(5)
26 Machine learning in bioinformatics
443(14)
Indrajeet Kumar
Surya Pratap Singh
Shivam
26.1 Introduction
443(1)
26.2 Supervised learning
443(2)
26.2.1 Classification
444(1)
26.2.2 Supervised machine learning in bioinformatics
444(1)
26.3 Unsupervised machine learning
445(1)
26.4 Problems to understand supervised learning and unsupervised learning
446(1)
26.5 Regression
446(4)
26.5.1 Hypothesis
447(3)
26.6 Clustering
450(2)
26.6.1 K-means clustering
450(1)
26.6.2 Density-based clustering
450(1)
26.6.3 Distribution-based clustering
451(1)
26.6.4 Fuzzy clustering
451(1)
26.7 Unsupervised learning in bioinformatics
452(1)
26.8 Application of machine learning
452(1)
26.8.1 Genome annotation
452(1)
26.8.2 Protein structure prediction
452(1)
26.8.3 Research area in bioinformatics with deep learning
453(1)
26.9 Discussion
453(1)
26.10 Conclusion
454(3)
Conflict of interest
454(1)
References
454(3)
27 Bioinformatics and biological data mining
457(1)
Aditya Harbola
Deepti Negi
Mahesh Manchanda
Rajesh Kumar Kesharwani
27 A Biological data mining
457(16)
27.2 Data mining applications
457(1)
27.3 Data mining process
458(3)
27.3.1 Classification
458(1)
27.3.2 Estimation
458(1)
27.3.3 Prediction
459(1)
27.3.4 Association
459(1)
27.3.5 Clustering
459(1)
27.3.6 Description and visualization
460(1)
27.3.7 Case studies using Waikato environment for knowledge analysis
461(1)
27.4 Feature selection technique in data mining
461(1)
27.4.1 Objective of feature selection
462(1)
27.5 Major data mining algorithms applicable to biological data
462(1)
27.5.1 C4.5 algorithm
462(1)
27.5.2 K-means algorithm
462(1)
27.5.3 Support vector machine
463(1)
27.6 Biological data evolution and related issues
463(3)
27.6.1 Biological data availability
463(1)
27.6.2 Biological data availability in computer-readable form
464(1)
27.6.3 Biological data cleaning
464(1)
27.6.4 Biological data quality
464(1)
27.6.5 Biological data dimensionality
464(2)
27.6.6 Biological data knowledge discovery
466(1)
27.7 Bioinformatics research areas and tools
466(3)
27.7.1 Sequence searching, comparison, and evolutionary analysis
467(1)
27.7.2 Annotation of gene/protein structure and function
467(1)
27.7.3 Gene and protein expression analysis
468(1)
27.7.4 Mutation and disease association study
468(1)
27.7.5 Protein structure prediction, docking, and protein--protein interaction analysis
468(1)
27.7.6 Biological systems modeling and network analysis
468(1)
27.7.7 Expressed sequence tag analysis
469(1)
27.7.8 MicroRNA and target prediction
469(1)
27.7.9 Medical and health data analysis and clinical decision support system
469(1)
27.8 Limitations
469(1)
27.9 Conclusion
470(3)
Conflict of interest
470(1)
References
470(3)
Index 473
Dr. Dev Bukhsh Singh is currently an Assistant Professor in the Department of Biotechnology at Siddharth University, Kapilvastu, Siddharth Nagar, India since 2021. He also served as Assistant Professor in the Department of Biotechnology at Chhatrapati Shahu Ji Maharaj University, Kanpur, India from 2009-2021. He received BSc (Biology) and MSc from the University of Allahabad, an MTech from the Indian Institute of Information Technology, Prayagraj India, and a PhD in Biotechnology from Gautam Buddha University, India. He has been actively involved in teaching BSc and MSc Biotechnology students since 2009. His areas of research are medicinal biology, lead compound search, and drug design. He published three edited books: Frontiers in Protein Structure, Function, and Dynamics Computer-Aided Drug Design and Bioinformatics: Methods and Applications. He is a member of various national and international academic bodies and is a reviewer for several international journals. Rajesh Kumar Pathak completed his BSc from Barkatullah University, Bhopal, MSc from Chhatrapati Shahu Ji Maharaj University, Kanpur, and PhD from Uttarakhand Technical University, Dehradun, India. He has authored several articles in international journals including, Scientific Reports, Computational Biology and Chemistry, OMICS: A Journal of Integrative Biology, Frontiers in Plant Science, 3 Biotech, Journal of Biomolecular Structure and Dynamics, etc. His research interests lie in the areas of the genome and transcriptome assembly and annotation, microarray data analysis, pathway modeling, and network analysis, molecular docking, virtual screening, and molecular dynamics simulation. He has worked as a Teaching Personnel at G. B. Pant University of Agriculture & Technology, Pantnagar, India, and Teaching Assistant at the School of Agricultural Biotechnology, Punjab Agricultural University, Ludhiana, India.
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