Note: Intro -- Automation in Proteomics and Genomics -- Contents -- Preface -- List of Contributors -- About the Editors -- SECTION 1 FUNDAMENTALS OF MOLECULAR AND CELLULAR BIOLOGY -- 1 The Central Dogma: From DNA to RNA, and to Protein -- 2 Genomes to Proteomes -- SECTION 2 ANALYSIS VIA AUTOMATION -- 3 High-Throughput DNA Sequencing -- 4 Modeling a Regulatory Network Using Temporal Gene Expression Data: Why and How? -- 5 Automated Prediction of Protein Attributes and Its Impact on Biomedicine and Drug Discovery -- 6 Molecular Interaction Networks: Topological and Functional Characterizations -- SECTION 3 DESIGN VIA AUTOMATION -- 7 DNA Synthesis -- 8 Computational and Experimental RNA Nanoparticle Design -- 9 New Paradigms in Droplet-Based Microfluidics and DNA Amplification -- 10 Synthetic Networks -- SECTION 4 INTEGRATION -- 11 Molecular Modeling of CYP Proteins and its Implication for Personal Drug Design -- 12 Recent Progress of Bioinformatics in Membrane Protein Structural Studies -- 13 Trends in Automation for Genomics and Proteomics -- Index.

Note: Intro -- Knowledge-Based Bioinformatics -- Contents -- Preface -- List of Contributors -- PART I FUNDAMENTALS -- Section 1 Knowledge-Driven Approaches -- 1 Knowledge-based bioinformatics -- 1.1 Introduction -- 1.2 Formal reasoning for bioinformatics -- 1.3 Knowledge representations -- 1.4 Collecting explicit knowledge -- 1.5 Representing common knowledge -- 1.6 Capturing novel knowledge -- 1.7 Knowledge discovery applications -- 1.8 Semantic harmonization: the power and limitation of ontologies -- 1.9 Text mining and extraction -- 1.10 Gene expression -- 1.11 Pathways and mechanistic knowledge -- 1.12 Genotypes and phenotypes -- 1.13 The Web's role in knowledge mining -- 1.14 New frontiers -- 1.14.1 Requirements for linked knowledge discovery -- 1.14.2 Information aggregation -- 1.14.3 The Linked Open Data initiative -- 1.14.4 Information articulation -- 1.14.5 Next-generation knowledge discovery -- 1.15 References -- 2 Knowledge-driven approaches to genome-scale analysis -- 2.1 Fundamentals -- 2.1.1 The genomic era and systems biology -- 2.1.2 The exponential growth of biomedical knowledge -- 2.1.3 The challenges of finding and interacting with biomedical knowledge -- 2.2 Challenges in knowledge-driven approaches -- 2.2.1 We need to read -- development of automatic methods to extract data housed in the biomedical literature -- 2.2.2 Implicit and implied knowledge -- the forgotten data source -- 2.2.3 Humans are visual beings: so should their knowledge be -- 2.3 Current knowledge-based bioinformatics tools -- 2.3.1 Enrichment tools -- 2.3.2 Integration and expansion: from gene lists to networks -- 2.3.3 Expanding the concept of an interaction -- 2.3.4 A systematic failure to support advanced scientific reasoning -- 2.4 3R systems: reading, reasoning and reporting the way towards biomedical discovery. , 2.4.1 3R knowledge networks populated by reading and reasoning -- 2.4.2 Implied association results in uncertainty -- 2.4.3 Reporting: using 3R knowledge networks to tell biological stories -- 2.5 The Hanalyzer: a proof of 3R concept -- 2.6 Acknowledgements -- 2.7 References -- 3 Technologies and best practices for building bio-ontologies -- 3.1 Introduction -- 3.2 Knowledge representation languages and tools for building bio-ontologies -- 3.2.1 RDF (resource description framework) -- 3.2.2 OWL (Web ontology language) -- 3.2.3 OBO format -- 3.3 Best practices for building bio-ontologies -- 3.3.1 Define the scope of the bio-ontology -- 3.3.2 Identity of the represented entities -- 3.3.3 Commit to agreed ontological principles -- 3.3.4 Knowledge acquisition -- 3.3.5 Ontology Design Patterns (ODPs) -- 3.3.6 Ontology evaluation -- 3.3.7 Documentation -- 3.4 Conclusion -- 3.5 Acknowledgements -- 3.6 References -- 4 Design, implementation and updating of knowledge bases -- 4.1 Introduction -- 4.2 Sources of data in bioinformatics knowledge bases -- 4.2.1 Data added by internal curators -- 4.2.2 Data submitted by external users and collaborators -- 4.2.3 Data added automatically -- 4.3 Design of knowledge bases -- 4.3.1 Understanding your end users and understanding their data -- 4.3.2 Interactions and interfaces: their impact on design -- 4.4 Implementation of knowledge bases -- 4.4.1 Choosing a database architecture -- 4.4.2 Good programming practices -- 4.4.3 Implementation of interfaces -- 4.5 Updating of knowledge bases -- 4.5.1 Manual curation and auto-annotation -- 4.5.2 Clever pipelines and data flows -- 4.5.3 Lessening data maintenance overheads -- 4.6 Conclusions -- 4.7 References -- Section 2 Data-Analysis Approaches -- 5 Classical statistical learning in bioinformatics -- 5.1 Introduction -- 5.2 Significance testing. , 5.2.1 Multiple testing and false discovery rate -- 5.2.2 Correlated errors -- 5.3 Exploratory analysis -- 5.3.1 Clustering -- 5.3.2 Principal components -- 5.3.3 Multidimensional scaling (MDS) -- 5.4 Classification and prediction -- 5.4.1 Discriminant analysis -- 5.4.2 Modern procedures -- 5.5 References -- 6 Bayesian methods in genomics and proteomics studies -- 6.1 Introduction -- 6.2 Bayes theorem and some simple applications -- 6.3 Inference of population structure from genetic marker data -- 6.4 Inference of protein binding motifs from sequence data -- 6.5 Inference of transcriptional regulatory networks from joint analysis of protein-DNA binding data and gene expression data -- 6.6 Inference of protein and domain interactions from yeast two-hybrid data -- 6.7 Conclusions -- 6.8 Acknowledgements -- 6.9 References -- 7 Automatic text analysis for bioinformatics knowledge discovery -- 7.1 Introduction -- 7.1.1 Knowledge discovery through text mining -- 7.1.2 Need for processing biomedical texts -- 7.1.3 Developing text mining solutions -- 7.2 Information needs for biomedical text mining -- 7.2.1 Efficient analysis of normalized information -- 7.2.2 Interactive seeking of textual information -- 7.3 Principles of text mining -- 7.3.1 Components -- 7.3.2 Methods -- 7.4 Development issues -- 7.4.1 Information needs -- 7.4.2 Corpus construction -- 7.4.3 Language analysis -- 7.4.4 Integration framework -- 7.4.5 Evaluation -- 7.5 Success stories -- 7.5.1 Interactive literature analysis -- 7.5.2 Integration into bioinformatics solutions -- 7.5.3 Discovery of knowledge from the literature -- 7.6 Conclusion -- 7.7 References -- PART II APPLICATIONS -- Section 3 Gene and Protein Information -- 8 Fundamentals of gene ontology functional annotation -- 8.1 Introduction -- 8.1.1 Data submission curation -- 8.1.2 Value-added curation -- 8.2 Gene Ontology (GO). , 8.2.1 Gene Ontology and the annotation of the human proteome -- 8.2.2 Gene Ontology Consortium data sets -- 8.2.3 GO annotation methods -- 8.2.4 Different approaches to manual annotation -- 8.2.5 Ontology development -- 8.3 Comparative genomics and electronic protein annotation -- 8.3.1 Manual methods of transferring functional annotation -- 8.3.2 Electronic methods of transferring functional annotation -- 8.3.3 Electronic annotation methods -- 8.4 Community annotation -- 8.4.1 Feedback forms -- 8.4.2 Wiki pages -- 8.4.3 Community annotation workshops -- 8.5 Limitations -- 8.5.1 GO cannot capture all relevant biological aspects -- 8.5.2 The ontology is always evolving -- 8.5.3 The volume of literature -- 8.5.4 Missing published data -- 8.5.5 Manual curation is expensive -- 8.6 Accessing GO annotations -- 8.6.1 Tools for browsing the GO -- 8.6.2 Functional classification -- 8.6.3 GO slims -- 8.6.4 GO displays in other databases -- 8.7 Conclusions -- 8.8 References -- 9 Methods for improving genome annotation -- 9.1 The basis of gene annotation -- 9.1.1 Introduction to gene annotation -- 9.1.2 Progression in ab initio gene prediction -- 9.1.3 Annotation based on transcribed evidence -- 9.1.4 A comparison of annotation processes -- 9.1.5 The CCDS project -- 9.1.6 Pseudogene annotation -- 9.1.7 The annotation of non-coding genes -- 9.2 The impact of next generation sequencing on genome annotation -- 9.2.1 The annotation of multispecies genomes -- 9.2.2 Community annotation -- 9.2.3 Alternative splicing and new transcriptomics data -- 9.2.4 The annotation of human genome variation -- 9.2.5 The annotation of polymorphic gene families -- 9.3 References -- 10 Sequences from prokaryotic, eukaryotic, and viral genomes available clustered according to phylotype on a Self-Organizing Map -- 10.1 Introduction. , 10.2 Batch-learning SOM (BLSOM) adapted for genome informatics -- 10.3 Genome sequence analyses using BLSOM -- 10.3.1 BLSOMs for 13 eukaryotic genomes -- 10.3.2 Diagnostic oligonucleotides for phylotype-specific clustering -- 10.3.3 A large-scale BLSOM constructed with all sequences available from species-known genomes -- 10.3.4 Phylogenetic estimation for environmental DNA sequences and microbial community comparison using the BLSOM -- 10.3.5 Reassociation of environmental genomic fragments according to species -- 10.4 Conclusions and discussion -- 10.5 References -- Section 4 Biomolecular Relationships and Meta-Relationships -- 11 Molecular network analysis and applications -- 11.1 Introduction -- 11.2 Topology analysis and applications -- 11.2.1 Global structure of molecular networks: scale-free, small-world, disassortative, and modular -- 11.2.2 Network statistics/measures -- 11.2.3 Applications of topology analysis -- 11.2.4 Challenges and future directions of topology analysis -- 11.3 Network motif analysis -- 11.3.1 Motif analysis: concept and method -- 11.3.2 Applications of motif analysis -- 11.3.3 Challenges and future directions of motif analysis -- 11.4 Network modular analysis and applications -- 11.4.1 Density-based clustering methods -- 11.4.2 Partition-based clustering methods -- 11.4.3 Centrality-based clustering methods -- 11.4.4 Hierarchical clustering methods -- 11.4.5 Applications of modular analysis -- 11.4.6 Challenges and future directions of modular analysis -- 11.5 Network comparison -- 11.5.1 Network comparison algorithms: from computer science to systems biology -- 11.5.2 Network comparison algorithms for molecular networks -- 11.5.3 Applications of molecular network comparison -- 11.5.4 Challenges and future directions of network comparison -- 11.6 Network analysis software and tools -- 11.7 Summary. , 11.8 Acknowledgement.