Data Processing Handbook for Complex Biological Data Sources

Description

Data Processing Handbook for Complex Biological Data provides relevant and to the point content for those who need to understand the different types of biological data and the techniques to process and interpret them. The book includes feedback the editor received from students studying at both undergraduate and graduate levels, and from her peers. In order to succeed in data processing for biological data sources, it is necessary to master the type of data and general methods and tools for modern data processing. For instance, many labs follow the path of interdisciplinary studies and get their data validated by several methods. Researchers at those labs may not perform all the techniques themselves, but either in collaboration or through outsourcing, they make use of a range of them, because, in the absence of cross validation using different techniques, the chances for acceptance of an article for publication in high profile journals is weakened. Explains how to interpret enormous amounts of data generated using several experimental approaches in simple terms, thus relating biology and physics at the atomic levelPresents sample data files and explains the usage of equations and web servers cited in research articles to extract useful information from their own biological dataDiscusses, in detail, raw data files, data processing strategies, and the web based sources relevant for data processing Cover Data Processing Handbook for Complex Biological Data Sources Copyright Dedication List of contributors Foreword Foreword Foreword Preface Acknowledgments 1 Mass spectroscopy 1.1 Introduction 1.1.1 Principle 1.1.2 Instrumentation 1.1.3 Types of ionization techniques 1.1.3.1 Electron ionization 1.1.3.2 Chemical ionization 1.1.3.3 Fast atom bombardment 1.1.3.4 Matrix assisted laser desorption ionization 1.1.3.5 Electrospray ionization 1.1.3.6 Fragmentation 1.1.4 Analyzers 1.1.4.1 Time of flight 1.1.4.2 Quadrupole 1.1.4.3 Ion trap analyzer 1.1.4.4 Fourier transform–ion cyclotron resistance 1.1.4.5 Orbitrap analyzer 1.1.4.6 Tandem mass spectrometer 1.1.5 Detectors 1.1.5.1 Faraday cup 1.1.5.2 Electron multipliers 1.1.5.3 Photomultipliers 1.1.5.4 Microchannel plate 1.1.6 Mass interpretation 1.1.6.1 Types of peaks 1.1.6.2 Fragmentation rules 1.1.6.3 Peptide mass fingerprinting 1.1.6.4 Peptide fragmentation fingerprinting 1.1.6.5 De novo peptide sequencing 1.2 Raw data 1.2.1 NanoLC principle 1.2.2 Gradient 1.2.3 Orbitrap technology 1.2.4 Data-dependent acquisition 1.3 Data processing 1.3.1 Trans-proteomic pipeline 1.3.2 Installation 1.3.3 Raw file conversion 1.3.4 Search engine support 1.3.5 PeptideProphet for corroboration 1.3.6 Pep3D to view chromatogram 1.3.7 iProphet for peptide-level corroboration 1.3.8 XPRESS/ASAP ratio for peptide quantitation 1.3.9 Protein prophet for protein interpretation and corroboration 1.4 Protein quantification and identification 1.4.1 Label-free quantification 1.4.2 Functional annotation and enrichment analysis 1.5 Applications of mass spectrometry 1.5.1 Application in proteomics 1.5.2 Application in metabolomics 1.5.3 Application in environment analysis 1.5.4 Application in pharmacy 1.5.5 Application in forensic science 1.5.6 Applications in medical research 1.6 Conclusion References Further Reading 2 Circular dichroism 2.1 Introduction 2.2 Principle 2.3 Raw data analyses 2.3.1 Case study 1: to study acid-induced transitions in a protein 2.3.2 Case study 2: to study pH-induced transitions in a protein 2.3.3 Case study 3: to study structural transitions in nucleic acids 2.3.4 Case study 4: determination of Tm and other thermodynamic parameters 2.4 Miscellaneous examples 2.5 Conclusion Acknowledgments References 3 Fluorescence spectroscopy 3.1 Introduction 3.2 Principle 3.2.1 Instrumentation 3.3 Intrinsic fluorescence 3.3.1 Protein stability studies 3.3.2 Protein interaction studies 3.4 Extrinsic fluorescence 3.5 Fluorescence polarization 3.6 Fluorescence resonance energy transfer 3.7 Conclusion Acknowledgment References 4 High-throughput sequencing 4.1 Introduction 4.2 High-throughput sequencing raw data 4.3 Databases for storing next generation sequencing raw data 4.4 Data processing 4.4.1 Read quality control 4.4.2 DNA-seq: the next generation sequencing genomics application 4.4.2.1 Short variant discovery 4.4.2.2 Structural variant discovery 4.4.2.3 Genome assembly 4.4.3 RNA-seq: the next generation sequencing transcriptomics application 4.4.3.1 Differential gene expression analysis 4.4.3.2 Transcriptome reconstruction 4.4.4 Next generation sequencing epigenetic application 4.5 High-throughput sequencing data analysis in an open source platform—galaxy 4.6 Conclusion Acknowledgment Reference 5 Nuclear magnetic resonance 5.1 Introduction 5.2 Theoretical and practical aspects 5.2.1 Fundamental concepts 5.2.2 Nuclear magnetic resonance relaxation mechanisms 5.3 Raw data 5.4 Data processing 5.4.1 Integrating data approaches including web-based sources 5.5 Essential analysis and processing software 5.5.1 Acquisition and initial processing 5.5.2 Computer-aided resonance assignment: spectra assignment 5.5.3 Spin works (Manitoba University): spectra processing and analysis 5.5.4 NMRFAM-SPARKY: assignment of integrated spectra 5.5.5 Collaborative computing project for nuclear magnetic resonance: spectral processing and analysis 5.5.6 Nuclear magnetic resonance pipe processing and analysis 5.6 Structure determination softwares 5.7 Molecular model visualization softwares 5.8 Examples 5.8.1 Isolated proteins 5.8.2 Protein interactions 5.8.3 Nucleic acids (DNA and RNA) 5.8.4 General overview 5.9 Conclusion References 6 Fourier transform infrared spectroscopy: Data interpretation and applications in structure elucidation and analysis of sm... 6.1 Introduction 6.2 Interpretation of biorelevant small molecules 6.3 Ligand-binding interactions 6.4 Drug–cell interactions 6.5 Biomolecules metal/metal complexes 6.5.1 Biomolecules: Fe-oxides 6.5.1.1 Small organic molecules as stabilizing agents 6.5.1.2 Nucleic bases—β-FeOOH nanoparticles 6.5.1.3 5′-Guanosine monophosphate-β-FeOOH 6.5.1.4 DNA- Fe (II) and Fe (III) nanoparticles 6.5.1.5 Peptide mediated biomineralization of Iron (III) oxyhydroxide nanoparticles 6.5.2 Guanosine monophosphate-cadmium sulfide nanostructures 6.6 Graphene-based nanomaterials 6.7 Conclusion 6.8 List of abbreviations Acknowledgments References 7 Microscopy 7.1 Introduction 7.2 Transmission electron microscopy 7.2.1 Sample preparation 7.2.1.1 Sample preparation: biological specimen 7.2.1.2 Sample preparation: nanomaterial samples 7.2.2 Three-dimensional tomography 7.3 Scanning electron microscopy 7.3.1 Sample preparation 7.3.1.1 Sample preparation: biological specimen 7.3.1.1.1 Coating the specimen surface 7.3.1.2 Sample preparation: nanomaterial specimen 7.3.2 Environmental scanning electron microscope 7.3.3 Three-dimensional scanning electron microscope 7.4 Atomic force microscopy 7.4.1 Sample preparation 7.4.1.1 Sample preparation for biological 7.4.1.2 Sample preparation for nanomaterial 7.5 Confocal microscopy 7.6 Data analysis: transmission electron microscopy, scanning electron microscope, atomic force microscope, and confocal mi... 7.7 Conclusions Acknowledgment References 8 Principles and applications of flow cytometry 8.1 Introduction 8.1.1 Light scattering and fluorescence 8.1.2 Cell labeling and compensation 8.2 Integrating data approaches and analytical tools 8.2.1 Phenotyping cell populations 8.2.2 Cell proliferation 8.2.3 Cell cycle 8.2.4 Apoptosis 8.3 Conclusion References 9 Isothermal titration calorimetry 9.1 Introduction 9.2 Raw data 9.3 Data processing 9.3.1 Web-based sources 9.4 Examples 9.4.1 Protein–metal interactions 9.4.2 Thermodynamic basis of calcium binding to EhCRD from Entamoeba histolytica 9.4.3 Protein carbohydrate interactions 9.5 Conclusion References 10 Metagenome analysis and interpretation 10.1 Introduction 10.2 Metagenomics 10.2.1 Metagenomic applications 10.2.1.1 Enzymes and metagenomics 10.2.1.2 Metagenomics and bioactive molecules 10.2.1.3 Novel biosynthetic pathways 10.2.2 Metagenome analysis 10.2.2.1 In silico analysis of the functional metagenomics datasets 10.2.2.2 In silico analysis of sequenced metagenome datasets 10.2.2.2.1 Quality filtration 10.2.2.2.1.1 FastQC 10.2.2.2.1.2 Fastqp 10.2.2.2.1.3 MetaQC chain 10.2.2.3 In silico analysis of 16S rRNA gene (SSU rRNA gene) datasets 10.2.2.3.1 QIIME 10.2.2.3.2 MEGAN 10.2.2.3.3 MOTHUR 10.2.2.3.4 JAGUC 10.2.2.3.5 UniFrac 10.2.2.3.6 PICRUSt 10.2.2.3.7 Galaxy (https://huttenhower.sph.harvard.edu/galaxy/) 10.2.2.4 In silico analysis of whole metagenome datasets 10.2.2.4.1 MG RAST [12] 10.2.2.4.2 SEED 10.2.2.4.3 MetaPath 10.3 Experimental and data analysis framework of metagenomic projects 10.3.1 Project I: Human gut microbiome structure 10.3.2 Project II Microbiome structure comparison of garden soil and hospital soil to define anthropogenic influence on soi... 10.3.3 Project III Metagenomic analysis of soil microbiome [45] 10.4 Conclusion References Index Back Cover