Genomic Approaches in Earth and Environmental Sciences

Gregory J. Dick, is Associate Professor of Earth and Environmental Sciences at the University of Michigan.

• Preface ix Acknowledgments x Abbreviations xi 1 Introduction 1 1.1 Exploring the Microbial World 1 1.2 The DNA Sequencing Revolution: Historical Perspectives 4 References 7 2 The Architecture of Microbial Genomes 11 Introduction 11 2.1 Genome Size, Organization, and Replication 11 2.2 Nucleotide Composition 14 2.3 Ecological and Evolutionary Aspects of Microbial Genomes 16 2.3.1 The Role of Viruses in Promoting Genomic Diversity 18 2.4 Genomic Diversity in Microbial Communities 19 2.5 Does Genomic Diversity Matter? 21 References 21 3 Application of Omics Approaches to Earth and Environmental Sciences: Opportunities and Challenges 27 Introduction 27 3.1 New Perspectives on Microbial Biogeochemistry 27 3.1.1 Redefining the Carbon and Nitrogen Cycles 27 3.1.2 Omics as Sensitive and Efficient Tracers of Biogeochemical Processes 29 3.1.3 Omics Data is Valuable for Biogeochemical Models 29 3.1.4 Understanding Biotic Responses and Feedbacks to Global Change 29 3.2 A Genomic Record of Biological and Geochemical Evolution 30 3.3 Challenges and Limitations of Omics Approaches 32 3.4 Omics as a Complement to Other Approaches 33 References 34 4 Overview of Approaches: From Whole-Community Shotgun Sequencing to Single-Cell Genomics 41 Introduction 41 4.1 Choosing the Right Approach 41 4.1.1 Whole-Community Approaches 41 4.1.2 Targeted Approaches: Physical, Microbiological, and Isotopic Enrichment 43 4.1.3 Single-Cell Genomics 44 4.2 Experimental Design and Sampling Considerations 45 4.2.1 Replication 45 4.2.2 Estimating Sequencing Effort: How Much Sequencing Do I Need to Do? 46 4.2.3 From Sample to Data: Biases Due to Preservation, Storage, Extraction, and Sequencing 47 4.2.4 Estimating Absolute Abundance with Internal Standards 49 4.3 Overview of Current DNA Sequencing Technologies 49 4.4 Quality Control and Sequence Processing 51 4.4.1 Dereplication 51 4.4.2 Trimming 52 References 53 5 Genomics of Single Species and Single Cells 59 Introduction 59 5.1 Algorithms for Genome Assembly 60 5.2 Challenges of Genome Assembly 61 5.3 Scaffolding 63 5.4 Programs and Pipelines for Genome Assembly 63 5.5 Evaluation of Genome Assemblies 66 5.6 Single-Cell Genomics 67 References 69 6 Metagenomics: Assembly and Database-Dependent Approaches 73 Introduction 73 6.1 To Assemble or Not To Assemble? 73 6.2 Database-Dependent Approaches 75 6.3 Database-Independent Approaches: De Novo Assembly 78 6.4 Evaluation of Metagenomic Assemblies 82 6.5 A Philosophy of Metagenome Assemblies 82 References 83 7 Metagenomic Binning 89 Introduction 89 7.1 Genomic Signatures of Nucleotide Composition 90 7.2 Binning Programs 91 7.3 Additional Signal and Steps for Binning: Coverage, Taxonomic Data, and Mini‐Assemblies 93 7.4 Identifying, Evaluating, and Assessing the Completeness of Genomic Bins 95 References 97 8 Annotation: Gene Calling, Taxonomy, and Function 101 Introduction 101 8.1 Gene Calling 102 8.2 Determining Taxonomic Composition 103 8.3 Functional Annotation 106 8.3.1 Overall Approach to Functional Annotation 106 8.3.2 Predicting Metabolic Pathways 107 8.3.3 The Importance of Experimental Annotation 108 References 109 9 Metatranscriptomics 113 Introduction 113 9.1 Sample Collection 114 9.2 RNA Extraction and Preparation of cDNA Libraries 115 9.2.1 Should rRNAs Be Removed Prior to Library Preparation and Sequencing? 115 9.3 Assigning Transcripts to Genes or Other Features 115 9.4 De Novo Assembly 116 9.5 Absolute Versus Relative Abundance and Normalization 118 9.6 Detecting Differential Expression 122 References 123 10 Metaproteomics 127 Introduction 127 10.1 Methodologies for Basic Proteomics 128 10.2 The Importance of Genomic Databases for Interpreting Proteomics Data 130 10.3 Quantitative Proteomics 131 10.4 Combining Stable Isotope Probing with Proteomics to Track Microbial Metabolism 133 References 133 11 Lipidomics and Metabolomics 137 Introduction 137 11.1 Lipidomics 137 11.2 Metabolomics 139 References 140 12 Downstream and Integrative Approaches and Future Outlook 145 Introduction 145 12.1 Comparative Omics 145 12.2 Statistical Approaches 146 12.3 Visualization 147 12.4 Cyberinfrastructure for Environmental Omics 148 12.4.1 Software Platforms for Integrated Analyses and Data Storage 149 12.5 Data and Sample Archival 151 12.6 Modeling 151 12.7 Emerging Trends and Future Outlook 153 References 155 Index 161