1. The ‘omics revolution in Apis: More data than meets the eye 2. Sample management 3. Genome sequencing 3.1. Introduction 3.2. Genome sequencing technologies 3.2.1. Sanger sequencing 3.2.2. Next generation sequencing 3.2.3. Long-read sequencing 3.3. The reference genome 3.3.1. Assembling the reference genome 3.3.1.1. Hi-C chromosome conformation capture 3.3.1.2. DNA source selection 3.3.2. Annotating the reference genome 3.3.3. High molecular weight DNA extraction 3.4. Small and large variant detection 3.4.1. RAD-seq 3.5. Sequencing museum specimens 3.5.1. Considerations 3.5.2. Materials 3.5.3. Procedure 3.5.3.1. Preparation and lysis 3.5.3.2. DNA extraction 3.5.3.3. Precipitation 3.5.3.4. Washing 3.5.3.5. Final solubilization 3.5.4. Sequencing of museum and ancient genomes 3.5.5. Guidance on the data analysis methods 3.5.6. Applications and limitations 4. Whole-genome population and association studies 4.1. Introduction 4.2. Ploidy and sampling considerations 4.2.1. Individual sampling 4.2.2. Pooled sampling 4.2.2.1. Groups of workers 4.2.2.2. Groups of drones 4.3. SNP and indel detection 4.3.1. Mapping reads with BWA-MEM: From FASTQ files to BAM files 4.3.2. Marking duplicate reads with Picard 4.3.3. Base quality score recalibration (BQSR) with GATK 4.3.4. Calling variants with GATK 4.3.5. Combining all samples and genotypes with GATK 4.3.6. Filtering variants with GATK: Technical filters 4.3.7. Filtering variants with VCFtools: Data quality 4.3.8. Genotype phasing 4.3.9. SNP annotation with SnpEff 4.3.10. SNP analysis by sequencing pooled samples 4.3.10.1. From FASTQ files to BAM files 4.3.10.2. SNP selection and pileup files 4.3.10.3. Counting reads per allele with PoPoolation 4.4. Comparing whole genomes 4.4.1. Conducting a pairwise genome comparison with LAST 4.5. Genome-wide association studies 4.5.1. Considerations for phenotypic data 4.5.2. Considerations for sample selection 4.5.2.1. Power analysis 4.5.3. Materials 4.5.3.1. Computational resources 4.5.3.2. Genotypic and phenotypic data 4.5.4. Methods 4.5.4.1. Preparation of phenotypic data 4.5.4.2. Preparation of genotypic data 4.5.4.3. Performing GWAS: Methods and software 4.5.4.4. Detecting signatures of selection 4.5.5. Sources of variation 4.5.6. Quality control and data interpretation 4.5.7. Applications and limitations 4.6. Population genomics: Experimental design 4.6.1. Sampling strategy 4.6.1.1. Sample sizes of individuals and markers 4.6.1.2. Sample breadth 4.6.1.3. Sampling design 4.6.1.4. Sampling workers versus drones 4.6.1.5. Sampling a single individual versus multiple individuals per colony 4.7. Population genomics: Filtering and summary statistics using PLINK 4.7.1. Download and installation 4.7.2. Input format and conversion 4.7.2.1. Variant call format (VCF) 4.7.2.2. PLINK 1 binary format (.bim) 4.7.2.3. Regular PLINK text files 4.7.2.4. Filtering and handling missing data 4.7.2.5. Computing and filtering based on allele frequency 4.7.2.6. Computing differentiation indices: Wright's FST 4.7.2.7. Estimating linkage disequilibrium 4.8. Population genomics: Inferring population structure using ADMIXTURE 4.8.1. Download and installation 4.8.2. Input files 4.8.3. Methods 4.9. Landscape genomics: An example using LFMM 4.9.1. Materials 4.9.2. Methods 4.10. Applying population genomics to conservation: Reduced SNP analysis 4.10.1. Materials 4.10.2. Methods 5. Epigenomics 5.1. Introduction 5.2. DNA methylation 5.2.1. Bisulfite-seq 5.2.1.1. Considerations 5.2.1.2. Materials 5.2.1.3. Methods 5.2.2. Methylated DNA immunoprecipitation-sequencing (MeDIP-seq) 5.2.2.1. Considerations 5.2.2.2. Materials 5.2.2.3. Methods 5.2.3. Data processing and analysis 5.2.3.1. Software recommendations 5.2.3.2. Data repository 5.2.3.3. Statistical analysis 5.3. Epitranscriptomics: RNA methylation of m6A 5.3.1. Considerations for testing global RNA methylation of m6A 5.3.2. Materials 5.3.3. Procedure 5.3.4. Identifying methylation sites 5.3.5. Software recommendations 5.4. Chromatin organization and histone modifications 5.4.1. Chromatin immunoprecipitation sequencing and transcription factor binding motifs 5.4.2. Hi-C & chromatin conformation 5.4.3. Chromatin accessibility and transcriptional factor motifs 5.4.4. Detecting histone modifications by mass spectrometry 5.5. Applications and limitations 6. Transcriptomics 6.1. Introduction 6.2. Sequencing technologies 6.2.1. Considerations 6.2.2. Illumina sequencing (short reads) 6.2.3. Third generation sequencing (long reads) 6.2.3.1. Considerations for choosing a long-read platform 6.3. Single-cell transcriptomics 6.3.1. Considerations 6.3.2. Materials 6.3.3. Sample preparation procedure for single-cell sequencing 6.4. Data handling and analysis 6.4.1. RNA-seq and differentially expressed genes (DEGs) 6.4.2. Gene network analysis 6.4.3. Single-cell transcriptomics 6.4.3.1. 10x Genomics specific software 6.4.3.2. Third party software 6.5. Applications and limitations 7. Functional genomics and xenobiotic treatment 7.1. Introduction 7.2. CRISPR 7.2.1. Considerations 7.2.2. Materials 7.2.3. Methods for CRISPR/Cas9 gene editing of embryos 7.2.3.1. Generating Cas9 protein 7.2.3.2. Generating sgRNA 7.2.3.3. Ribonucleoprotein assembly 7.2.3.4. Egg collection and microinjection 7.3. RNA interference 7.3.1. RNAi considerations 7.3.2. Methods for nanoparticle-mediated RNAi 7.3.2.1. Materials 7.3.2.2. Procedure 7.4. Xenobiotic treatment 7.4.1 Xenobiotic treatment considerations 7.4.2. Materials 7.4.3 Procedure 7.4.3.1 Thorax application 7.4.3.2. Injection 7.4.3.3. Feeding individual bees 7.4.3.4. Flight cage feeding 7.5. Applications and limitations 8. Proteomics 8.1. Introduction 8.2. Standard methods for shot-gun proteomics sample preparation 8.2.1. Considerations 8.2.1.1. General 8.2.1.2. Sample handling 8.2.1.3. Reagent handling 8.2.2. Materials 8.2.3. Proteomics methods 8.2.3.1. Lysis and precipitation 8.2.3.2. Solubilization and digestion 8.2.3.3. Peptide desalting and resuspension 8.3. Liquid chromatography and mass spectrometry 8.4. Proteomics data processing 8.4.1. Software recommendations 8.4.1.1. MaxQuant and DIA-NN search parameters 8.4.1.2. Choosing an appropriate protein database 8.4.1.3 Statistical analysis 8.5. Applications and limitations 9. Metabolomics 9.1. Introduction 9.2. Sample preparation for metabolomics 9.2.1. Considerations 9.2.1.1. General 9.2.1.2. Sample handling 9.2.1.3. Reagent handling 9.2.2. Materials 9.2.3. Metabolomics methods 9.2.3.1. Sample homogenization 9.2.3.2. Extraction 9.3. Chromatography and mass spectrometry 9.4. Metabolomics data processing 9.5. Metabolomics applications and limitations 10. Microbiome analysis 10.1. Introduction 10.2. Sampling and DNA extraction 10.2.1. Considerations 10.2.1.1. General 10.2.1.2. Tissue sample handling 10.2.1.3. Hive material sample handling 10.2.2. Protocol for tissue samples 10.2.2.1. Materials 10.2.2.2. Dissection methods 10.2.2.3. DNA extraction methods 10.2.3. Protocol for sampling hive materials 10.2.3.1. Materials 10.2.3.2. Methods for bee bread sampling and DNA extraction 10.2.3.3. Methods for hive entrance sampling and DNA extraction 10.3. Amplicon sequencing 10.3.1. Considerations 10.3.2. Materials for amplicon sequencing 10.3.3. Methods for amplicon sequencing 10.4. Microbiome data analysis 10.4.1. Recommended software 10.4.2. Guidance on the data analysis methods: An example with QIIME 2 10.4.2.1. Importing data 10.4.2.2. Non-biological sequence removal 10.4.2.3. Sequence quality control (denoising) 10.4.2.4. Removing biological contamination 10.5. Applications and limitations 11. Data management and open access sharing 11.1. Metadata standardization 11.1.1. Common problems with Apis-related BioProjects 11.1.2. Common problems with Apis-related BioSamples 11.2. Sharing pipelines and scripts 12. The future of Apis omics: Biological integration