Introduction | Installation | General information on the mzTab-M format | R mzTab-M API | High-level, user faced functions | Get the Metabonaut result object | Define the data set's metadata (MTD) | Add feature abundance matrix (SMF) | Completing the metadata content | Export to an MTD+SMF mzTab-M file | Importing an MTD+SMF mzTab-M file | Validation of the mzTab-M file | Low-level functions | Formatting and exporting | Metadata | Small Molecule Feature (SMF) Table | Small Molecule (SML) Table | Small Evidence (SME) Table | General utility functions | Session information | References

Introduction | Installation | Accessing data from Bruker TimsTOF files | Session information

Introduction | Installation | Importing MS Data from MetaboLights | General information for a MetaboLights data set | Session information

Introduction | Installation | A stash for MsExperiment objects | Creating self-contained stashes | Retrieve MS experiments from MetaboLights | Session information

Introduction | Installation | A stash for MsExperiment objects | Creating self-contained stashes | Retrieve MS experiments from MetaboLights | Session information

Introduction | Installation | A stash for Spectra objects | Creating self-contained stashes | Stashes for Spectra with in-memory backends | Session information

Introduction | Installation | Example implementations | Suggested properties of implemented methods | PlainTextParam | AlabasterParam | Session information

Introduction | System requirements | Installation | Translating data structures between R and Python | Library loading and system setup | Converting MS data from R to Python | Converting MS data from Python to R | Using a dedicated MS data backend for MS data in Python | Replacing data and ensuring data consistency | Conversion of spectra variables | Combined MS data analysis | Summary | Appendix | General comments | Session information

Introduction | Installation | Extracting data from Spectra objects with MassQL | MassQL definition | Type of data | Condition | Filter | Differences of the SpectraQL implementation to the MassQL definition | Examples | Filtering and subsetting | Choosing which data to return | Filtering peaks within spectra | Session information

Introduction | Installation | Creating and using MsBackendSql SQL databases | Performance considerations | Database systems and data storage modes | Database systems | MsBackendSql database layouts/storage modes | Performance comparison with other backends | Considerations for database systems/servers | Other properties of the MsBackendSql | Summary | Session information

Introduction | Installation | General description | Example use cases | Matching of m/z values | Matching of m/z and retention time values | Matching of SummarizedExperiment or QFeatures objects | Matching of MS/MS spectra | Using alternative spectra similarity functions | Query against multiple reference databases | Finding MS2 spectra for selected m/z and retention times | Performance and parallel processing | Utility functions | Creating mixes of standard compounds | Input format | Using the function | Session information | References

Introduction | Creating CompDb databases | CompDb from HMDB data | CompDb from custom data | CompDb from MoNA data | CompDb by sequentially filling with data | Extending CompDb databases | Session information

Introduction | Installation | Configure reticulate with host system Python environment (optional) | SpectriPy pre-requisites and installation instructions | Installing Bioconductor | Installing SpectriPy | Check installation completed | Appendix | Installation instructions for first-time R users | Installing R and RStudio | How to Download and Install R | Windows | Mac | Linux | Using R | RStudio | Opening R | Startup and Python configuration | Fixing package installation or loading problems | Session information

Introduction | Using ensembldb annotation packages to retrieve specific annotations | Extracting gene/transcript/exon models for RNASeq feature counting | Retrieving sequences for gene/transcript/exon models | Integrating annotations from Ensembl based EnsDb packages with UCSC based annotations | Interactive annotation lookup using the shiny web app | Plotting gene/transcript features using ensembldb and Gviz and ggbio | Using EnsDb objects in the AnnotationDbi framework | Important notes | Getting or building EnsDb databases/packages | Getting EnsDb databases | Building annotation packages | Database layout | Session information | Footnotes

Introduction | Fetch protein annotation for genes and transcripts | Use methods from the AnnotationDbi package to query protein annotation | Retrieve proteins from the database | Map peptide features within proteins to the genome | Session information

Introduction | Using ensembldb with a MySQL server | Session information

Introduction | Installation | General usage | Querying compound annotations | Additional functionality for CompDb databases | Accessing and using MS/MS data | Ion databases | Session information | References

Introduction | Installation | Examples | Conversion between ion m/z and compound masses | Working with chemical formulas | Kendrick mass defect calculation | Retention time indexing | Linear model-based adjustment of LC-MS feature abundances | Basic quality assessment and pre-filtering of metabolomics data | Contributions | Session information | References

Introduction | Installation | MSP file format | Importing MS/MS data from MSP files | Session information | References

Introduction | Fetch MassBank CompDb Databases from AnnotationHub | Creating CompDb Databases from MassBank | Session Information

Query for helix-loop-helix transcription factors on chromosome 21 | Mapping of genomic coordinates to protein-relative positions | Session information | References

Introduction | Basic pedigree operations | Pedigree analysis methods | Additional plotting options | Graph utilities | Importing and exporting pedigree data | Testing for familial aggregation | Genealogical index of familiality | Familial incidence rate (FIR) | Kinship sum test | Kinship group test | Binomial test | References

Introduction | Installation | Mass Spectrometry Feature Grouping | Grouping of features by similar retention time | Grouping of features by abundance correlation across samples | Performing feature grouping on a subset of features | Session information

Introduction | Mapping genomic coordinates to transcript-relative coordinates | Mapping genomic coordinates to protein-relative coordinates | Mapping protein coordinates to transcript coordinates | Mapping protein coordinates to the genome | Mapping transcript coordinates to genomic coordinates | Mapping transcript coordinates to protein coordinates | Session information