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Low level infrastructure to handle MS spectra

Project Status: Active – The project has reached a stable, usable state and is being actively developed. R-CMD-check-bioc codecov license years in bioc Ranking by downloads build release build devel

The Spectra package defines an efficient infrastructure for storing and handling mass spectrometry spectra and functionality to subset, process, visualize and compare spectra data. It provides different implementations (backends) to store mass spectrometry data. These comprise backends tuned for fast data access and processing and backends for very large data sets ensuring a small memory footprint.

A (possibly incomplete) list of available backends (along with a link to the R package providing it) is shown below:

  • MsBackendCompDb (package CompoundDb: provides access to spectra data (spectra and peaks variables) from a CompDb database. Has a small memory footprint because all data (except precursor m/z values) are retrieved on-the-fly from the database.

  • MsBackendDataFrame (package: Spectra): alternative to the MsBackendMemory also keeping all data in memory, but supporting S4 objects as spectra variables because the data is stored internally in a DataFrame.

  • MsBackendHdf5Peaks (package: Spectra): on-disk backend similar to MsBackendMzR, but the peaks data is stored in HDF5 files (general spectra variables are kept in memory).

  • MsBackendHmdbXml (package MsbackendHmdb): allows import of MS data from xml files of the Human Metabolome Database (HMDB). Extends the MsBackendDataFrame and keeps thus all data, after import, in memory.

  • MsBackendMassbank (package MsBackendMassbank): allows to import/export data in MassBank text file format. Extends the MsBackendDataFrame and keeps thus all data, after import, in memory.

  • MsBackendMassbankSql (package MsBackendMassbank): allows to directly connect to a MassBank SQL database to retrieve all MS data and variables. Has a minimal memory footprint because all data is retrieved on-the-fly from the SQL database.

  • MsBackendMemory (package: Spectra): default backend which keeps all data in memory. Optimized for fast processing.

  • MsBackendMetaboLights (package MsBackendMetaboLights): retrieves and caches MS data files from MetaboLights.

  • MsBackendMgf (package MsBackendMgf): allows to import/export data in mascot generic format (MGF). Extends the MsBackendDataFrame and keeps thus all data, after import, in memory.

  • MsBackendMsp (package MsbackendMsp): allows to import/export data in NIST MSP format. Extends the MsBackendDataFrame and keeps thus all data, after import, in memory.

  • MsBackendMzR (package: Spectra): by using the mzR package it supports import of MS data from mzML, mzXML and CDF files. This backend keeps only general spectra variables in memory and retrieves the peaks data (m/z and intensity values) on-the-fly from the original data files. The backend has thus a smaller memory footprint compared to in-memory backends.

  • MsBackendOfflineSql (package MsBackendSql): stores all MS data in a SQL database and has thus a minimal memory footprint. Does, in contrast to MsBackendSql, not keep an active SQL database connection and can thus support parallel processing.

  • MsBackendRawFileReader (package MsBackendRawFileReader): implements a backend for reading MS data from Thermo Fisher Scientific’s raw data files using the manufacturer’s NewRawFileReader .Net libraries. The package generalizes the functionality introduced by the rawrr package.

  • MsBackendSql (package MsBackendSql): stores all MS data in a SQL database and has thus a minimal memory footprint.

  • MsBackendTimsTof (package MsBackendTimsTof: allows import of data from Bruker TimsTOF raw data files (using the opentimsr R package).

  • MsBackendWeizMass (package MsBackendWeizMass: allows to access MS data from WeizMass MS/MS spectral databases.

For more information see the package homepage.

Installation

The package can be installed with

install.packages("BiocManager")
BiocManager::install("Spectra")

Contributions

Contributions are highly welcome and should follow the contribution guidelines. Also, please check the coding style guidelines in the RforMassSpectrometry vignette and importantly, follow our code of conduct.