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https://rformassspectrometry.github.io/Spectra/
Low level infrastructure to handle MS spectra
https://rformassspectrometry.github.io/Spectra/
bioconductor mass-spectrometry metabolomics proteomics rstats
Last synced: 3 months ago
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Low level infrastructure to handle MS spectra
- Host: GitHub
- URL: https://rformassspectrometry.github.io/Spectra/
- Owner: rformassspectrometry
- Created: 2019-04-05T08:22:17.000Z (over 5 years ago)
- Default Branch: main
- Last Pushed: 2024-08-01T12:48:45.000Z (3 months ago)
- Last Synced: 2024-08-01T13:10:18.942Z (3 months ago)
- Topics: bioconductor, mass-spectrometry, metabolomics, proteomics, rstats
- Language: R
- Homepage: https://rformassspectrometry.github.io/Spectra/
- Size: 20.4 MB
- Stars: 34
- Watchers: 10
- Forks: 24
- Open Issues: 35
-
Metadata Files:
- Readme: README.md
- Changelog: NEWS.md
Awesome Lists containing this project
README
# Low level infrastructure to handle MS spectra
[![Project Status: Active – The project has reached a stable, usable state and is being actively developed.](https://www.repostatus.org/badges/latest/active.svg)](https://www.repostatus.org/#active)
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[![build devel](http://bioconductor.org/shields/build/devel/bioc/Spectra.svg)](https://bioconductor.org/checkResults/devel/bioc-LATEST/Spectra/)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:- `MsBackendMemory` (package: *Spectra*): *default* backend which keeps all data
in memory. Optimized for fast processing.
- `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`.
- `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.
- `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).
- `MsBackendMgf` (package
[*MsBackendMgf*](https://github.com/rformassspectrometry/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*](https://github.com/rformassspectrometry/MsBackendMsp): allows
to import/export data in NIST MSP format. Extends the `MsBackendDataFrame` and
keeps thus all data, after import, in memory.
- `MsBackendMassbank` (package
[*MsBackendMassbank*](https://github.com/rformassspectrometry/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*](https://github.com/rformassspectrometry/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.
- `MsBackendRawFileReader` (package
[*MsBackendRawFileReader*](https://github.com/fgcz/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.
- `MsBackendHmdbXml` (package
[*MsbackendHmdb*](https://github.com/rformassspectrometry/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.
- `MsBackendSql` (package
[*MsBackendSql*](https://github.com/rformassspectrometry/MsBackendSql)):
stores all MS data in a SQL database and has thus a minimal memory footprint.
- `MsBackendCompDb` (package
[*CompoundDb*](https://github.com/rformassspectrometry/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.
- `MsBackendTimsTof` (package
[*MsBackendTimsTof*](https://github.com/rformassspectrometry/MsBackendTimsTof):
allows import of data from Bruker TimsTOF raw data files (using the
`opentimsr` R package).
- `MsBackendWeizMass` (package
[*MsBackendWeizMass*](https://github.com/rformassspectrometry/MsBackendWeizMass):
allows to access MS data from WeizMass MS/MS spectral databases.For more information see the package
[homepage](https://rformassspectrometry.github.io/Spectra).# Installation
The package can be installed with
```r
install.packages("BiocManager")
BiocManager::install("Spectra")
```# Contributions
Contributions are highly welcome and should follow the [contribution
guidelines](https://rformassspectrometry.github.io/RforMassSpectrometry/articles/RforMassSpectrometry.html#contributions).
Also, please check the coding style guidelines in the [RforMassSpectrometry
vignette](https://rformassspectrometry.github.io/RforMassSpectrometry/articles/RforMassSpectrometry.html).