MALDIquant: a versatile R package for the analysis of mass spectrometry data

By Sebastian Gibb, Korbinian Strimmer. Bioinform., 2012

615 citations recorded by the source corpus.

UNLABELLED MALDIquant is an R package providing a complete and modular analysis pipeline for quantitative analysis of mass spectrometry data. MALDIquant is specifically designed with application in clinical diagnostics in mind and implements sophisticated routines for importing raw data, preprocessing, non-linear peak alignment and calibration. It also handles technical replicates as well as spectra with unequal resolution. AVAILABILITY MALDIquant and its associated R packages readBrukerFlexData and readMzXmlData are freely available from the R archive CRAN (http://cran.r-project.org). The software is distributed under the GNU General Public License (version 3 or later) and is accompanied by example files and data. Additional documentation is available from http://strimmerlab.org/software/maldiquant/.

Subfields: Ecology, Molecular Biology, Spectroscopy

View Paper: https://www.semanticscholar.org/paper/0b5d4a2be2c3de217f809b60db36ea01727b064e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Ecology

1. Vasile Palade
2. Andreas Holzinger
3. Sabine Grunwald
4. Massimo Ferri
5. Randy Goebel

In Molecular Biology

1. Vasile Palade
2. Andreas Holzinger
3. Massimo Ferri
4. Randy Goebel

In Spectroscopy

1. W. P. Filho
2. Vasile Palade
3. Manish Kumar
4. R. S. Dalmolin
5. Andreas Holzinger
6. Sabine Grunwald
7. Jugsharan Singh Virdi
8. Massimo Ferri

Citations in the corpus

• Two preprocessing techniques to reduce model covariables in soil property predictions by Vis-NIR spectroscopy (2017, methodology): “This technique was applied by detectPeaks function in MALDIquant R package (Gibb and Strimmer, 2012).”
• Towards Integrative Machine Learning and Knowledge Extraction (2017, methodology): “A proper method should be able to identify peaks with different intensities, so five groups of data-sets with different intensities were simulated [24], which are divided into two groups (same as the real life problems which are divided into two classes as diseased and healthy, here called as case a”
• MALDI-TOF mass spectrometry: an emerging technology for microbial identification and diagnosis (2015, background): “…of DNA in less than an hour • Easy to use • No sophisticated equipment is required • Developed for only a small number of microorganisms as yet Metagenomic assay • Useful for random detection of pathogens • Data acquisition and data analysis is time consuming • Trained laboratory personnel required”