Integrating Multiple Quantitative Proteomic Analyses Using MetaMSD

Ryu, Seok Chang; Yun, Miriam P; Kim, Su-Jung

doi:10.1007/978-1-0716-1967-4_16

Cited by 2 publications

(2 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A number of recent studies aiming at benchmarking the protein quantitation were focused mostly on the entire quantitation workflows, rather than on the specifics of the peptide feature detection methods 23 . While some previous reports have demonstrated integration of quantitation methods, such as combining label-free and labeled approaches 24 , or combination of the quantitation results obtained for different datasets 25 , there is a lack of implementations of the integration of different standalone quantitation workflows at the peptide feature detection level. In this study we analyzed the differences in quantitation results between feature detection algorithms combined with a particular quantitation method.…”

Section: Introductionmentioning

confidence: 99%

IQMMA: an efficient MS1 intensity extraction using multiple feature detection algorithms for DDA proteomics

Postoenko

Garibova

Levitsky

et al. 2023

Preprint

View full text Add to dashboard Cite

One of the key steps in data dependent acquisition (DDA) proteomics is detection of peptide isotopic clusters, also called "features", in MS1 spectra and matching them to MS/MS-based peptide identifications. A number of peptide feature detection tools became available in recent years each relying on its own matching algorithm. Here, we provide an integrated solution, Intensity-based Quantitative Mix and Match Approach (IQMMA), which integrates a number of peptide feature detection algorithms and returns the most probable intensity values for the MS/MS-based identifications. IQMMA was tested using available proteomic data acquired for both well-characterized (ground truth) and real-world biological samples, including a mix of E. coli digest spiked at different concentrations into the HeLa digest used as a background and a set of glioblastoma cell lines. Three open-source feature detection algorithms were integrated: Dinosaur, biosaur2 and OpenMS FeatureFinder. Neither of them was found optimal when applied individually to all the datasets employed in this work, however, their combined use in IQMMA improved efficiency of subsequent protein quantitation. The software implementing IQMMA is freely available at https://github.com/PostoenkoVI/IQMMA under Apache 2.0 license.

show abstract

Section: Introductionmentioning

confidence: 99%

IQMMA: an efficient MS1 intensity extraction using multiple feature detection algorithms for DDA proteomics

Postoenko

Garibova

Levitsky

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…A number of recent studies aiming at benchmarking the protein quantitation were focused mostly on the entire quantitation workflows, rather than on the specifics of the peptide feature detection methods. 27 While some previous reports have demonstrated integration of quantitation methods, such as combining label-free and labeled approaches, 28 or combination of the quantitation results obtained for different data sets, 29 there is a lack of implementations of the integration of different standalone quantitation workflows at the peptide feature detection level. In this study, we analyzed the differences in quantitation results between feature detection algorithms combined with a particular quantitation method.…”

Section: ■ Introductionmentioning

confidence: 99%

IQMMA: Efficient MS1 Intensity Extraction Pipeline Using Multiple Feature Detection Algorithms for DDA Proteomics

Postoenko,

Garibova,

Levitsky

et al. 2023

J. Proteome Res.

View full text Add to dashboard Cite

One of the key steps in data dependent acquisition (DDA) proteomics is detection of peptide isotopic clusters, also called "features", in MS1 spectra and matching them to MS/MS-based peptide identifications. A number of peptide feature detection tools became available in recent years, each relying on its own matching algorithm. Here, we provide an integrated solution, the intensity-based Quantitative Mix and Match Approach (IQMMA), which integrates a number of untargeted peptide feature detection algorithms and returns the most probable intensity values for the MS/MS-based identifications. IQMMA was tested using available proteomic data acquired for both well-characterized (ground truth) and real-world biological samples, including a mix of Yeast and E. coli digests spiked at different concentrations into the Human K562 digest used as a background, and a set of glioblastoma cell lines. Three open-source feature detection algorithms were integrated: Dinosaur, biosaur2, and OpenMS FeatureFinder. None of them was found optimal when applied individually to all the data sets employed in this work; however, their combined use in IQMMA improved efficiency of subsequent protein quantitation. The software implementing IQMMA is freely available at https://github.com/PostoenkoVI/ IQMMA under Apache 2.0 license.

show abstract

Integrating Multiple Quantitative Proteomic Analyses Using MetaMSD

Cited by 2 publications

References 23 publications

IQMMA: an efficient MS1 intensity extraction using multiple feature detection algorithms for DDA proteomics

IQMMA: an efficient MS1 intensity extraction using multiple feature detection algorithms for DDA proteomics

IQMMA: Efficient MS1 Intensity Extraction Pipeline Using Multiple Feature Detection Algorithms for DDA Proteomics

Contact Info

Product

Resources

About