| Title: | Estimating Absolute Protein Quantities from Label-Free LC-MS/MS Proteomics Data |
|---|---|
| Description: | Determination of absolute protein quantities is necessary for multiple applications, such as mechanistic modeling of biological systems. Quantitative liquid chromatography tandem mass spectrometry (LC-MS/MS) proteomics can measure relative protein abundance on a system-wide scale. To estimate absolute quantitative information using these relative abundance measurements requires additional information such as heavy-labeled references of known concentration. Multiple methods have been using different references and strategies; some are easily available whereas others require more effort on the users end. Hence, we believe the field might benefit from making some of these methods available under an automated framework, which also facilitates validation of the chosen strategy. We have implemented the most commonly used absolute label-free protein abundance estimation methods for LC-MS/MS modes quantifying on either MS1-, MS2-levels or spectral counts together with validation algorithms to enable automated data analysis and error estimation. Specifically, we used Monte-carlo cross-validation and bootstrapping for model selection and imputation of proteome-wide absolute protein quantity estimation. Our open-source software is written in the statistical programming language R and validated and demonstrated on a synthetic sample. |
| Authors: | George Rosenberger, Hannes Roest, Christina Ludwig, Ruedi Aebersold, Lars Malmstroem |
| Maintainer: | George Rosenberger <[email protected]> |
| License: | GPL (>= 3) |
| Version: | 1.3.6 |
| Built: | 2025-02-13 04:43:35 UTC |
| Source: | https://github.com/cran/aLFQ |
Estimating Absolute Protein Quantities from Label-Free LC-MS/MS Proteomics Data
| Package: | aLFQ |
| Type: | Package |
| Version: | 1.3.5 |
| Date: | 2019-04-30 |
| Author: | George Rosenberger, Hannes Roest, Christina Ludwig, Ruedi Aebersold, Lars Malmstroem |
| Maintainer: | George Rosenberger <[email protected]> |
| Depends: | R (>= 2.15.0) |
| Imports: | data.table, plyr, caret, seqinr, lattice, randomForest, ROCR, reshape2, protiq, bio3d |
| Suggests: | testthat |
| License: | GPL version 3 or newer |
| URL: | https://github.com/aLFQ |
| NeedsCompilation: | no |
| Repository: | CRAN |
Determination of absolute protein quantities is necessary for multiple applications, such as mechanistic modeling of biological systems. Quantitative liquid chromatography tandem mass spectrometry (LC-MS/MS) proteomics can measure relative protein abundance on a system-wide scale. To estimate absolute quantitative information using these relative abundance measurements requires additional information such as heavy-labeled references of known concentration. Multiple methods have been using different references and strategies; some are easily available whereas others require more effort on the users end. Hence, we believe the field might benefit from making some of these methods available under an automated framework, which also facilitates validation of the chosen strategy. We have implemented the most commonly used absolute label-free protein abundance estimation methods for LC-MS/MS modes quantifying on either MS1-, MS2-levels or spectral counts together with validation algorithms to enable automated data analysis and error estimation. Specifically, we used Monte-carlo cross-validation and bootstrapping for model selection and imputation of proteome-wide absolute protein quantity estimation. Our open-source software is written in the statistical programming language R and validated and demonstrated on a synthetic sample.
import, ProteinInference, AbsoluteQuantification, ALF, APEX, apexFeatures, proteotypic
## Not run: help(package="aLFQ")## Not run: help(package="aLFQ")
Absolute label-free quantification of mass spectrometry proteomics experiments.
## Default S3 method: AbsoluteQuantification(data, total_protein_concentration = 1, ...) ## S3 method for class 'AbsoluteQuantification' cval(object, cval_method = "mc", mcx = 1000, ...) ## S3 method for class 'AbsoluteQuantification' print(x, ...) ## S3 method for class 'AbsoluteQuantification' plot(x, ...) ## S3 method for class 'AbsoluteQuantification' hist(x, ...) ## S3 method for class 'AbsoluteQuantification' pivot(x, ...) ## S3 method for class 'AbsoluteQuantification' export(x, file, ...)## Default S3 method: AbsoluteQuantification(data, total_protein_concentration = 1, ...) ## S3 method for class 'AbsoluteQuantification' cval(object, cval_method = "mc", mcx = 1000, ...) ## S3 method for class 'AbsoluteQuantification' print(x, ...) ## S3 method for class 'AbsoluteQuantification' plot(x, ...) ## S3 method for class 'AbsoluteQuantification' hist(x, ...) ## S3 method for class 'AbsoluteQuantification' pivot(x, ...) ## S3 method for class 'AbsoluteQuantification' export(x, file, ...)
data |
a mandatory data frame containing the columns |
total_protein_concentration |
the total protein concentration in the sample in any unit. This will be used for the normalized protein and concentration columns. |
object |
an |
cval_method |
a method for doing crossvalidation: |
mcx |
a positive integer value of the number of folds for cross-validation. |
file |
the location of the output csv file. |
x |
an |
... |
future extensions. |
If absolute quantity estimation based on anchor peptides or proteins is demanded, the calibration peptide or protein abundance must be provided. Both estimated calibration protein intensities and separately determined calibration protein concentrations are log transformed and a first order linear least-squares regression of this log-log data is calculated. The abundance of the target proteins is predicted based on this regression. The error of the regression arises from biological and technical variation as well from the protein and peptide intensity estimators. To perform model selection and to estimate the error of the predicted protein concentrations, bootstrapping and Monte Carlo cross-validation as suggested (Malmstrom et al., 2009; Ludwig et al., 2012) were implemented. For both methods, the objective function is the minimization of the mean fold-error.
If, on the other hand, the total protein concentration per cell is supplied in proteome-wide experiments, the absolute protein concentrations are estimated by normalization of the MS intensities or spectral counts to this number (Lu et al., 2006).
An object of class AbsoluteQuantification.
George Rosenberger [email protected]
Malmstrom, J. et al. Proteome-wide cellular protein concentrations of the human pathogen Leptospira interrogans. Nature 460, 762-765 (2009).
Ludwig, C., Claassen, M., Schmidt, A. & Aebersold, R. Estimation of Absolute Protein Quantities of Unlabeled Samples by Selected Reaction Monitoring Mass Spectrometry. Molecular & Cellular Proteomics 11, M111.013987-M111.013987 (2012).
Lu, P., Vogel, C., Wang, R., Yao, X. & Marcotte, E. M. Absolute protein expression profiling estimates the relative contributions of transcriptional and translational regulation. Nat Biotech 25, 117-124 (2006).
import, ProteinInference, ALF, APEX, apexFeatures, proteotypic
data(UPS2MS) UPS2_SRM<-head(UPS2_SRM,100) # Remove this line for real applications data_PI <- ProteinInference(UPS2_SRM) data_AQ <- predict(cval(AbsoluteQuantification(data_PI),mcx=2)) print(data_AQ) plot(data_AQ) hist(data_AQ) pivot(data_AQ)data(UPS2MS) UPS2_SRM<-head(UPS2_SRM,100) # Remove this line for real applications data_PI <- ProteinInference(UPS2_SRM) data_AQ <- predict(cval(AbsoluteQuantification(data_PI),mcx=2)) print(data_AQ) plot(data_AQ) hist(data_AQ) pivot(data_AQ)
Estimation of Absolute Protein Quantities of Unlabeled Samples by Targeted Mass Spectrometry.
## Default S3 method: ALF(data, report_filename="ALF_report.pdf", prediction_filename="ALF_prediction.csv", peptide_methods = c("top"), peptide_topx = c(1,2,3), peptide_strictness = "loose", peptide_summary = "mean", transition_topx = c(1,2,3), transition_strictness = "loose", transition_summary = "sum", fasta = NA, apex_model = NA, combine_precursors = FALSE, combine_peptide_sequences = FALSE, consensus_proteins = TRUE, consensus_peptides = TRUE, consensus_transitions = TRUE, cval_method = "boot", cval_mcx = 1000, ...)## Default S3 method: ALF(data, report_filename="ALF_report.pdf", prediction_filename="ALF_prediction.csv", peptide_methods = c("top"), peptide_topx = c(1,2,3), peptide_strictness = "loose", peptide_summary = "mean", transition_topx = c(1,2,3), transition_strictness = "loose", transition_summary = "sum", fasta = NA, apex_model = NA, combine_precursors = FALSE, combine_peptide_sequences = FALSE, consensus_proteins = TRUE, consensus_peptides = TRUE, consensus_transitions = TRUE, cval_method = "boot", cval_mcx = 1000, ...)
data |
a mandatory data frame containing the columns |
report_filename |
the path and filename of the PDF report. |
prediction_filename |
the path and filename of the predictions of the optimal model. |
peptide_methods |
a vecter containing a combination of |
peptide_topx |
( |
peptide_strictness |
( |
peptide_summary |
( |
transition_topx |
a positive integer value of the top x transitions to consider for transition to peptide intensity estimation methods. |
transition_strictness |
whether |
transition_summary |
how to summarize the transition intensities: |
fasta |
( |
apex_model |
( |
combine_precursors |
whether to sum all precursors of the same peptide. |
combine_peptide_sequences |
whether to sum all variants (modifications) of the same peptide sequence. |
consensus_proteins |
if multiple runs are provided, select identical proteins among all runs. |
consensus_peptides |
if multiple runs are provided, select identical peptides among all runs. |
consensus_transitions |
if multiple runs are provided, select identical transitions among all runs. |
cval_method |
a method for doing crossvalidation: |
cval_mcx |
a positive integer value of the number of folds for cross-validation. |
... |
future extensions. |
The ALF module enables model selection for TopN transitions and peptides for protein quantification (Ludwig et al., 2012). The workflow is completely automated and a report and prediction (using the best model) is generated.
The reports specified in the function call.
George Rosenberger [email protected]
Ludwig, C., Claassen, M., Schmidt, A. \& Aebersold, R. Estimation of Absolute Protein Quantities of Unlabeled Samples by Selected Reaction Monitoring Mass Spectrometry. Molecular \& Cellular Proteomics 11, M111.013987-M111.013987 (2012).
import, ProteinInference, AbsoluteQuantification, APEX, apexFeatures, proteotypic
## Not run: data(UPS2MS) ## Not run: ALF(UPS2_SRM) ## Not run: data(LUDWIGMS) ## Not run: ALF(LUDWIG_SRM)## Not run: data(UPS2MS) ## Not run: ALF(UPS2_SRM) ## Not run: data(LUDWIGMS) ## Not run: ALF(LUDWIG_SRM)
Calculating absolute and relative protein abundance from mass spectrometry-based protein expression data.
## Default S3 method: APEX(data, ...) ## S3 method for class 'APEX' predict(object, newdata=NULL, ...) ## S3 method for class 'APEX' cval(object, folds=10, ...) ## S3 method for class 'APEX' print(x, ...) ## S3 method for class 'APEX' plot(x, ...)## Default S3 method: APEX(data, ...) ## S3 method for class 'APEX' predict(object, newdata=NULL, ...) ## S3 method for class 'APEX' cval(object, folds=10, ...) ## S3 method for class 'APEX' print(x, ...) ## S3 method for class 'APEX' plot(x, ...)
data |
an R object of type |
object |
an |
newdata |
an R object of type |
folds |
a positive integer value of the number of folds for cross-validation. |
x |
an |
... |
future extensions. |
The APEX module is a reimplementation of the original algorithm (Lu et al., 2006; Vogel et al., 2008) using the randomForest package. It requires apexFeatures input objects and reports the results in an APEX object, which can be used by the ProteinInference module for protein quantification.
An object of class APEX.
George Rosenberger [email protected]
Lu, P., Vogel, C., Wang, R., Yao, X. & Marcotte, E. M. Absolute protein expression profiling estimates the relative contributions of transcriptional and translational regulation. Nat Biotech 25, 117-124 (2006).
Vogel, C. & Marcotte, E. M. Calculating absolute and relative protein abundance from mass spectrometry-based protein expression data. Nat Protoc 3, 1444-1451 (2008).
import, ProteinInference, AbsoluteQuantification, ALF, apexFeatures, proteotypic
set.seed(131) data(APEXMS) APEX_ORBI<-head(APEX_ORBI,50) # Remove this line for real applications APEX_ORBI.af <- apexFeatures(APEX_ORBI) APEX_ORBI.apex <- APEX(data=APEX_ORBI.af) print(APEX_ORBI.apex) APEX_ORBI_cval.apex <- cval(APEX_ORBI.apex, folds=2) plot(APEX_ORBI_cval.apex)set.seed(131) data(APEXMS) APEX_ORBI<-head(APEX_ORBI,50) # Remove this line for real applications APEX_ORBI.af <- apexFeatures(APEX_ORBI) APEX_ORBI.apex <- APEX(data=APEX_ORBI.af) print(APEX_ORBI.apex) APEX_ORBI_cval.apex <- cval(APEX_ORBI.apex, folds=2) plot(APEX_ORBI_cval.apex)
Calculation of physicochemical amino acid properties for APEX.
## Default S3 method: apexFeatures(x, ...) ## S3 method for class 'apexFeatures' print(x, ...)## Default S3 method: apexFeatures(x, ...) ## S3 method for class 'apexFeatures' print(x, ...)
x |
a mandatory data frame containing the variables in the model.
The data frame requires the columns |
... |
future extensions. |
The apexFeatures function computes the APEX or PeptideSieve features (Mallick et al., 2006; Vogel et al., 2008) based on AAindex (Kawashima et al., 2008) and returns them in an apexFeatures object for further usage in the APEX module.
An object of class apexFeatures.
George Rosenberger [email protected]
Kawashima, S. et al. AAindex: amino acid index database, progress report 2008. Nucleic Acids Research 36, D202-5 (2008).
Mallick, P. et al. Computational prediction of proteotypic peptides for quantitative proteomics. Nat Biotech 25, 125-131 (2006).
Vogel, C. & Marcotte, E. M. Calculating absolute and relative protein abundance from mass spectrometry-based protein expression data. Nat Protoc 3, 1444-1451 (2008).
import, ProteinInference, AbsoluteQuantification, ALF, APEX, proteotypic
data(APEXMS) # APEX_ORBI APEX_ORBI<-head(APEX_ORBI,20) # Remove this line for real applications APEX_ORBI.af <- apexFeatures(APEX_ORBI) print(APEX_ORBI.af) # APEX_LCQ APEX_LCQ<-head(APEX_LCQ,20) # Remove this line for real applications APEX_LCQ.af <- apexFeatures(APEX_LCQ) print(APEX_LCQ.af)data(APEXMS) # APEX_ORBI APEX_ORBI<-head(APEX_ORBI,20) # Remove this line for real applications APEX_ORBI.af <- apexFeatures(APEX_ORBI) print(APEX_ORBI.af) # APEX_LCQ APEX_LCQ<-head(APEX_LCQ,20) # Remove this line for real applications APEX_LCQ.af <- apexFeatures(APEX_LCQ) print(APEX_LCQ.af)
This dataset contains training ThermoFinnigan LTQ-OrbiTrap (ORBI) and ThermoFinnigan Surveyor/DecaXP+ iontrap (LCQ) data for training of an APEX classifier. The dataset was generated by Christine Vogel and Edward M. Marcotte (see references).
data(APEXMS)data(APEXMS)
A data.frame with the following components:
peptide_sequence character vector: Peptide sequence.
apex character vector: observed in experiment: 0 = no; 1 = yes.
The dataset was obtained from:
http://marcottelab.org/APEX_Protocol/.
Vogel, C. & Marcotte, E. M. Calculating absolute and relative protein abundance from mass spectrometry-based protein expression data. Nat Protoc 3, 1444-1451 (2008).
import, ProteinInference, AbsoluteQuantification, ALF, APEX, apexFeatures, proteotypic
data(APEXMS)data(APEXMS)
import of mass spectrometry proteomics data analysis software reports.
## Default S3 method: import(ms_filenames, ms_filetype, concentration_filename=NA, averageruns=FALSE, sumruns=FALSE, mprophet_cutoff=0.01, openswath_superimpose_identifications=FALSE, openswath_replace_run_id=FALSE, openswath_filtertop=FALSE, openswath_removedecoys=TRUE, peptideprophet_cutoff=0.95, abacus_column="ADJNSAF", pepxml2csv_runsplit="~", ...)## Default S3 method: import(ms_filenames, ms_filetype, concentration_filename=NA, averageruns=FALSE, sumruns=FALSE, mprophet_cutoff=0.01, openswath_superimpose_identifications=FALSE, openswath_replace_run_id=FALSE, openswath_filtertop=FALSE, openswath_removedecoys=TRUE, peptideprophet_cutoff=0.95, abacus_column="ADJNSAF", pepxml2csv_runsplit="~", ...)
ms_filenames |
the paths and filenames of files to import in a character or array class. |
ms_filetype |
one of |
concentration_filename |
the filename of a csv with concentrations (in any unit). Needs to have
the columns |
averageruns |
whether different MS runs should be averaged. |
sumruns |
whether different MS runs should be summed. |
mprophet_cutoff |
(openswath and mprophet only:) the FDR cutoff (m_score) for OpenSWATH and mProphet reports. |
openswath_superimpose_identifications |
(openswath only:) enables propagation of identification among several runs if feature alignment or requantification was conducted. |
openswath_replace_run_id |
whether the run_id of the MS data should be replaced by the filename. |
openswath_filtertop |
(openswath only:) whether only the top peakgroup should be considered. |
openswath_removedecoys |
(openswath only:) whether decoys should be removed. |
peptideprophet_cutoff |
(abacus and pepxml2csv only:) the PeptideProphet probability cutoff for Abacus reports. |
abacus_column |
(abacus only:) target score: one of |
pepxml2csv_runsplit |
(pepxml2csv only:) the separator of the run_id and spectrum_id column. |
... |
future extensions. |
The import function provides unified access to the results of various standard proteomic quantification applications like OpenSWATH (Roest et al., 2014), mProphet (Reiter et al., 2011), OpenMS (Sturm et al., 2008; Weisser et al., 2013),Skyline (MacLean et al., 2010) and Abacus (Fermin et al., 2011). This enables generic application of all further steps using the same data structure and enables extension to support other data formats. If multiple runs, i.e. replicates, are supplied, the averaged or summed values can be used to summarize the experimental data. In addition to the input from the analysis software, an input table with the anchor peptides or proteins and sample specific absolute abundance, or an estimate of the total protein concentration in the sample is required. The endpoint of this step is a unified input data structure.
A standard aLFQ import data frame, either on transition, peptide (precursor) or protein level.
George Rosenberger [email protected]
Roest H. L. et al. A tool for the automated, targeted analysis of data-independent acquisition (DIA) MS-data: OpenSWATH. Nat Biotech, in press.
Reiter, L. et al. mProphet: automated data processing and statistical validation for large-scale SRM experiments. Nat Meth 8, 430-435 (2011).
Sturm, M. et al. OpenMS - An open-source software framework for mass spectrometry. BMC Bioinformatics 9, 163 (2008).
Weisser, H. et al. An automated pipeline for high-throughput label-free quantitative proteomics. J. Proteome Res. 130208071745007 (2013). doi:10.1021/pr300992u
MacLean, B. et al. Skyline: an open source document editor for creating and analyzing targeted proteomics experiments. Bioinformatics 26, 966-968 (2010).
Fermin, D., Basrur, V., Yocum, A. K. & Nesvizhskii, A. I. Abacus: A computational tool for extracting and pre-processing spectral count data for label-free quantitative proteomic analysis. PROTEOMICS 11, 1340-1345 (2011).
ProteinInference, AbsoluteQuantification, ALF, APEX, apexFeatures, proteotypic
import(ms_filenames = system.file("extdata","example_openswath.txt",package="aLFQ"), ms_filetype = "openswath", concentration_filename=NA, averageruns=FALSE, sumruns=FALSE, mprophet_cutoff=0.01, openswath_superimpose_identifications=FALSE, openswath_replace_run_id=FALSE, openswath_filtertop=FALSE, openswath_removedecoys=TRUE) import(ms_filenames = system.file("extdata","example_mprophet.txt",package="aLFQ"), ms_filetype = "mprophet", concentration_filename = system.file("extdata","example_concentration_peptide.csv", package="aLFQ"), averageruns=FALSE, sumruns=FALSE, mprophet_cutoff=0.01) import(ms_filenames = system.file("extdata","example_openmslfq.csv",package="aLFQ"), ms_filetype = "openmslfq", concentration_filename=NA, averageruns=FALSE, sumruns=FALSE) import(ms_filenames = system.file("extdata","example_skyline.csv",package="aLFQ"), ms_filetype = "skyline", concentration_filename = system.file("extdata","example_concentration_protein.csv",package="aLFQ"), averageruns=FALSE, sumruns=FALSE) import(ms_filenames = system.file("extdata","example_abacus_protein.txt",package="aLFQ"), ms_filetype = "abacus", concentration_filename = system.file("extdata","example_concentration_protein.csv", package="aLFQ"), averageruns=FALSE, sumruns=FALSE, peptideprophet_cutoff=0.95, abacus_column="ADJNSAF")import(ms_filenames = system.file("extdata","example_openswath.txt",package="aLFQ"), ms_filetype = "openswath", concentration_filename=NA, averageruns=FALSE, sumruns=FALSE, mprophet_cutoff=0.01, openswath_superimpose_identifications=FALSE, openswath_replace_run_id=FALSE, openswath_filtertop=FALSE, openswath_removedecoys=TRUE) import(ms_filenames = system.file("extdata","example_mprophet.txt",package="aLFQ"), ms_filetype = "mprophet", concentration_filename = system.file("extdata","example_concentration_peptide.csv", package="aLFQ"), averageruns=FALSE, sumruns=FALSE, mprophet_cutoff=0.01) import(ms_filenames = system.file("extdata","example_openmslfq.csv",package="aLFQ"), ms_filetype = "openmslfq", concentration_filename=NA, averageruns=FALSE, sumruns=FALSE) import(ms_filenames = system.file("extdata","example_skyline.csv",package="aLFQ"), ms_filetype = "skyline", concentration_filename = system.file("extdata","example_concentration_protein.csv",package="aLFQ"), averageruns=FALSE, sumruns=FALSE) import(ms_filenames = system.file("extdata","example_abacus_protein.txt",package="aLFQ"), ms_filetype = "abacus", concentration_filename = system.file("extdata","example_concentration_protein.csv", package="aLFQ"), averageruns=FALSE, sumruns=FALSE, peptideprophet_cutoff=0.95, abacus_column="ADJNSAF")
This dataset contains the Leptospira interrogans MS data from Ludwig C., et al. (2012).
data(LUDWIGMS)data(LUDWIGMS)
The data structure for LUDWIG_SRM represents a data.frame containing the following column header: "run_id" (freetext), "protein_id" (freetext), "peptide_id" (freetext), "transition_id" (freetext), "peptide_sequence" (unmodified, natural amino acid sequence in 1-letter nomenclature), "precursor_charge" (positive integer value), "transition_intensity" (positive non-logarithm floating value) and "concentration" (calibration: positive non-logarithm floating value, prediction: "?").
Ludwig, C., Claassen, M., Schmidt, A. & Aebersold, R. Estimation of Absolute Protein Quantities of Unlabeled Samples by Selected Reaction Monitoring Mass Spectrometry. Molecular & Cellular Proteomics 11, M111.013987-M111.013987 (2012).
import, ProteinInference, AbsoluteQuantification, ALF, APEX, apexFeatures, proteotypic
data(LUDWIGMS)data(LUDWIGMS)
Peptide inference for aLFQ import data frame.
## Default S3 method: PeptideInference(data, transition_topx = 3, transition_strictness = "strict",transition_summary = "sum", consensus_proteins = TRUE, consensus_transitions = TRUE, ...)## Default S3 method: PeptideInference(data, transition_topx = 3, transition_strictness = "strict",transition_summary = "sum", consensus_proteins = TRUE, consensus_transitions = TRUE, ...)
data |
a mandatory data frame containing the |
transition_topx |
a positive integer value of the top x transitions to consider for transition to peptide intensity estimation methods. |
transition_strictness |
whether |
transition_summary |
how to summarize the transition intensities: |
consensus_proteins |
if multiple runs are provided, select identical proteins among all runs. |
consensus_transitions |
if multiple runs are provided, select identical transitions among all runs. |
... |
future extensions. |
The PeptideInference module provides functionality to infer peptide / precursor quantities from the measured precursor or fragment intensities or peptide spectral counts.
A standard aLFQ import data frame on peptide / precursor level.
George Rosenberger [email protected]
Ludwig, C., Claassen, M., Schmidt, A. & Aebersold, R. Estimation of Absolute Protein Quantities of Unlabeled Samples by Selected Reaction Monitoring Mass Spectrometry. Molecular & Cellular Proteomics 11, M111.013987-M111.013987 (2012).
import, AbsoluteQuantification, ALF, APEX, apexFeatures, proteotypic
data(UPS2MS) data_PI <- PeptideInference(UPS2_SRM) print(data_PI)data(UPS2MS) data_PI <- PeptideInference(UPS2_SRM) print(data_PI)
Protein inference for aLFQ import data frame.
## Default S3 method: ProteinInference(data, peptide_method = "top", peptide_topx = 2, peptide_strictness = "strict",peptide_summary = "mean", transition_topx = 3, transition_strictness = "strict",transition_summary = "sum", fasta = NA, apex_model = NA, combine_precursors = FALSE, combine_peptide_sequences = FALSE, consensus_proteins = TRUE, consensus_peptides = TRUE, consensus_transitions = TRUE, ...)## Default S3 method: ProteinInference(data, peptide_method = "top", peptide_topx = 2, peptide_strictness = "strict",peptide_summary = "mean", transition_topx = 3, transition_strictness = "strict",transition_summary = "sum", fasta = NA, apex_model = NA, combine_precursors = FALSE, combine_peptide_sequences = FALSE, consensus_proteins = TRUE, consensus_peptides = TRUE, consensus_transitions = TRUE, ...)
data |
a mandatory data frame containing the columns |
peptide_method |
one of |
peptide_topx |
( |
peptide_strictness |
( |
peptide_summary |
( |
transition_topx |
a positive integer value of the top x transitions to consider for transition to peptide intensity estimation methods. |
transition_strictness |
whether |
transition_summary |
how to summarize the transition intensities: |
fasta |
( |
apex_model |
( |
combine_precursors |
whether to sum all precursors of the same peptide. |
combine_peptide_sequences |
whether to sum all variants (modifications) of the same peptide sequence. |
consensus_proteins |
if multiple runs are provided, select identical proteins among all runs. |
consensus_peptides |
if multiple runs are provided, select identical peptides among all runs. |
consensus_transitions |
if multiple runs are provided, select identical transitions among all runs. |
... |
future extensions. |
The ProteinInference module provides functionality to infer protein quantities from the measured precursor or fragment intensities or peptide spectral counts. If the dataset contains targeted MS2-level data, the paired precursor and fragment ion signals, the transitions, are first summarized to the precursor level. Different methods for aggregation can be specified, e.g. sum, mean or median and a limit for the selection of the most intense transitions can be provided. It is further possible to exclude precursors, which do not have sufficient transitions to fulfill this boundary. To summarize precursor intensities or spectral counts to theoretical protein intensities, the mean, TopN (Silva et al., 2006; Malmstrom et al., 2009; Schmidt et al., 2011; Ludwig et al., 2012), APEX (Lu et al., 2006), iBAQ (Schwanhausser et al., 2011) and NSAF (Zybailov et al., 2006) protein intensity estimators are provided. For APEX, iBAQ and NSAF, the protein database in FASTA format needs to be supplied. In terms of targeted data acquisition, for both APEX and iBAQ methods all peptides of a protein must be targeted. The results are reported in the same unified data structure as from the previous step
A standard aLFQ import data frame on protein level.
George Rosenberger [email protected]
Silva, J. C., Gorenstein, M. V., Li, G.-Z., Vissers, Johannes P. C. & Geromanos, S. J. Absolute quantification of proteins by LCMSE: a virtue of parallel MS acquisition. Mol. Cell Proteomics 5, 144-156 (2006).
Malmstrom, J. et al. Proteome-wide cellular protein concentrations of the human pathogen Leptospira interrogans. Nature 460, 762-765 (2009).
Schmidt, A. et al. Absolute quantification of microbial proteomes at different states by directed mass spectrometry. Molecular Systems Biology 7, 1-16 (2011).
Ludwig, C., Claassen, M., Schmidt, A. & Aebersold, R. Estimation of Absolute Protein Quantities of Unlabeled Samples by Selected Reaction Monitoring Mass Spectrometry. Molecular & Cellular Proteomics 11, M111.013987-M111.013987 (2012).
Lu, P., Vogel, C., Wang, R., Yao, X. & Marcotte, E. M. Absolute protein expression profiling estimates the relative contributions of transcriptional and translational regulation. Nat Biotech 25, 117-124 (2006).
Schwanhausser, B. et al. Global quantification of mammalian gene expression control. Nature 473, 337-342 (2011).
Zybailov, B. et al. Statistical Analysis of Membrane Proteome Expression Changes in Saccharomyces c erevisiae. J. Proteome Res. 5, 2339-2347 (2006).
Gerster S., Kwon T., Ludwig C., Matondo M., Vogel C., Marcotte E. M., Aebersold R., Buhlmann P. Statistical approach to protein quantification. Molecular & Cellular Proteomics 13, M112.02445 (2014).
import, AbsoluteQuantification, ALF, APEX, apexFeatures, proteotypic
data(UPS2MS) data_ProteinInference <- ProteinInference(UPS2_SRM) print(data_ProteinInference)data(UPS2MS) data_ProteinInference <- ProteinInference(UPS2_SRM) print(data_ProteinInference)
Prediction of the flyability of proteotypic peptides.
## Default S3 method: proteotypic(fasta, apex_model, min_aa=4 , max_aa=20, ...)## Default S3 method: proteotypic(fasta, apex_model, min_aa=4 , max_aa=20, ...)
fasta |
a amino acid FASTA file. |
apex_model |
an |
min_aa |
the minimum number of amino acids for proteotypic peptides. |
max_aa |
the maximum number of amino acids for proteotypic peptides. |
... |
future extensions. |
This function provides prediction of the "flyability" of proteotypic peptides using the APEX method (Lu et al., 2006; Vogel et al., 2008). The APEX scores are probabilities that indicate detectability of the peptide amino acid sequence in LC-MS/MS experiments.
A data.frame containing peptide sequences and associated APEX scores.
George Rosenberger [email protected]
Lu, P., Vogel, C., Wang, R., Yao, X. & Marcotte, E. M. Absolute protein expression profiling estimates the relative contributions of transcriptional and translational regulation. Nat Biotech 25, 117-124 (2006).
Vogel, C. & Marcotte, E. M. Calculating absolute and relative protein abundance from mass spectrometry-based protein expression data. Nat Protoc 3, 1444-1451 (2008).
import, ProteinInference, AbsoluteQuantification, ALF, APEX, apexFeatures
set.seed(131) data(APEXMS) APEX_ORBI<-head(APEX_ORBI,20) # Remove this line for real applications APEX_ORBI.af <- apexFeatures(APEX_ORBI) APEX_ORBI.apex <- APEX(data=APEX_ORBI.af) peptides <- proteotypic(fasta=system.file("extdata","example.fasta",package="aLFQ"), apex_model=APEX_ORBI.apex, min_aa=4 , max_aa=20) ## Not run: print(peptides)set.seed(131) data(APEXMS) APEX_ORBI<-head(APEX_ORBI,20) # Remove this line for real applications APEX_ORBI.af <- apexFeatures(APEX_ORBI) APEX_ORBI.apex <- APEX(data=APEX_ORBI.af) peptides <- proteotypic(fasta=system.file("extdata","example.fasta",package="aLFQ"), apex_model=APEX_ORBI.apex, min_aa=4 , max_aa=20) ## Not run: print(peptides)
We assessed the performance of aLFQ and the different quantification estimation methods it supports by investigating a commercially available synthetic sample. The Universal Proteomic Standard 2 (UPS2) consists of 48 proteins spanning a dynamic range of five orders of magnitude in bins of eight proteins. The sample was measured in a complex background consisting of Mycobacterium bovis BCG total cell lysate in shotgun and targeted MS modes. Three datasets are available: UPS2_SC (spectral counts), UPS2_LFQ (MS1 intensity), UPS2_SRM (MS2 intensity).
data(UPS2MS)data(UPS2MS)
The data structure for UPS2_SRM represents a data.frame containing the following column header: "run_id" (freetext), "protein_id" (freetext), "peptide_id" (freetext), "transition_id" (freetext), "peptide_sequence" (unmodified, natural amino acid sequence in 1-letter nomenclature), "precursor_charge" (positive integer value), "transition_intensity" (positive non-logarithm floating value) and "concentration" (calibration: positive non-logarithm floating value, prediction: "?").
The data structure for UPS2_LFQ (MS1-level intensity) / UPS2_SC (spectral counts) represents a data.frame containing the columns "run_id" (freetext), "protein_id" (freetext), "peptide_id" (freetext), "peptide_sequence" (unmodified, natural amino acid sequence in 1-letter nomenclature), "precursor_charge" (positive integer value), "peptide_intensity" (positive non-logarithm floating value) and "concentration" (calibration: positive non-logarithm floating value, prediction: "?"). It should be noted, that the spectral count value is also represented by "peptide_intensity".
import, ProteinInference, AbsoluteQuantification, ALF, APEX, apexFeatures, proteotypic
data(UPS2MS)data(UPS2MS)