aLFQ: an R-package for estimating absolute protein quantities from label-free LC-MS/MS proteomics data

Abstract

Motivation: The determination of absolute quantities of proteins in biological samples is necessary for multiple types of scientific inquiry. While relative quantification has been commonly used in proteomics, few proteomic datasets measuring absolute protein quantities have been reported to date. Various technologies have been applied using different types of input data, e.g. ion intensities or spectral counts, as well as different absolute normalization strategies. To date, a user-friendly and transparent software supporting large-scale absolute protein quantification has been lacking.

Results: We present a bioinformatics tool, termed aLFQ, which supports the commonly used absolute label-free protein abundance estimation methods (TopN, iBAQ, APEX, NSAF and SCAMPI) for LC-MS/MS proteomics data, together with validation algorithms enabling automated data analysis and error estimation.

Availability and implementation: aLFQ is written in R and freely available under the GPLv3 from CRAN (http://www.cran.r-project.org). Instructions and example data are provided in the R-package. The raw data can be obtained from the PeptideAtlas raw data repository (PASS00321).

Supplementary information: Supplementary data are available at Bioinformatics online.

Fig. 1.

Diagram for exemplary aLFQ workflow with TopX transition and TopN peptide model selection to mediate estimation of protein abundance using SIS peptides. 1. import: generates a generic aLFQ input data structure. 2. ProteinInference: different protein intensity estimation methods can be used to infer protein intensities from measured peptides and transitions. 3. AbsoluteQuantification: using SIS peptides, a model is built and cross-validation is conducted to examine the performance. 4. ALF: different models for varying numbers of transitions and peptides are generated and evaluated and the model with the smallest MFE is selected