{
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  "Package": "aLFQ",
  "Type": "Package",
  "Title": "Estimating Absolute Protein Quantities from Label-Free LC-MS/MS\nProteomics Data",
  "Version": "1.3.6",
  "Date": "2020-01-07",
  "Author": "George Rosenberger, Hannes Roest, Christina Ludwig, Ruedi\nAebersold, Lars Malmstroem",
  "Maintainer": "George Rosenberger <gr2578@cumc.columbia.edu>",
  "Description": "Determination of absolute protein quantities is necessary\nfor multiple applications, such as mechanistic modeling of\nbiological systems. Quantitative liquid chromatography tandem\nmass spectrometry (LC-MS/MS) proteomics can measure relative\nprotein abundance on a system-wide scale. To estimate absolute\nquantitative information using these relative abundance\nmeasurements requires additional information such as\nheavy-labeled references of known concentration. Multiple\nmethods have been using different references and strategies;\nsome are easily available whereas others require more effort on\nthe users end. Hence, we believe the field might benefit from\nmaking some of these methods available under an automated\nframework, which also facilitates validation of the chosen\nstrategy. We have implemented the most commonly used absolute\nlabel-free protein abundance estimation methods for LC-MS/MS\nmodes quantifying on either MS1-, MS2-levels or spectral counts\ntogether with validation algorithms to enable automated data\nanalysis and error estimation. Specifically, we used\nMonte-carlo cross-validation and bootstrapping for model\nselection and imputation of proteome-wide absolute protein\nquantity estimation. Our open-source software is written in the\nstatistical programming language R and validated and\ndemonstrated on a synthetic sample.",
  "License": "GPL (>= 3)",
  "URL": "https://github.com/aLFQ",
  "NeedsCompilation": "no",
  "Packaged": {
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    "User": "root"
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  "Date/Publication": "2020-01-08 10:10:03 UTC",
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