bioRxivBiochemistryPreprint
Reporter Ion Data Analysis Reduction (R.I.D.A.R) for isobaric proteomics quantification studies
Conor Jenkins, Alexis L. NorrisiD, Maura O’Neill, Sudipto Das, Thorkell Andresson, Ben OrsburniD
Abstract
Isobaric labeling-based relative quantification techniques such as iTRAQ and TMT were introduced 15 years ago and are now nearly ubiquitous in shotgun proteomics labs around the world. The methods for data processing in these experiments has changed little since inception, with peptide database searching of all MS/MS spectra occurring concurrent or asynchronous to the quantification of the reporter fragment regions. In this study we present an alternative method for data processing whereby the reporter ion region of all MS/MS spectra are first examined and spectra that are not quantitatively interesting to the end user are discarded. The remaining MS/MS spectra that are retained can then be more rapidly searched for computationally expensive database alterations such as post-translational modifications and single amino acid variations in more practical time. We have termed this method Reporter Ion Data Analysis Reduction (RIDAR). To demonstrate the application of RIDAR, we reprocess a recent CPTAC 2 study containing approximately 7.8 million MS/MS spectra. Post RIDAR processing we can search this public dataset versus a human canonical FASTA database and a compiled proteogenomic database of over 875,000 known cancer mutations in a single day on a standard desktop computer, a time reduction of 85% compared to the conventional workflow. With the rapidly increasing size and density of shotgun proteomics data files, RIDAR facilitates rapid analysis of large proteomics datasets for researchers without access to high performance computational resources.\n\nAbstract Graphic\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=115 SRC=\"FIGDIR/small/437210_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (14K):\norg.highwire.dtl.DTLVardef@1bcf00eorg.highwire.dtl.DTLVardef@134510eorg.highwire.dtl.DTLVardef@1b594b4org.highwire.dtl.DTLVardef@35e268_HPS_FORMAT_FIGEXP M_FIG C_FIG
- organism: human
- assay: proteomics
- research focus: variant calling
- research focus: database / resource
- approach: deep learning
- approach: statistical model
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