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Identifying proteomic LC-MS/MS data sets with Bumbershoot and IDPicker.

ABSTRACT: The identification of peptides and proteins by LC-MS/MS requires the use of bioinformatics. Tools developed in the Tabb Laboratory contribute significant flexibility and discrimination to this process. The Bumbershoot tools (MyriMatch, DirecTag, TagRecon, and Pepitome) enable the identification of peptides represented by MS/MS scans. All of these tools can work directly from instrument capture files of multiple vendors, such as Thermo RAW format, or from standard XML-based formats, such as mzML or mzXML. Peptide identifications are written to mzIdentML or pepXML format. Protein assembly is handled by the IDPicker algorithm. Raw identifications are filtered to a confident set by use of the target-decoy strategy. IDPicker arranges large sets of input files into a hierarchy for reporting, and the software applies a parsimony algorithm to report the smallest possible number of proteins to explain the observed peptides. This protocol details the use of these tools for new users.

SUBMITTER: Holman JD

PROVIDER: S-EPMC4547833 | biostudies-literature | 2012 Mar

REPOSITORIES: biostudies-literature

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Identifying proteomic LC-MS/MS data sets with Bumbershoot and IDPicker.

Holman Jerry D JD Ma Ze-Qiang ZQ Tabb David L DL

Current protocols in bioinformatics 20120301

The identification of peptides and proteins by LC-MS/MS requires the use of bioinformatics. Tools developed in the Tabb Laboratory contribute significant flexibility and discrimination to this process. The Bumbershoot tools (MyriMatch, DirecTag, TagRecon, and Pepitome) enable the identification of peptides represented by MS/MS scans. All of these tools can work directly from instrument capture files of multiple vendors, such as Thermo RAW format, or from standard XML-based formats, such as mzML ...[more]

PMID: 22389012

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Project description:Despite immense interest in the proteome as a source of biomarkers in cancer, mass spectrometry has yet to yield a clinically useful protein biomarker for tumor classification. To explore the potential of a particular class of mass spectrometry-based quantitation approaches, label-free alignment of liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) data sets, for the identification of biomarkers for acute leukemias, we asked whether a label-free alignment algorithm could distinguish known classes of leukemias on the basis of their proteomes. This approach to quantitation involves (1) computational alignment of MS1 peptide peaks across large numbers of samples; (2) measurement of the relative abundance of peptides across samples by integrating the area under the curve of the MS1 peaks; and (3) assignment of peptide IDs to those quantified peptide peaks on the basis of the corresponding MS2 spectra. We extracted proteins from blasts derived from four patients with acute myeloid leukemia (AML, acute leukemia of myeloid lineage) and five patients with acute lymphoid leukemia (ALL, acute leukemia of lymphoid lineage). Mobilized CD34+ cells purified from peripheral blood of six healthy donors and mononuclear cells (MNC) from the peripheral blood of two healthy donors were used as healthy controls. Proteins were analyzed by LC-MS/MS and quantified with a label-free alignment-based algorithm developed in our laboratory. Unsupervised hierarchical clustering of blinded samples separated the samples according to their known biological characteristics, with each sample group forming a discrete cluster. The four proteins best able to distinguish CD34+, AML, and ALL were all either known biomarkers or proteins whose biological functions are consistent with their ability to distinguish these classes. We conclude that alignment-based label-free quantitation of LC-MS/MS data sets can, at least in some cases, robustly distinguish known classes of leukemias, thus opening the possibility that large scale studies using such algorithms can lead to the identification of clinically useful biomarkers.

Dataset Information

Identifying proteomic LC-MS/MS data sets with Bumbershoot and IDPicker.

Publications

Identifying proteomic LC-MS/MS data sets with Bumbershoot and IDPicker.

Similar Datasets

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