Computational protocol: Proteomic analysis of cerebrospinal fluid from children with central nervous system tumors identifies candidate proteins relating to tumor metastatic spread

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Protocol publication

[…] MS data was collected from one single analysis per sample. Proteins were identified with SEQUEST. A stringent filter after SEQUEST analysis was applied. An estimated false discovery rate (FDR) of 1% was calculated, based on forward-reverse decoy. The data were searched against a fully tryptic indexed human protein database maintained by the NCBI with variable oxidized methionine and static carboxyamidomethylated cysteine modification. The search results were filtered using the following criteria: minimum XCorr=2.2 (+2), 3.5 (+3), minimum dCn=0.1, and a maximum precursor ion mass deviation of 15 ppm. An MS1-based comparative data analysis was run using BioSieve (Thermo and Vast Scientific). Spectral counting (MS2-based) analysis was done using Scaffold (Proteome Software Inc., Portland, OR). The gene ontology annotations for selected proteins were obtained using the Panther classification system 9.0 (http://www.pantherbd.org) []. The non-redundant CSF proteome was compared with the Sys-BodyFluid proteome database at http://lifecenter.sgst.cn/bodyfluid/ []. The Database for Annotation, Visualization and Integrated Discovery (David) (https://david.ncifcrf.gov) bioinformatic resource was used to convert protein identifiers []. […]

Pipeline specifications

Software tools Comet, PANTHER, DAVID
Databases Sys-BodyFluid
Applications MS-based untargeted proteomics, Protein sequence analysis
Organisms Homo sapiens, Martes pennanti
Diseases Brain Neoplasms, Lymphoma, Non-Hodgkin, Multiple Sclerosis, Neoplasms, Central Nervous System Neoplasms