Dataset Information

A bioinformatics workflow for variant peptide detection in shotgun proteomics

ABSTRACT: We created a protein sequence database that comprehensively annotates thousands of cancer-related coding variants collected in the Cancer Proteome Variation Database as well as noncancer-specific ones from the Single Nucleotide Polymorphism Database (dbSNP). Using this database, we then developed a data analysis workflow for variant peptide identification in shotgun proteomics. The high risk of false positive variant identifications was addressed by a modified false discovery rate estimation method. Analysis of colorectal cancer cell lines SW480, RKO, and HCT-116 revealed a total of 81 peptides that contain either noncancer-specific or can- cer-related variations.

INSTRUMENT(S): LTQ Orbitrap

ORGANISM(S): Homo Sapiens (ncbitaxon:9606)

SUBMITTER: Daniel C Liebler

PROVIDER: MSV000082473 | MassIVE | Thu Jun 14 05:40:00 BST 2018

REPOSITORIES: MassIVE

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A bioinformatics workflow for variant peptide detection in shotgun proteomics.

Li Jing J Su Zengliu Z Ma Ze-Qiang ZQ Slebos Robbert J C RJ Halvey Patrick P Tabb David L DL Liebler Daniel C DC Pao William W Zhang Bing B

Molecular & cellular proteomics : MCP 20110309 5

Shotgun proteomics data analysis usually relies on database search. However, commonly used protein sequence databases do not contain information on protein variants and thus prevent variant peptides and proteins from been identified. Including known coding variations into protein sequence databases could help alleviate this problem. Based on our recently published human Cancer Proteome Variation Database, we have created a protein sequence database that comprehensively annotates thousands of can ...[more]

PMID: 21389108

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Project description:Before stimulation, the cells were washed and the culture media was changed to RPMI-1640. After stimulation, cell culture media were collected, concentrated, and the proteins were precipitated with 2-D Clean-Up Kit (GE Healthcare) followed by protein alkylation, trypsin digestion and iTRAQ labelling of the resulting peptides according to manufacturer´s instructions (Applied Biosystems). Control sample was labeled with 114, LPS-stimulated with 115, Curdlan-stimulated with 116, and GBY-stimulated with 117 isobaric tag. After labelling the samples were pooled and dried, and the peptides were fractionated by strong cation exchange chromatography using an Ettan HPLC system (Amersham Biosciences) connected to a PolySULFOETHYL A column. Each SCX-fraction containing labelled peptides was analyzed twice with nano-liquid chromatography-tandem mass spectrometry using Ultimate 3000 nano-liquid chromatograph (Dionex) and QSTAR Elite hybrid quadrupole time-of-flight mass spectrometer (Applied Biosystems / MDS Sciex) with nano-ESI ionization as previously described (Lietzén et al., 2011). MS data were acquired automatically using Analyst QS 2.0 software. Protein identification and relative quantitation were performed with ParagonTM search algorithm (Shilov et al., 2007) using ProteinPilot 2.0 interface (AB Sciex). Data files from both technical replicates of an iTRAQ sample set were processed together. Database searching was done against UniProt human database (version 2008-01-28 with 20330 human sequences) and ´decoy` database (the reverse amino acid sequence for false discovery rate estimation). The search criteria were: cysteine alkylation with MMTS, trypsin digestion, biological modifications allowed, thorough search and detected protein threshold of 95% confidence (Unused ProtScore > 1.3). Importantly, automatic bias correction was not used in quantitation. The false discovery rates were calculated as previously described (Elias and Gygi, 2007) and were 1.4% and 2% for the two biological replicates.