Peak bagging for peptide mass fingerprinting

نویسندگان

  • Zengyou He
  • Can Yang
  • Weichuan Yu
چکیده

MOTIVATION Mass Spectrometry (MS)-based protein identification via peptide mass fingerprinting (PMF) is a key component in high-throughput proteome research. While PMF was the first commonly used protein identification method, provided higher throughput than the tandem MS-based method, its accuracy is lower than that of the tandem MS method. Thus, it is desirable to develop PMF-based algorithm with higher protein identification accuracy to facilitate proteome research. RESULTS We propose a peak bagging method for single MS-based protein identification. It combines results from multiple PMF algorithms, where each PMF algorithm takes a random peak subset as input. Evaluation with a set of real MALDI-TOF MS spectra shows that the new peak bagging method provides consistent improvements over the single PMF algorithm.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Enhanced peptide mass fingerprinting through high mass accuracy: Exclusion of non-peptide signals based on residual mass.

Peptide mass fingerprinting (PMF) is among the principle methods of contemporary proteomic analysis. While PMF is routinely practiced in many laboratories, the complexity of protein tryptic digests is such that PMF based on unrefined mass spectrometric peak lists is often inconclusive. A number of data processing strategies have thus been designed to improve the quality of PMF peak lists, and t...

متن کامل

Detection and identification of protein isoforms using cluster analysis of MALDI-MS mass spectra.

We describe an approach to screen large sets of MALDI-MS mass spectra for protein isoforms separated on two-dimensional electrophoresis gels. Mass spectra are matched against each other by utilizing extracted peak mass lists and hierarchical clustering. The output is presented as dendrograms in which protein isoforms cluster together. Clustering could be applied to mass spectra from different s...

متن کامل

A peptide identification-free, genome sequence-independent shotgun proteomics workflow for strain-level bacterial differentiation

Shotgun proteomics is an emerging tool for bacterial identification and differentiation. However, the identification of the mass spectra of peptides to genome-derived peptide sequences remains a key issue that limits the use of shotgun proteomics to bacteria with genome sequences available. In this proof-of-concept study, we report a novel bacterial fingerprinting method that enjoys the resolvi...

متن کامل

Identification of polypeptides expressed in response to vinyl chloride, ethene, and epoxyethane in Nocardioides sp. strain JS614 by using peptide mass fingerprinting.

Enzymes expressed in response to vinyl chloride, ethene, and epoxyethane by Nocardioides sp. strain JS614 were identified by using a peptide mass fingerprinting (PMF) approach. PMF provided insight concerning vinyl chloride biodegradation in strain JS614 and extends the use of matrix-assisted laser desorption-ionization time of flight mass spectrometry as a tool to enhance characterization of b...

متن کامل

Iterative data analysis is the key for exhaustive analysis of peptide mass fingerprints from proteins separated by two-dimensional electrophoresis.

Peptide mass fingerprinting (PMF) is a powerful tool for identification of proteins separated by two-dimensional electrophoresis (2-DE). With the increase in sensitivity of peptide mass determination it becomes obvious that even spots looking well separated on a 2-DE gel may consist of several proteins. As a result the number of mass peaks in PMFs increased dramatically leaving many unassigned ...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:
  • Bioinformatics

دوره 24 10  شماره 

صفحات  -

تاریخ انتشار 2008