Parallel processing of large datasets from NanoLC-FTICR-MS measurements.
A new approach for automatic parallel processing of large mass spectral datasets in a distributed computing environment is demonstrated to significantly decrease the total processing time. The implementation of this novel approach is described and evaluated for large nanoLC-FTICR-MS datasets. The speed benefits are determined by the network speed and file transfer protocols only and allow almost real-time analysis of complex data (e.g., a 3-gigabyte raw dataset is fully processed within 5 min). Key advantages of this approach are not limited to the improved analysis speed, but also include the improved flexibility, reproducibility, and the possibility to share and reuse the pre- and postprocessing strategies. The storage of all raw data combined with the massively parallel processing approach described here allows the scientist to reprocess data with a different set of parameters (e.g., apodization, calibration, noise reduction), as is recommended by the proteomics community. This approach of parallel processing was developed in the Virtual Laboratory for e-Science (VL-e), a science portal that aims at allowing access to users outside the computer research community. As such, this strategy can be applied to all types of serially acquired large mass spectral datasets such as LC-MS, LC-MS/MS, and high-resolution imaging MS results.
van der Burgt
Y E M
YE
FOM Institute for Atomic and Molecular Physics (AMOLF), Amsterdam, The Netherlands.
Taban
I M
IM
Konijnenburg
M
M
Biskup
M
M
Duursma
M C
MC
Heeren
R M A
RM
Römpp
A
A
van Nieuwpoort
R V
RV
Bal
H E
HE
D000465:Algorithms; D030562:Databases, Protein; D036103:Nanotechnology / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods; D040901:Proteomics / Q000379:methods*; D012984:Software; D017550:Spectroscopy, Fourier Transform Infrared / Q000379:methods*