Parallel processing of large datasets from NanoLC-FTICR-MS measurements

Abstract

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. (J Am Soc Mass Spectrom 2007, 18, 152-161)

Big Data and Clinicians: A Review on the State of the Science

“…A large number of biostatistics software packages have been used to handle large clinical datasets, some of which enabled the features of cloud-based or distributed computing. Popular software packages include, but are not limited to, SAS [ 36 , 51 - 53 ], Mplus [ 51 ], SPSS [ 36 , 39 , 45 ], PP-VLAM [ 89 ], Stata [ 90 ], and R [ 91 ]. These technologies and tools greatly facilitate the handling of big data.…”

MZDASoft: a software architecture that enables large‐scale comparison of protein expression levels over multiple samples based on liquid chromatography/tandem mass spectrometry

“…PPE extracts features without requiring knowledge of the analyte, and it can be readily used in various applications based on LC/MS. In contrast, features are extracted after deisotoping [32] and the pre-processing part needs to be re-adjusted for different labeling methods. 4.…”

“…PPE extracts features without requiring knowledge of the analyte, and it can be readily used in various applications based on LC/MS. In contrast, features are extracted after deisotoping and the pre‐processing part needs to be re‐adjusted for different labeling methods. High‐Performance Computing (HPC) support: The architecture has been implemented on a HPC center, TACC, which is accessible to most academic users in Texas. Similar centers exist in other states.…”

“…However, OpenMS only outputs very few peak parameters, and does not output elution profiles required by PeakLink. Another parallel processing architecture, PP‐VLAM, was not designed to link peptides, and does not output enough peak features.…”

“…Thus the architecture can be easily updated and modified for novel applications. In contrast, in past parallel implementation features are summarized after deisotoping, and important feature information such as elution profile is not provided. Thus we cannot create new applications by developing new modules.…”