Using ontologies for preprocessing and mining spectra data on the grid
Future Generation Computer Systems - Special section: Data mining in grid computing environments
Combinatorial Approaches for Mass Spectra Recalibration
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
MaSDA: A system for analyzing mass spectrometry data
Computer Methods and Programs in Biomedicine
Services, standards, and technologies for high performance computational proteomics
ISPA'07 Proceedings of the 2007 international conference on Frontiers of High Performance Computing and Networking
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Motivation: The analysis of biological samples with high-throughput mass spectrometers has increased greatly in recent years. As larger datasets are processed, it is important that the spectra are aligned to ensure that the same protein intensities are correctly identified in each sample. Without such an alignment procedure it is possible to make errors in identifying the signals from peptides with similar molecular weight. Two algorithms are provided that can improve the alignment among samples. One algorithm is designed to work with SELDI data produced from a Ciphergen instrument, and the other can be used with data in a more general format. Results: The two algorithms were applied to samples drawn from a common pool of reference serum. The results indicate substantial improvement in consistently identifying peptide signals in different samples. Availability: The two algorithms are programmed using the R language and are available from http://krisa.ninds.nih.gov/alignment/ Contact: neal.jeffries@nih.gov