Methodology article
msmsEval: tandem mass spectral quality assignment for high-throughput proteomics
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* Corresponding author: Gerard Cagney gerard.cagney@ucd.ie
1 Chemistry Department, Oxford University, Physical and Theoretical Chemistry Laboratory, South Parks Road, Oxford OX1 3QZ, UK
2 Conway Institute, University College Dublin, Belfield, Dublin 4, Republic of Ireland
BMC Bioinformatics 2007, 8:51 doi:10.1186/1471-2105-8-51
Published: 9 February 2007Additional files
Additional file 1:
Supplementary figure 1. Venn diagram showing the breakdown of successful spectrum annotations for the UCD dataset by different strategies.
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Additional file 2:
Appendices. Contains details referred to in the manuscript regarding, 1) denoising of training datasets with k-nearest neighbor procedure, 2) comparison of classification procedures and 3) feature selection for the discrimination model.
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Additional file 3:
Supplementary figure 2. Predicted distributions of identifiable and unidentifiable for a sample from the UCD test dataset (A) and sample A1 from the ISB dataset (B). The number of spectra is calculated with spectra placed in bins of 0.25 for the discriminant score. Spectra with fewer than five peaks are removed in line with the EM-algorithm. The solid line represents the actual distribution of spectra of the complete dataset and the dotted line represents the estimated distributions of identifiable and unidentifiable spectra using the expectation-maximization algorithm.
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