Narrow Results

De novo peptide sequencing that determines the amino acid sequence of a peptide via tandem mass spectrometry (MS/MS) has been increasingly used nowadays in proteomics for protein identification. Current de novo methods generally employ a graph theory which usually produces a large number of candidate sequences and causes heavy computational cost while trying to determine a sequence with less ambiguity. In this paper, we will present an efficient and effective de novo sequencing algorithm which greatly reduces the number of candidate sequences. By utilizing certain properties of b- and y-ion series in MS/MS spectrum, we first propose a reliable two-way parallel searching algorithm to filter out the peptide candidates which are further pruned by an intensity evidence based screening criterion. Then, the best candidate is singled out using a scoring function by consideration of total intensity evidence within certain local region. Results of our algorithm were compared with those of PEAKS, a well-known de novo sequencing software. Experimental results demonstrated the efficiency and potency of our approach.

Cancer is a proteomic disease. Though MALDI-TOF mass spectrometry allows direct measurement of the protein signature of tissue, blood, or their biological samples, and holds tremendous potential for disease diagnosis and treatment, key challenges remain in the processing of proteomic data. In this chapter, we will introduce a wavelet based mathematical framework and computational tools for proteomic data processing, feature selection, and statistical analysis in cancer study.