New computational approaches for de novo peptide sequencing from MS/MS experiments

Lubeck, O.; Sewell, Carrock; Gu, Sheng; Chen, Xian; Cai, Deng

doi:10.1109/jproc.2002.805301

Cited by 24 publications

(13 citation statements)

References 12 publications

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“…Most of the approaches (e.g. [14][15][16][17][18][19][20][21][22]) use a spectrum graph, in which the nodes correspond to ions of the spectrum and edges correspond to valid mass differences. A valid mass difference means that the difference between the m/z ratios of two ions in the spectrum corresponds to the mass of one of the common amino acids.…”

Section: Introductionmentioning

confidence: 99%

De novo peptide sequencing by tandem MS using complementary CID and electron transfer dissociation

et al. 2009

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De novo sequencing of peptides using tandem MS is difficult due to missing fragment ions in the spectra commonly obtained after CID of peptide precursor ions. Complementing CID spectra with spectra obtained in an ion-trap mass spectrometer upon electron transfer dissociation (ETD) significantly increases the sequence coverage with diagnostic ions. In the de novo sequencing algorithm CompNovo presented here, a divide-and-conquer approach was combined with an efficient mass decomposition algorithm to exploit the complementary information contained in CID and ETD spectra. After optimizing the parameters for the algorithm on a well-defined training data set obtained for peptides from nine known proteins, the CompNovo algorithm was applied to the de novo sequencing of peptides derived from a whole protein extract of Sorangium cellulosum bacteria. To 2406 pairs of CID and ETD spectra contained in this data set, 675 fully correct sequences were assigned, which represent a success rate of 28.1%. It is shown that the CompNovo algorithm yields significantly improved sequencing accuracy as compared with published approaches using only CID spectra or combined CID and ETD spectra.

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Section: Introductionmentioning

confidence: 99%

De novo peptide sequencing by tandem MS using complementary CID and electron transfer dissociation

et al. 2009

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“…Traditionally, there have been two approaches to peptide interpretation: the database search [4,5,6,7] and de novo sequencing [8,9,10,11,12,13,14,15,16,17,18,19]. However, this separation is somewhat artificial since the de novo search can be viewed as a search in the very large database of all possible peptides.…”

Section: Introductionmentioning

confidence: 99%

De Novo Peptide Sequencing and Identification with Precision Mass Spectrometry

et al. 2006

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The recent proliferation of novel mass spectrometers such as Fourier-Transform, Qtof and OrbiTrap marks a transition into the era of precision mass spectrometry, providing a two orders of magnitude boost to the mass resolution, as compared to low precision ion-trap detectors. We investigate peptide de novo sequencing by precision mass spectrometry and explore some of the differences when compared to analysis of low precision data. We demonstrate how the dramatically improved performance of de novo sequencing with precision mass spectrometry paves the way for novel approaches to peptide identification that are based on direct sequence lookups, rather than comparisons of spectra to a database. With the direct sequence lookup it is not only possible to search a database very efficiently, but it is also opens the possibility for using the database in novel ways, such as searching for products of alternative splicing or products of fusion proteins in cancer. Our de novo sequencing software is available for download at http://peptide.ucsd.edu/.

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“…De novo methods have received considerable interest because they are the only efficient means for applications such as finding novel proteins, amino acid mutations, and studying the proteome before the genome. A prominent methodology for the de novo peptide identification problem is a spectrum graph approach,17–27 which in many methods is solved using a dynamic programming algorithm 18, 20, 25, 26. This approach is based on graph theory, where each ion peak in the experimental spectrum generates multiple nodes (corresponding to different fragment ion types) in a directed acyclic graph, known as a sequence graph or a spectrum graph.…”

Section: Introductionmentioning

confidence: 99%

Distribution of p63, a novel myoepithelial marker, in fine-needle aspiration biopsies of the breast

Reis‐Filho

Milanezi

Amendoeira

et al. 2003

Cancer

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A novel methodology for the de novo identification of peptides by mixed‐integer optimization and tandem mass spectrometry is presented in this article. The various features of the mathematical model are presented and examples are used to illustrate the key concepts of the proposed approach. Several problems are examined to illustrate the proposed method's ability to address (1) residue‐dependent fragmentation properties and (2) the variability of resolution in different mass analyzers. A preprocessing algorithm is used to identify important m/z values in the tandem mass spectrum. Missing peaks, resulting from residue‐dependent fragmentation characteristics, are dealt with using a two‐stage algorithmic framework. A cross‐correlation approach is used to resolve missing amino acid assignments and to identify the most probable peptide by comparing the theoretical spectra of the candidate sequences that were generated from the MILP sequencing stages with the experimental tandem mass spectrum. © 2006 American Institute of Chemical Engineers AIChE J 2007

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New computational approaches for de novo peptide sequencing from MS/MS experiments

Cited by 24 publications

References 12 publications

De novo peptide sequencing by tandem MS using complementary CID and electron transfer dissociation

De novo peptide sequencing by tandem MS using complementary CID and electron transfer dissociation

De Novo Peptide Sequencing and Identification with Precision Mass Spectrometry

Distribution of p63, a novel myoepithelial marker, in fine-needle aspiration biopsies of the breast

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