FAST <i>DE NOVO</i> PEPTIDE SEQUENCING AND SPECTRAL ALIGNMENT VIA TREE DECOMPOSITION

Liu, Chunmei; Song, Yinglei; Yan, Bo; Xu, Ying; Cai, Liming

doi:10.1142/9789812701626_0024

Cited by 15 publications

(12 citation statements)

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“…Although many de novo sequencing algorithms include the ability to search for static, variable, multiple, or a combination of PTMs, due to the infancy of the de novo prediction algorithms for PTM detection, they have not been discussed in this review. In the near future more tools will be developed that either predict PTMs directly from MS/MS data or use multi-pass analysis on a collection of spectra to assign PTMs [111][112][113]. With these methods or a combination of methods, proper assignment of PTMs will become possible.…”

Section: Discussionmentioning

confidence: 99%

Algorithms for thede novosequencing of peptides from tandem mass spectra

Allmer¹

2011

Expert Review of Proteomics

107

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Proteomics is the study of the proteins, their time and location dependent expression profiles as well as their modifications and interactions. Mass spectrometry is useful to investigate many of the questions asked in proteomics. Typically database search methods are employed to identify proteins from complex mixtures. However, often databases are not available or despite their availability some sequences are not readily found therein. To overcome this problem de novo sequencing can be used to directly assign a peptide sequence to an MS/MS spectrum. Many algorithms have been proposed for de novo sequencing and a selection of them is detailed in this review. Although a standard accuracy measure has not been agreed upon in the field, relative algorithm performance is discussed. The current state of the de novo sequencing is assessed thereafter and finally, examples are used to construct possible future perspectives of the field. Running title:De novo sequencing of MS/MS spectra

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Section: Discussionmentioning

confidence: 99%

Algorithms for thede novosequencing of peptides from tandem mass spectra

Allmer¹

2011

Expert Review of Proteomics

107

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“…The latter constraint can be addressed by dynamically generating multiple variants of each database peptide to account for the various modifications. The second approach attempts to identify the peptide without reference to a sequence database, and is referred to in the literature as de novo peptide identification [4], [6], [9], [12], [14], [15]. This approach has traditionally been handicapped by the large number of peaks that can be missing from an experimental spectrum.…”

Section: A Related Workmentioning

confidence: 99%

A Scalable Parallel Approach for Peptide Identification from Large-Scale Mass Spectrometry Data

Kulkarni

Kalyanaraman

Cannon

et al. 2009

2009 International Conference on Parallel Processing Workshops

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Identifying peptides, which are short polymeric chains of amino acid residues in a protein sequence, is of fundamental importance in systems biology research. The most popular approach to identify peptides is through database search. In this approach, an experimental spectrum ("query") generated from fragments of a target peptide using mass spectrometry is computationally compared with a database of already known protein sequences. The goal is to detect database peptides that are most likely to have generated the target peptide. The exponential growth rates and overwhelming sizes of biomolecular databases make this an ideal application to benefit from parallel computing. However, the present generation of software tools is not expected to scale to the magnitudes and complexities of data that will be generated in the next few years. This is because they are all either serial algorithms or parallel strategies that have been designed over inherently serial methods, thereby requiring high spaceand time-requirements. In this paper, we present an efficient parallel approach for peptide identification through database search. Three key factors distinguish our approach from that of existing solutions: i) (space) Given p processors and a database with N residues, we provide the first space-optimal algorithm (O( N p )) under distributed memory machine model; ii) (time) Our algorithm uses a combination of parallel techniques such as one-sided communication and masking of communication with computation to ensure that the overhead introduced due to parallelism is minimal; and iii) (quality) The run-time savings achieved using parallel processing has allowed us to incorporate highly accurate statistical models that have previously been demonstrated to ensure high quality prediction albeit on smaller scale data. We present the design and evaluation of two different algorithms to implement our approach. Experimental results using 2.65 million microbial proteins show linear scaling up to 128 processors of a Linux commodity cluster, with parallel efficiency at ∼50%. We expect that this new approach will be critical to meet the data-intensive and qualitative demands stemming from this important application domain.

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“…The restrictions limit the possible interpretations of each peak to at most one N-terminal (usually b-ion) and one C-terminal (usually y-ion) ion type. Liu and Cai [16] use tree-decomposition to solve the restricted problem. Bafna and Edwards [17] propose a variant of the dynamic programming approach that also allows for more interpretations leading to a polynomial algorithm of a higher degree.…”

Section: Introductionmentioning

confidence: 99%

Antilope—A Lagrangian Relaxation Approach to the de novo Peptide Sequencing Problem

Andreotti

Klau

Reinert

2012

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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Peptide sequencing from mass spectrometry data is a key step in proteome research. Especially de novo sequencing, the identification of a peptide from its spectrum alone, is still a challenge even for state-of-the-art algorithmic approaches. In this paper, we present ANTILOPE, a new fast and flexible approach based on mathematical programming. It builds on the spectrum graph model and works with a variety of scoring schemes. ANTILOPE combines Lagrangian relaxation for solving an integer linear programming formulation with an adaptation of Yen’s k shortest paths algorithm. It shows a significant improvement in running time compared to mixed integer optimization and performs at the same speed like other state-of-the-art tools. We also implemented a generic probabilistic scoring scheme that can be trained automatically for a data set of annotated spectra and is independent of the mass spectrometer type. Evaluations on benchmark data show that ANTILOPE is competitive to the popular state-of-the-art programs PepNovo and NovoHMM both in terms of runtime and accuracy. Furthermore, it offers increased flexibility in the number of considered ion types. ANTILOPE will be freely available as part of the open source proteomics library OpenMS.

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FAST DE NOVO PEPTIDE SEQUENCING AND SPECTRAL ALIGNMENT VIA TREE DECOMPOSITION

Cited by 15 publications

References 0 publications

Algorithms for thede novosequencing of peptides from tandem mass spectra

Algorithms for thede novosequencing of peptides from tandem mass spectra

A Scalable Parallel Approach for Peptide Identification from Large-Scale Mass Spectrometry Data

Antilope—A Lagrangian Relaxation Approach to the de novo Peptide Sequencing Problem

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