De Novo Sequencing and Homology Searching

Ma, Bin; Johnson, Richard S.

doi:10.1074/mcp.o111.014902

Cited by 144 publications

(128 citation statements)

References 108 publications

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“…As pointed out by Ma and Johnson (2011), de novo sequences not only are valuable for the analysis of the novel peptides that are not present in proteome databases but also can facilitate the homology-based database searches. Since the reconstructions reported by UniNovo contain mass gaps representing the total mass of multiple amino acids [termed gapped peptides (Jeong et al, 2011;Kim et al, 2009b)], MS-BPM algorithm (Ng et al, 2011) can be used for fast exact or homology searches (UniNovoÈMS-BPM).…”

Section: Resultsmentioning

confidence: 99%

UniNovo : A Universal Tool for de Novo Peptide Sequencing

Jeong

Kim

Pevzner

2013

Lecture Notes in Computer Science

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Motivation: Mass spectrometry (MS) instruments and experimental protocols are rapidly advancing, but de novo peptide sequencing algorithms to analyze tandem mass (MS/MS) spectra are lagging behind. Although existing de novo sequencing tools perform well on certain types of spectra [e.g. Collision Induced Dissociation (CID) spectra of tryptic peptides], their performance often deteriorates on other types of spectra, such as Electron Transfer Dissociation (ETD), Higher-energy Collisional Dissociation (HCD) spectra or spectra of non-tryptic digests. Thus, rather than developing a new algorithm for each type of spectra, we develop a universal de novo sequencing algorithm called UniNovo that works well for all types of spectra or even for spectral pairs (e.g. CID/ETD spectral pairs). UniNovo uses an improved scoring function that captures the dependences between different ion types, where such dependencies are learned automatically using a modified offset frequency function. Results: The performance of UniNovo is compared with PepNovoþ, PEAKS and pNovo using various types of spectra. The results show that the performance of UniNovo is superior to other tools for ETD spectra and superior or comparable with others for CID and HCD spectra. UniNovo also estimates the probability that each reported reconstruction is correct, using simple statistics that are readily obtained from a small training dataset. We demonstrate that the estimation is accurate for all tested types of spectra (including CID, HCD, ETD, CID/ETD and HCD/ETD spectra of trypsin, LysC or AspN digested peptides). Availability: UniNovo is implemented in JAVA and tested on Windows, Ubuntu and OS X machines. UniNovo is available at http://proteomics. ucsd.edu/Software/UniNovo.html along with the manual.

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Section: Resultsmentioning

confidence: 99%

UniNovo : A Universal Tool for de Novo Peptide Sequencing

Jeong

Kim

Pevzner

2013

Lecture Notes in Computer Science

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“…Since the actual output of an MS experiment is a set of MS/MS spectra, the first step typically consists of identifying the origin of these spectra, i.e., match spectra to peptides. While various options exist for this goal (including de novo sequencing (Frank et al 2007;Ma and Johnson 2012) and tagbased approaches (Tabb et al 2008;Tabb et al 2003)), database searching is by far the most common spectrum identification method. Here, acquired spectra are matched to a set of known spectra to find the best match and thereby transitively assign identification.…”

Section: From Acquired Data To Processed Resultsmentioning

confidence: 99%

Crowdsourcing in proteomics: public resources lead to better experiments

Barsnes

Martens

2013

Amino Acids

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With the growing interest in the field of proteomics, the amount of publicly available proteome resources has also increased dramatically. This means that there are many useful resources available for almost all aspects of a proteomics experiment.However, it remains vital to use the right resource, for the right purpose, at the right time. This review is therefore meant to aid the reader in obtaining an overview of the available resources and their application, thus providing the necessary background to choose the appropriate resources for the experiment at hand. Many of the resources are also taking advantage of so-called crowdsourcing to maximize the potential of the resource. What this means and how this can improve future experiments will also be discussed. The text roughly follows the steps involved in a proteomics experiment, starting with the planning of the experiment, via the processing of the data and the analysis of the results, to the community-wide sharing of the produced data. 3 Main Text Background

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“…In the second approach, instead of searching acquired MS/MS spectra against theoretically predicted spectra, one can assign MS/MS spectra to peptides by matching against a spectral library (spectral library searching) [132,27,42,63]. In the third approach, peptide sequences are extracted directly from the spectra, i.e., without referring to a sequence database for help (de novo sequencing approach) [122,82,81]. The forth category is hybrid approaches, such as those based on the extraction of short sequence tags (a piece of information about a peptide that contains three to five amino acid residues) followed by "error-tolerant" database searching, where the error tolerant search of uninterpreted MS/MS data is a powerful way of finding additional peptide matches [85,121,41].…”

Section: Research Objective and Motivationmentioning

confidence: 99%

“…The relations between users and objects that can be represented by a bipartite graph is the basic information contained by every recommender system. 81 4.2 The illustration of a bipartite network (a), its X projection (b), and Y projection (c) [141]. The The vertical axis is the mass, and the maximum mass is approximate 9,000. .…”

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confidence: 99%

NBPMF: Novel network-based inference methods for peptide mass fingerprinting

Liang

Lajoie

Zhang

2017

2017 IEEE 16th International Conference on Cognitive Informatics &Amp; Cognitive Computing (ICCI*CC)

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Proteins are large, complex molecules that perform a vast array of functions in every living cell. A proteome is a set of proteins produced in an organism, and proteomics is the large-scale study of proteomes. Several high-throughput technologies have been developed in proteomics, where the most commonly applied are mass spectrometry (MS) based approaches.MS is an analytical technique for determining the composition of a sample. Recently it has become a primary tool for protein identification, quantification, and post translational modification (PTM) characterization in proteomics research. There are usually two different ways to identify proteins: top-down and bottom-up. Top-down approaches are based on subjecting intact protein ions and large fragment ions to tandem MS directly, while bottom-up methods are based on mass spectrometric analysis of peptides derived from proteolytic digestion, usually with trypsin.In bottom-up techniques, peptide mass fingerprinting (PMF) is widely used to identify proteins from MS dataset. Conventional PMF representatives such as probabilistic MOWSE algorithm, is based on mass distribution of tryptic peptides. In this thesis, we developed a novel network-based inference software termed NBPMF. By analyzing peptide-protein bipartite network, we designed new peptide protein matching score functions. We present two methods: the static one, ProbS, is based on an independent probability framework; and the dynamic one, HeatS, depicts input dataset as dependent peptides. Moreover, we use linear regression to adjust the matching score according to the masses of proteins. In addition, we consider the order of retention time to further correct the score function. In the post processing, we design two algorithms: assignment of peaks, and protein filtration. The former restricts that a peak can only be assigned to one peptide in order to reduce random matches; and the latter assumes each peak can only be assigned to one protein. In the result validation, we propose two new target-decoy search strategies to estimate the false discovery rate (FDR). The experiments on simulated, authentic, and simulated authentic dataset demonstrate that our NBPMF approaches lead to significantly improved performance compared to several state-of-the-art methods.i

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De Novo Sequencing and Homology Searching

Cited by 144 publications

References 108 publications

UniNovo : A Universal Tool for de Novo Peptide Sequencing

UniNovo : A Universal Tool for de Novo Peptide Sequencing

Crowdsourcing in proteomics: public resources lead to better experiments

NBPMF: Novel network-based inference methods for peptide mass fingerprinting

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