Literature DB >> 24508333

Improving the quality of protein identification in non-model species. Characterization of Quercus ilex seed and Pinus radiata needle proteomes by using SEQUEST and custom databases.

M Cristina Romero-Rodríguez1, Jesús Pascual2, Luis Valledor3, Jesús Jorrín-Novo4.   

Abstract

Nowadays the most used pipeline for protein identification consists in the comparison of the MS/MS spectra to reference databases. Search algorithms compare obtained spectra to an in silico digestion of a sequence database to find exact matches. In this context, the database has a paramount importance and will determine in a great deal the number of identifications and its quality, being this especially relevant for non-model plant species. Using a single Viridiplantae database (NCBI, UniProt) and TAIR is not the best choice for non-model species since they are underrepresented in databases resulting in poor identification rates. We demonstrate how it is possible to improve the rate and quality of identifications in two orphan species, Quercus ilex and Pinus radiata, by using SEQUEST and a combination of public (Viridiplantae NCBI, UniProt) and a custom-built specific database which contained 593,294 and 455,096 peptide sequences (Quercus and Pinus, respectively). These databases were built after gathering and processing (trimming, contiging, 6-frame translation) publicly available RNA sequences, mostly ESTs and NGS reads. A total of 149 and 1533 proteins were identified from Quercus seeds and Pinus needles, representing a 3.1- or 1.5-fold increase in the number of protein identifications and scores compared to the use of a single database. Since this approach greatly improves the identification rate, and is not significantly more complicated or time consuming than other approaches, we recommend its routine use when working with non-model species. BIOLOGICAL SIGNIFICANCE: In this work we demonstrate how the construction of a custom database (DB) gathering all available RNA sequences and its use in combination with Viridiplantae public DBs (NCBI, UniProt) significantly improve protein identification when working with non-model species. Protein identification rate and quality is higher to those obtained in routine procedures based on using only one database (commonly Viridiplantae from NCBI), as we demonstrated analyzing Quercus seeds and Pine needles. The proposed approach based on the building of a custom database is not difficult or time consuming, so we recommend its routine use when working with non-model species. This article is part of a Special Issue entitled: Proteomics of non-model organisms.
Copyright © 2014 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Custom databases; ESTs; NGS; Non-model species; Protein identification; SEQUEST

Mesh:

Substances:

Year:  2014        PMID: 24508333     DOI: 10.1016/j.jprot.2014.01.027

Source DB:  PubMed          Journal:  J Proteomics        ISSN: 1874-3919            Impact factor:   4.044


  16 in total

1.  Integrated Physiological, Proteomic, and Metabolomic Analysis of Ultra Violet (UV) Stress Responses and Adaptation Mechanisms in Pinus radiata.

Authors:  Jesús Pascual; María Jesús Cañal; Mónica Escandón; Mónica Meijón; Wolfram Weckwerth; Luis Valledor
Journal:  Mol Cell Proteomics       Date:  2017-01-17       Impact factor: 5.911

2.  Scientific standards and MIAPEs in plant proteomics research and publications.

Authors:  Jesús V Jorrín Novo
Journal:  Front Plant Sci       Date:  2015-06-30       Impact factor: 5.753

3.  A Proteomic Approach to Investigate the Drought Response in the Orphan Crop Eragrostis tef.

Authors:  Rizqah Kamies; Jill M Farrant; Zerihun Tadele; Gina Cannarozzi; Mohammed Suhail Rafudeen
Journal:  Proteomes       Date:  2017-11-15

4.  A curated gluten protein sequence database to support development of proteomics methods for determination of gluten in gluten-free foods.

Authors:  Sophie Bromilow; Lee A Gethings; Mike Buckley; Mike Bromley; Peter R Shewry; James I Langridge; E N Clare Mills
Journal:  J Proteomics       Date:  2017-04-04       Impact factor: 4.044

5.  Proteogenomic Analysis Greatly Expands the Identification of Proteins Related to Reproduction in the Apogamous Fern Dryopteris affinis ssp. affinis.

Authors:  Jonas Grossmann; Helena Fernández; Pururawa M Chaubey; Ana E Valdés; Valeria Gagliardini; María J Cañal; Giancarlo Russo; Ueli Grossniklaus
Journal:  Front Plant Sci       Date:  2017-03-22       Impact factor: 5.753

6.  Comprehensive Proteomic Profiling of Wheat Gluten Using a Combination of Data-Independent and Data-Dependent Acquisition.

Authors:  Sophie N L Bromilow; Lee A Gethings; James I Langridge; Peter R Shewry; Michael Buckley; Michael J Bromley; E N Clare Mills
Journal:  Front Plant Sci       Date:  2017-01-10       Impact factor: 5.753

7.  Multiplex staining of 2-DE gels for an initial phosphoproteome analysis of germinating seeds and early grown seedlings from a non-orthodox specie: Quercus ilex L. subsp. ballota [Desf.] Samp.

Authors:  M Cristina Romero-Rodríguez; Nieves Abril; Rosa Sánchez-Lucas; Jesús V Jorrín-Novo
Journal:  Front Plant Sci       Date:  2015-08-11       Impact factor: 5.753

8.  Dataset of UV induced changes in nuclear proteome obtained by GeLC-Orbitrap/MS in Pinus radiata needles.

Authors:  Sara Alegre; Jesús Pascual; Matthias Nagler; Wolfram Weckwerth; María Jesús Cañal; Luis Valledor
Journal:  Data Brief       Date:  2016-04-07

Review 9.  Proteomics and Metabolomics: Two Emerging Areas for Legume Improvement.

Authors:  Abirami Ramalingam; Himabindu Kudapa; Lekha T Pazhamala; Wolfram Weckwerth; Rajeev K Varshney
Journal:  Front Plant Sci       Date:  2015-12-24       Impact factor: 5.753

10.  Holm Oak (Quercus ilex) Transcriptome. De novo Sequencing and Assembly Analysis.

Authors:  Victor M Guerrero-Sanchez; Ana M Maldonado-Alconada; Francisco Amil-Ruiz; Jesús V Jorrin-Novo
Journal:  Front Mol Biosci       Date:  2017-10-06
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