Literature DB >> 16991202

The pitfalls of proteomics experiments without the correct use of bioinformatics tools.

David G Biron1, Christine Brun, Thierry Lefevre, Camille Lebarbenchon, Hugh D Loxdale, François Chevenet, Jean-Paul Brizard, Frédéric Thomas.   

Abstract

The elucidation of the entire genomic sequence of various organisms, from viruses to complex metazoans, most recently man, is undoubtedly the greatest triumph of molecular biology since the discovery of the DNA double helix. Over the past two decades, the focus of molecular biology has gradually moved from genomes to proteomes, the intention being to discover the functions of the genes themselves. The postgenomic era stimulated the development of new techniques (e.g. 2-DE and MS) and bioinformatics tools to identify the functions, reactions, interactions and location of the gene products in tissues and/or cells of living organisms. Both 2-DE and MS have been very successfully employed to identify proteins involved in biological phenomena (e.g. immunity, cancer, host-parasite interactions, etc.), although recently, several papers have emphasised the pitfalls of 2-DE experiments, especially in relation to experimental design, poor statistical treatment and the high rate of 'false positive' results with regard to protein identification. In the light of these perceived problems, we review the advantages and misuses of bioinformatics tools - from realisation of 2-DE gels to the identification of candidate protein spots - and suggest some useful avenues to improve the quality of 2-DE experiments. In addition, we present key steps which, in our view, need to be to taken into consideration during such analyses. Lastly, we present novel biological entities named 'interactomes', and the bioinformatics tools developed to analyse the large protein-protein interaction networks they form, along with several new perspectives of the field.

Entities:  

Mesh:

Substances:

Year:  2006        PMID: 16991202     DOI: 10.1002/pmic.200600223

Source DB:  PubMed          Journal:  Proteomics        ISSN: 1615-9853            Impact factor:   3.984


  23 in total

Review 1.  Finding biomarkers is getting easier.

Authors:  Brian Patrick Bradley
Journal:  Ecotoxicology       Date:  2012-03-13       Impact factor: 2.823

2.  Biomarker discovery and clinical proteomics.

Authors:  Jerzy Silberring; Pawel Ciborowski
Journal:  Trends Analyt Chem       Date:  2010-02-01       Impact factor: 12.296

3.  A new method for 2D gel spot alignment: application to the analysis of large sample sets in clinical proteomics.

Authors:  Sabine Pérès; Laurence Molina; Nicolas Salvetat; Claude Granier; Franck Molina
Journal:  BMC Bioinformatics       Date:  2008-10-28       Impact factor: 3.169

Review 4.  Image analysis tools and emerging algorithms for expression proteomics.

Authors:  Andrew W Dowsey; Jane A English; Frederique Lisacek; Jeffrey S Morris; Guang-Zhong Yang; Michael J Dunn
Journal:  Proteomics       Date:  2010-12       Impact factor: 3.984

5.  Plasma proteomic profiles of bovine growth hormone transgenic mice as they age.

Authors:  Juan Ding; Darlene E Berryman; John J Kopchick
Journal:  Transgenic Res       Date:  2011-03-02       Impact factor: 2.788

6.  Phylogenetic character mapping of proteomic diversity shows high correlation with subspecific phylogenetic diversity in Trypanosoma cruzi.

Authors:  Jenny Telleria; David G Biron; Jean-Paul Brizard; Edith Demettre; Martial Séveno; Christian Barnabé; Francisco J Ayala; Michel Tibayrenc
Journal:  Proc Natl Acad Sci U S A       Date:  2010-11-08       Impact factor: 11.205

Review 7.  Genomic, proteomic, and systems biology approaches in biomarker discovery for multiple sclerosis.

Authors:  Carol Chase Huizar; Itay Raphael; Thomas G Forsthuber
Journal:  Cell Immunol       Date:  2020-09-20       Impact factor: 4.868

Review 8.  Proteomics of plant pathogenic fungi.

Authors:  Raquel González-Fernández; Elena Prats; Jesús V Jorrín-Novo
Journal:  J Biomed Biotechnol       Date:  2010-05-27

9.  Changes in the proteomes of the hemocytes and fat bodies of the flesh fly Sarcophaga bullata larvae after infection by Escherichia coli.

Authors:  Alice Masova; Miloslav Sanda; Jiri Jiracek; Irena Selicharova
Journal:  Proteome Sci       Date:  2010-01-13       Impact factor: 2.480

10.  A statistical model to identify differentially expressed proteins in 2D PAGE gels.

Authors:  Steven H Wu; Michael A Black; Robyn A North; Kelly R Atkinson; Allen G Rodrigo
Journal:  PLoS Comput Biol       Date:  2009-09-18       Impact factor: 4.475

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.