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      The proteome of Toxoplasma gondii: integration with the genome provides novel insights into gene expression and annotation

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          Abstract

          A proteomics analysis identifies one third of the predicted Toxoplasma gondii proteins and integrates proteomics and genomics data to refine genome annotation.

          Abstract

          Background

          Although the genomes of many of the most important human and animal pathogens have now been sequenced, our understanding of the actual proteins expressed by these genomes and how well they predict protein sequence and expression is still deficient. We have used three complementary approaches (two-dimensional electrophoresis, gel-liquid chromatography linked tandem mass spectrometry and MudPIT) to analyze the proteome of Toxoplasma gondii, a parasite of medical and veterinary significance, and have developed a public repository for these data within ToxoDB, making for the first time proteomics data an integral part of this key genome resource.

          Results

          The draft genome for Toxoplasma predicts around 8,000 genes with varying degrees of confidence. Our data demonstrate how proteomics can inform these predictions and help discover new genes. We have identified nearly one-third (2,252) of all the predicted proteins, with 2,477 intron-spanning peptides providing supporting evidence for correct splice site annotation. Functional predictions for each protein and key pathways were determined from the proteome. Importantly, we show evidence for many proteins that match alternative gene models, or previously unpredicted genes. For example, approximately 15% of peptides matched more convincingly to alternative gene models. We also compared our data with existing transcriptional data in which we highlight apparent discrepancies between gene transcription and protein expression.

          Conclusion

          Our data demonstrate the importance of protein data in expression profiling experiments and highlight the necessity of integrating proteomic with genomic data so that iterative refinements of both annotation and expression models are possible.

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          Most cited references43

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          Pfam: clans, web tools and services

          Pfam is a database of protein families that currently contains 7973 entries (release 18.0). A recent development in Pfam has enabled the grouping of related families into clans. Pfam clans are described in detail, together with the new associated web pages. Improvements to the range of Pfam web tools and the first set of Pfam web services that allow programmatic access to the database and associated tools are also presented. Pfam is available on the web in the UK (), the USA (), France () and Sweden ().
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            Large-scale analysis of the yeast proteome by multidimensional protein identification technology.

            We describe a largely unbiased method for rapid and large-scale proteome analysis by multidimensional liquid chromatography, tandem mass spectrometry, and database searching by the SEQUEST algorithm, named multidimensional protein identification technology (MudPIT). MudPIT was applied to the proteome of the Saccharomyces cerevisiae strain BJ5460 grown to mid-log phase and yielded the largest proteome analysis to date. A total of 1,484 proteins were detected and identified. Categorization of these hits demonstrated the ability of this technology to detect and identify proteins rarely seen in proteome analysis, including low-abundance proteins like transcription factors and protein kinases. Furthermore, we identified 131 proteins with three or more predicted transmembrane domains, which allowed us to map the soluble domains of many of the integral membrane proteins. MudPIT is useful for proteome analysis and may be specifically applied to integral membrane proteins to obtain detailed biochemical information on this unwieldy class of proteins.
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              Discovery of gene function by expression profiling of the malaria parasite life cycle.

              The completion of the genome sequence for Plasmodium falciparum, the species responsible for most malaria human deaths, has the potential to reveal hundreds of new drug targets and proteins involved in pathogenesis. However, only approximately 35% of the genes code for proteins with an identifiable function. The absence of routine genetic tools for studying Plasmodium parasites suggests that this number is unlikely to change quickly if conventional serial methods are used to characterize encoded proteins. Here, we use a high-density oligonucleotide array to generate expression profiles of human and mosquito stages of the malaria parasite's life cycle. Genes with highly correlated levels and temporal patterns of expression were often involved in similar functions or cellular processes.
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                Author and article information

                Journal
                Genome Biol
                Genome Biology
                BioMed Central
                1465-6906
                1465-6914
                2008
                21 July 2008
                : 9
                : 7
                : R116
                Affiliations
                [1 ]Department of Pre-clinical Veterinary Science, Faculty of Veterinary Science, University of Liverpool, Liverpool L69 7ZJ, UK
                [2 ]Department of Cell Biology, The Scripps Research Institute, North Torrey Pines Road, La Jolla, CA 92037, USA
                [3 ]Division of Microbiology, Institute for Animal Health, Compton, Berkshire, RG20 7NN, UK
                [4 ]The Division of Cell and Molecular Biology, Imperial College London, London, SW7 2AZ, UK
                [5 ]Department of Biology, University of Pennsylvania, Philadelphia, PA 19104, USA
                [6 ]Veterinary Pathology, Faculty of Veterinary Science, University of Liverpool, Liverpool L69 7ZJ, UK
                Article
                gb-2008-9-7-r116
                10.1186/gb-2008-9-7-r116
                2530874
                18644147
                7ce1eb01-08bc-4071-aef8-821665cc4dfd
                Copyright © 2008 Xia et al.; licensee BioMed Central Ltd.

                This is an open access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 8 April 2008
                : 17 June 2008
                : 21 July 2008
                Categories
                Research

                Genetics
                Genetics

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