The C-HPP Team for Chromosome 17 has documented meeting the neXt-MP50 Missing Protein Challenge. ... more The C-HPP Team for Chromosome 17 has documented meeting the neXt-MP50 Missing Protein Challenge. Protein-level PE1 evidence including MS and protein-protein interaction data from the global proteomics community has reduced the number of PE2,3,4 proteins coded on Chromosome 17 by 50 since the 2016 baseline in neXtProt. As a follow-up to our analysis and prioritization of Chr 17 missing proteins last year, we describe what predictions and guidance were successful in this update and we explain the dynamics of a net reduction of PE2,3,4 Missing Proteins. This analysis can serve as a guide for other C-HPP chromosome teams seeking high-quality evidence for the 2129 remaining missing proteins.
Gene Ontology (GO) has been widely used to annotate functions of genes and gene products. We prop... more Gene Ontology (GO) has been widely used to annotate functions of genes and gene products. We proposed a new method (TripletGO) to deduce GO terms of protein-coding and non-coding genes, through the integration of four complementary pipelines built on transcript expression profiling, genetic sequence alignment, protein sequence alignment and naïve probability, respectively. TripletGO was tested on a large set of 5,754 genes from 8 species (human, mouse, arabidopsis, rat, fly, budding yeast, fission yeast, and nematoda) and 2,433 proteins with available expression data from the CAFA3 experiment and achieved function annotation accuracy significantly beyond the current state-of-the-art approaches. Detailed analyses show that the major advantage of TripletGO lies in the coupling of a new triplet-network based profiling method with the feature space mapping technique which can accurately recognize function patterns from transcript expressions. Meanwhile, the combination of multiple compl...
Proteomic technologies, such as yeast twohybrid, mass spectrometry (MS), protein/peptide arrays a... more Proteomic technologies, such as yeast twohybrid, mass spectrometry (MS), protein/peptide arrays and fluorescence microscopy, yield multi-dimensional data sets, which are often quite large and either not published or published as supplementary information that is not easily searchable. Without a system in place for standardizing and sharing data, it is not fruitful for the biomedical community to contribute these types of data to centralized repositories. Even more difficult is the annotation and display of pertinent information in the context of the corresponding proteins. Wikipedia, an online encyclopedia that anyone can edit, has already proven quite successful1 and can be used as a model for sharing biological data. However, the need for experimental evidence, data standardization and ownership of data creates scientific obstacles. Here, we describe Human Proteinpedia (http://www.humanproteinpedia.org/) as a portal that overcomes many of these obstacles to provide an integrated view of the human proteome. Human Proteinpedia also allows users to contribute and edit proteomic data with two significant differences from Wikipedia: first, the contributor is expected to provide experimental evidence for the data annotated; and second, only the original contributor can edit their data. Human Proteinpedia's annotation system provides investigators with multiple options for contributing data including web forms and annotation servers. Although registration is required to contribute data, anyone can freely access the data in the repository. The web forms simplify submission through the use of pull-down menus for certain data fields and pop-up menus for standardized vocabulary terms. Distributed annotation servers using modified protein DAS (distributed annotation system) protocols developed by us (DAS protocols were originally developed for sharing mRNA and DNA data) permit contributing laboratories to maintain protein annotations locally. All protein annotations are visualized in the context of corresponding proteins in the Human Protein Reference Database (HPRD)3. Figure 1 shows tissue expression data for alpha-2-HS glycoprotein derived from three different types of experiments. Our unique effort differs significantly from existing repositories, such as PeptideAtlas and PRIDE5 in several respects. First, most proteomic repositories are restricted to one or two experimental platforms, whereas Human Proteinpedia can accommodate data from diverse platforms, including yeast two-hybrid screens, MS, peptide/protein arrays, immunohistochemistry, western blots, coimmunoprecipitation and fluorescence microscopy-type experiments. Second, Human Proteinpedia allows contributing laboratories to annotate data pertaining to six features of proteins (posttranslational modifications, tissue expression, cell line expression, subcellular localization, enzyme substrates and protein-protein interactions;). No existing repository currently permits annotation of all these features in proteins. Third, all data submitted to Human Proteinpedia are viewable through HPRD in the context of other features of the corresponding proteins. To aid comparison and interpretation, meta-annotations pertaining to samples, method of isolation and experimental platform-specific information are provided (e.g., labeling method, protease used, ionization method, details of primary antibody used). And fourth, in spite of accommodating multiple data types, the data submission is simplified. This means that a biologist with no technical expertise can login and contribute data. Thus far, a considerable body of data has been contributed to Human Proteinpedia by the community, with a total of >1.8 million peptides and >4 million MS/MS-spectra deposited.
The contribution of microenvironment to tumor growth has important implications for optimizing ch... more The contribution of microenvironment to tumor growth has important implications for optimizing chemotherapeutic response and understanding the biology of recurrent tumors. In this study, we tested the effects of locally administered topotecan on a rat model of glioblastoma that is induced by intracerebral injection of PDGF (platelet-derived growth factor)-IRES (internal ribosome entry site)-GFP (green fluorescent protein)–expressing retrovirus, treated the tumors by convection-enhanced delivery (CED) of topotecan (136 μmol/L) for 1, 4, or 7 days, and then characterized the effects on both the retrovirus-transformed tumor cells (GFP+ cells) as well as the uninfected glial progenitor cells (GFP− cells) that are recruited to the tumor. Topotecan treatment reduced GFP+ cells about 10-fold and recruited progenitors by about 80-fold while providing a significant survival advantage that improved with greater treatment duration. Regions of glial progenitor ablation occurred corresponding to...
Background The identification of differentially expressed tumor-associated proteins and genomic a... more Background The identification of differentially expressed tumor-associated proteins and genomic alterations driving neoplasia is critical in the development of clinical assays to detect cancers and forms the foundation for understanding cancer biology. One of the challenges in the analysis of pancreatic ductal adenocarcinoma (PDAC) is the low neoplastic cellularity and heterogeneous composition of bulk tumors. To enrich neoplastic cells from bulk tumor tissue, coring, and laser microdissection (LMD) sampling techniques have been employed. In this study, we assessed the protein and KRAS mutation changes associated with samples obtained by these enrichment techniques and evaluated the fraction of neoplastic cells in PDAC for proteomic and genomic analyses. Methods Three fresh frozen PDAC tumors and their tumor-matched normal adjacent tissues (NATs) were obtained from three sampling techniques using bulk, coring, and LMD; and analyzed by TMT-based quantitative proteomics. The protein p...
Canonical isoforms in different databases have been defined as the most prevalent, most conserved... more Canonical isoforms in different databases have been defined as the most prevalent, most conserved, most expressed, longest, or the one with the clearest description of domains or posttranslational modifications. In this article, we revisit these definitions of canonical isoforms based on functional genomics and proteomics evidence, focusing on mouse data. We report a novel functional relationship network-based approach for identifying the Highest Connected Isoforms (HCIs). We show that 46% of these HCIs are not the longest transcripts. In addition, this approach revealed many genes that have more than one highly connected isoforms. Averaged across 175 RNA-seq datasets covering diverse tissues and conditions, 65% of the HCIs show higher expression levels than non-highest connected isoforms (NCIs) at the transcript level. At the protein level, these HCIs highly overlap with the expressed splice variants, based on proteomic data from eight different normal tissues. These results suggest that a more confident definition of canonical isoforms can be made through integration of multiple lines of evidence, including highest connected isoforms defined by biological processes and pathways, expression prevalence at the transcript level, and relative or absolute abundance at the protein level. This integrative proteogenomics approach can successfully identify principal isoforms that are responsible for the canonical functions of genes.
Every data-rich community research effort requires a clear plan for ensuring the quality of the d... more Every data-rich community research effort requires a clear plan for ensuring the quality of the data interpretation and comparability of analyses. To address this need within the Human Proteome Project (HPP) of the Human Proteome Organization (HUPO), we have developed through broad consultation a set of mass spectrometry data interpretation guidelines that should be applied to all HPP data contributions. For submission of manuscripts reporting HPP protein identification results, the guidelines are presented as a one-page checklist containing fifteen essential points followed by two pages of expanded description of each. Here, we present an overview of the guidelines and provide an in-depth description of each of the fifteen elements to facilitate understanding of the intentions and rationale behind the guidelines, both for authors and for reviewers. Broadly, these guidelines provide specific directions regarding how HPP data are to be submitted to mass spectrometry data repositories...
<p>We collected RNA-seq data from the sequence read archive (SRA) database and estimated is... more <p>We collected RNA-seq data from the sequence read archive (SRA) database and estimated isoform-level expression values using state-of-the-art software <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003314#pcbi.1003314-Trapnell1" target="_blank">[32]</a>, <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003314#pcbi.1003314-Trapnell2" target="_blank">[34]</a>. We then generated a gene-level gold standard using Gene Ontology (GO) annotations. For each biological function, this gold standard contains positive genes (annotated to the function under investigation) and negative genes (other genes). Our study contains two major parts: cross-validation for performance estimation and bootstrap bagging for generating final predictions as well as performance evaluation. For cross-validation, we partitioned the examples into a training set for model development and a test set for model validation. For generating final predictions for all isoforms, we sampled with replacement to construct a training set, and then used this training set to construct models to assign prediction probabilities to the out-of-bag set. The final predictions for all isoforms were made by calculating the median prediction values of all out-of-bag sets. For each training set, a model was derived from the RNA-seq data to delineate the positives and the negatives. This model was used to classify the training set and update the labels of the isoforms of the positive genes, under the criterion that at least one isoform of a positive gene must remain positive. This new assignment is then used to construct the model in the next iteration. This process is iterated until the assignment of positive isoforms no longer changes, and then the final model was used to assign a prediction value to the test or the out-of-the-bag set. Bootstrap was done for 30 iterations and the median value for each out-of-the-bag isoform was taken as the final prediction value. The predictive performance of our model was assessed through three approaches: (1) cross-validation of gene-level predictive performances, focusing on comparison between single-isoform genes and multiple-isoform genes, (2) literature validation and (3) experimental validation of top predictions using proteomic data.</p
This paper summarizes the recent activities of the Chromosome-Centric Human Proteome Project (C-H... more This paper summarizes the recent activities of the Chromosome-Centric Human Proteome Project (C-HPP) consortium, which develops new technologies to identify yet-to-be annotated proteins (termed "missing proteins") in biological samples that lack sufficient experimental evidence at the protein level for confident protein identification. The C-HPP also aims to identify new protein forms that may be caused by genetic variability, post-translational modifications, and alternative splicing. Proteogenomic data integration forms the basis of the C-HPP's activities; therefore, we have summarized some of key approaches and their roles in the project. We present new analytical technologies that improve the chemical space and lower detection limits coupled with bioinformatics tools and some publicly available resources that can be used to improve data analysis or support the development of analytical assays. Most of this paper's contents have been compiled from posters, slides, and discussions presented in the series of C-HPP workshops held during 2014. All data (posters, presentations) used are available at the C-HPP Wiki (http://c-hpp.webhosting.rug.nl/) and in the supporting information.
iMOP-the Initiative on Model Organism Proteomes-was accepted as a new HUPO initiative at the Nint... more iMOP-the Initiative on Model Organism Proteomes-was accepted as a new HUPO initiative at the Ninth HUPO meeting in Sydney in 2010. A goal of iMOP is to integrate research groups working on a great diversity of species into a model organism community. At the Tenth HUPO meeting in Geneva this variety was reflected in the iMOP session on Tuesday September 6, 2011. The presentations covered the quantitative proteome database PaxDb, proteomics projects studying farm animals, Arabidopsis thaliana, as well as host-pathogen interactions.
The mission of the Chromosome-Centric Human Proteome Project (C-HPP), is to map and annotate the ... more The mission of the Chromosome-Centric Human Proteome Project (C-HPP), is to map and annotate the entire predicted human protein set (~20,000 proteins) encoded by each chromosome. The initial steps of the project are focused on 'missing proteins (MPs)', which lacked documented evidence for existence at protein level. In addition to remaining 2,579 MPs, we also target those annotated proteins having unknown functions, uPE1 proteins, alternative splice isoforms and post-translational modifications. We also consider how to investigate various protein functions involved in cis-regulatory phenomena, amplicons lncRNAs and smORFs. Areas covered: We will cover the scope, historic background, progress, challenges and future prospects of C-HPP. This review also addresses the question of how we can best improve the methodological approaches, select the optimal biological samples, and recommend stringent protocols for the identification and characterization of MPs. A new strategy for fun...
The C-HPP Team for Chromosome 17 has documented meeting the neXt-MP50 Missing Protein Challenge. ... more The C-HPP Team for Chromosome 17 has documented meeting the neXt-MP50 Missing Protein Challenge. Protein-level PE1 evidence including MS and protein-protein interaction data from the global proteomics community has reduced the number of PE2,3,4 proteins coded on Chromosome 17 by 50 since the 2016 baseline in neXtProt. As a follow-up to our analysis and prioritization of Chr 17 missing proteins last year, we describe what predictions and guidance were successful in this update and we explain the dynamics of a net reduction of PE2,3,4 Missing Proteins. This analysis can serve as a guide for other C-HPP chromosome teams seeking high-quality evidence for the 2129 remaining missing proteins.
This is an open access article published under an ACS AuthorChoice License, which permits copying... more This is an open access article published under an ACS AuthorChoice License, which permits copying and redistribution of the article or any adaptations for non-commercial purposes.
The C-HPP Team for Chromosome 17 has documented meeting the neXt-MP50 Missing Protein Challenge. ... more The C-HPP Team for Chromosome 17 has documented meeting the neXt-MP50 Missing Protein Challenge. Protein-level PE1 evidence including MS and protein-protein interaction data from the global proteomics community has reduced the number of PE2,3,4 proteins coded on Chromosome 17 by 50 since the 2016 baseline in neXtProt. As a follow-up to our analysis and prioritization of Chr 17 missing proteins last year, we describe what predictions and guidance were successful in this update and we explain the dynamics of a net reduction of PE2,3,4 Missing Proteins. This analysis can serve as a guide for other C-HPP chromosome teams seeking high-quality evidence for the 2129 remaining missing proteins.
Gene Ontology (GO) has been widely used to annotate functions of genes and gene products. We prop... more Gene Ontology (GO) has been widely used to annotate functions of genes and gene products. We proposed a new method (TripletGO) to deduce GO terms of protein-coding and non-coding genes, through the integration of four complementary pipelines built on transcript expression profiling, genetic sequence alignment, protein sequence alignment and naïve probability, respectively. TripletGO was tested on a large set of 5,754 genes from 8 species (human, mouse, arabidopsis, rat, fly, budding yeast, fission yeast, and nematoda) and 2,433 proteins with available expression data from the CAFA3 experiment and achieved function annotation accuracy significantly beyond the current state-of-the-art approaches. Detailed analyses show that the major advantage of TripletGO lies in the coupling of a new triplet-network based profiling method with the feature space mapping technique which can accurately recognize function patterns from transcript expressions. Meanwhile, the combination of multiple compl...
Proteomic technologies, such as yeast twohybrid, mass spectrometry (MS), protein/peptide arrays a... more Proteomic technologies, such as yeast twohybrid, mass spectrometry (MS), protein/peptide arrays and fluorescence microscopy, yield multi-dimensional data sets, which are often quite large and either not published or published as supplementary information that is not easily searchable. Without a system in place for standardizing and sharing data, it is not fruitful for the biomedical community to contribute these types of data to centralized repositories. Even more difficult is the annotation and display of pertinent information in the context of the corresponding proteins. Wikipedia, an online encyclopedia that anyone can edit, has already proven quite successful1 and can be used as a model for sharing biological data. However, the need for experimental evidence, data standardization and ownership of data creates scientific obstacles. Here, we describe Human Proteinpedia (http://www.humanproteinpedia.org/) as a portal that overcomes many of these obstacles to provide an integrated view of the human proteome. Human Proteinpedia also allows users to contribute and edit proteomic data with two significant differences from Wikipedia: first, the contributor is expected to provide experimental evidence for the data annotated; and second, only the original contributor can edit their data. Human Proteinpedia's annotation system provides investigators with multiple options for contributing data including web forms and annotation servers. Although registration is required to contribute data, anyone can freely access the data in the repository. The web forms simplify submission through the use of pull-down menus for certain data fields and pop-up menus for standardized vocabulary terms. Distributed annotation servers using modified protein DAS (distributed annotation system) protocols developed by us (DAS protocols were originally developed for sharing mRNA and DNA data) permit contributing laboratories to maintain protein annotations locally. All protein annotations are visualized in the context of corresponding proteins in the Human Protein Reference Database (HPRD)3. Figure 1 shows tissue expression data for alpha-2-HS glycoprotein derived from three different types of experiments. Our unique effort differs significantly from existing repositories, such as PeptideAtlas and PRIDE5 in several respects. First, most proteomic repositories are restricted to one or two experimental platforms, whereas Human Proteinpedia can accommodate data from diverse platforms, including yeast two-hybrid screens, MS, peptide/protein arrays, immunohistochemistry, western blots, coimmunoprecipitation and fluorescence microscopy-type experiments. Second, Human Proteinpedia allows contributing laboratories to annotate data pertaining to six features of proteins (posttranslational modifications, tissue expression, cell line expression, subcellular localization, enzyme substrates and protein-protein interactions;). No existing repository currently permits annotation of all these features in proteins. Third, all data submitted to Human Proteinpedia are viewable through HPRD in the context of other features of the corresponding proteins. To aid comparison and interpretation, meta-annotations pertaining to samples, method of isolation and experimental platform-specific information are provided (e.g., labeling method, protease used, ionization method, details of primary antibody used). And fourth, in spite of accommodating multiple data types, the data submission is simplified. This means that a biologist with no technical expertise can login and contribute data. Thus far, a considerable body of data has been contributed to Human Proteinpedia by the community, with a total of >1.8 million peptides and >4 million MS/MS-spectra deposited.
The contribution of microenvironment to tumor growth has important implications for optimizing ch... more The contribution of microenvironment to tumor growth has important implications for optimizing chemotherapeutic response and understanding the biology of recurrent tumors. In this study, we tested the effects of locally administered topotecan on a rat model of glioblastoma that is induced by intracerebral injection of PDGF (platelet-derived growth factor)-IRES (internal ribosome entry site)-GFP (green fluorescent protein)–expressing retrovirus, treated the tumors by convection-enhanced delivery (CED) of topotecan (136 μmol/L) for 1, 4, or 7 days, and then characterized the effects on both the retrovirus-transformed tumor cells (GFP+ cells) as well as the uninfected glial progenitor cells (GFP− cells) that are recruited to the tumor. Topotecan treatment reduced GFP+ cells about 10-fold and recruited progenitors by about 80-fold while providing a significant survival advantage that improved with greater treatment duration. Regions of glial progenitor ablation occurred corresponding to...
Background The identification of differentially expressed tumor-associated proteins and genomic a... more Background The identification of differentially expressed tumor-associated proteins and genomic alterations driving neoplasia is critical in the development of clinical assays to detect cancers and forms the foundation for understanding cancer biology. One of the challenges in the analysis of pancreatic ductal adenocarcinoma (PDAC) is the low neoplastic cellularity and heterogeneous composition of bulk tumors. To enrich neoplastic cells from bulk tumor tissue, coring, and laser microdissection (LMD) sampling techniques have been employed. In this study, we assessed the protein and KRAS mutation changes associated with samples obtained by these enrichment techniques and evaluated the fraction of neoplastic cells in PDAC for proteomic and genomic analyses. Methods Three fresh frozen PDAC tumors and their tumor-matched normal adjacent tissues (NATs) were obtained from three sampling techniques using bulk, coring, and LMD; and analyzed by TMT-based quantitative proteomics. The protein p...
Canonical isoforms in different databases have been defined as the most prevalent, most conserved... more Canonical isoforms in different databases have been defined as the most prevalent, most conserved, most expressed, longest, or the one with the clearest description of domains or posttranslational modifications. In this article, we revisit these definitions of canonical isoforms based on functional genomics and proteomics evidence, focusing on mouse data. We report a novel functional relationship network-based approach for identifying the Highest Connected Isoforms (HCIs). We show that 46% of these HCIs are not the longest transcripts. In addition, this approach revealed many genes that have more than one highly connected isoforms. Averaged across 175 RNA-seq datasets covering diverse tissues and conditions, 65% of the HCIs show higher expression levels than non-highest connected isoforms (NCIs) at the transcript level. At the protein level, these HCIs highly overlap with the expressed splice variants, based on proteomic data from eight different normal tissues. These results suggest that a more confident definition of canonical isoforms can be made through integration of multiple lines of evidence, including highest connected isoforms defined by biological processes and pathways, expression prevalence at the transcript level, and relative or absolute abundance at the protein level. This integrative proteogenomics approach can successfully identify principal isoforms that are responsible for the canonical functions of genes.
Every data-rich community research effort requires a clear plan for ensuring the quality of the d... more Every data-rich community research effort requires a clear plan for ensuring the quality of the data interpretation and comparability of analyses. To address this need within the Human Proteome Project (HPP) of the Human Proteome Organization (HUPO), we have developed through broad consultation a set of mass spectrometry data interpretation guidelines that should be applied to all HPP data contributions. For submission of manuscripts reporting HPP protein identification results, the guidelines are presented as a one-page checklist containing fifteen essential points followed by two pages of expanded description of each. Here, we present an overview of the guidelines and provide an in-depth description of each of the fifteen elements to facilitate understanding of the intentions and rationale behind the guidelines, both for authors and for reviewers. Broadly, these guidelines provide specific directions regarding how HPP data are to be submitted to mass spectrometry data repositories...
<p>We collected RNA-seq data from the sequence read archive (SRA) database and estimated is... more <p>We collected RNA-seq data from the sequence read archive (SRA) database and estimated isoform-level expression values using state-of-the-art software <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003314#pcbi.1003314-Trapnell1" target="_blank">[32]</a>, <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003314#pcbi.1003314-Trapnell2" target="_blank">[34]</a>. We then generated a gene-level gold standard using Gene Ontology (GO) annotations. For each biological function, this gold standard contains positive genes (annotated to the function under investigation) and negative genes (other genes). Our study contains two major parts: cross-validation for performance estimation and bootstrap bagging for generating final predictions as well as performance evaluation. For cross-validation, we partitioned the examples into a training set for model development and a test set for model validation. For generating final predictions for all isoforms, we sampled with replacement to construct a training set, and then used this training set to construct models to assign prediction probabilities to the out-of-bag set. The final predictions for all isoforms were made by calculating the median prediction values of all out-of-bag sets. For each training set, a model was derived from the RNA-seq data to delineate the positives and the negatives. This model was used to classify the training set and update the labels of the isoforms of the positive genes, under the criterion that at least one isoform of a positive gene must remain positive. This new assignment is then used to construct the model in the next iteration. This process is iterated until the assignment of positive isoforms no longer changes, and then the final model was used to assign a prediction value to the test or the out-of-the-bag set. Bootstrap was done for 30 iterations and the median value for each out-of-the-bag isoform was taken as the final prediction value. The predictive performance of our model was assessed through three approaches: (1) cross-validation of gene-level predictive performances, focusing on comparison between single-isoform genes and multiple-isoform genes, (2) literature validation and (3) experimental validation of top predictions using proteomic data.</p
This paper summarizes the recent activities of the Chromosome-Centric Human Proteome Project (C-H... more This paper summarizes the recent activities of the Chromosome-Centric Human Proteome Project (C-HPP) consortium, which develops new technologies to identify yet-to-be annotated proteins (termed "missing proteins") in biological samples that lack sufficient experimental evidence at the protein level for confident protein identification. The C-HPP also aims to identify new protein forms that may be caused by genetic variability, post-translational modifications, and alternative splicing. Proteogenomic data integration forms the basis of the C-HPP's activities; therefore, we have summarized some of key approaches and their roles in the project. We present new analytical technologies that improve the chemical space and lower detection limits coupled with bioinformatics tools and some publicly available resources that can be used to improve data analysis or support the development of analytical assays. Most of this paper's contents have been compiled from posters, slides, and discussions presented in the series of C-HPP workshops held during 2014. All data (posters, presentations) used are available at the C-HPP Wiki (http://c-hpp.webhosting.rug.nl/) and in the supporting information.
iMOP-the Initiative on Model Organism Proteomes-was accepted as a new HUPO initiative at the Nint... more iMOP-the Initiative on Model Organism Proteomes-was accepted as a new HUPO initiative at the Ninth HUPO meeting in Sydney in 2010. A goal of iMOP is to integrate research groups working on a great diversity of species into a model organism community. At the Tenth HUPO meeting in Geneva this variety was reflected in the iMOP session on Tuesday September 6, 2011. The presentations covered the quantitative proteome database PaxDb, proteomics projects studying farm animals, Arabidopsis thaliana, as well as host-pathogen interactions.
The mission of the Chromosome-Centric Human Proteome Project (C-HPP), is to map and annotate the ... more The mission of the Chromosome-Centric Human Proteome Project (C-HPP), is to map and annotate the entire predicted human protein set (~20,000 proteins) encoded by each chromosome. The initial steps of the project are focused on 'missing proteins (MPs)', which lacked documented evidence for existence at protein level. In addition to remaining 2,579 MPs, we also target those annotated proteins having unknown functions, uPE1 proteins, alternative splice isoforms and post-translational modifications. We also consider how to investigate various protein functions involved in cis-regulatory phenomena, amplicons lncRNAs and smORFs. Areas covered: We will cover the scope, historic background, progress, challenges and future prospects of C-HPP. This review also addresses the question of how we can best improve the methodological approaches, select the optimal biological samples, and recommend stringent protocols for the identification and characterization of MPs. A new strategy for fun...
The C-HPP Team for Chromosome 17 has documented meeting the neXt-MP50 Missing Protein Challenge. ... more The C-HPP Team for Chromosome 17 has documented meeting the neXt-MP50 Missing Protein Challenge. Protein-level PE1 evidence including MS and protein-protein interaction data from the global proteomics community has reduced the number of PE2,3,4 proteins coded on Chromosome 17 by 50 since the 2016 baseline in neXtProt. As a follow-up to our analysis and prioritization of Chr 17 missing proteins last year, we describe what predictions and guidance were successful in this update and we explain the dynamics of a net reduction of PE2,3,4 Missing Proteins. This analysis can serve as a guide for other C-HPP chromosome teams seeking high-quality evidence for the 2129 remaining missing proteins.
This is an open access article published under an ACS AuthorChoice License, which permits copying... more This is an open access article published under an ACS AuthorChoice License, which permits copying and redistribution of the article or any adaptations for non-commercial purposes.
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