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Proteome Coverage Prediction for Integrated Proteomics Datasets [chapter]

Manfred Claassen, Ruedi Aebersold, Joachim M. Buhmann
2010 Lecture Notes in Computer Science  
methods and prediction methods designed for non-integrated datasets.  ...  To date, there does not exist any method that reliably predicts proteome coverage from integrated datasets.  ...  Acknowledgments We thank Alexander Schmidt and Lukas Reiter for carefully reading the manuscript. We further thank Alexander Schmidt for kindly providing the L. interrogans data set.  ... 
doi:10.1007/978-3-642-12683-3_7 fatcat:ov5iysh7bjdghdeq47fir4ytwu

Integration of RNA-seq and proteomics data with genomics for improved genome annotation in Apicomplexan parasites

Natalie C. Silmon de Monerri, Louis M. Weiss
2015 Proteomics  
They applied a systematic approach to combine new and previously published proteomics data from T. gondii and N. caninum with transcriptomics data, leading to substantially improved gene models for these  ...  While high quality genomic sequence data is available for many pathogenic organisms, the corresponding gene annotations are often plagued with inaccuracies that can hinder research that utilizes such genomic  ...  They build new RNA-seq-derived genetic models and provide the first global proteomic dataset for N. caninum.  ... 
doi:10.1002/pmic.201500253 pmid:26152714 pmcid:PMC4552184 fatcat:7d2tmdb6fjfhhpwxqri4k2fk2u

proBAMsuite, a Bioinformatics Framework for Genome-Based Representation and Analysis of Proteomics Data

Xiaojing Wang, Robbert J. C. Slebos, Matthew C. Chambers, David L. Tabb, Daniel C. Liebler, Bing Zhang
2015 Molecular & Cellular Proteomics  
Applying proBAMsuite to three recently published proteomics datasets, we demonstrated its utility in facilitating efficient genome-based sharing, interpretation, and integration of proteomics data.  ...  Third, using the genome as a common reference, proBAMsuite facilitates seamless proteomics and proteogenomics data integration.  ...  Integrating all three datasets used in the study achieved an overall coverage of 18% for the whole human coding genome.  ... 
doi:10.1074/mcp.m115.052860 pmid:26657539 pmcid:PMC4813696 fatcat:3vlyqxv55fddtku4pca5fh3xze

Approaching Drosophila development through proteomic tools and databases: At the hub of the post-genomic era

Ana Carmena
2009 Mechanisms of Development  
Here, we briefly summarize the emerging analytical tools and databases that are paving the way for studying Drosophila development by proteomic approaches.  ...  The past decade has witnessed an explosion in the growth of proteomics.  ...  However, the two-step strategy followed for the analysis of Drosophila proteome has been particularly successful, representing the first high-coverage proteome map for a multicellular eukaryote (Loevenich  ... 
doi:10.1016/j.mod.2009.08.001 pmid:19679183 fatcat:3mu4oixr2fhj5ndhbvbbhth6ji

MitProNet: A Knowledgebase and Analysis Platform of Proteome, Interactome and Diseases for Mammalian Mitochondria

Jiabin Wang, Jian Yang, Song Mao, Xiaoqiang Chai, Yuling Hu, Xugang Hou, Yiheng Tang, Cheng Bi, Xiao Li, Miguel A. Andrade-Navarro
2014 PLoS ONE  
Furthermore, we utilized the network to predict candidate genes for mitochondrial diseases using prioritization algorithms.  ...  An organelle-specific network of functional linkages among mitochondrial proteins was then generated by integrating genomic features encoded by a wide range of datasets including genomic context, gene  ...  by integrating the proteomic datasets from a wide range of mammalian mitochondria.  ... 
doi:10.1371/journal.pone.0111187 pmid:25347823 pmcid:PMC4210245 fatcat:ydqlwzje75co7n26byvtgjhzri

The structural coverage of the human proteome before and after AlphaFold [article]

Eduard Porta-Pardo, Victoria Ruiz-Serra, Alfonso Valencia
2021 bioRxiv   pre-print
Our results indicate that our current baseline for structural coverage of 47%, considering experimentally-derived or template-based homology models, elevates up to 75% when including AlphaFold predictions  ...  , reducing the fraction of dark proteome from 22% to just 7% and the number of proteins without structural information from 4.832 to just 29.  ...  The authors would like to thank the DeepMind team for sharing their models of human proteins.  ... 
doi:10.1101/2021.08.03.454980 fatcat:wx5gggbiurfhnnag5yktnfy42a

Proteome coverage prediction with infinite Markov models

M. Claassen, R. Aebersold, J. M. Buhmann
2009 Bioinformatics  
The predicted progression enabled us to specify maximal coverage for the test sample.  ...  Proteome coverage prediction is important to enhance the design of efficient proteomics studies.  ...  ACKNOWLEDGEMENTS We would like to thank Cheng Soon Ong for careful reading of the manuscript and Alexander Schmidt for kindly providing the test dataset.  ... 
doi:10.1093/bioinformatics/btp233 pmid:19477982 pmcid:PMC2687987 fatcat:wqks43eskfaydcvfjhndo3p2pa

PSORTb 3.0: improved protein subcellular localization prediction with refined localization subcategories and predictive capabilities for all prokaryotes

Nancy Y. Yu, James R. Wagner, Matthew R. Laird, Gabor Melli, Sébastien Rey, Raymond Lo, Phuong Dao, S. Cenk Sahinalp, Martin Ester, Leonard J. Foster, Fiona S. L. Brinkman
2010 Computer applications in the biosciences : CABIOS  
Results: We developed PSORTb version 3.0 with improved recall, higher proteome-scale prediction coverage, and new refined localization subcategories.  ...  complemented by Proteome Analyst predictions.  ...  ACKNOWLEDGEMENTS The authors would like to thank Francis Lim for protein sample processing for mass spectrometry analysis, as well as Kurt McMillan and Yifeng Liu for providing PA 3.0 whole genome prediction  ... 
doi:10.1093/bioinformatics/btq249 pmid:20472543 pmcid:PMC2887053 fatcat:2xak454utjf3xbm55harjgtntm

Integrative Analysis of the Mitochondrial Proteome in Yeast

Holger Prokisch, Curt Scharfe, David G Camp, Wenzhong Xiao, Lior David, Christophe Andreoli, Matthew E Monroe, Ronald J Moore, Marina A Gritsenko, Christian Kozany, Kim K Hixson, Heather M Mottaz (+9 others)
2004 PLoS Biology  
the integration of 22 datasets.  ...  By expression analysis and comparison to other proteome studies, we demonstrate that the proteomic approach identifies primarily highly abundant proteins.  ...  The predictive score for a mitochondrial protein (MitoP2) was based on the integration of 22 datasets, most of which are shown, and was calculated for different thresholds.  ... 
doi:10.1371/journal.pbio.0020160 pmid:15208715 pmcid:PMC423137 fatcat:a4afyntopbdmtmorvemwe5t2pe

Protein cleavage strategies for an improved analysis of the membrane proteome

Frank Fischer, Ansgar Poetsch
2006 Proteome Science  
For the membrane proteomes from all three analyzed organisms, we identified cleavage conditions that achieve better sequence and proteome coverage than trypsin.  ...  It was demonstrated for bacteriorhodopsin that the in silico predictions agree well with the experimental observations.  ...  The translated predicted ORFs for Corynebacterium glutamicum were supplied by J. Kalinowski (Bielefeld University) and are part of the publicly available dataset at EBI.  ... 
doi:10.1186/1477-5956-4-2 pmid:16512920 pmcid:PMC1458320 fatcat:oxlyczojv5hxtasmxvuuwwkk7e

Computational Analysis and Experimental Validation of Gene Predictions in Toxoplasma gondii

Joseph M. Dybas, Carlos J. Madrid-Aliste, Fa-Yun Che, Edward Nieves, Dmitry Rykunov, Ruth Hogue Angeletti, Louis M. Weiss, Kami Kim, Andras Fiser, Steven L. Salzberg
2008 PLoS ONE  
Mass spectrometry analysis validated 2,477 gene coding regions with 6,438 possible alternative gene predictions; approximately one third of the T. gondii proteome.  ...  Commonly used gene prediction algorithms produce very disparate sets of protein sequences, with pairwise overlaps ranging from 1.4% to 12%.  ...  Acknowledgments We thank for making Toxoplasma related experimental and computational data available. Author Contributions Validation of Gene Predictions PLoS ONE |  ... 
doi:10.1371/journal.pone.0003899 pmid:19065262 pmcid:PMC2587701 fatcat:of4il3u6lrahpah4b344bjbiky

The structural coverage of the human proteome before and after AlphaFold

Eduard Porta-Pardo, Victoria Ruiz-Serra, Samuel Valentini, Alfonso Valencia, Silvio C. E. Tosatto
2022 PLoS Computational Biology  
Our results indicate that our current baseline for structural coverage of 48%, considering experimentally-derived or template-based homology models, elevates up to 76% when including AlphaFold predictions  ...  Finally, we show how the contribution of AlphaFold models to the structural coverage of non-human organisms, including important pathogenic bacteria, is significantly larger than that of the human proteome  ...  Acknowledgments The authors would like to thank the DeepMind team for sharing their models of human proteins.  ... 
doi:10.1371/journal.pcbi.1009818 pmid:35073311 pmcid:PMC8812986 fatcat:3tw5etg77vfy7oswnzpwltudu4

AlphaMap: An open-source Python package for the visual annotation of proteomics data with sequence specific knowledge [article]

Eugenia Voytik, Isabell Bludau, Sander Willems, Fynn Hansen, Andreas-David Brunner, Maximilian T Strauss, Matthias Mann
2021 bioRxiv   pre-print
Integrating experimental information across proteomic datasets with the wealth of publicly available sequence annotations is a crucial part in many proteomic studies that currently lacks an automated analysis  ...  The functionality of AlphaMap can be accessed via an intuitive graphical user interface or - more flexibly - as a Python package that allows its integration into common analysis workflows for data visualization  ...  Tanzer for valuable discussions and for providing experimental data.  ... 
doi:10.1101/2021.07.30.454433 fatcat:ed5a6754urdtbcsyrxxbnd6qde

PSORTb v.2.0: Expanded prediction of bacterial protein subcellular localization and insights gained from comparative proteome analysis

J. L. Gardy, M. R. Laird, F. Chen, S. Rey, C. J. Walsh, M. Ester, F. S. L. Brinkman
2004 Bioinformatics  
The program attains a precision of 96% for Gram-positive and Gram-negative bacteria and predictive coverage comparable to other tools for whole proteome analysis.  ...  However, the program's predictive coverage and recall are low and the method is only applicable to Gram-negative bacteria.  ...  Funding for this work was provided by the Natural Sciences and Engineering Research Council of Canada.  ... 
doi:10.1093/bioinformatics/bti057 pmid:15501914 fatcat:wpkhw5qocnhdjp7xcvzh5oxfai

Proteome-wide Prediction of Signal Flow Direction in Protein Interaction Networks Based on Interacting Domains

Wei Liu, Dong Li, Jian Wang, Hongwei Xie, Yunping Zhu, Fuchu He
2009 Molecular & Cellular Proteomics  
Based on the pairwise interaction domains, here we defined a novel parameter protein interaction directional score and then used it to predict the direction of signal flow between proteins in proteome-wide  ...  Using 5-fold cross-validation, our approach obtained a satisfied performance with the accuracy 89.79%, coverage 48.08%, and error ratio 16.91%.  ...  Jiangqi Li, Lei Dou, and Songfeng Wu for their excellent advice and assistance as well as all the members in the bioinformatics lab of Beijing Proteome Research Center for helpful discussions.  ... 
doi:10.1074/mcp.m800354-mcp200 pmid:19502588 pmcid:PMC2742434 fatcat:5t2aayje4zacrhutonp56udrti
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