1
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Tong M, Liu Z, Li J, Wei X, Shi W, Liang C, Yu C, Huang R, Lin Y, Wang X, Wang S, Wang Y, Huang J, Wang Y, Li T, Qin J, Zhan D, Ji ZL. PhosMap: An ensemble bioinformatic platform to empower interactive analysis of quantitative phosphoproteomics. Comput Biol Med 2024; 174:108391. [PMID: 38613887 DOI: 10.1016/j.compbiomed.2024.108391] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2024] [Revised: 03/18/2024] [Accepted: 04/01/2024] [Indexed: 04/15/2024]
Abstract
BACKGROUND Liquid chromatography-mass spectrometry (LC-MS)-based quantitative phosphoproteomics has been widely used to detect thousands of protein phosphorylation modifications simultaneously from the biological specimens. However, the complicated procedures for analyzing phosphoproteomics data has become a bottleneck to widening its application. METHODS Here, we develop PhosMap, a versatile and scalable tool to accomplish phosphoproteomics data analysis. A standardized phosphorylation data format was created for data analyses, from data preprocessing to downstream bioinformatic analyses such as dimension reduction, differential phosphorylation analysis, kinase activity, survival analysis, and so on. For better usability, we distribute PhosMap as a Docker image for easy local deployment upon any of Windows, Linux, and Mac system. RESULTS The source code is deposited at https://github.com/BADD-XMU/PhosMap. A free PhosMap webserver (https://huggingface.co/spaces/Bio-Add/PhosMap), with easy-to-follow fashion of dashboards, is curated for interactive data analysis. CONCLUSIONS PhosMap fills the technical gap of large-scale phosphorylation research by empowering researchers to process their own phosphoproteomics data expediently and efficiently, and facilitates better data interpretation.
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Affiliation(s)
- Mengsha Tong
- State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Faculty of Medicine and Life Sciences, Xiamen University, Xiamen, Fujian, 361102, China; National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, Fujian, 361102, China
| | - Zan Liu
- State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Faculty of Medicine and Life Sciences, Xiamen University, Xiamen, Fujian, 361102, China; National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, Fujian, 361102, China
| | - Jiaao Li
- State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Faculty of Medicine and Life Sciences, Xiamen University, Xiamen, Fujian, 361102, China
| | - Xin Wei
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, 102206, China
| | - Wenhao Shi
- Analysis Center, Chemistry Department, Tsinghua University, Beijing, 100084, China
| | - Chenyu Liang
- National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, Fujian, 361102, China
| | - Chunyu Yu
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Hangzhou Normal University, Hangzhou, 311121, China
| | - Rongting Huang
- Department of Biomedical Informatics, School of Basic Medical Sciences, Peking University Health Science Center, Beijing, 100191, China
| | - Yuxiang Lin
- National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, Fujian, 361102, China
| | - Xinkang Wang
- National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, Fujian, 361102, China
| | - Shun Wang
- Departments of Pathology, National Cancer Center, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Yi Wang
- Beijing Pineal Diagnostics Co., Ltd., Beijing, 102206, China; State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, 102206, China
| | - Jialiang Huang
- State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Faculty of Medicine and Life Sciences, Xiamen University, Xiamen, Fujian, 361102, China; National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, Fujian, 361102, China
| | - Yini Wang
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, 102206, China
| | - Tingting Li
- Department of Biomedical Informatics, School of Basic Medical Sciences, Peking University Health Science Center, Beijing, 100191, China.
| | - Jun Qin
- Beijing Pineal Diagnostics Co., Ltd., Beijing, 102206, China; State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, 102206, China.
| | - Dongdong Zhan
- Beijing Pineal Diagnostics Co., Ltd., Beijing, 102206, China.
| | - Zhi-Liang Ji
- State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Faculty of Medicine and Life Sciences, Xiamen University, Xiamen, Fujian, 361102, China; National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, Fujian, 361102, China.
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2
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Takimoto K, Takeuchi K, Ton NNT, Taniike T. Exploring stabilizer formulations for light-induced yellowing of polystyrene by high-throughput experimentation and machine learning. Polym Degrad Stab 2022. [DOI: 10.1016/j.polymdegradstab.2022.109967] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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3
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Theodorakis E, Antonakis AN, Baltsavia I, Pavlopoulos GA, Samiotaki M, Amoutzias GD, Theodosiou T, Acuto O, Efstathiou G, Iliopoulos I. ProteoSign v2: a faster and evolved user-friendly online tool for statistical analyses of differential proteomics. Nucleic Acids Res 2021; 49:W573-W577. [PMID: 33963869 PMCID: PMC8262687 DOI: 10.1093/nar/gkab329] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Revised: 04/08/2021] [Accepted: 04/21/2021] [Indexed: 12/30/2022] Open
Abstract
Bottom-up proteomics analyses have been proved over the last years to be a powerful tool in the characterization of the proteome and are crucial for understanding cellular and organism behaviour. Through differential proteomic analysis researchers can shed light on groups of proteins or individual proteins that play key roles in certain, normal or pathological conditions. However, several tools for the analysis of such complex datasets are powerful, but hard-to-use with steep learning curves. In addition, some other tools are easy to use, but are weak in terms of analytical power. Previously, we have introduced ProteoSign, a powerful, yet user-friendly open-source online platform for protein differential expression/abundance analysis designed with the end-proteomics user in mind. Part of Proteosign's power stems from the utilization of the well-established Linear Models For Microarray Data (LIMMA) methodology. Here, we present a substantial upgrade of this computational resource, called ProteoSign v2, where we introduce major improvements, also based on user feedback. The new version offers more plot options, supports additional experimental designs, analyzes updated input datasets and performs a gene enrichment analysis of the differentially expressed proteins. We also introduce the deployment of the Docker technology and significantly increase the speed of a full analysis. ProteoSign v2 is available at http://bioinformatics.med.uoc.gr/ProteoSign.
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Affiliation(s)
- Evangelos Theodorakis
- Division of Basic Sciences, University of Crete Medical School, Heraklion 71110, Greece.,Department of Informatics, Technical University of Munich, Boltzmannstr. 3, 85748 Garching, Germany
| | - Andreas N Antonakis
- Division of Basic Sciences, University of Crete Medical School, Heraklion 71110, Greece
| | - Ismini Baltsavia
- Division of Basic Sciences, University of Crete Medical School, Heraklion 71110, Greece
| | - Georgios A Pavlopoulos
- Institute for Fundamental Biomedical Research, BSRC "Alexander Fleming", 34 Fleming Street, 16672 Vari, Greece
| | - Martina Samiotaki
- Institute for Bioinnovation, BSRC "Alexander Fleming", 34 Fleming Street, 16672 Vari, Greece
| | - Grigoris D Amoutzias
- Bioinformatics Laboratory, Department of Biochemistry and Biotechnology, University of Thessaly, Larisa 41500, Greece
| | - Theodosios Theodosiou
- Division of Basic Sciences, University of Crete Medical School, Heraklion 71110, Greece
| | - Oreste Acuto
- Sir William Dunn School of Pathology, University of Oxford, South Parks Road, Oxford OX13RE, UK
| | - Georgios Efstathiou
- Division of Basic Sciences, University of Crete Medical School, Heraklion 71110, Greece.,Sir William Dunn School of Pathology, University of Oxford, South Parks Road, Oxford OX13RE, UK
| | - Ioannis Iliopoulos
- Division of Basic Sciences, University of Crete Medical School, Heraklion 71110, Greece
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4
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Terkelsen T, Krogh A, Papaleo E. CAncer bioMarker Prediction Pipeline (CAMPP)-A standardized framework for the analysis of quantitative biological data. PLoS Comput Biol 2020; 16:e1007665. [PMID: 32176694 PMCID: PMC7108742 DOI: 10.1371/journal.pcbi.1007665] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2019] [Revised: 03/31/2020] [Accepted: 01/18/2020] [Indexed: 01/21/2023] Open
Abstract
With the improvement of -omics and next-generation sequencing (NGS) methodologies, along with the lowered cost of generating these types of data, the analysis of high-throughput biological data has become standard both for forming and testing biomedical hypotheses. Our knowledge of how to normalize datasets to remove latent undesirable variances has grown extensively, making for standardized data that are easily compared between studies. Here we present the CAncer bioMarker Prediction Pipeline (CAMPP), an open-source R-based wrapper (https://github.com/ELELAB/CAncer-bioMarker-Prediction-Pipeline -CAMPP) intended to aid bioinformatic software-users with data analyses. CAMPP is called from a terminal command line and is supported by a user-friendly manual. The pipeline may be run on a local computer and requires little or no knowledge of programming. To avoid issues relating to R-package updates, a renv .lock file is provided to ensure R-package stability. Data-management includes missing value imputation, data normalization, and distributional checks. CAMPP performs (I) k-means clustering, (II) differential expression/abundance analysis, (III) elastic-net regression, (IV) correlation and co-expression network analyses, (V) survival analysis, and (VI) protein-protein/miRNA-gene interaction networks. The pipeline returns tabular files and graphical representations of the results. We hope that CAMPP will assist in streamlining bioinformatic analysis of quantitative biological data, whilst ensuring an appropriate bio-statistical framework.
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Affiliation(s)
- Thilde Terkelsen
- Computational Biology Laboratory, Danish Cancer Society Research Center and Center for Autophagy, Recycling and Disease, Copenhagen, Denmark
| | - Anders Krogh
- Unit of Computational and RNA biology, Department of Biology, University of Copenhagen, Copenhagen Denmark
| | - Elena Papaleo
- Computational Biology Laboratory, Danish Cancer Society Research Center and Center for Autophagy, Recycling and Disease, Copenhagen, Denmark
- Translational Disease System Biology, Faculty of Health and Medical Science, Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark
- * E-mail:
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5
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Kennedy SA, Jarboui MA, Srihari S, Raso C, Bryan K, Dernayka L, Charitou T, Bernal-Llinares M, Herrera-Montavez C, Krstic A, Matallanas D, Kotlyar M, Jurisica I, Curak J, Wong V, Stagljar I, LeBihan T, Imrie L, Pillai P, Lynn MA, Fasterius E, Al-Khalili Szigyarto C, Breen J, Kiel C, Serrano L, Rauch N, Rukhlenko O, Kholodenko BN, Iglesias-Martinez LF, Ryan CJ, Pilkington R, Cammareri P, Sansom O, Shave S, Auer M, Horn N, Klose F, Ueffing M, Boldt K, Lynn DJ, Kolch W. Extensive rewiring of the EGFR network in colorectal cancer cells expressing transforming levels of KRAS G13D. Nat Commun 2020; 11:499. [PMID: 31980649 PMCID: PMC6981206 DOI: 10.1038/s41467-019-14224-9] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2019] [Accepted: 12/05/2019] [Indexed: 02/07/2023] Open
Abstract
Protein-protein-interaction networks (PPINs) organize fundamental biological processes, but how oncogenic mutations impact these interactions and their functions at a network-level scale is poorly understood. Here, we analyze how a common oncogenic KRAS mutation (KRASG13D) affects PPIN structure and function of the Epidermal Growth Factor Receptor (EGFR) network in colorectal cancer (CRC) cells. Mapping >6000 PPIs shows that this network is extensively rewired in cells expressing transforming levels of KRASG13D (mtKRAS). The factors driving PPIN rewiring are multifactorial including changes in protein expression and phosphorylation. Mathematical modelling also suggests that the binding dynamics of low and high affinity KRAS interactors contribute to rewiring. PPIN rewiring substantially alters the composition of protein complexes, signal flow, transcriptional regulation, and cellular phenotype. These changes are validated by targeted and global experimental analysis. Importantly, genetic alterations in the most extensively rewired PPIN nodes occur frequently in CRC and are prognostic of poor patient outcomes.
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Affiliation(s)
- Susan A Kennedy
- Systems Biology Ireland, University College Dublin, Dublin, Ireland
| | - Mohamed-Ali Jarboui
- Institute for Ophthalmic Research, University of Tübingen, Tübingen, Germany
- Werner Siemens Imaging Center, University of Tübingen, Tübingen, Germany
| | - Sriganesh Srihari
- EMBL Australia Group, South Australian Health and Medical Research Institute, Adelaide, SA, 5000, Australia
- QIMR-Berghofer Medical Research Institute, Brisbane, QLD, 4006, Australia
| | - Cinzia Raso
- Systems Biology Ireland, University College Dublin, Dublin, Ireland
| | - Kenneth Bryan
- EMBL Australia Group, South Australian Health and Medical Research Institute, Adelaide, SA, 5000, Australia
| | - Layal Dernayka
- Institute for Ophthalmic Research, University of Tübingen, Tübingen, Germany
| | - Theodosia Charitou
- Systems Biology Ireland, University College Dublin, Dublin, Ireland
- EMBL Australia Group, South Australian Health and Medical Research Institute, Adelaide, SA, 5000, Australia
| | - Manuel Bernal-Llinares
- EMBL Australia Group, South Australian Health and Medical Research Institute, Adelaide, SA, 5000, Australia
| | | | | | - David Matallanas
- Systems Biology Ireland, University College Dublin, Dublin, Ireland
| | - Max Kotlyar
- Krembil Research Institute, University Health Network, Toronto, Canada
| | - Igor Jurisica
- Krembil Research Institute, University Health Network, Toronto, Canada
- Departments of Medical Biophysics and Computer Science, University of Toronto, Toronto, Canada
- Institute of Neuroimmunology, Slovak Academy of Sciences, Bratislava, Slovak Republic
| | - Jasna Curak
- Donnelly Centre, University of Toronto, Toronto, Canada
- Department of Biochemistry, University of Toronto, Toronto, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Canada
| | - Victoria Wong
- Donnelly Centre, University of Toronto, Toronto, Canada
- Department of Biochemistry, University of Toronto, Toronto, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Canada
| | - Igor Stagljar
- Donnelly Centre, University of Toronto, Toronto, Canada
- Department of Biochemistry, University of Toronto, Toronto, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Canada
- Mediterranean Institute for Life Sciences, Split, Croatia
| | - Thierry LeBihan
- Synthetic and Systems Biology, University of Edinburgh, Edinburgh, UK
| | - Lisa Imrie
- Synthetic and Systems Biology, University of Edinburgh, Edinburgh, UK
| | - Priyanka Pillai
- EMBL Australia Group, South Australian Health and Medical Research Institute, Adelaide, SA, 5000, Australia
| | - Miriam A Lynn
- EMBL Australia Group, South Australian Health and Medical Research Institute, Adelaide, SA, 5000, Australia
| | - Erik Fasterius
- School of Biotechnology, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Cristina Al-Khalili Szigyarto
- School of Biotechnology, KTH Royal Institute of Technology, Stockholm, Sweden
- Science for Life Laboratory, KTH Royal Institute of Technology, Stockholm, Sweden
| | - James Breen
- School of Biological Sciences, University of Adelaide Bioinformatics Hub, Adelaide, SA, Australia
- Computational & Systems Biology Program, South Australian Health and Medical Research Institute, Adelaide, SA, Australia
| | - Christina Kiel
- Systems Biology Ireland, University College Dublin, Dublin, Ireland
- Centre for Genomic Regulation, Barcelona Institute of Science and Technology, Barcelona, Spain
- Conway Institute, University College Dublin, Dublin, Ireland
| | - Luis Serrano
- Centre for Genomic Regulation, Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Nora Rauch
- Systems Biology Ireland, University College Dublin, Dublin, Ireland
| | | | - Boris N Kholodenko
- Systems Biology Ireland, University College Dublin, Dublin, Ireland
- Conway Institute, University College Dublin, Dublin, Ireland
- Department of Pharmacology, Yale University School of Medicine, New Haven, CT, USA
| | | | - Colm J Ryan
- Systems Biology Ireland, University College Dublin, Dublin, Ireland
- School of Computer Science, University College Dublin, Dublin, Ireland
| | - Ruth Pilkington
- Systems Biology Ireland, University College Dublin, Dublin, Ireland
| | | | - Owen Sansom
- Cancer Research UK Beatson Institute, Glasgow, UK
- Institute of Cancer Studies, Glasgow University, Glasgow, UK
| | - Steven Shave
- School of Biological Sciences and School of Biomedical Sciences, University of Edinburgh, Edinburgh, UK
| | - Manfred Auer
- School of Biological Sciences and School of Biomedical Sciences, University of Edinburgh, Edinburgh, UK
| | - Nicola Horn
- Institute for Ophthalmic Research, University of Tübingen, Tübingen, Germany
| | - Franziska Klose
- Institute for Ophthalmic Research, University of Tübingen, Tübingen, Germany
| | - Marius Ueffing
- Institute for Ophthalmic Research, University of Tübingen, Tübingen, Germany
| | - Karsten Boldt
- Institute for Ophthalmic Research, University of Tübingen, Tübingen, Germany.
| | - David J Lynn
- EMBL Australia Group, South Australian Health and Medical Research Institute, Adelaide, SA, 5000, Australia.
- College of Medicine and Public Health, Flinders University, Bedford Park, SA, 5042, Australia.
| | - Walter Kolch
- Systems Biology Ireland, University College Dublin, Dublin, Ireland.
- Conway Institute, University College Dublin, Dublin, Ireland.
- School of Medicine, University College Dublin, Dublin, Ireland.
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6
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Efstathiou G, Antonakis AN, Pavlopoulos GA, Theodosiou T, Divanach P, Trudgian DC, Thomas B, Papanikolaou N, Aivaliotis M, Acuto O, Iliopoulos I. ProteoSign: an end-user online differential proteomics statistical analysis platform. Nucleic Acids Res 2019; 45:W300-W306. [PMID: 28520987 PMCID: PMC5793730 DOI: 10.1093/nar/gkx444] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2017] [Accepted: 05/04/2017] [Indexed: 12/17/2022] Open
Abstract
Profiling of proteome dynamics is crucial for understanding cellular behavior in response to intrinsic and extrinsic stimuli and maintenance of homeostasis. Over the last 20 years, mass spectrometry (MS) has emerged as the most powerful tool for large-scale identification and characterization of proteins. Bottom-up proteomics, the most common MS-based proteomics approach, has always been challenging in terms of data management, processing, analysis and visualization, with modern instruments capable of producing several gigabytes of data out of a single experiment. Here, we present ProteoSign, a freely available web application, dedicated in allowing users to perform proteomics differential expression/abundance analysis in a user-friendly and self-explanatory way. Although several non-commercial standalone tools have been developed for post-quantification statistical analysis of proteomics data, most of them are not end-user appealing as they often require very stringent installation of programming environments, third-party software packages and sometimes further scripting or computer programming. To avoid this bottleneck, we have developed a user-friendly software platform accessible via a web interface in order to enable proteomics laboratories and core facilities to statistically analyse quantitative proteomics data sets in a resource-efficient manner. ProteoSign is available at http://bioinformatics.med.uoc.gr/ProteoSign and the source code at https://github.com/yorgodillo/ProteoSign.
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Affiliation(s)
- Georgios Efstathiou
- Medical School, Division of Basic Sciences, University of Crete, Heraklion 71003, Greece.,Sir William Dunn School of Pathology, University of Oxford, South Parks Road, Oxford OX1 3RE, UK
| | - Andreas N Antonakis
- Medical School, Division of Basic Sciences, University of Crete, Heraklion 71003, Greece
| | - Georgios A Pavlopoulos
- Medical School, Division of Basic Sciences, University of Crete, Heraklion 71003, Greece.,Lawrence Berkeley National Labs, DOE Joint Genome Institute, Walnut Creek, 2800 Mitchell Drive, CA 94598, USA
| | - Theodosios Theodosiou
- Medical School, Division of Basic Sciences, University of Crete, Heraklion 71003, Greece
| | - Peter Divanach
- Institute of Molecular Biology and Biotechnology, FORTH, Heraklion 70013, Greece
| | - David C Trudgian
- Department of Bioinformatics, UT Southwestern Medical Center, Dallas, TX 75390, USA
| | - Benjamin Thomas
- Sir William Dunn School of Pathology, University of Oxford, South Parks Road, Oxford OX1 3RE, UK
| | - Nikolas Papanikolaou
- Medical School, Division of Basic Sciences, University of Crete, Heraklion 71003, Greece
| | - Michalis Aivaliotis
- Institute of Molecular Biology and Biotechnology, FORTH, Heraklion 70013, Greece
| | - Oreste Acuto
- Sir William Dunn School of Pathology, University of Oxford, South Parks Road, Oxford OX1 3RE, UK
| | - Ioannis Iliopoulos
- Medical School, Division of Basic Sciences, University of Crete, Heraklion 71003, Greece
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7
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Misra BB. Updates on resources, software tools, and databases for plant proteomics in 2016-2017. Electrophoresis 2018; 39:1543-1557. [PMID: 29420853 DOI: 10.1002/elps.201700401] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2017] [Revised: 01/23/2018] [Accepted: 02/02/2018] [Indexed: 11/05/2022]
Abstract
Proteomics data processing, annotation, and analysis can often lead to major hurdles in large-scale high-throughput bottom-up proteomics experiments. Given the recent rise in protein-based big datasets being generated, efforts in in silico tool development occurrences have had an unprecedented increase; so much so, that it has become increasingly difficult to keep track of all the advances in a particular academic year. However, these tools benefit the plant proteomics community in circumventing critical issues in data analysis and visualization, as these continually developing open-source and community-developed tools hold potential in future research efforts. This review will aim to introduce and summarize more than 50 software tools, databases, and resources developed and published during 2016-2017 under the following categories: tools for data pre-processing and analysis, statistical analysis tools, peptide identification tools, databases and spectral libraries, and data visualization and interpretation tools. Intended for a well-informed proteomics community, finally, efforts in data archiving and validation datasets for the community will be discussed as well. Additionally, the author delineates the current and most commonly used proteomics tools in order to introduce novice readers to this -omics discovery platform.
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Affiliation(s)
- Biswapriya B Misra
- Department of Internal Medicine, Section of Molecular Medicine, Medical Center Boulevard, Winston-Salem, NC, USA
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