scholarly journals CEDAR, an online resource for the reporting and exploration of complexome profiling data

2020 ◽  
Author(s):  
Joeri van Strien ◽  
Alexander Haupt ◽  
Uwe Schulte ◽  
Hans-Peter Braun ◽  
Alfredo Cabrero-Orefice ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Protein Complexes ◽  
Mass Spectrometry Data ◽  
Analysis Tool ◽  
Data Resource ◽  
Online Resource ◽  
Comprehensive Information ◽  
Central Resource ◽  
Rest Api ◽  
User Friendly

Complexome profiling is an emerging 'omics approach that systematically interrogates the composition of protein complexes (the complexome) of a sample, by combining biochemical separation of native protein complexes with mass-spectrometry based quantitation proteomics. The resulting fractionation profiles hold comprehensive information on the abundance and composition of the complexome, and have a high potential for reuse by experimental and computational researchers. However, the lack of a central resource that provides access to these data, reported with adequate descriptions and an analysis tool, has limited their reuse. Therefore, we established the ComplexomE profiling DAta Resource (CEDAR, www3.cmbi.umcn.nl/cedar/), an openly accessible database for depositing and exploring mass spectrometry data from complexome profiling studies. Compatibility and reusability of the data is ensured by a standardized data and reporting format containing the "minimum information required for a complexome profiling experiment" (MIACE). The data can be accessed through a user-friendly web interface, as well as programmatically using the REST API portal. Additionally, all complexome profiles available on CEDAR can be inspected directly on the website with the profile viewer tool that allows the detection of correlated profile sand inference of potential complexes. In conclusion, CEDAR is a unique,growing and invaluable resource for the study of protein complex composition and dynamics across biological systems.

scholarly journals NAguideR: performing and prioritizing missing value imputations for consistent bottom-up proteomic analyses

2020 ◽  
Vol 48 (14) ◽  
pp. e83-e83 ◽  
Author(s):  
Shisheng Wang ◽  
Wenxue Li ◽  
Liqiang Hu ◽  
Jingqiu Cheng ◽  
Hao Yang ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Quantitative Proteomics ◽  
Missing Values ◽  
Protein Complexes ◽  
Label Free ◽  
Missing Value ◽  
Imputation Methods ◽  
Data Independent Acquisition ◽  
Low Performance ◽  
User Friendly

Abstract Mass spectrometry (MS)-based quantitative proteomics experiments frequently generate data with missing values, which may profoundly affect downstream analyses. A wide variety of imputation methods have been established to deal with the missing-value issue. To date, however, there is a scarcity of efficient, systematic, and easy-to-handle tools that are tailored for proteomics community. Herein, we developed a user-friendly and powerful stand-alone software, NAguideR, to enable implementation and evaluation of different missing value methods offered by 23 widely used missing-value imputation algorithms. NAguideR further evaluates data imputation results through classic computational criteria and, unprecedentedly, proteomic empirical criteria, such as quantitative consistency between different charge-states of the same peptide, different peptides belonging to the same proteins, and individual proteins participating protein complexes and functional interactions. We applied NAguideR into three label-free proteomic datasets featuring peptide-level, protein-level, and phosphoproteomic variables respectively, all generated by data independent acquisition mass spectrometry (DIA-MS) with substantial biological replicates. The results indicate that NAguideR is able to discriminate the optimal imputation methods that are facilitating DIA-MS experiments over those sub-optimal and low-performance algorithms. NAguideR further provides downloadable tables and figures supporting flexible data analysis and interpretation. NAguideR is freely available at http://www.omicsolution.org/wukong/NAguideR/ and the source code: https://github.com/wangshisheng/NAguideR/.

Software for the Analysis and Interpretation of Native Mass Spectrometry Data

2019 ◽  
Author(s):  
Timothy Allison ◽  
Perdita Barran ◽  
Justin Benesch ◽  
Sarah Cianferani ◽  
Matteo Degiacomi ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Ion Mobility Spectrometry ◽  
Ion Mobility ◽  
Protein Complexes ◽  
Native Mass Spectrometry ◽  
Mass Spectrometry Data ◽  
Full Potential ◽  
Research Groups ◽  
Future Developments ◽  
New Instrumentation

<div><div><div><p>The last few years have seen a dramatic increase in applications of native mass and ion mobility spectrometry, especially for the study of proteins and protein complexes. This increase has been catalysed by the availability of commercial instrumentation capable of carrying out such analyses. Like in most fields, however, the software to process the data generated from new instrumentation lags behind. Recently, a number of research groups have started addressing this by developing software, but further improvements are still required in order to realise the full potential of the datasets generated. Here we describe practical aspects as well as challenges in processing native mass spectrometry (MS) and ion mobility-MS datasets, and provide a brief overview of currently available tools. We then set out our vision of future developments that would bring the community together and lead to the development of a common platform to expedite future computational developments, provide standardised processing approaches and serve as a location for the deposition of data for this emerging field.</p></div></div></div>

scholarly journals DNAproDB: an expanded database and web-based tool for structural analysis of DNA–protein complexes

2019 ◽  
Author(s):  
Jared M Sagendorf ◽  
Nicholas Markarian ◽  
Helen M Berman ◽  
Remo Rohs
Keyword(s):  
Structural Analysis ◽  
Protein Complexes ◽  
Structural Data ◽  
Data Bank ◽  
Structural Features ◽  
Analysis Tool ◽  
Web Based ◽  
Initial Release ◽  
Data Files ◽  
User Friendly

Abstract DNAproDB (https://dnaprodb.usc.edu) is a web-based database and structural analysis tool that offers a combination of data visualization, data processing and search functionality that improves the speed and ease with which researchers can analyze, access and visualize structural data of DNA–protein complexes. In this paper, we report significant improvements made to DNAproDB since its initial release. DNAproDB now supports any DNA secondary structure from typical B-form DNA to single-stranded DNA to G-quadruplexes. We have updated the structure of our data files to support complex DNA conformations, multiple DNA–protein complexes within a DNAproDB entry and model indexing for analysis of ensemble data. Support for chemically modified residues and nucleotides has been significantly improved along with the addition of new structural features, improved structural moiety assignment and use of more sequence-based annotations. We have redesigned our report pages and search forms to support these enhancements, and the DNAproDB website has been improved to be more responsive and user-friendly. DNAproDB is now integrated with the Nucleic Acid Database, and we have increased our coverage of available Protein Data Bank entries. Our database now contains 95% of all available DNA–protein complexes, making our tools for analysis of these structures accessible to a broad community.

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