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      The PeptideAtlas of a widely cultivated fish Labeo rohita: A resource for the Aquaculture Community

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          Abstract

          Labeo rohita (Rohu) is one of the most important fish species produced in world aquaculture. Integrative omics research provides a strong platform to understand the basic biology and translate this knowledge into sustainable solutions in tackling disease outbreak, increasing productivity and ensuring food security. Mass spectrometry-based proteomics has provided insights to understand the biology in a new direction. Very little proteomics work has been done on ‘Rohu’ limiting such resources for the aquaculture community. Here, we utilised an extensive mass spectrometry based proteomic profiling data of 17 histologically normal tissues, plasma and embryo of Rohu to develop an open source PeptideAtlas. The current build of “Rohu PeptideAtlas” has mass-spectrometric evidence for 6015 high confidence canonical proteins at 1% false discovery rate, 2.9 million PSMs and ~150 thousand peptides. This is the first open-source proteomics repository for an aquaculture species. The ‘Rohu PeptideAtlas’ would promote basic and applied aquaculture research to address the most critical challenge of ensuring nutritional security for a growing population.

          Abstract

          Measurement(s) Proteins and Peptides
          Technology Type(s) Mass Spectrometry
          Sample Characteristic - Organism Labeo rohita
          Sample Characteristic - Location India

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          Most cited references41

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          Cleavage of Structural Proteins during the Assembly of the Head of Bacteriophage T4

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            eggNOG 5.0: a hierarchical, functionally and phylogenetically annotated orthology resource based on 5090 organisms and 2502 viruses

            Abstract eggNOG is a public database of orthology relationships, gene evolutionary histories and functional annotations. Here, we present version 5.0, featuring a major update of the underlying genome sets, which have been expanded to 4445 representative bacteria and 168 archaea derived from 25 038 genomes, as well as 477 eukaryotic organisms and 2502 viral proteomes that were selected for diversity and filtered by genome quality. In total, 4.4M orthologous groups (OGs) distributed across 379 taxonomic levels were computed together with their associated sequence alignments, phylogenies, HMM models and functional descriptors. Precomputed evolutionary analysis provides fine-grained resolution of duplication/speciation events within each OG. Our benchmarks show that, despite doubling the amount of genomes, the quality of orthology assignments and functional annotations (80% coverage) has persisted without significant changes across this update. Finally, we improved eggNOG online services for fast functional annotation and orthology prediction of custom genomics or metagenomics datasets. All precomputed data are publicly available for downloading or via API queries at http://eggnog.embl.de
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              Skyline: an open source document editor for creating and analyzing targeted proteomics experiments.

              Skyline is a Windows client application for targeted proteomics method creation and quantitative data analysis. It is open source and freely available for academic and commercial use. The Skyline user interface simplifies the development of mass spectrometer methods and the analysis of data from targeted proteomics experiments performed using selected reaction monitoring (SRM). Skyline supports using and creating MS/MS spectral libraries from a wide variety of sources to choose SRM filters and verify results based on previously observed ion trap data. Skyline exports transition lists to and imports the native output files from Agilent, Applied Biosystems, Thermo Fisher Scientific and Waters triple quadrupole instruments, seamlessly connecting mass spectrometer output back to the experimental design document. The fast and compact Skyline file format is easily shared, even for experiments requiring many sample injections. A rich array of graphs displays results and provides powerful tools for inspecting data integrity as data are acquired, helping instrument operators to identify problems early. The Skyline dynamic report designer exports tabular data from the Skyline document model for in-depth analysis with common statistical tools. Single-click, self-updating web installation is available at http://proteome.gs.washington.edu/software/skyline. This web site also provides access to instructional videos, a support board, an issues list and a link to the source code project.
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                Author and article information

                Contributors
                mukugoswami@gmail.com
                sanjeeva@iitb.ac.in
                Journal
                Sci Data
                Sci Data
                Scientific Data
                Nature Publishing Group UK (London )
                2052-4463
                13 April 2022
                13 April 2022
                2022
                : 9
                : 171
                Affiliations
                [1 ]GRID grid.417971.d, ISNI 0000 0001 2198 7527, Department of Biosciences and Bioengineering, , Indian Institute of Technology Bombay, Powai, ; Mumbai, 400076 India
                [2 ]GRID grid.64212.33, ISNI 0000 0004 0463 2320, Institute for Systems Biology, ; Seattle, WA 98109 USA
                [3 ]GRID grid.418105.9, ISNI 0000 0001 0643 7375, Central Institute of Fisheries Education, , Indian Council of Agricultural Research, Versova, ; Mumbai, Maharashtra 400061 India
                [4 ]GRID grid.502122.6, ISNI 0000 0004 1774 5631, Regional Centre for Biotechnology, ; Faridabad, 121001 India
                Author information
                http://orcid.org/0000-0002-0581-1510
                http://orcid.org/0000-0002-8783-6315
                http://orcid.org/0000-0002-3216-9447
                http://orcid.org/0000-0001-5159-6834
                Article
                1259
                10.1038/s41597-022-01259-9
                9008064
                35418183
                fecd15ee-92fd-442f-9daf-bb195e2ee164
                © The Author(s) 2022

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 5 August 2021
                : 11 March 2022
                Funding
                Funded by: FundRef https://doi.org/10.13039/100000052, U.S. Department of Health & Human Services | NIH | NIH Office of the Director (OD);
                Award ID: 1S10OD026936
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100000057, U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (NIGMS);
                Award ID: GM087221
                Award Recipient :
                Funded by: National Institute on Aging Grant- U19AG023122 NSF Award- 1920268
                Funded by: FundRef https://doi.org/10.13039/501100001407, Department of Biotechnology, Ministry of Science and Technology (DBT);
                Award ID: BT/PR15285/AAQ/3/753/2015
                Award ID: BT/PR13114/INF/22/206/2015
                Award Recipient :
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                Data Descriptor
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                © The Author(s) 2022

                protein-protein interaction networks,agriculture

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