A Service Computing Framework for Proteomics Analysis and Collaboration of Pathogenic Mechanism Studies

The booming of proteomics data has positioned multiple disciplines and research areas in a more complicated and challenging place. Moreover, the proteomics data of any defined research interests, such as for pathogenic mechanism studies of infectious diseases, have presented unstructured and heterogeneous characteristics. Thus, a service computing framework for proteomics analysis is desired to bring biologists and computer scientists into this area seamlessly and efficiently. With this regard, this work is dedicated to detail the proteomics analysis and collaboration process of pathogenic mechanism studies. We articulate this framework to serve the requirements and ease the task design by broadly reviewing the state-of-the- art research and development efforts and collectively designing different informative stages. Thus, the framework has a focus of distilling different aspects, including data curation, resources distribution, standard construction and computational tasks identification, into the proteomics analysis. The framework is designed as Proteomics Analysis as a Service to deepen the understanding of the interdisciplinary research.

[1]  Gary D Bader,et al.  PSICQUIC and PSISCORE: accessing and scoring molecular interactions , 2011, Nature Methods.

[2]  Enis Afgan,et al.  Federated Galaxy: Biomedical Computing at the Frontier , 2018, 2018 IEEE 11th International Conference on Cloud Computing (CLOUD).

[3]  Andrzej M. Goscinski,et al.  A Survey of Cloud-Based Service Computing Solutions for Mammalian Genomics , 2014, IEEE Transactions on Services Computing.

[4]  Lennart Martens,et al.  Human Proteome Organization Proteomics Standards Initiative: data standardization, a view on developments and policy. , 2007, Molecular & cellular proteomics : MCP.

[5]  Lei Wang,et al.  Towards Biological Sequence Data Service with Insights , 2018, 2018 IEEE International Conference on Big Data (Big Data).

[6]  Lizy Kurian John,et al.  Benchmarking Big Data Systems: A Review , 2018, IEEE Transactions on Services Computing.

[7]  Yan Zhang,et al.  PATRIC, the bacterial bioinformatics database and analysis resource , 2013, Nucleic Acids Res..

[8]  Neil A Ranson,et al.  Securing the future of research computing in the biosciences , 2019, PLoS Comput. Biol..

[9]  Chris F. Taylor,et al.  The work of the Human Proteome Organisation's Proteomics Standards Initiative (HUPO PSI). , 2006, Omics : a journal of integrative biology.

[10]  Kara Dolinski,et al.  The BioGRID interaction database: 2017 update , 2016, Nucleic Acids Res..

[11]  I. Foster,et al.  Service-Oriented Science , 2005, Science.

[12]  Fatih Erdogan Sevilgen,et al.  PHISTO: pathogen-host interaction search tool , 2013, Bioinform..

[13]  Johannes Goll,et al.  Protein interaction data curation: the International Molecular Exchange (IMEx) consortium , 2012, Nature Methods.

[14]  The UniProt Consortium,et al.  UniProt: a worldwide hub of protein knowledge , 2018, Nucleic Acids Res..

[15]  C. Sander,et al.  The HUPO PSI's Molecular Interaction format—a community standard for the representation of protein interaction data , 2004, Nature Biotechnology.

[16]  B. Langmead,et al.  Cloud computing for genomic data analysis and collaboration , 2018, Nature Reviews Genetics.

[17]  L. Castagnoli,et al.  mentha: a resource for browsing integrated protein-interaction networks , 2013, Nature Methods.

[18]  Jiangning Song,et al.  Systematic evaluation of machine learning methods for identifying human-pathogen protein-protein interactions , 2020, Briefings Bioinform..

[19]  Gos Micklem,et al.  Encompassing new use cases - level 3.0 of the HUPO-PSI format for molecular interactions , 2018, BMC Bioinformatics.

[20]  Magnus Palmblad,et al.  Scientific Workflow Management in Proteomics , 2012, Molecular & Cellular Proteomics.

[21]  Rajkumar Buyya,et al.  HPC Cloud for Scientific and Business Applications , 2017, ACM Comput. Surv..