Methods for Proteogenomics Data Analysis, Challenges, and Scalability Bottlenecks: A Survey

IEEE Access

Reza M. Parizi¹  Mohammed Aledhari¹  Rehma Razzak¹  Fahad Saeed²  Muhammad Haseeb²  Muhammad Usman Tariq² 

[1] College of Computing and Software Engineering, Kennesaw State University, Marietta, GA, USA;School of Computing and Information Sciences, Florida International University, Miami, FL, USA;

关键词: Proteogenomics;    proteomics;    high-performance computing;    workflow;    genomics;    big data;   

DOI  :  10.1109/ACCESS.2020.3047588

来源: DOAJ

【 摘 要 】

Big Data Proteogenomics lies at the intersection of high-throughput Mass Spectrometry (MS) based proteomics and Next Generation Sequencing based genomics. The combined and integrated analysis of these two high-throughput technologies can help discover novel proteins using genomic, and transcriptomic data. Due to the biological significance of integrated analysis, the recent past has seen an influx of proteogenomic tools that perform various tasks, including mapping proteins to the genomic data, searching experimental MS spectra against a six-frame translation genome database, and automating the process of annotating genome sequences. To date, most of such tools have not focused on scalability issues that are inherent in proteogenomic data analysis where the size of the database is much larger than a typical protein database. These state-of-the-art tools can take more than half a month to process a small-scale dataset of one million spectra against a genome of 3 GB. In this article, we provide an up-to-date review of tools that can analyze proteogenomic datasets, providing a critical analysis of the techniques' relative merits and potential pitfalls. We also point out potential bottlenecks and recommendations that can be incorporated in the future design of these workflows to ensure scalability with the increasing size of proteogenomic data. Lastly, we make a case of how high-performance computing (HPC) solutions may be the best bet to ensure the scalability of future big data proteogenomic data analysis.

【 授权许可】

Unknown