Methods for Proteogenomics Data Analysis, Challenges, and Scalability Bottlenecks: A Survey
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...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9309010/ |
| _version_ | 1818411767646126080 |
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| author | Muhammad Usman Tariq Muhammad Haseeb Mohammed Aledhari Rehma Razzak Reza M. Parizi Fahad Saeed |
| author_facet | Muhammad Usman Tariq Muhammad Haseeb Mohammed Aledhari Rehma Razzak Reza M. Parizi Fahad Saeed |
| author_sort | Muhammad Usman Tariq |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-14T10:36:39Z |
| format | Article |
| id | doaj.art-c0f8574a8ff64b949a36eef5a93f9b57 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T10:36:39Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-c0f8574a8ff64b949a36eef5a93f9b572022-12-21T23:05:53ZengIEEEIEEE Access2169-35362021-01-0195497551610.1109/ACCESS.2020.30475889309010Methods for Proteogenomics Data Analysis, Challenges, and Scalability Bottlenecks: A SurveyMuhammad Usman Tariq0Muhammad Haseeb1https://orcid.org/0000-0002-0697-6894Mohammed Aledhari2https://orcid.org/0000-0002-5380-6003Rehma Razzak3https://orcid.org/0000-0002-5301-8955Reza M. Parizi4https://orcid.org/0000-0002-0049-4296Fahad Saeed5https://orcid.org/0000-0002-3410-9552School of Computing and Information Sciences, Florida International University, Miami, FL, USASchool of Computing and Information Sciences, Florida International University, Miami, FL, USACollege of Computing and Software Engineering, Kennesaw State University, Marietta, GA, USACollege of Computing and Software Engineering, Kennesaw State University, Marietta, GA, USACollege of Computing and Software Engineering, Kennesaw State University, Marietta, GA, USASchool of Computing and Information Sciences, Florida International University, Miami, FL, USABig 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.https://ieeexplore.ieee.org/document/9309010/Proteogenomicsproteomicshigh-performance computingworkflowgenomicsbig data |
| spellingShingle | Muhammad Usman Tariq Muhammad Haseeb Mohammed Aledhari Rehma Razzak Reza M. Parizi Fahad Saeed Methods for Proteogenomics Data Analysis, Challenges, and Scalability Bottlenecks: A Survey IEEE Access Proteogenomics proteomics high-performance computing workflow genomics big data |
| title | Methods for Proteogenomics Data Analysis, Challenges, and Scalability Bottlenecks: A Survey |
| title_full | Methods for Proteogenomics Data Analysis, Challenges, and Scalability Bottlenecks: A Survey |
| title_fullStr | Methods for Proteogenomics Data Analysis, Challenges, and Scalability Bottlenecks: A Survey |
| title_full_unstemmed | Methods for Proteogenomics Data Analysis, Challenges, and Scalability Bottlenecks: A Survey |
| title_short | Methods for Proteogenomics Data Analysis, Challenges, and Scalability Bottlenecks: A Survey |
| title_sort | methods for proteogenomics data analysis challenges and scalability bottlenecks a survey |
| topic | Proteogenomics proteomics high-performance computing workflow genomics big data |
| url | https://ieeexplore.ieee.org/document/9309010/ |
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