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

Abstract

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.

Review of Liquid Chromatography-Mass Spectrometry-Based Proteomic Analyses of Body Fluids to Diagnose Infectious Diseases

“…The most abundant precursor ions in a given spectrum are then selected and fragmented into MS/MS for further analysis (Figure 1B) [80]. Various protein identification programs have been developed [64,81]. The most common approach for protein identification is the sequence database matching algorithm, in which real spectra obtained from MS/MS analysis are comparatively analyzed with in silico spectra derived from peptide sequences from a reference database.…”

“…However, although the previously described body fluid proteomics studies succeeded in identifying bacterial-derived markers, in many cases researchers failed to identify bacterial proteins because of intrinsic limitations, low quantity target proteins relative to the host proteins, and/or the absence of target proteins in existing databases, as mentioned above [65]. Spectral library searching is an alternative method for overcoming sensitivityrelated limitations [81]. This is described in more detail in the next section.…”

“…Although DIA is an extremely powerful method, it is more complex than DDA because of the difficulties of MS/MS spectral data analysis. Previously used peptide identification algorithms are not appropriate for DIA because of the complexity of the MS/MS spectrum of DIA [58,81]. In order to deconvolute complex spectra, spectral libraries are essential as reference databases.…”

“…This shows the importance of the spectral library approach [88,142]. However, the quality of spectral libraries from individual experiments is questionable, and the lack of publicly available libraries makes it difficult to apply spectral library search strategies to common proteomic data analysis [81,88,89,[143][144][145][146]. For this reason, several synthetic peptide libraries, library-free approaches, or spectral prediction approaches have been developed as alternatives [147][148][149][150][151][152].…”

Quantitative Proteogenomic Characterization of Inflamed Murine Colon Tissue Using an Integrated Discovery, Verification, and Validation Proteogenomic Workflow

“…Although proteogenomics has been shown to be a powerful approach for studying cancer [ 15 , 17 ], potential false-positive matches to non-canonical sequences remains a concern [ 18 ], requiring methods to verify the accuracy of PSMs using bioinformatic and/or analytic approaches. To aid in analysis, these assorted bioinformatics processes can be combined into simple workflows for automated, streamlined proteogenomic analyses [ 19 ].…”