ArticleIEEE access : practical innovations, open solutions2021
Methods for Proteogenomics Data Analysis, Challenges, and Scalability Bottlenecks: A Survey.
Article in IEEE access : practical innovations, open solutions, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
15 citing papers in PubMed.
- pAnno: a comprehensive, precise, and fast proteogenomic workflow for the discovery of novel coding regions.Genome biology · 2026Article
- Review
- LanthMS: A Computational Tool for the Structure Elucidation of Lanthipeptides from Tandem Mass Spectrometry Data.Protein and peptide letters · 2026Article
- PTMFusionNet: A Deep Learning Approach for Predicting Disease Related Post-translational Modification and Classifying Disease Subtypes.Molecular & cellular proteomics : MCP · 2025Article
- Article
- Proteomic insights into the extracellular matrix: a focus on proteoforms and their implications in health and disease.Expert review of proteomics · 2024Review
- Using flux theory in dynamic omics data sets to identify differentially changing signals using DPoP.BMC bioinformatics · 2024Article
- Phenotyping Tumor Heterogeneity through Proteogenomics: Study Models and Challenges.International journal of molecular sciences · 2024Review
- Proteogenomics in Nephrology: A New Frontier in Nephrological Research.Current issues in molecular biology · 2024Review
- Article
- Data-Independent Acquisition (DIA) Is Superior for High Precision Phospho-Peptide Quantification inJournal of fungi (Basel, Switzerland) · 2022Article
- Nano-omics: nanotechnology-based multidimensional harvesting of the blood-circulating cancerome.Nature reviews. Clinical oncology · 2022Review
- Article
- An overview of technologies for MS-based proteomics-centric multi-omics.Expert review of proteomics · 2022Article
- Review of Liquid Chromatography-Mass Spectrometry-Based Proteomic Analyses of Body Fluids to Diagnose Infectious Diseases.International journal of molecular sciences · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
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.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.