Connecting Proteomics to Next‐Generation Sequencing: Proteogenomics and Its Current Applications in Biology

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Abstract

Understanding the relationship between genotypes and phenotypes is essential to disentangle biological mechanisms and to unravel the molecular basis of diseases. Genes and proteins are closely linked in biological systems. However, genomics and proteomics have developed separately into two distinct disciplines whereby crosstalk among scientists from the two domains is limited and this constrains the integration of both fields into a single data modality of useful information. The emerging field of proteogenomics attempts to address this by building bridges between the two disciplines. In this review, how genomics and transcriptomics data in different formats can be utilized to assist proteogenomics application is briefly discussed. Subsequently, a much larger part of this review focuses on proteogenomics research articles that are published in the last five years that answer two important questions. First, how proteogenomics can be applied to tackle biological problems is discussed, covering genome annotation and precision medicine. Second, the latest developments in analytical technologies for data acquisition and the bioinformatics tools to interpret and visualize proteogenomics data are covered.

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Most cited references 82

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Jeffrey A Martin, Zhong Wang (2011)

Transcriptomics studies often rely on partial reference transcriptomes that fail to capture the full catalogue of transcripts and their variations. Recent advances in sequencing technologies and assembly algorithms have facilitated the reconstruction of the entire transcriptome by deep RNA sequencing (RNA-seq), even without a reference genome. However, transcriptome assembly from billions of RNA-seq reads, which are often very short, poses a significant informatics challenge. This Review summarizes the recent developments in transcriptome assembly approaches - reference-based, de novo and combined strategies - along with some perspectives on transcriptome assembly in the near future.

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Alexey I Nesvizhskii (2014)

Proteogenomics is an area of research at the interface of proteomics and genomics. In this approach, customized protein sequence databases generated using genomic and transcriptomic information are used to help identify novel peptides (not present in reference protein sequence databases) from mass spectrometry-based proteomic data; in turn, the proteomic data can be used to provide protein-level evidence of gene expression and to help refine gene models. In recent years, owing to the emergence of new sequencing technologies such as RNA-seq and dramatic improvements in the depth and throughput of mass spectrometry-based proteomics, the pace of proteogenomic research has greatly accelerated. Here I review the current state of proteogenomic methods and applications, including computational strategies for building and using customized protein sequence databases. I also draw attention to the challenge of false positive identifications in proteogenomics and provide guidelines for analyzing the data and reporting the results of proteogenomic studies.

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A Pan-Cancer Proteogenomic Atlas of PI3K/AKT/mTOR Pathway Alterations.

Yiqun Zhang, Patrick Kwok-Shing Ng, Melanie H. Kucherlapati … (2017)

Molecular alterations involving the PI3K/AKT/mTOR pathway (including mutation, copy number, protein, or RNA) were examined across 11,219 human cancers representing 32 major types. Within specific mutated genes, frequency, mutation hotspot residues, in silico predictions, and functional assays were all informative in distinguishing the subset of genetic variants more likely to have functional relevance. Multiple oncogenic pathways including PI3K/AKT/mTOR converged on similar sets of downstream transcriptional targets. In addition to mutation, structural variations and partial copy losses involving PTEN and STK11 showed evidence for having functional relevance. A substantial fraction of cancers showed high mTOR pathway activity without an associated canonical genetic or genomic alteration, including cancers harboring IDH1 or VHL mutations, suggesting multiple mechanisms for pathway activation.