Integrating Genomic, Transcriptomic, and Interactome Data to Improve Peptide and Protein Identification in Shotgun Proteomics

Wang, Xiaojing; Zhang, Bing

doi:10.1021/pr500194t

Cited by 34 publications

(24 citation statements)

References 63 publications

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“…Sample‐specific integration of genomic, transcriptomic, epigenomic, proteomic, and possibly metabolomics information is required to understand the system of interconnectivity from the gene to functional levels. For example, integrating genomic, transcriptomic, and interactome information in a bioinformatics platform was shown to facilitate improved protein and peptide identification in proteomics analysis (Wang & Zhang, ). Integrating transcriptome sequencing with GS was previously shown to enhance the efficiency of identifying functionally relevant variants in the human genome (Lappalainen et al., ).…”

Section: Promise Of Functional Genomics and In Vivo In Vitro Validatmentioning

confidence: 99%

Inferring the effect of genomic variation in the new era of genomics

Chakravorty

Hegde

2018

Human Mutation

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Accurate and detailed understanding of the effects of variants in the coding and noncoding regions of the genome is the next big challenge in the new genomic era of personalized medicine, especially to tackle newer findings of genetic and phenotypic heterogeneity of diseases. This is necessary to resolve the gene-variant-disease relationship, the pathogenic variant spectrum of genes, pathogenic variants with variable clinical consequences, and multiloci diseases. In turn, this will facilitate patient recruitment for relevant clinical trials. In this review, we describe the trends in research at the intersection of basic and clinical genomics aiming to (a) overcome molecular diagnostic challenges and increase the clinical utility of next-generation sequencing (NGS) platforms, (b) elucidate variants associated with disease, (c) determine overall genomic complexity including epistasis, complex inheritance patterns such as "synergistic heterozygosity," digenic/multigenic inheritance, modifier effect, and rare variant load. We describe the newly emerging field of integrated functional genomics, in vivo or in vitro large-scale functional approaches, statistical bioinformatics algorithms that support NGS genomics data to interpret variants for timely clinical diagnostics and disease management. Thus, facilitating the discovery of new therapeutic or biomarker options, and their roles in the future of personalized medicine.

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Section: Promise Of Functional Genomics and In Vivo In Vitro Validatmentioning

confidence: 99%

Inferring the effect of genomic variation in the new era of genomics

Chakravorty

Hegde

2018

Human Mutation

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“…Given the recent increase in DNA and RNA sequencing experiments, as well as protein interaction data, several methods and databases have been developed and curated to improve protein inference and the classification of non-unique peptides. Exon-exon junctions, splicing and sequence variants and even novel protein coding genes can be identified using sequencing data (see Wang et al [30] for a review of data integration for the improvement of peptide and protein identification).…”

Section: Experimental Protocolsmentioning

confidence: 99%

Comparative analysis of statistical methods used for detecting differential expression in label-free mass spectrometry proteomics

Langley

Mayr

2015

Journal of Proteomics

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“…To complement already published review papers that focus on specific sub-domains of the broad proteogenomics research area (21)(22)(23)(24), we systematically classified existing methods and tools for various types of integrative proteogenomic studies into four major sections. Sequence-centric Proteogenomics describes aspects of sequence-centric proteogenomics and the combined use of genomic and proteomic data to augment gene or protein annotation (Fig.…”

mentioning

confidence: 99%

Methods, Tools and Current Perspectives in Proteogenomics

Ruggles

Krug

Wang

et al. 2017

Molecular & Cellular Proteomics

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138

110

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With combined technological advancements in high-throughput next-generation sequencing and deep mass spectrometry-based proteomics, proteogenomics, the integrative analysis of proteomic and genomic data, has emerged as a new research field. Early efforts in the field were focused on improving protein identification using sample-specific genomic and transcriptomic sequencing data. More recently, integrative analysis of quantitative measurements from genomic and proteomic studies have identified novel insights into gene expression regulation, cell signaling, and disease. Many methods and tools have been developed or adapted to enable an array of integrative proteogenomic approaches and in this article, we systematically classify published methods and tools into four major categories, (1) Sequence-centric proteogenomics; (2) Analysis of proteogenomic relationships; (3) Integrative modeling of proteogenomic data; and (4) Data sharing and visualization. We provide a comprehensive review of methods and available tools in each category and highlight their typical applications.

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Integrating Genomic, Transcriptomic, and Interactome Data to Improve Peptide and Protein Identification in Shotgun Proteomics

Cited by 34 publications

References 63 publications

Inferring the effect of genomic variation in the new era of genomics

Inferring the effect of genomic variation in the new era of genomics

Comparative analysis of statistical methods used for detecting differential expression in label-free mass spectrometry proteomics

Methods, Tools and Current Perspectives in Proteogenomics

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