Bridging the gap: the need for genomic and clinical -omics data integration and standardization in overcoming the bottleneck of variant interpretation

Furness, L. Mike

doi:10.1080/23808993.2017.1322897

Cited by 5 publications

(5 citation statements)

References 45 publications

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“…However, data sharing remains a major bottleneck due to the lack of standardization of experimental models and data structure and should be handled with caution. The problems arising from integration of -omics data from different research groups has been addressed in a review by Furness [ 172 ]. Nonetheless, a recent initiative has been made by the group of Gagneux, to define a reference set of genetically well-characterized MTBC isolates, spanning all seven known human-adapted MTBC [ 173 ].…”

Section: Systems Biology Methodology and Novel Applications In mentioning

confidence: 99%

Metabolic Versatility of Mycobacterium tuberculosis during Infection and Dormancy

Chang

Guan

2021

Metabolites

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Mycobacterium tuberculosis (Mtb), the causative agent of tuberculosis (TB), is a highly successful intracellular pathogen with the ability to withstand harsh conditions and reside long-term within its host. In the dormant and persistent states, the bacterium tunes its metabolism and is able to resist the actions of antibiotics. One of the main strategies Mtb adopts is through its metabolic versatility—it is able to cometabolize a variety of essential nutrients and direct these nutrients simultaneously to multiple metabolic pathways to facilitate the infection of the host. Mtb further undergo extensive remodeling of its metabolic pathways in response to stress and dormancy. In recent years, advancement in systems biology and its applications have contributed substantially to a more coherent view on the intricate metabolic networks of Mtb. With a more refined appreciation of the roles of metabolism in mycobacterial infection and drug resistance, and the success of drugs targeting metabolism, there is growing interest in further development of anti-TB therapies that target metabolism, including lipid metabolism and oxidative phosphorylation. Here, we will review current knowledge revolving around the versatility of Mtb in remodeling its metabolism during infection and dormancy, with a focus on central carbon metabolism and lipid metabolism.

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Section: Systems Biology Methodology and Novel Applications In mentioning

confidence: 99%

Metabolic Versatility of Mycobacterium tuberculosis during Infection and Dormancy

Chang

Guan

2021

Metabolites

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“…In the setting of rare disease, such as mitochondrial disease, power is in numbers and large datasets of mineable information are necessitated to be able to definitively label a variant as 'pathogenic' [14]. Examples of such databases are the Exome Aggregation Consortium (http://exac.broadinstitute.org) (ExAC) database, comprising the sequence data of 60000 individuals and the Genome Aggregation Database (gnomAD) comprising 123136 exomes and 15496 genomes from unrelated individuals [56].…”

Section: Repositories Of Patient Data and Data Sharingmentioning

confidence: 99%

“…Quantificative '-omics' analyses, such as proteomics, metabolomics and transcriptomics are predicted to play an increasing role in variant prioritization [14]. Interrogation of proteomic data has proved a powerful approach to elucidate the pathogenicity of novel variants due to detection of reduced or diminished protein level [15] and in detection of specific proteomic signatures [60] in mitochondrial disease.…”

Section: Integration Of Functional '-Omics' Datamentioning

confidence: 99%

“…As a consequence, the primary challenge has now shifted from the capacity to discover variants to the ability to interpret function and clinical impact [11,12] for which comprehensive American College of Medical Genetics and Genomics (ACMG) guidance has been developed [13]. A further promising approach to increase diagnostic yield is to integrate functional 'multi-omics' data [14] such as transcriptomics into the analysis pipeline. Thus facilitating detection of variants which have evaded detection or prioritization in WES and WGS approaches [15,16].…”

Section: Introductionmentioning

confidence: 99%

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Advancing genomic approaches to the molecular diagnosis of mitochondrial disease

Stenton

Prokisch

2018

Essays in Biochemistry

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Mitochondrial diseases present a diagnostic challenge due to their clinical and genetic heterogeneity. Achieving comprehensive molecular diagnosis via a conventional candidate-gene approach is likely, therefore, to be labour- and cost-intensive given the expanding number of mitochondrial disease genes. The advent of whole exome sequencing (WES) and whole genome sequencing (WGS) hold the potential of higher diagnostic yields due to the universality and unbiased nature of the methods. However, these approaches are subject to the escalating challenge of variant interpretation. Thus, integration of functional 'multi-omics' data, such as transcriptomics, is emerging as a powerful complementary tool in the diagnosis of mitochondrial disease patients for whom extensive prior analysis of DNA sequencing has failed to return a genetic diagnosis.

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“…Systems Medicine Disease must link the subsystems of the organism and include features of both organism and disease at their Interface. The multi-level spatial constraints of biology and feature change that occur over time characterize these processes (Furness, 2017 ). Time expressed as aging has an effect on disease and organism response (Zierer et al, 2016 ) that a supplemental New Taxonomy must inform in conjunction with current classification and coding methods.…”

Section: Omic Patterns and Rates Of Change—scalability Toward A New Tmentioning

confidence: 99%

Systems Medicine Disease: Disease Classification and Scalability Beyond Networks and Boundary Conditions

Berlin

Gruen

Best

2018

Front. Bioeng. Biotechnol.

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In order to accommodate the forthcoming wealth of health and disease related information, from genome to body sensors to population and the environment, the approach to disease description and definition demands re-examination. Traditional classification methods remain trapped by history; to provide the descriptive features that are required for a comprehensive description of disease, systems science, which realizes dynamic processes, adaptive response, and asynchronous communication channels, must be applied (Wolkenhauer et al., 2013). When Disease is viewed beyond the thresholds of lines and threshold boundaries, disease definition is not only the result of reductionist, mechanistic categories which reluctantly face re-composition. Disease is process and synergy as the characteristics of Systems Biology and Systems Medicine are included. To capture the wealth of information and contribute meaningfully to medical practice and biology research, Disease classification goes beyond a single spatial biologic level or static time assignment to include the interface of Disease process and organism response (Bechtel, 2017a; Green et al., 2017).

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Bridging the gap: the need for genomic and clinical -omics data integration and standardization in overcoming the bottleneck of variant interpretation

Cited by 5 publications

References 45 publications

Metabolic Versatility of Mycobacterium tuberculosis during Infection and Dormancy

Metabolic Versatility of Mycobacterium tuberculosis during Infection and Dormancy

Advancing genomic approaches to the molecular diagnosis of mitochondrial disease

Systems Medicine Disease: Disease Classification and Scalability Beyond Networks and Boundary Conditions

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