Modeling and interoperability of heterogeneous genomic big data for integrative processing and querying

Masseroli, Marco; Kaitoua, Abdulrahman; Pinoli, Pietro; Ceri, Stefano

doi:10.1016/j.ymeth.2016.09.002

Cited by 40 publications

(28 citation statements)

References 25 publications

(23 reference statements)

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“…172 Successfully achieving this goal will require precise mathematical models, efficient integrated tools and enhanced knowledge bases. [173][174][175][176][177] Furthermore, development in big data analysis techniques, including artificial intelligence, will benefit data exploitation and will foster personalised treatment strategies for the patient, depending on efficient decision-support algorithms. [178][179][180][181][182][183] Thus, big data will be useful for discovery of signatures involved in pathogenesis or response to treatment, by enabling the study of large patient cohorts.…”

Section: Clinical Evaluation Of Viral Genome Variabilitymentioning

confidence: 99%

How viral genetic variants and genotypes influence disease and treatment outcome of chronic hepatitis B. Time for an individualised approach?

Rajoriya

Combet

Zoulim

et al. 2017

Journal of Hepatology

131

125

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Chronic hepatitis B virus (HBV) infection remains a global problem. Several HBV genotypes exist with different biology and geographical prevalence. Whilst the future aim of HBV treatment remains viral eradication, current treatment strategies aim to suppress the virus and prevent the progression of liver disease. Current strategies also involve identification of patients for treatment, namely those at risk of progressive liver disease. Identification of HBV genotype, HBV mutants and other predictive factors allow for tailoured treatments, and risk-surveillance pathways, such as hepatocellular cancer screening. In the future, these factors may enable stratification not only of treatment decisions, but also of patients at risk of higher relapse rates when current therapies are discontinued. Newer technologies, such as next-generation sequencing, to assess drug-resistant or immune escape variants and quasi-species heterogeneity in patients, may allow for more information-based treatment decisions between the clinician and the patient. This article serves to discuss how HBV genotypes and genetic variants impact not only upon the disease course and outcomes, but also current treatment strategies. Adopting a personalised genotypic approach may play a role in future strategies to combat the disease. Herein, we discuss new technologies that may allow more informed decision-making for response guided therapy in the battle against HBV.

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Section: Clinical Evaluation Of Viral Genome Variabilitymentioning

confidence: 99%

How viral genetic variants and genotypes influence disease and treatment outcome of chronic hepatitis B. Time for an individualised approach?

Rajoriya

Combet

Zoulim

et al. 2017

Journal of Hepatology

131

125

View full text Add to dashboard Cite

show abstract

“…The important aspect of this repository is the format unification achieved across the different sources. Data files available at the sources are transformed to a same representation, called the Genomic Data Model, GDM [12], which essentially forces every data type used by the data files to become a mapping from regions to a data type-specific feature vector. Format transformations come as the result of significant efforts: for instance, the transformation of TCGA-supported data types to GDM is a long process, with several syntactic and semantic transformations (see TCGA2BED [13]).…”

Section: Geco Resourcesmentioning

confidence: 99%

Data Science for Genomic Data Management: Challenges, Resources, Experiences

Ceri

Pinoli

2019

SN COMPUT. SCI.

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We highlight several challenges which are faced by data scientists who use public datasets for solving biological and clinical problems. In spite of the large efforts in building such public datasets, they are dispersed over many sources and heterogeneous for their formats and sequencing/calling techniques, often meeting highly variable quality standards. Moreover, for most research questions, scientists hardly find datasets with enough samples for building and training machine learning models. Data scarcity depends on the complexity of the genomic domain, with its multi-facets, as well as the lack of organic initiatives to provide standardization across communities. In this paper, we discuss our approach to genomic data management, that can strongly improve the problems of data dispersion and format heterogeneity through high-level abstractions for genomics. We briefly present the computational resources that were recently developed by the GeCo project (ERC Advanced Grant); they include GDM, a Genomic Data Model providing interoperability across data formats; GMQL, a genometric query language for answering data science queries over genomic datasets; and an in-house integrated repository providing attribute-based and keyword-based search over normalized metadata from several open data repositories. We describe these resources at work on a specific research question, and we highlight how we managed to produce a model for addressing such specific research question by overcoming the lack of sufficient samples and labelled datasets.

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“…The Genomic Data Model (GDM) [19] is based on the notions of datasets and samples and on two abstractions: one for genomic regions, which represent portions of the DNA and their features, and one for their metadata. Datasets are collections of samples and each sample consists of two parts: the region data, which describe the characteristics of genomic features called during secondary analysis, and the metadata, which describe general properties of the sample.…”

Section: Data Modelmentioning

confidence: 99%

Overview of GeCo: A Project for Exploring and Integrating Signals from the Genome

Ceri

Bernasconi

Canakoglu

et al. 2018

Communications in Computer and Information Science

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Next Generation Sequencing is a 10-year old technology for reading the DNA, capable of producing massive amounts of genomic data-in turn, reshaping genomic computing. In particular, tertiary data analysis is concerned with the integration of heterogeneous regions of the genome; this is an emerging and increasingly important problem of genomic computing, because regions carry important signals and the creation of new biological or clinical knowledge requires the integration of these signals into meaningful messages. We specifically focus on how the GeCo project is contributing to tertiary data analysis, by overviewing the main results of the project so far and by describing its future scenarios.

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Modeling and interoperability of heterogeneous genomic big data for integrative processing and querying

Cited by 40 publications

References 25 publications

How viral genetic variants and genotypes influence disease and treatment outcome of chronic hepatitis B. Time for an individualised approach?

How viral genetic variants and genotypes influence disease and treatment outcome of chronic hepatitis B. Time for an individualised approach?

Data Science for Genomic Data Management: Challenges, Resources, Experiences

Overview of GeCo: A Project for Exploring and Integrating Signals from the Genome

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