GenoMetric Query Language: a novel approach to large-scale genomic data management

Masseroli, Marco; Pinoli, Pietro; Venco, Francesco; Kaitoua, Abdulrahman; Jalili, Vahid; Palluzzi, Fernando; Müller, Heiko; Ceri, Stefano

doi:10.1093/bioinformatics/btv048

Cited by 83 publications

(86 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The GenoMetric Query Language [22] is a high-level, declarative query language for genomic data, and a toolkit is available for its use at http://www.bioinformatics.deib.polimi.it/GMQL/. The TCGA data in BED format are fully supported by the GMQL data model and can be seamlessly processed by GMQL, which is a key instrument for the integration of genomic and clinical data also from heterogeneous sources.…”

Section: Resultsmentioning

confidence: 99%

“…Such formats facilitate the use of these data in knowledge discovery analyses, providing an intuitive and high-quality access to the valuable large-scale cancer genomics and clinical data from TCGA. Moreover, they allow the seamless application on these data of the recently proposed GenoMetric Query Language (GMQL) [22] for their comprehensive management, processing, and querying for knowledge extraction.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

TCGA2BED: extracting, extending, integrating, and querying The Cancer Genome Atlas

et al. 2017

Self Cite

View full text Add to dashboard Cite

BackgroundData extraction and integration methods are becoming essential to effectively access and take advantage of the huge amounts of heterogeneous genomics and clinical data increasingly available. In this work, we focus on The Cancer Genome Atlas, a comprehensive archive of tumoral data containing the results of high-throughout experiments, mainly Next Generation Sequencing, for more than 30 cancer types.ResultsWe propose TCGA2BED a software tool to search and retrieve TCGA data, and convert them in the structured BED format for their seamless use and integration. Additionally, it supports the conversion in CSV, GTF, JSON, and XML standard formats. Furthermore, TCGA2BED extends TCGA data with information extracted from other genomic databases (i.e., NCBI Entrez Gene, HGNC, UCSC, and miRBase). We also provide and maintain an automatically updated data repository with publicly available Copy Number Variation, DNA-methylation, DNA-seq, miRNA-seq, and RNA-seq (V1,V2) experimental data of TCGA converted into the BED format, and their associated clinical and biospecimen meta data in attribute-value text format.ConclusionsThe availability of the valuable TCGA data in BED format reduces the time spent in taking advantage of them: it is possible to efficiently and effectively deal with huge amounts of cancer genomic data integratively, and to search, retrieve and extend them with additional information. The BED format facilitates the investigators allowing several knowledge discovery analyses on all tumor types in TCGA with the final aim of understanding pathological mechanisms and aiding cancer treatments.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1419-5) contains supplementary material, which is available to authorized users.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

TCGA2BED: extracting, extending, integrating, and querying The Cancer Genome Atlas

et al. 2017

Self Cite

View full text Add to dashboard Cite

show abstract

“…As the amount of genomic and epigenomic resources grows and need to be confronted with reference datasets like those from the ENCODE (The Encode Project Consortium, 2011) or the TCGA consortium (Cancer Genome Atlas Network, 2012), new querying tools as the GenoMetric Query Language (Masseroli et al, 2015) will make it possible to interrogate and compare large scale genomic information based on experimental and phenotypic properties.…”

Section: Perspectivesmentioning

confidence: 99%

Integrated Systems for NGS Data Management and Analysis: Open Issues and Available Solutions

et al. 2016

View full text Add to dashboard Cite

Next-generation sequencing (NGS) technologies have deeply changed our understanding of cellular processes by delivering an astonishing amount of data at affordable prices; nowadays, many biology laboratories have already accumulated a large number of sequenced samples. However, managing and analyzing these data poses new challenges, which may easily be underestimated by research groups devoid of IT and quantitative skills. In this perspective, we identify five issues that should be carefully addressed by research groups approaching NGS technologies. In particular, the five key issues to be considered concern: (1) adopting a laboratory management system (LIMS) and safeguard the resulting raw data structure in downstream analyses; (2) monitoring the flow of the data and standardizing input and output directories and file names, even when multiple analysis protocols are used on the same data; (3) ensuring complete traceability of the analysis performed; (4) enabling non-experienced users to run analyses through a graphical user interface (GUI) acting as a front-end for the pipelines; (5) relying on standard metadata to annotate the datasets, and when possible using controlled vocabularies, ideally derived from biomedical ontologies. Finally, we discuss the currently available tools in the light of these issues, and we introduce HTS-flow, a new workflow management system conceived to address the concerns we raised. HTS-flow is able to retrieve information from a LIMS database, manages data analyses through a simple GUI, outputs data in standard locations and allows the complete traceability of datasets, accompanying metadata and analysis scripts.

show abstract

“…it/GMQL/queries/. Thus, while [8] proves user focused innovation for biologists, this paper presents solid domainspecific innovation for genomics in cloud-based data management, associated with a new implementation which operates on any server or cloud supporting Hadoop and Pig. The remaining of this paper is structured as follows. Section 2 presents the state of the art and discusses other related current approaches and implementations.…”

Section: Introductionmentioning

confidence: 99%

“…In [8], we introduced the novel GenoMetric Query Language (GMQL), built on an abstracted model for genomic data; we sketched out its main operations and demonstrated its usefulness, expressive power and flexibility through multiple different examples of biological interest (including finding distal bindings in transcription regulatory regions, associating transcriptomics and epigenomics, and finding somatic mutations in exons).…”

Section: Introductionmentioning

confidence: 99%

Data Management for Heterogeneous Genomic Datasets

Ceri

Kaitoua

Masseroli

et al. 2017

IEEE/ACM Trans. Comput. Biol. and Bioinf.

Self Cite

View full text Add to dashboard Cite

Next Generation Sequencing (NGS), a family of technologies for reading DNA and RNA, is changing biological research, and will soon change medical practice, by quickly providing sequencing data and high-level features of numerous individual genomes in different biological and clinical conditions. The availability of millions of whole genome sequences may soon become the biggest and most important "big data" problem of mankind. In this exciting framework, we recently proposed a new paradigm to raise the level of abstraction in NGS data management, by introducing a GenoMetric Query Language (GMQL) and demonstrating its usefulness through several biological query examples. Leveraging on that effort, here we motivate and formalize GMQL operations, especially focusing on the most characteristic and domain-specific ones. Furthermore, we address their efficient implementation and illustrate the architecture of the new software system that we have developed for their execution on big genomic data in a cloud computing environment, providing the evaluation of its performance. The new system implementation is available for download at the GMQL website (http://www.bioinformatics.deib.polimi.it/GMQL/); GMQL can also be tested through a set of predefined queries on ENCODE and Roadmap Epigenomics data at http://www.bioinformatics.deib.polimi.it/GMQL/queries/.

show abstract

GenoMetric Query Language: a novel approach to large-scale genomic data management

Abstract: The GMQL toolkit is freely available for non-commercial use at http://www.bioinformatics.deib.polimi.it/GMQL/.

Cited by 83 publications

References 24 publications

TCGA2BED: extracting, extending, integrating, and querying The Cancer Genome Atlas

TCGA2BED: extracting, extending, integrating, and querying The Cancer Genome Atlas

Integrated Systems for NGS Data Management and Analysis: Open Issues and Available Solutions

Data Management for Heterogeneous Genomic Datasets

Contact Info

Product

Resources

About