Scalable Genomic Data Management System on the Cloud

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

doi:10.1109/hpcs.2017.19

Cited by 4 publications

(3 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With an exponential growth in datasets, the real challenge will be to store and access haplotyping data in an efficient way, which can potentially be achieved by applying massive parallelism (detailed reviews in [ 141 , 142 ]). In addition, cloud-based strategies will be required for storing, accessing, and sharing data (for example, https://vgp.github.io/genomeark/ ).…”

Section: Remaining Challenges and Perspectivesmentioning

confidence: 99%

Computational methods for chromosome-scale haplotype reconstruction

Garg

2021

Genome Biol

View full text Add to dashboard Cite

High-quality chromosome-scale haplotype sequences of diploid genomes, polyploid genomes, and metagenomes provide important insights into genetic variation associated with disease and biodiversity. However, whole-genome short read sequencing does not yield haplotype information spanning whole chromosomes directly. Computational assembly of shorter haplotype fragments is required for haplotype reconstruction, which can be challenging owing to limited fragment lengths and high haplotype and repeat variability across genomes. Recent advancements in long-read and chromosome-scale sequencing technologies, alongside computational innovations, are improving the reconstruction of haplotypes at the level of whole chromosomes. Here, we review recent and discuss methodological progress and perspectives in these areas.

show abstract

“…With an exponential growth in datasets, the real challenge will be to store and access haplotyping data in an efficient way, which can potentially be achieved by applying massive parallelism (detailed reviews in [ 141 , 142 ]). In addition, cloud-based strategies will be required for storing, accessing, and sharing data (for example, https://vgp.github.io/genomeark/ ).…”

Section: Remaining Challenges and Perspectivesmentioning

confidence: 99%

Computational methods for chromosome-scale haplotype reconstruction

Garg

2021

Genome Biol

View full text Add to dashboard Cite

show abstract

“…With an exponential growth in datasets, the real challenge will be to store and access haplotyping data in an efficient way, which can potentially be achieved by applying massive parallelism (detailed reviews in 130,131 benefit from a public collection of high-quality benchmarks, for example in the form of a community-driven assessment initiative similar to the Critical Assessment of Metagenome Interpretation 83 (CAMI), Assemblathon 133,134 and Genome Assembly Gold-standard Evaluations 135 (GAGE). As the field advances to produce high-quality chromosome-scale phased sequences, the next critical step will be in the development of new gene annotation tools 136 to enable more precise downstream analyses in the coming decade.…”

Section: [H2] Scalementioning

confidence: 99%

Computational Methods for Chromosome-Scale Haplotype Reconstruction

Garg

2021

Preprint

View full text Add to dashboard Cite

High-quality chromosome-scale haplotype sequences— of diploid genomes, polyploid genomes and metagenomes — provide important insights into genetic variation associated with disease and biodiversity. However, whole-genome short read sequencing does not yield haplotype information that spans whole chromosomes directly. Computational assembly of shorter haplotype fragments is required for haplotype reconstruction, which can be challenging owing to limited fragment lengths and high haplotype and repeat variability across genomes. Recent advancements in long-read and chromosome-scale sequencing technologies, alongside computational innovations, are improving the reconstruction of haplotypes at the level of whole chromosomes. Here, we review recent methodological progress in these areas and discuss perspectives that could enable routine high-quality haplotype reconstruction in clinical and evolutionary studies.

show abstract

“…The BioMart implementation is based upon Structured Query Language (SQL) and supported by several SQL engines, including MySQL, PostgreSQL, Oracle, DB2 and MS SQL. The main difference of Federated GMQL with BioMart is that the former exposes the full power of GMQL, a high-level declarative language allowing the expression of queries over genomic regions and metadata, and is implemented on Spark, which scales better than relational databases on the cloud [25], [26].…”

Section: Federated Genomic Data Management System Comparisonmentioning

confidence: 99%

Federated sharing and processing of genomic datasets for tertiary data analysis

Canakoglu

Pinoli

Gulino

et al. 2020

Briefings in Bioinformatics

Self Cite

View full text Add to dashboard Cite

Abstract Motivation With the spreading of biological and clinical uses of next-generation sequencing (NGS) data, many laboratories and health organizations are facing the need of sharing NGS data resources and easily accessing and processing comprehensively shared genomic data; in most cases, primary and secondary data management of NGS data is done at sequencing stations, and sharing applies to processed data. Based on the previous single-instance GMQL system architecture, here we review the model, language and architectural extensions that make the GMQL centralized system innovatively open to federated computing. Results A well-designed extension of a centralized system architecture to support federated data sharing and query processing. Data is federated thanks to simple data sharing instructions. Queries are assigned to execution nodes; they are translated into an intermediate representation, whose computation drives data and processing distributions. The approach allows writing federated applications according to classical styles: centralized, distributed or externalized. Availability The federated genomic data management system is freely available for non-commercial use as an open source project at http://www.bioinformatics.deib.polimi.it/FederatedGMQLsystem/ Contact {arif.canakoglu, pietro.pinoli}@polimi.it Summary

show abstract

Scalable Genomic Data Management System on the Cloud

Cited by 4 publications

References 9 publications

Computational methods for chromosome-scale haplotype reconstruction

Computational methods for chromosome-scale haplotype reconstruction

Computational Methods for Chromosome-Scale Haplotype Reconstruction

Federated sharing and processing of genomic datasets for tertiary data analysis

Contact Info

Product

Resources

About