Critical assessment of on-premise approaches to scalable genome analysis

BMC Bioinformatics

Research

Habiba Alsafar¹  Gihan Daw Elbait²  Amira Al-Aamri³  Syafiq Kamarul Azman³  Andreas Henschel⁴ 

[1] Center for Biotechnology (BTC), Khalifa University, P.O. Box 127788, Abu Dhabi, United Arab Emirates;Department of Biomedical Engineering, Khalifa University, P.O. Box 127788, Abu Dhabi, United Arab Emirates;Department of Biology, College of Arts and Sciences, Khalifa University, P.O. Box 127788, Abu Dhabi, United Arab Emirates;Center for Biotechnology (BTC), Khalifa University, P.O. Box 127788, Abu Dhabi, United Arab Emirates;Department of Electrical Engineering and Computer Science, College of Engineering, Khalifa University, P.O. Box 127788, Abu Dhabi, United Arab Emirates;Department of Electrical Engineering and Computer Science, College of Engineering, Khalifa University, P.O. Box 127788, Abu Dhabi, United Arab Emirates;Center for Biotechnology (BTC), Khalifa University, P.O. Box 127788, Abu Dhabi, United Arab Emirates;

关键词: Genomic data science;    Big data;    Genomic databases;    SQL;    VCF;    NoSQL;    Horizontal scaling;   

DOI  :  10.1186/s12859-023-05470-2

 received in 2023-05-27, accepted in 2023-09-08,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundPlummeting DNA sequencing cost in recent years has enabled genome sequencing projects to scale up by several orders of magnitude, which is transforming genomics into a highly data-intensive field of research. This development provides the much needed statistical power required for genotype–phenotype predictions in complex diseases.MethodsIn order to efficiently leverage the wealth of information, we here assessed several genomic data science tools. The rationale to focus on on-premise installations is to cope with situations where data confidentiality and compliance regulations etc. rule out cloud based solutions. We established a comprehensive qualitative and quantitative comparison between BCFtools, SnpSift, Hail, GEMINI, and OpenCGA. The tools were compared in terms of data storage technology, query speed, scalability, annotation, data manipulation, visualization, data output representation, and availability.ResultsTools that leverage sophisticated data structures are noted as the most suitable for large-scale projects in varying degrees of scalability in comparison to flat-file manipulation (e.g., BCFtools, and SnpSift). Remarkably, for small to mid-size projects, even lightweight relational database.ConclusionThe assessment criteria provide insights into the typical questions posed in scalable genomics and serve as guidance for the development of scalable computational infrastructure in genomics.

【 授权许可】

CC BY   
© BioMed Central Ltd., part of Springer Nature 2023

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