Critical assessment of on-premise approaches to scalable genome analysis

Abstract

Background: Plummeting 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.

Methods: In 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.

Results: Tools 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.

Conclusion: The 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.

Keywords: Big data; Genomic data science; Genomic databases; Horizontal scaling; NoSQL; SQL; VCF.

Fig. 1

The general workflow of a genomics data science solution. The input is a VCF file after a variant calling pipeline which could undergo transformation into a storage system. Variants are then annotated with a variety of sources and fed back into the storage. The contents of the VCF file can be queried via a client or a program for later analysis

Fig. 2

Query performance comparison for all studied tools to query for a unique variant by its identifier with and without providing the chromosome. Chromosome regions are shown as bands of dark and light rectangles. BCFtools and GEMINI results are presented in a log scale: as the query time between chromosome-bound queries and regular queries differ by order of magnitude, the log scale is more favorable to display the intricate patterns when querying with region indexing

Fig. 3

Query performance comparison between all studied tools to query for all INDEL-typed variants located in chromosome 5

Fig. 4

Query performance comparison between all studied tools to query for all variant sites where all samples in the study have homozygous genotype

Fig. 5

Time (in hours) taken by the studied tools to annotate the variants by patients and controls’ allele frequency. The annotation time is shown for a different number of samples

Fig. 6

Query performance comparison of studied tools for different numbers of samples to retrieve all variants that appear in more than 40% of control samples and less than or equal to 40% of patient samples