SVI: A Simple Single-Nucleotide Human Variant Interpretation Tool for Clinical Use

Missier, Paolo; Wijaya, Eldarina; Kirby, R. A. Y.; Keogh, Michael J.

doi:10.1007/978-3-319-21843-4_14

Cited by 7 publications

(8 citation statements)

References 22 publications

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“…''Simple Variant Interpretation (SVI)'' [6] is an experiment which was developed as part of Recomp project (recomp.org.uk). SVI tracks the entire diagnostic process through storing of data consumed and data produced at each step of process.…”

Section: Use Case 1: Simple Variant Interpretation (Svi)mentioning

confidence: 99%

“…Understanding the causes for these observed variations is arguably important. For instance, in our own study on providing a genetic interpretation for suspected diseases, we have observed that the analysis of human variants coming from high-throughput genomic data is sensitive to the specific version of each of the reference databases used by the analysis process [6]. In particular, changes in database version may sometime cause the diagnosis for important neuro-degenerative diseases such as Alzheimer to change over time for the same patient.…”

Section: Introductionmentioning

confidence: 99%

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Why-Diff: Exploiting Provenance to Understand Outcome Differences From Non-Identical Reproduced Workflows

2019

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Data analytics processes such as scientific workflows tend to be executed repeatedly, with varying dependencies and input datasets. The case has been made in the past for tracking the provenance of the final information products through the workflow steps, to enable their reproducibility. In this paper, we explore the hypothesis that provenance traces recorded during execution are also instrumental to answering questions about the observed differences between sets of results obtained from similar but not identical workflow configurations. Such differences in configurations may be introduced deliberately, i.e., to explore process variations or accidentally, typically as the result of porting efforts or of changes in the computing environment. Using a commonly used workflow programming model as a reference, we consider both structural variations in the workflows as well as variations within their individual components. Our why-diff algorithm compares the graph representations of two provenance traces derived from two workflow variations. It produces a delta graph that can be used to produce human-readable explanations of the impact of workflow differences on observed output differences. We report with Neo4j graph database. Further, we report explanations of the difference between workflow results using a suite of synthetic workflows as well as real-world workflows. INDEX TERMS Why-diff, provenance, reproducibility, big data, eScience Central, workflow.

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Section: Use Case 1: Simple Variant Interpretation (Svi)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Why-Diff: Exploiting Provenance to Understand Outcome Differences From Non-Identical Reproduced Workflows

2019

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“…One instance of such a process is Simple Variant Interpretation (SVI) [5], developed in our group, which uses knowledge from the ClinVar and Gene Map reference databases. Fig.…”

Section: Reference Case Study: Genetic Variants Analysismentioning

confidence: 99%

“…The work presented here is carried out as part of the ReComp project. 5 Although the project's ambition is to develop generic techniques that are applicable to a broad range of data analytics processes, in this paper we focus on a singularly important and timely use case in genomics. It is a complex and resource-intensive process of identifiying potentially deleterious genomic mutations in humans, in order to help with the diagnosis of rare genetic diseases.…”

Section: Introductionmentioning

confidence: 99%

Selective and Recurring Re-computation of Big Data Analytics Tasks: Insights from a Genomics Case Study

Cała

Missier

2018

Big Data Research

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In Data Science, knowledge generated by a resource-intensive analytics process is a valuable asset. Such value, however, tends to decay over time as a consequence of the evolution of any of the elements the process depends on: external data sources, libraries, and system dependencies. It is therefore important to be able to (i) detect changes that may partially or completely invalidate prior outcomes, (ii) determine the impact that those changes will have on those prior outcomes, ideally without having to perform expensive re-computations, and (iii) optimise the process re-execution needed to selectively refresh affected outcomes. This paper presents an extensive experimental study on how the selective re-computation problem manifests itself in a relevant analytics task for Genomics, namely variant calling and clinical interpretation, and how the problem can be addressed using a combination of approaches. Starting from this experience, we then offer a blueprint for a generic re-computation meta-process that makes use of process history metadata to make informed decisions about selective recomputations in reaction to a variety of changes in the data.

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“…12 Lastly, our pipeline includes an automatic annotation step using ANNOVAR [21] in addition to our own in-house annotation tool, which makes the output ready for a researcher to analyse. This also means that output from the pipeline can be directly fed into our variant interpretation tool [30].…”

Section: Related Workmentioning

confidence: 99%

Scalable and efficient whole-exome data processing using workflows on the cloud

Cała

Marei

et al. 2016

Future Generation Computer Systems

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Dataow-style workows o_er a simple, high-level programming model for exible prototyping of scienti_c applications as an attractive alternative to low-level scripting. At the same time, workow management systems (WfMS) may support data parallelism over big datasets by providing scalable, distributed deployment and execution of the workow over a cloud infrastructure. In theory, the combination of these properties makes workows a natural choice for implementing Big Data processing pipelines, common for instance in bioinformatics. In practice, however, correct workow design for parallel Big Data problems can be complex and very time-consuming. In this paper we present our experience in porting a genomics data processing pipeline from an existing scripted implementation deployed on a closed HPC cluster, to a workow-based design deployed on the Microsoft Azure public cloud. We draw two contrasting and general conclusions from this project. On the positive side, we show that our solution based on the e-Science Central WfMS and deployed in the cloud clearly outperforms the original HPC-based implementation achieving up to 2.3x speed-up. However, in order to deliver such performance we describe the importance of optimising the workow deployment model to best suit the characteristics of the cloud computing infrastructure. The main reason for the performance gains was the availability of fast, node-local SSD disks delivered by Dseries Azure VMs combined with the implicit use of local disk resources by e-Science Central workow engines. These conclusions suggest that, on parallel Big Data problems, it is important to couple understanding of the cloud computing architecture and its software stack with simplicity of design, and that further e_orts in automating parallelisation of complex pipelines are required.

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SVI: A Simple Single-Nucleotide Human Variant Interpretation Tool for Clinical Use

Cited by 7 publications

References 22 publications

Why-Diff: Exploiting Provenance to Understand Outcome Differences From Non-Identical Reproduced Workflows

Why-Diff: Exploiting Provenance to Understand Outcome Differences From Non-Identical Reproduced Workflows

Selective and Recurring Re-computation of Big Data Analytics Tasks: Insights from a Genomics Case Study

Scalable and efficient whole-exome data processing using workflows on the cloud

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