Back Module: SBI126

Competency: 5

This is a compentency for the module "IT for Advanced Bioinformatics Applications".

Competency information

Details

Deploy the workflow, including maintenance and upgrading, ensuring compliance with quality assurance procedures, including version control.

Considerations

  • How to develop a data and meta-data capture strategy for a genomics laboratory.
  • How to perform a strategic analysis of the computational requirements of a genomics laboratory through the use of computational analysis and design tools, e.g. structured systems analysis and design method (SSADM).
  • How to evaluate computing solutions for their fitness for purpose within the security and IT governance frameworks of the NHS.
  • How to evaluate data integration strategies for their fitness for purpose within the security and IT governance frameworks of the NHS.
  • How to create simple workflows capable of integrating and analysing clinical functional genomics data.

Computational infrastructure

  • Data encryption and data encryption standards.
  • Governance and security issues for large data in the NHS.
  • Basic cloud computing architectures (software as service, compute as service, etc.).
  • Public and private cloud architectures (including commercial systems such as Azure and EC2).
  • A basic introduction to workflows in computer science.
  • An introduction to workflow tools (Taverna, Galaxy, etc.).

Functional genomics and genomics data sets

  • The concept of meta-data.
  • The role of minimum information standards to allow effective sharing.
  • Tools to capture minimal information data (Extensible Markup Language; XML).
  • An introduction to ontologies.
  • Community annotation through ontology.
  • Interoperating with ontologies.
  • Strategies for large-scale data integration and data mining.
  • The pros and cons of data warehouses versus data integration over distributed heterogeneous data.
  • Examples of ontology-driven data integration.
  • Examples of data warehouses for genomic integration (Ensembl).

Workflows

  • The basic theory of computational workflows.
  • The architecture of workflow systems.
  • Examples of workflows in genetics (Galaxy assembly of NGS data).
  • Analysis of current literature and data integration and workflows in genetics and medicine.

Relevant learning outcomes

# Outcome
# 1 Outcome Identify a clinical and/or laboratory bioinformatics requirement and develop, validate and deploy a bespoke workflow for clinical or public health analysis.
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