Module 2

NewIntegrative interpretation of large-scale data

 

Module summary:

The module provides conceptual understanding and hands on experience of Bioinformatic tools for accessing and interpreting high-throughput -omics data.


Topics include:

  • Data sharing standards and accessing publicly available large-scale data repositories (ENA, GEO, ArrayExpress, Metabolights, PRIDE).
  • Genome Browsers as a resource for visualising and accessing information
  • Annotation data repositories (for example, NCBI-ENTREZ, BRENDA, KEGG, SEED/RAST)
  • Sequence homology for annotation (BLAST-based tools)
  • Sequence analysis (RSAT)
  • Gene ontology
  • Functional enrichment analysis
  • Molecular interaction data (IntAct, STRING)
  • Using Cytoscape to integrate information and visualise as networks
  • Examples of data integration

This online module aims to:

  • Provide conceptual understanding of Bioinformatic approaches for analysing and interpreting high-throughput -omics data
  • Allow students to critically analyse, interpret and integrate high-throughput data for understanding the response of a system given a particular perturbation.
  • To increase awareness of the utility and need of computational sciences in the biosciences.
  • To increase awareness of the major molecular concepts relevant to current systems biology research.
  • To enable delegates to evaluate the quality of data and information.

On successful completion of this module students will be able to:

  • Be able to independently identify suitable Bioinformatic approaches for analysing and interpreting high-throughput –omics data.
  • Demonstrate an ability to interpret –omics data.
  • Critically evaluate and interpret the analyses of different –omics methods.
  • Be critically aware of the advantages and disadvantages of each data set.
  • Comprehensively describe what each data set contributes to understanding a system.
  • Integrate different –omic information.

Module Leader

Emma Laing

Dr Emma Laing

Lecturer in Bioinformatics 

e.laing@surrey.ac.uk 

 

 


Related modules

Quantitative data analysis

Systems modelling and network analysis

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Key Information

  • Start date

    Start Date - On-going  (applicants can apply year round) 


  • Duration

    Duration: 15 weeks/150 hours


  • Cost

    Cost: £1,250


  • Credits

    Award: Certificate of achievement, subject to validation - 15 credits will be awarded as Recognition of Prior Learning (RPL)


  • Delivery of module

    Delivery of Module: Online lectures, live discussion events with academics and industry experts, interactive and self-directed learning exercises based on industry-directed case studies. Delivered via SurreyLearn virtual learning environment.


  • Assessment

    Assessment: TBC


Entry Requirements

At least a 2.2 degree (or equivalent) in a relevant biological sciences discipline. Applicants not possessing these qualifications may be considered, depending on the length and quality of their practical experience and recommendations from employers/supervisors.

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