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Last updated: Apr 25, 2018

Generating and analysing genomic data

Key considerations:
  • The laboratory methods, sequencing technologies, analysis tools and parameters will all have an effect on the tools and analysis methodologies that can be used.
  • Tool selection is often a trade-off between sensitivity, precision and speed (cost).
  • Quality control of biological samples, sequences, alignment and results can prevent the return of false positives and negatives.
  • The biological context is critical ensuring that the results make sense. If a result does not make biological sense then it could be a sequencing artefact, data mix up, in a poorly sequenced region, etc.
  • Genomic analysis requires considerable computer resources and data storage. 

Recent developments in high throughput genomics technologies have made genomic research more affordable than ever before. Researchers are now generating high volumes of data which has shifted the bottleneck from generating sequences information to analysing, interpreting, validating and managing the deluge of data [1].                                                                                                                                 

Most clinical genomic sequencing experiments aimed at detecting variants, follow these key steps; sample preparation and sequencing, processing and alignment of sequencing data, identifying, filtering, annotating, biological and clinical interpretation and validation of variants. At each step, there should be various quality checks to ensure that no errors have occurred.

There are countless genomic methodologies and steps that cannot be described as part of this resource. More information can be found in research publications and by talking with sequencing providers or a bioinformatician.

This section addresses: 



[1] Katsanis & Katsanis, Nature Reviews Genetics 14, 415–426 (2013) doi:10.1038/nrg3493