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Implementation of semi-automated sequencing

Implementation of semi-automated sequencing. Birmingham Genetic Technologist Meeting 01.10.09. Michael Day, Carolyn Tysoe, Katie Guegan, Ann-Marie Patch and Sian Ellard. Introduction. Why Semi-automated Analysis?

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Implementation of semi-automated sequencing

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  1. Implementation of semi-automated sequencing Birmingham Genetic Technologist Meeting 01.10.09 Michael Day, Carolyn Tysoe, Katie Guegan, Ann-Marie Patch and Sian Ellard

  2. Introduction • Why Semi-automated Analysis? • The case for unidirectional sequence analysis and the sensitivity of semi-automated analysis • CMGS Sequencing guidelines 2009 • Implementation of semi-automated sequence analysis in Exeter

  3. Why semi-automated analysis? • Sequencing quality has improved with the introduction of capillary sequencers and better chemistry. • Sequence analysis software packages are now “fit for purpose”. • Volume of sequence data has increased hugely in the past 5 years. • We can’t justify the cost of NOT using the software to its full potential (ie cost of licences vs staff costs)

  4. 1995 2001 2009 Sequencing quality from 1995 to 2009

  5. Exeter laboratory activity data 2001- 2008 92% of genotypes are now generated by sequencing (vs 36% in 2001-2002)

  6. The case for unidirectional sequence analysis • Sensitivity of semi-automated analysis

  7. CFTR intron 8 The case for unidirectional sequencing • Some regions can only be examined in one direction eg. Heterozygous intronic insertions/deletions • No biological reason for a mutation to be visible only in one direction • Improved sequence quality gives higher confidence in unidirectional analysis

  8. What is the sensitivity of Mutation Surveyor for detecting heterozygous bases in 1D sequencing? Software detected 701 unique heterozygous base substitutions in 27 genes No mutations were missed (comparison with visual inspection) Sensitivity estimated as ≥99.6%

  9. CMGS Sequencing guidelines 2009 • Define the extent of the analysis (region of interest or ROI) • Bi-directional sequencing is not essential for the detection of heterozygous (or homozygous) mutations provided that high quality sequence data is obtained for the complete ROI in one direction. • Bi-directional sequencing isrecommended for the detection of mosaic mutations. • Sole reliance on unassisted visual inspection of the sequence data is not acceptable. 

  10. Semi-automated mutation scanning • Semi-automated analysis relies upon the automatic analysis of sequence data with visual inspection confined to checking of variants and sequences, or bases within a sequence, that do not meet defined quality parameters. • It is acceptable to use semi-automated analysis, but the thresholds for visual inspection should be determined using an evidence-based approach within individual laboratories to minimise the risk that a mutation is not detected due to poor quality sequence.

  11. Implementation of semi-automated sequence analysis in Exeter

  12. Definition of semi-automated analysis • All mutations and polymorphisms inspected visually and nomenclature checked (especially important for frameshifts) • Lower quality sequences must be flagged for visual inspection/repeat testing • Low quality bases flagged for visual inspection

  13. Requirements for semi-automated analysis • Define sensitivity of analysis software • Define region of interest (ROI) • Define quality criteria (at the level of the sequence read and individual base)

  14. How does Mutation Surveyor (v3.24) assess sequence quality? (1) PHRED scores for each base with an average for the ROI (2) Quality score - represents signal: noise ratio with an average for the ROI QS 100 = 1% noise

  15. Requirements for semi-automated analysis • Define sensitivity of analysis software • Define region of interest (ROI) • Define quality criteria (at the level of the sequence read and individual base) • ROI Quality score ≥60 (v3.24) • Nucleotide PHRED-like score ≥20 (?replace with Q value)

  16. Quality score validation data 1 Ellard et al 2009 Genetic testing and molecular biomarkers 2 Raw data, others are final data

  17. Step1: Generate mutation report

  18. Step 2: Confirm/delete mutations and polymorphisms

  19. Step 3: Identify sequences for visual inspection or repeat testing

  20. Step 4: Visual inspection

  21. Internal quality assurance • Automated DNA extraction, robotic DNA dilution, PCR set-up and sequencing with barcode checks throughout • Each 96-well or 384-well PCR plate includes one PCR water blank • Each 384-well sequencing plate includes a pGEM sequencing control • Check for contamination (signal strength of water blank), sequencing quality (signal strength, resolution of homopolymer tracts and signal:noise ratio for pGEM control)

  22. SNPs uploaded “Fail” – repeat PCR QS <60 – visual inspection list Mutations uploaded QS ≥60, no variants, no bases <20 - “Normal” Towards STARLIMS…

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