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Towards semi-automated analysis of sequence data using Mutation Surveyor. Michael Day Department of Molecular Genetics Royal Devon & Exeter Foundation Trust. Outline. What is semi-automated analysis? Defining quality parameters for semi-automated analysis

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Presentation Transcript
slide1

Towards semi-automated analysis of

sequence data using Mutation Surveyor

Michael Day

Department of Molecular Genetics

Royal Devon & Exeter Foundation Trust

slide2

Outline

What is semi-automated analysis?

Defining quality parameters for semi-automated analysis

Quality analysis of routine sequencing data (ABCC8, n=50)

Conclusions and future work

slide3

Mutation Surveyor

Annotation and trace comparison

Visual inspection of trace

Sequence analysis software has improved

slide5

Quality score - represents signal: noise ratio with an average for the ROI

  • (eg QS 20 = 2.5% noise)

1% noise

(2) PHRED scores for each base with an average for the ROI

How does Mutation Surveyor assess sequence quality?

slide6

Peak spacing

uncalled/called peak ratio (7 peaks)

uncalled/called peak ratio (3 peaks)

Peak resolution

PHRED scores are numerical values representing the quality of base calling

MS uses PHRED-like scores which are calculated using a modified algorithm

Peak resolution

Comparable to PHRED except in regions of low quality > may differ by a score of 5

slide7

Contains features requested at NHS user meeting

MS quality parameters are displayed in the HGVS table

slide8

Highlight traces in which

the ROI is not covered

Quality score showing the average

PHRED-like score across the ROI

Highlight traces which fall below

a user defined quality score

Highlight bases within the ROI that do not

meet a user-defined quality score

Quality score showing the

average signal/noise (S/N) ratio

across the region of interest (ROI)

slide9

Sequencing data for ABCC8 gene in a consecutive series of 50 unselected patients

ABCC8 gene has 39 exons

50 patients = 1950 amplicons

slide10

Quality score

(S/N ratio)

11

20

30

40

48.3

50

1942 amplicons > 99.6%

PHRED-like score

37

40

50

57.1

59

1903 amplicons > 97.6%

1947 amplicons > 99.8%

Distribution of mean quality scores (ROI) within 1950 amplicons

97.6% amplicons have a QS ≥ 30 or PHRED-like score ≥ 50

slide11

322,100 bases within ROI had a visual inspection

Distribution of quality scores for individual bases within the region of interest (ROI)

1950 amplicons

slide12

How many bases within the ROI are low quality?

(have a PHRED-like score ≤ 20)

419 / 322,100 = 0.13% bases had PHRED-like score ≤ 20

and would need a visual inspection

slide13

2/3 of bases with PHRED <20 are within the

context of a heterozygous base

143 poor quality bases > need visual check (0.05%)

266 are in the context of a heterozygous base

Bases flanking SNPs may have low PHRED scores

419 / 322,100 = 0.13% bases had PHRED-like score ≤ 20

and would need a visual inspection

slide14

Low Phred score would prompt visual inspection

Example of heterozygous base not called by Mutation Surveyor

examples of poor quality sequence which require visual inspection
Examples of Poor Quality Sequence which Require Visual Inspection

n-1 Sequence

Non-specific PCR Product

can quality scores be used to determine whether a sequence is reportable
Can quality scores be used to determine whether a sequence is reportable?

No visual inspection required - report

High quality sequencing (>30 S/N ratio)

Average quality sequencing (20-30 S/N ratio)

Visual Inspection required

No visual inspection required - repeat

Poor quality sequencing (<20 S/N ratio)

slide17

Development of new CMGS Best Practice Guidelines to include semi-automated analysis of sequence data

Towards semi-automated sequence analysis

A visual inspection would be required for:

  • Sequences containing mutations or polymorphisms
  • (from Mutation report)
  • Low quality bases (ideally by link from HGVS table)
  • Sequences with ROI quality scores or average PHRED-like
  • scores that fall below a threshold

Further work is required across multiple laboratories in order to establish appropriate thresholds for visual inspection

slide18

Acknowledgements

All the Exeter team - Dr. Ann-Marie Patch, Dr Bev Shields

Piers Fulton (ABCC8 data)

SoftGenetics

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