Base quality and read quality: How should data quality be measured?

Gabor T. MarthBoston College Biology Department

1000 Genomes MeetingCold Spring Harbor LaboratoryMay 5-6. 2008

Read accuracy / quality values

• quality values help distinguish between sequencing error and allelic difference• some aligners (e.g. MAQ) use quality values to find correct read mapping position

• the more accurate the read, the easier and faster it is to map• the more errors the aligner must tolerate, the less reads can be uniquely aligned0 1 2

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

Fra

ctio

n of

gen

ome

Number of mismatches allowed

Read accuracy

Well-calibrated quality values

How to tabulate sequencing error rates?

…atggatgagtataacgtcaggctaaactgtagtatatggataaaatgacca*acga…

tatgcataaaatgaccatacg

S Itggatgagtataa*gtcagg

D

measured fragment length (L)

• Align a set of reads to the corresponding organismal reference genome sequences

PE read

reference sequence

• Register positions of mismatches / gaps

Caveat #1 – paralogous mapping

aaccttactttgccttgtactgaaattactacgtacaccttactttaccttgtactgc

accttactttaccttgtactg

correct map location

spurious error

• can be avoided by using exhaustive alignment that reveals the fact of multiple possible map locations. Reads that don’t map uniquely should not be used for error analysis…

accttactttaccttgtactg

incorrect map location

Caveat #2 – local misalignment

aaccttactttgccttgtactgaaattactacgtacaccttactttaccttgtactgc

accttactttgccttgtact*a

correct alignment

D

spurious S

• typically happens at the ends of reads• consequence of the scoring scheme… difficult to fix…

accttactttgccttgtacta

incorrect alignment

Caveat #3 – polymorphic dataset / ref errors

aaccttactttgccttgtactgaaattactacgtacaccttactttaccttgtactgc

aaccttactttgccttgcactgaaattactacgtacaccttactttaccttgtactgc

reference sequence

resequenced individualactttgccttgcactgaaatt

spurious S

• important source of error: Θ ≈ 1/1,000 for humans• use resequencing data from haploid DNA source (e.g. BAC)• in polymorphic datasets, maybe do SNP calling, and exclude reads that overlap a SNP (this should also work for errors in the reference sequence)

SNP

Caveat #4 – very low quality reads

aaccttactttgccttgtactgaaattactacgtacaccttactttaccttgtactgc

acaatgcgttgca***agatt

• these reads are hard to even align elude error statistics• only tabulate error stats for reads that will be aligned?

Study design

• Took 3 random lanes each from 3 random runs of PE Illumina reads from Sanger (100,000 random pairs per lane)

• Mapped the reads to NCBI build36 using MosaikPE

• Alignment conditions: paired-end alignments, unique-unique end-readsmaximum 4 mismatches per end-read

• Fraction aligned:on average, X fraction of the reads used

Analysis by Derek Barnett

Fragment length distribution

Base error rate and error type

Base error rate by substitution type

Per-read error rates

Position-specific base error rates

Accuracy across lanes and runs

Assigned Q-value vs. base error rate

Does the Q-value depend on base cycle?

Quality value calibration

base cycle

raw

Q

30

10

32.0 31.1 30.9 30.0 31.1

31.3 31.3 31.4 30.2 30.7

31.3 30.0 28 28 26.0

29.7 28.3 27.7 26.7 25.4

28.3 28.6 27.8 26.2 25.1

Q-values for read simulations

0.7 0.9 0.8 0.8 0.1

3.6 2.3 2.1 2.0 1.0

20.1 19.4 18.3 18.2 16.3

9.0 10.1 9.2 7.9 7.5

7.9 6.7 7.1 5.4 4.3

Weichun Huang, see poster at Genome Meeting

base cycle

Q

30

10

introduce error in read at base cycle 10 with P=0.001

Thanks

Derek Aaron Weichun

Michael Chip