Module 4.2 Sequencing Run QC

Question 1

tiles

Answer 1

regions on a flow cell

Question 2

Summary Table categories

14

Answer 2

1. Number of lanes
2. Number of tiles per lane
3. Density (K/mm2)
4. Cluster PF (%)
5. Phase/Prephase (%)
6. Reads (M)
7. Reads PF (M)
8. % > Q30
9. Yield (G)
10. Cycles Err Rated
11. Aligned (%)
12. Error Rate (%)
13. Error Rate 35 cycle (%)
14. Error Rate 75 cycle (%)

Question 3

Summary Table

Answer 3

• statistics showing means and standard deviations over all the tiles used in one lane

Question 4

Summary Table

Density
(K/mm2)

Answer 4

• concentration of clusters detected by image analysis
• expressed as thousands of clusters per square millimeter

Question 5

Summary Table

Cluster PF
(%)

PF = Passing Filter

Answer 5

• reads that pass Chastity filter
• Reads PF / Reads = ANSWER

Question 6

Chastity

used in Chastity Filter

Answer 6

• ratio of the brightest base intensity divided by the sum of the brightest and second brightest base intensities
• Illumina internal quality filtering procedure
• Clusters pass filter if no more than one base call has ANSWER value below 0.6 in first 25 cycles.

Question 7

Summary Table

Reads
(M)

Answer 7

• total number of clusters
• measured in millions

Question 8

Summary Table

Reads PF
(M)

Answer 8

• number of clusters that pass Chastity filter
• measured in millions

Question 9

Yield
(G)

Answer 9

• number of bases sequenced that pass filter.
• 1 cluster = 1 read sequence
• answer = (number of clusters passing filter) x (number of bases sequenced in each read)

Question 10

Summary Table

% >= Q30

Answer 10

percentage of bases with Quality Score of 30 or higher

Question 11

Phred Quality Score

Answer 11

• prediction of probability of an error in base calling
• Q = -10log₁₀P
• P = base calling error probability
• generally range 0-40
• error prone due to extreme GC bias, specific patterns or homopolymers
• data with less than Q 20 not valuable

Question 12

Phred

Analyzing process

Answer 12

1. calculates several parameters related to peak shape and peak resolution at each base position
2. use parameters to predict error probabilities generated from sequence trace, where correct sequence was known
3. score (gray bar) matches to each colored peak (base)

-No score = N base assignment (not good enough peak for base call)

Question 13

Illumina quality scores calculation

steps (3)

Answer 13

1. intensity profiles and signal to noise ratios measure base calling reliability
2. compute quality predictor values for a new base call and compare to values in pre-calibrated Quality (Q) table/model. Quality scores recorded for each cycle in base call (BCL) files
3. quality scores converted to FASTQ format

Question 14

Quality Score

FASTQ format

Answer 14

4 lines with separated fields per sequence read

1. @Sequence_Identifier
2. ATGC Raw Sequencing Data
3. +(sequence id again or description
4. Quality values for each base (ASCII)

Question 15

FASTQ file

Answer 15

• text-based format for storing both the nucleotide sequence and its corresponding quality data
• quality scores encoded in compact ASCII character format so it uses only one byte per quality value.
• Quality score = ASCII character code -33

Question 16

Error probability (P)

Answer 16

• estimated probability of a base call being wrong
• P = 10^(-Q/10)
• Q 30 -> P = 10^-3 = 1 in 1000 (0.1%) -> 99.9% base call accuracy

Question 17

Q Score plot features

Answer 17

• x-axis: position in read (bp)
• y-axis: Q score
• box plot with statistical values
• median value: central red line
• inter quartile range (25, 75%): yellow box
• 10% and 90%: lower and upper viscus
• mean Q score: solid blue line
• Green, orange, and red zones
• median quality score lower for first 5-7 bases then rises, then drop toward end of read

Question 18

Overclustering

Answer 18

• small distance between clusters = signal overlap
• two clusters can be interpreted as a single cluster with mixed fluorescence signals,
• generates low quality scores across entire read.

Question 19

Reduced Q score

some causes

Answer 19

• fluorescent signal decay
• overclustering
• instrument breakdown

Question 20

Summary Table

Phase/Prephase
(%)

Answer 20

• calculates rate at which molecules in cluster fall behind or jump ahead during read
• an estimate for first 25 cycles of read
• relies on comparison to Phi X control

Question 21

Summary Table

Cycles Err Rated

Answer 21

number of cycles that have been used for calculating error rate starting from cycle one (paired end cycles)

Question 22

Summary Table

Aligned
(%)

Answer 22

percentage of reads that can be aligned to control sample genome (Phi X)
- should match percent of Phi X spiked in during experiment
- if different, possible that quantification of library was off or the sample library couldn’t cluster or sequence well.
- sequencing run might have quality issue reflected by another metric

Question 23

Summary Table

Error rate
(%)

Answer 23

• actual calculated error rate which is determined by aligning actual reads produced in run from the control Phi X samples against the known Phi X genome sequence
• 3 different error rates: 35 cycles, 75 cycles, and entire run

Question 24

Phi X Control Libraries

Answer 24

• small single stranded DNA genome.
• 5000bp long
• 45% GC / 55% AT.
• well-defined genome sequence which enables quick alignment and estimation of error rate
• mean insert size ~335 bp, 500bp with adapters
• added to sample libraries after pooling and denaturation for use as positive control during clustering and sequencing process.
• allows the quick calculation for % phasing/prephasing, percent alignment, and estimation of error rate
• spiking with Phi X can increase overall nucleotide diversity of sample (1% for diverse library samples)

Question 25

Diverse/balanced libraries

Answer 25

- has equal proportions of ATGC nucleotides at each base position - Illumina optimized around balanced representation of ATGC - sequencing software use images from first 47 cycles of Read 1 to identify location of each cluster - first 25 cycles used for % passing filter evaluation in base calling and quality score calculations

Question 26

# diverse libraries per base sequence content data plot

Answer 26

-QC measure that shows representation of each base position by cycle - in well-balanced library percent of base plot shows even and horizontal curves centered around 25% for each base

Question 27

low diversity libraries

Answer 27

- single base per cycle - eg. same primers used = all clusters have exact same sequences at the beginning of the reads, - per base plot shows large intensity spikes at each cycle.

Question 28

unbalanced libraries

Answer 28

- have one base at much lower percentage than the others

Question 29

# QC metric Percent of Adapter

Answer 29

- matches adapter sequences that can be subtracted from base sequence data - some library inserts are shorter than read lengths so they're read through to adapter at 3’ end

Question 30

# QC metric Duplication Rate

Answer 30

- measures number of reads derived from one single original molecule - high level could indicate PCR enrichment bias due to nature of sample or library method - can be truly overrepresented sequences such as a very abundant transcripts in RNA sequencing library or in amplicon data (expected and normal) Artificial causes - optical - clustering

Question 31

# artificial duplication Optical duplication

Answer 31

- single cluster that has been called as two clusters by software - NOT on patterned flow cells

Question 32

Clustering duplication

Answer 32

- library molecule occupies two adjacent wells, due to overflow into neighboring wells - two clusters of same original molecule - Unique to patterned flow cells