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Quality Control of Illumina Data Mick Watson Director of ARK-Genomics The Roslin Institute

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QUALITY SCORES

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Quality scores The sequencer outputs base calls at each position of a read It also outputs a quality value at each position – This relates to the probability that that base call is incorrect The most common Quality value is the Sanger Q score, or Phred score – Q sanger -10 * log 10 (p) – Where p is the probability that the call is incorrect – If p = 0.05, there is a 5% chance, or 1 in 20 chance, it is incorrect – If p = 0.01, there is a 1% chance, or 1 in 100 chance, it is incorrect – If p = 0.001, there is a 0.1% chance, or 1 in 1000 chance, it is incorrect Using the equation: – p=0.05, Q sanger = 13 – p=0.01, Q sanger = 20 – p=0.001, Q sanger = 30

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For the geeks…. In R, you can investigate this: sangerq <- function(x) {return(-10 * log10(x))} sangerq(0.05) sangerq(0.01) sangerq(0.001) plot(seq(0,1,by=0.00001),sangerq(seq(0,1,by=0.00001)), type="l")

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The plot

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For the geeks…. And the other way round…. qtop <- function(x) {return(10^(x/-10))} qtop(30) qtop(20) qtop(13) plot(seq(40,1,by=-1), qtop(seq(40,1,by=-1)), type="l")

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The important stuff Q30 – 1 in 1000 chance base is incorrect Q20 – 1 in 100 chance base is incorrect

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QUALITY ENCODING

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Quality Encoding Bioinformaticians do not like to make your life easy! Q scores of 20, 30 etc take two digits Bioinformaticians would prefer they only took 1 In computers, letters have a corresponding ASCII code: Therefore, to save space, we convert the Q score (two digits) to a single letter using this scheme

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The process in full p (probability base is wrong) : 0.01 Q (-10 * log10(p)) : 30 Add 33 : 63 Encode as character : ? PQCode 0.0513. 0.01205 0.00130?

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For the geeks…. code2Q <- function(x) { return(utf8ToInt(x)-33) } code2Q(".") code2Q("5") code2Q("?") code2P <- function(x) { return(10^((utf8ToInt(x)-33)/-10)) } code2P(".") code2P("5") code2P("?")

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QC OF ILLUMINA DATA

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FastQC FastQC is a free piece of software Written by Babraham Bioinformatics group http://www.bioinformatics.babraham.ac.uk/projects/fastqc/ Available on Linux, Windows etc Command-line or GUI

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Read the documentation Follow the course notes

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Per sequence quality One of the most important plots from FastQC Plots a box at each position The box shows the distribution of quality values at that position across all reads

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Obvious problems

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Less obvious problems

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Really bad problems

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Other useful plots Per sequence N content – May identify cycles that are unreliable Over-represented sequences – May identify Illumina adapters and primers

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