시퀀스 품질

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Paula Andrea Martinez, PhD.

Data Scientist

품질 점수 - Phred 테이블

품질 값	오류 확률	정확도 (%)
10	1/10	90
20	1/100	99
30	1/1000	99.9
40	1/10000	99.99
50	1/100000	99.999

인코딩 - Phred +33

# quality encoding
encoding(quality(fqsample))

인코딩 문자와 점수

 !  "  #  $  %  &  '  (  )  *  +  ,  -  .   #  encoding
 0  1  2  3  4  5  6  7  8  9 10 11 12 13   #  score

 /  0  1  2  3  4  5  6  7  8  9  :  ;  <   #  encoding 
14 15 16 17 18 19 20 21 22 23 24 25 26 27   #  score 

 =  >  ?  @  A  B  C  D  E  F  G  H  I      #  encoding
28 29 30 31 32 33 34 35 36 37 38 39 40      #  score

fastq 품질

library(ShortRead)
quality(fqsample)

class: FastqQuality
A BStringSet instance 

# Quality is represented with ASCII characters 
[1]    40 ?@@DDDDDHDFDHE>AHFEGFIIEBGDBHH<3FEBEEEEG
[2]    40 BCCDFFFFHHHHHJJJJJJJJJJEHHGHIJJJJJJJJJJJ
[3]    40 BCCFFFFFHFHHHJJJJJJIIJJIIIIIGIIJJIJGIJII
[4]    40 CCCFFFFFHHHHHJJJJJJJJJJIJJJJJJJJJJJJJJJJ

library(ShortRead)

sread(fqsample)[1]

# Quality is represented with ASCII characters 
quality(fqsample)[1]

50 GTCCCATTTACCTCTGACTCTTTTGATGCTGCAATTGCTGCTCATATACT

50 ?@@DDDDDHDFDHE>AHFEGFIIEBGDBHH<3FEBEEEEGGIGIIGHGHC

## PhredQuality instance
pq <- PhredQuality(quality(fqsample))

# transform encoding into scores 
qs <- as(pq, "IntegerList")
qs # print scores

30 31 31 35 35 35 35 35 39 35 37 35 39 36 29 32 39 37 36 38 37 40 40 36 33 38 35 33 39 39 27 18 37 36 33 36
36 36 36 38 38 40 38 40 40 38 39 38 39 34

품질 평가

library(ShortRead)
# Quality assessment
qaSummary <- qa(fqsample, lane = 1)    # optional lane

# class: ShortReadQQA(10)
# Names accessible with the quality assessment summary
names(qaSummary)

 [1] "readCounts"           "baseCalls"            "readQualityScore"          "baseQuality"
 [5] "alignQuality"         "frequentSequences"    "sequenceDistribution"      "perCycle"  
 [9] "perTile"              "adapterContamination"
# QA elements are accessed with qa[["name"]]

# Get a HTML report
browseURL(report(qaSummary))

library(ShortRead)

# sequences alphabet
alphabet(sread(fullSample))

A,C,G,T,M,R,W,S,Y,K,V,H,D,B,N,-,+,.

abc <- alphabetByCycle(sread(fullSample))

# Each observation is a letter and each variable is a cycle. First, select the 4 first rows nucleotides A, C, G, T 
# Then transpose
nucByCycle <- t(abc[1:4,])

nucByCycle <- nucByCycle %>% as_tibble() %>% # convert to tibble
                      mutate(cycle = 1:50) # add cycle numbers
nucByCycle

    A     C     G     T cycle
16839 16335 16740 10878     1     
13056 13327 12064 22389     2     
13666 15617 13198 18355     3     
14723 15439 14239 16435     4

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