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SeqShop: Variant Calling and Filtering for INDELs Practical, June 2014 (view source)

Revision as of 21:50, 16 June 2014

14,843 bytes added ,  21:50, 16 June 2014
→‎Examining GotCloud indel Ouptut
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=== Looking at the INDEL Variant Call File (VCF) ===
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We will use [[Vt]] to look at the INDEL VCF file.
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==== Header ====
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First, let's look at the header:
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  ${GC}/bin/vt view -H ${OUT}/final/all.genotypes.vcf.gz
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The header is as follows:
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  ##fileformat=VCFv4.2
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  ##FILTER=<ID=PASS,Description="All filters passed">
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  ##contig=<ID=22,length=51304566>
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  ##FORMAT=<ID=GT,Number=1,Type=String,Description="Genotype">
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  ##FORMAT=<ID=PL,Number=G,Type=Integer,Description="Normalized, Phred-scaled likelihoods for genotypes">
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  ##FORMAT=<ID=DP,Number=1,Type=Integer,Description="Depth">
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  ##FORMAT=<ID=AD,Number=3,Type=Integer,Description="Allele Depth">
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  ##FORMAT=<ID=GQ,Number=1,Type=Integer,Description="Genotype Quality">
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  ##INFO=<ID=AC,Number=A,Type=Integer,Description="Alternate Allele Counts">
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  ##INFO=<ID=AN,Number=1,Type=Integer,Description="Total Number Allele Counts">
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  ##INFO=<ID=NS,Number=1,Type=Integer,Description="Number of Samples With Data">
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  ##INFO=<ID=AF,Number=A,Type=Float,Description="Alternate Allele Frequency">
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  ##INFO=<ID=GC,Number=G,Type=Integer,Description="Genotype Counts">
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  ##INFO=<ID=GN,Number=1,Type=Integer,Description="Total Number of Genotypes Counts">
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  ##INFO=<ID=GF,Number=G,Type=Float,Description="Genotype Frequency">
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  ##INFO=<ID=HWEAF,Number=A,Type=Float,Description="Genotype likelihood based MLE Allele Frequency assuming HWE">
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  ##INFO=<ID=HWEGF,Number=G,Type=Float,Description="Genotype likelihood based MLE Genotype Frequency assuming HWE">
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  ##INFO=<ID=MLEAF,Number=A,Type=Float,Description="Genotype likelihood based MLE Allele Frequency">
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  ##INFO=<ID=MLEGF,Number=G,Type=Float,Description="Genotype likelihood based MLE Genotype Frequency">
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  ##INFO=<ID=HWE_LLR,Number=1,Type=Float,Description="Genotype likelihood based Hardy Weinberg ln(Likelihood Ratio)">
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  ##INFO=<ID=HWE_LPVAL,Number=1,Type=Float,Description="Genotype likelihood based Hardy Weinberg Likelihood Ratio Test Statistic ln(p-value)">
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  ##INFO=<ID=HWE_DF,Number=1,Type=Integer,Description="Degrees of freedom for Genotype likelihood based Hardy Weinberg Likelihood Ratio Test Statistic">
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  ##INFO=<ID=FIC,Number=1,Type=Float,Description="Genotype likelihood based Inbreeding Coefficient">
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  ##INFO=<ID=AB,Number=1,Type=Float,Description="Genotype likelihood based Allele Balance">
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  ##FILTER=<ID=PASS,Description="Temporary pass">
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  ##FILTER=<ID=overlap,Description="Overlapping variant">
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====Records====
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To view a specific region of records:
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  ${GC}/bin/vt view -i 22:36990878-36990879 ${OUT}/final/all.genotypes.vcf.gz
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* -i specifies the region
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* You can leave it out and look at all the records
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The columns are CHROM, POS, ID, REF, ALT, QUAL, FILTER, INFO, FORMAT, Genotype fields denoted by the sample name.
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  22 36990878 . GGT G 455 PASS AC=32;AN=116;AF=0.275862;GC=32,20,6;GN=58;
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                                                                GF=0.551724,0.344828,0.103448;NS=58;
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                                                                HWEAF=0.275797;HWEGF=0.52447,0.399466,0.0760642;
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                                                                MLEAF=0.27366; MLEGF=0.494275,0.464129,0.0415952;
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                                                                HWE_LLR=-0.453098;HWE_LPVAL=-1.0755;HWE_DF=1;
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                                                                FIC=-0.0718807;AB=0.6129
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                                                        GT:PL:DP:AD:GQ 0/0:0,9,108:9:3,0,6:10
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Here is a description of the record's fields.
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  22            : chromosome
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  36990878      : genome position
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  .              : this is the ID field that is left blank.
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  GGT            : the reference sequence that is replaced by the alternative sequence below.
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  G              : so this is basically a deletion of GT
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  455            : QUAL field denoting validity of this variant, higher the better.
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  PASS          : a passed variant.
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  INFO          : fields containing information about the variant.
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  FORMAT        : format field labels for the genotype columns.
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  0/0:0,9,108:9:3,0,6:10 :  genotype information.
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=====INFO field=====
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The information field are as follows:
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  AC=32                : alternate allele count
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  AN=116              : total number of alleles
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  AF=0.28              : allele frequency based on AC/AN
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  GC=32,20,6          : genotype counts for 0/0, 0/1, 1/1
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  GN=58;              : total number of genotypes 
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  GF=0.55,0.34,0.10    : genotype frequencies based on GC
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  NS=58                : no. of samples
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  HWEAF=0.28          : genotype likelihood based estimation of the allele frequency assuming Hardy Weinberg equilibrium
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  HWEGF=0.52,0.40,0.08 : genotype frequency derived from HWEAF
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  MLEAF=0.27          : genotype likelihood based estimation of the genotype frequency
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  MLEGF=0.49,0.46,0.04 : genotype frequency derived from MLEAF
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  HWE_LLR=-0.45        : log likelihood ratio of HWE test
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  HWE_LPVAL=-1.08      : log p value of HWE test
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  HWE_DF=1            : degrees of freedom of the HWE test
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  FIC=-0.07            : genotype likelihood based inbreeding coefficient
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  AB=0.61              : genotype likelihood based allele balance
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=====GENOTYPE field=====
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The genotype fields are described as follows:
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  0/0      : homozygous reference chosen based on PL
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  0,9,108  : PHRED scaled genotype likelihoods
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  9        : no. of reads covering this variant
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  3,0,6    : allele depth
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            counts of reads supporting the reference allele,
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            the alternative allele and neither alleles respectively.
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            The last category might be due to insufficient
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            coverage of the read over the locus
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            or simply an allele that is not accounted for.
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  10      : genotype quality
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== INDEL Analysis ==
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The following section details some simple analyses we can perform.
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== Summary  ==
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First you want to know what is in the vcf file.
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  ${GC}/bin/vt peek ${OUT}/final/all.genotypes.vcf.gz
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  stats: no. of samples                    :        62
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        no. of chromosomes                :          1 <br>
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        no. Indels                        :        720
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            2 alleles (ins/del)            :            720 (0.84) [328/392] #(insertion deletion ratio) [#insertions, #deletions]
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            >=3 alleles (ins/del)          :              0 (-nan) [0/0] <br>
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        no. of observed variants          :        720
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* Our VCF only contains INDELs so there are lots of <code>0 (-nan) [0/0]</code> values in the output.  <code>vt</code> can be used to analyze VCFs with more variants than just INDELs.
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The variants have filter labels. PASS meaning a temporary pass and overlap, meaning that the variants are overlapping with another variant, implying multiallelicity.
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We can count the number of variants with different filters with the following commands.
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  ${GC}/bin/vt peek ${OUT}/final/all.genotypes.vcf.gz -f "FILTER.PASS"
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  stats: no. of samples                    :        62
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        no. of chromosomes                :          1 <br>
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        no. Indels                        :        584
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            2 alleles (ins/del)            :            584 (0.69) [239/345]
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            >=3 alleles (ins/del)          :              0 (-nan) [0/0]
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  ${GC}/bin/vt peek ${OUT}/final/all.genotypes.vcf.gz -f "FILTER.overlap"
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  stats: no. of samples                    :        62
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        no. of chromosomes                :          1 <br>
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        no. Indels                        :        136
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            2 alleles (ins/del)            :            136 (1.89) [89/47]  #notice the difference in insertion deletion ratios
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            >=3 alleles (ins/del)          :              0 (-nan) [0/0]
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  #passed singletons only
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  ${GC}/bin/vt peek ${OUT}/final/all.genotypes.vcf.gz -f "FILTER.PASS&&INFO.AC==1"
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  #passed indels of length 1 only
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  ${GC}/bin/vt peek ${OUT}/final/all.genotypes.vcf.gz -f "FILTER.PASS&&LEN==1"
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  #passed indels of length >4
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  ${GC}/bin/vt peek ${OUT}/final/all.genotypes.vcf.gz -f "FILTER.PASS&&LEN>1"
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  #passed singletons of length 4 or insertions of length 3
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  ${GC}/bin/vt peek ${OUT}/final/all.genotypes.vcf.gz -f "FILTER.PASS&&(LEN==4||DLEN==3)"
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== Comparison with other data sets ==
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It is usually useful to examine the call sets against known data sets for the passed variants.
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  ${GC}/bin/vt profile_indels -g indel.reference.txt  -r hs37d5.fa ${OUT}/final/all.genotypes.vcf.gz -i 22:36000000-37000000 -f "PASS"
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  data set
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    No Indels        :        613 [0.72]    #613 passed variants with an insertion deletion ratio of 0.72
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      FS/NFS        :      0.50 (2/2)    #frame shift / non frameshift indels proportion, the bracket gives the counts of the frame shift and non frameshift indels
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      Low complexity :      0.46 (283/613) #fraction of indels in low complexity region, the bracket gives the counts of the indels <br>
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  1000G  #1000 Genomes Phase 1 data set
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    A-B        371 [0.76] #variants found in call set only, square brackets contain insertion deletion ratio
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    A&B        242 [0.66] #variants found in both data sets
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    B-A        276 [0.89] #variants found in 1000G phase 1 data set only
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    Precision    39.5%    #39.5% of the call set are previously known, so 60.5% are novel variants.
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    Sensitivity  46.7%    #sensitivity of variant calling, 46,7% of known variants from 1000 Genomes were rediscovered  <br>
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  mills  #The gold standard Mills et al. indel set
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    A-B        542 [0.68]
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    A&B        71 [1.03]
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    B-A        31 [1.07]
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    Precision    11.6%
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    Sensitivity  69.6%  <br>
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  dbsnp  #Indels from dbSNP
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    A-B        405 [0.68]
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    A&B        208 [0.79]
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    B-A        494 [2.03]
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    Precision    33.9%
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    Sensitivity  29.6%
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Ins/Del ratios:  Reference alignment based methods tend to be biased towards the detection of deletions.  This provides a useful measure for discovery Indel sets to show the varying degree of biasness.  It also appears that as coverage increases, the ins/del ratio tends to 1.
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Coding region analysis:  Coding region Indels may be categorised as Frame shift Indels and Non frameshift Indels.  A lower proportion of Frameshift Indels may indicate a better quality data set but this depends also on the individuals sequenced.
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Complexity region analysis: Indels in regions marked by DUST - a low complexity region masker used in the NCBI pipeline.
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Overlap analysis:  overlap analysis with other data sets is an indicator of sensitivity.
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* 1000G: contains Indels from 1000 Genomes, represent a wide spectrum of variants from many different populations.  Variants here have an allele frequency above 0.005.
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* Mills:  contains doublehit common indels from the Mills. et al paper and is a relatively good measure of sensitivity for common variants.  Because not all Indels in this set is expected to be present in your sample, this actually gives you an underestimate of sensitivity.
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* dbsnp: contains Indels submitted from everywhere, I am not sure what does this represent exactly.  But assuming most are real, then precision is a useful estimated quantity from this reference data set.
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We perform the same analysis for the failed variants again, the relatively low overlap with known data sets imply a reasonable tradeoff in sensitivity and specificity.
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  ${GC}/bin/vt profile_indels -g indel.reference.txt  -r hs37d5.fa all.genotypes.bcf -i 22:36000000-37000000 -f "~PASS"
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  data set
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    No Indels        :        107 [2.06]
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      FS/NFS        :      -nan (0/0)
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      Low complexity :      0.79 (85/107) <br>
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  1000G
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    A-B        107 [2.06]
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    A&B          0 [-nan]
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    B-A        518 [0.77]
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    Precision    0.0%
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    Sensitivity  0.0% <br>
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  mills
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    A-B        105 [2.09]
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    A&B          2 [1.00]
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    B-A        100 [1.04]
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    Precision    1.9%
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    Sensitivity  2.0% <br>
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  dbsnp
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    A-B        102 [2.00]
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    A&B          5 [4.00]
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    B-A        697 [1.51]
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    Precision    4.7%
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    Sensitivity  0.7%
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This analysis supports filters too.
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==Normalization==
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A slight digression here, when analyzing indels, it is important to normalize it.  While it is a simple concept,
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it is hardly standardized.  The call set here had already been normalized but we feel that this is an important
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concept so we discuss this a bit here.
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Indel representation is not unique, you should normalize them and remove duplicates.
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  Variant normalization is implemented in [[vt#Normalization|vt]] and this page explains the algorithm
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  and also provides a simple proof of correctness - [[Variant_Normalization|Variant Normalization]]
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The following table shows the number of variants that had to be normalized and the corresponding
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type of normalization performed and the ensuing
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number of duplicate variants found for some of the 1000 Genomes Trio High Coverage call sets.
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Although left alignment seems to be a trivial concept, it is easily overlooked and remain a common
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mistake. 
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{| class="wikitable"
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|-
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! scope="col"| Dataset
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! scope="col"| Freebayes
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! scope="col"| Haplotyecaller
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! scope="col"| PINDEL
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! scope="col"| Platypus
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! scope="col"| RTG
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! scope="col"| Samtools
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! scope="col"| SGA
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|-
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| Biallelic
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|-
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| Left trim
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| 27069
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| 1
 +
| 0
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| 0
 +
| 0
 +
| 0
 +
| 15047
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|-
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| Left aligned
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| 3
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| 1
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| 1
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| 0
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| 12262
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| 2
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| 1892
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|-
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| Multi-allelic
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|-
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| Left trim
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| 40782
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| 0
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| 0
 +
| 0
 +
| 374
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| 0
 +
| 0
 +
|-
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| Left aligned
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| 1892
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| 0
 +
| 0
 +
| 0
 +
| 1329
 +
| 1
 +
| 0
 +
|-
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| Right trimmed
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| 0
 +
| 0
 +
| 0
 +
| 25393
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| 0
 +
| 11
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| 0
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|-
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|
 +
|-
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| Duplicate variants
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| 0
 +
| 1
 +
| 155
 +
| 3143
 +
| 286
 +
| 8
 +
| 7541
 +
|}
 +
 +
Another example is the Mills et al. data set which followed up with 10004 Indels for validation.
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Out of 9996 passed variants, it was found that after normalization, only 8904 distinct Indels remain
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- about a loss of 11% of variant thought distinct.
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To normalize and remove duplicate variants:
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  ${GC}/bin/vt normalize  mills.genotypes.bcf -r hs37d5.fa  | ${GC}/bin/vt mergedups - -o mills.normalized.genotypes.bcf
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and you will observe that 3994 variants had to be left aligned and 1092 variants were removed.
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  stats: biallelic
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          no. left trimmed                      : 0
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          no. left trimmed and left aligned    : 0
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          no. left trimmed and right trimmed    : 0
 +
          no. left aligned                      : 3994
 +
          no. right trimmed                    : 0 <br>
 +
        multiallelic
 +
          no. left trimmed                      : 0
 +
          no. left trimmed and left aligned    : 0
 +
          no. left trimmed and right trimmed    : 0
 +
          no. left aligned                      : 0
 +
          no. right trimmed                    : 0 <br>
 +
      no. variants observed                    : 9996 <br>
 +
  <br>
 +
  stats: Total number of observed variants  9996
 +
        Total number of unique variants    8904 <br>
 +
 +
 +
UMICH's algorithm for normalization has been adopted by Petr Danecek in bcftools and is also used in GKNO.
Retrieved from "http://genome.sph.umich.edu/wiki/Special:MobileDiff/10807"

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