Sequencing Workshop Analysis of Indels

Introduction

This wiki page details some standard Indel analyses for the sequencing workshop in the example indel data set.

Viewing the BCF file

The file generated from the indel calling is a binary version [BCFv2.1] of the Variant Call Format (VCF). BCFv2.1 is more efficient to process as the data is already stored in computer readable format on the hard disk. It is however not necessarily more compact than VCF4.2 especially when the format fields are rich in details.

Header

You can access the header by running the command:

 vt view -H all.genotypes.bcf.

The header is as follows:

 ##fileformat=VCFv4.2
 ##FILTER=<ID=PASS,Description="All filters passed">
 ##contig=<ID=22,length=51304566>
 ##FORMAT=<ID=GT,Number=1,Type=String,Description="Genotype">
 ##FORMAT=<ID=PL,Number=G,Type=Integer,Description="Normalized, Phred-scaled likelihoods for genotypes">
 ##FORMAT=<ID=DP,Number=1,Type=Integer,Description="Depth">
 ##FORMAT=<ID=AD,Number=3,Type=Integer,Description="Allele Depth">
 ##FORMAT=<ID=GQ,Number=1,Type=Integer,Description="Genotype Quality">
 ##INFO=<ID=AC,Number=A,Type=Integer,Description="Alternate Allele Counts">
 ##INFO=<ID=AN,Number=1,Type=Integer,Description="Total Number Allele Counts">
 ##INFO=<ID=NS,Number=1,Type=Integer,Description="Number of Samples With Data">
 ##INFO=<ID=AF,Number=A,Type=Float,Description="Alternate Allele Frequency">
 ##INFO=<ID=GC,Number=G,Type=Integer,Description="Genotype Counts">
 ##INFO=<ID=GN,Number=1,Type=Integer,Description="Total Number of Genotypes Counts">
 ##INFO=<ID=GF,Number=G,Type=Float,Description="Genotype Frequency">
 ##INFO=<ID=HWEAF,Number=A,Type=Float,Description="Genotype likelihood based MLE Allele Frequency assuming HWE">
 ##INFO=<ID=HWEGF,Number=G,Type=Float,Description="Genotype likelihood based MLE Genotype Frequency assuming HWE">
 ##INFO=<ID=MLEAF,Number=A,Type=Float,Description="Genotype likelihood based MLE Allele Frequency">
 ##INFO=<ID=MLEGF,Number=G,Type=Float,Description="Genotype likelihood based MLE Genotype Frequency">
 ##INFO=<ID=HWE_LLR,Number=1,Type=Float,Description="Genotype likelihood based Hardy Weinberg ln(Likelihood Ratio)">
 ##INFO=<ID=HWE_LPVAL,Number=1,Type=Float,Description="Genotype likelihood based Hardy Weinberg Likelihood Ratio Test Statistic ln(p-value)">
 ##INFO=<ID=HWE_DF,Number=1,Type=Integer,Description="Degrees of freedom for Genotype likelihood based Hardy Weinberg Likelihood Ratio Test Statistic">
 ##INFO=<ID=FIC,Number=1,Type=Float,Description="Genotype likelihood based Inbreeding Coefficient">
 ##INFO=<ID=AB,Number=1,Type=Float,Description="Genotype likelihood based Allele Balance">
 ##FILTER=<ID=TPASS,Description="Temporary pass">
 ##FILTER=<ID=overlap,Description="Overlapping variant">

Records

To view the records:

 vt view all.genotypes.bcf.

The columns are CHROM, POS, ID, REF, ALT, QUAL, FILTER, INFO, FORMAT, Genotype fields denoted by the sample name.

 22	36990877	.	GGT	G	.	TPASS	AC=32;AN=116;AF=0.275862;GC=32,20,6;GN=58;
                                                                GF=0.551724,0.344828,0.103448;NS=58; 
                                                                HWEAF=0.275797;HWEGF=0.52447,0.399466,0.0760642;
                                                                MLEAF=0.27366; MLEGF=0.494275,0.464129,0.0415952;
                                                                HWE_LLR=-0.453098;HWE_LPVAL=-1.0755;HWE_DF=1;
                                                                FIC=-0.0718807;AB=0.6129	
                                                        GT:PL:DP:AD:GQ	0/0:0,9,108:9:3,0,6:10

Let's look at the first 5 records.

 22             : chromosome
 36990877       : genome position
 .              : this is the ID field that is left blank.
 GGT            :  the reference sequence that is replaced by the alternative sequence below.
 G              :  so this is basically a deletion of GT
 .              : QUAL field which is left missing.
 TPASS          :  a temporary passed variant.
 INFO           :  fields containing information about the variant.
 FORMAT         : format field labels for the genotype columns.
 0/0:0,9,108:9:3,0,6:10 :  genotype information.

INFO field

The information field are as follows:

 AC=32        : alternate allele count
 AN=116       : total number of alleles
 AF=0.275862  : allele frequency based on AC/AN
 GC=32,20,6   : genotype counts for 0/0, 0/1, 1/1
 GN=58;       : total number of genotypes   
 GF=0.55,0.34,0.10 :    genotype frequencies based on GC
 NS=58        : no. of samples.
 HWEAF=0.28                    : genotype likelihood based estimation of the allele frequency assuming Hardy Weinberg equilibrium.
 HWEGF=0.52,0.40,0.08 : genotype frequency derived from HWEAF
 MLEAF=0.27: genotype likelihood based estimation of the genotype frequency
 MLEGF=0.49,0.46,0.04 : genotype frequency derived from MLEAF
 HWE_LLR=-0.45 : log likelihood ratio of HWE test
 HWE_LPVAL=-1.08 : log p value of HWE test
 HWE_DF=1 : degrees of freedom of the HWE test
 FIC=-0.07:  genotype likelihood based inbreeding coefficient
 AB=0.61 : genotype likelihood based allele balance

GENOTYPE field

 0/0: homozygous reference chosen based on in PL.
 0,9,108: PHRED scaled genotype likelihoods
 9:  no. of reads covering this variant
 3,0,6: allele depth
          counts of reads supporting the reference allele, the alternative allele and neither.
        The last category might be due to insufficient coverage of the read over the locus 
       or simply a mis specified allele.
 10 :  genotype quality.

Analysis

First you want to know what is in the bcf file.

Analyses

vt peek all.genotypes.bcf

options: input VCF file run/final/all.genotypes.bcf

        [o] output VCF file           -

stats: no. of samples : 62

      no. of chromosomes                 :          1

      no. of SNPs                        :          0
          2 alleles (ts/tv)              :               0 (-nan) [0/0]
          3 alleles (ts/tv)              :               0 (-nan) [0/0]
          4 alleles (ts/tv)              :               0 (-nan) [0/0]

      no. of MNPs                        :          0
          2 alleles (ts/tv)              :               0 (-nan) [0/0]
          >=3 alleles (ts/tv)            :               0 (-nan) [0/0]

      no. Indels                         :        720
          2 alleles (ins/del)            :             720 (0.84) [328/392]
          >=3 alleles (ins/del)          :               0 (-nan) [0/0]

      no. SNP/MNP                        :          0
          3 alleles (ts/tv)              :               0 (-nan) [0/0] 
          >=4 alleles (ts/tv)            :               0 (-nan) [0/0] 

      no. SNP/Indels                     :          0
          2 alleles (ts/tv) (ins/del)    :               0 (-nan) [0/0] (-nan) [0/0]
          >=3 alleles (ts/tv) (ins/del)  :               0 (-nan) [0/0] (-nan) [0/0]

      no. MNP/Indels                     :          0
          2 alleles (ts/tv) (ins/del)    :               0 (-nan) [0/0] (-nan) [0/0]
          >=3 alleles (ts/tv) (ins/del)  :               0 (-nan) [0/0] (-nan) [0/0]

      no. SNP/MNP/Indels                 :          0
          3 alleles (ts/tv) (ins/del)    :               0 (-nan) [0/0] (-nan) [0/0]
          4 alleles (ts/tv) (ins/del)    :               0 (-nan) [0/0] (-nan) [0/0]
          >=5 alleles (ts/tv) (ins/del)  :               0 (-nan) [0/0] (-nan) [0/0]

      no. of clumped variants            :          0
          2 alleles                      :               0 (-nan) [0/0] (-nan) [0/0]
          3 alleles                      :               0 (-nan) [0/0] (-nan) [0/0]
          4 alleles                      :               0 (-nan) [0/0] (-nan) [0/0]
          >=5 alleles                    :               0 (-nan) [0/0] (-nan) [0/0]

      no. of reference                   :          0

      no. of observed variants           :        720
      no. of unclassified variants       :          0

Time elapsed: 0.01s

Comparison with other data sets

Note that about 47% of the i

vt profile_indels -g /net/fantasia/home/atks/ref/vt/grch37/indel.reference.txt -r /net/fantasia/home/atks/ref/vt/grch37/hs37d5.fa run/final/all.genotypes.bcf -i 22:36000000-37000000

profile_indels v0.5

 data set
   No Indels         :        720 [0.84] #720 indels, with and insertion deletion ratio of 0.84
      FS/NFS         :       0.50 (2/2) #only 4 variants overlap with coding regions, half of which are frameshift variants
      Low complexity :       0.47 (335/720)   #47% of the variants are in low complexity regions 

 1000G
   A-B        719 [0.83]  
   A&B          1 [inf]       #only one variant overlaps with 1000 Genomes phase 1 data set.
   B-A        517 [0.77]
   Precision     0.1%
   Sensitivity   0.2% 

 mills
   A-B        720 [0.84]
   A&B          0 [-nan]  #no variants overlaps with Mills et al. double hit variants.
   B-A        102 [1.04]
   Precision     0.0%
   Sensitivity   0.0% 

 dbsnp
   A-B        720 [0.84]
   A&B          0 [-nan] #no variants overlaps with Mills et al. double hit variants.
   B-A        702 [1.52]
   Precision     0.0%
   Sensitivity   0.0%

This discovery set appears to have many novel variants! (or false positives)

Peek

You can see what you have in the file with:

 vt peek mills.genotypes.bcf

You can also focus on a chromosome:

 vt peek mills.genotypes.bcf -i 20

Or with just passed variants:

 vt peek mills.genotypes.bcf -i 20 -f PASS

Or with failed variants:

 vt peek mills.genotypes.bcf -i 20 -f ~PASS

Or with just 1bp indels:

 vt peek mills.genotypes.bcf -i 20 -f "PASS&&DLEN==1"

Or with just 1bp deletions:

 vt peek mills.genotypes.bcf -i 20 -f "PASS&&LEN==-1"

Or with just biallelic 1bp indels:

 vt peek mills.genotypes.bcf -i 20 -f "PASS&&N_ALLELE==2&&LEN==1"

Or with just biallelic 1bp indels that are somewhat rare:

 vt peek mills.sites.bcf -f "PASS&&N_ALLELE==2&&LEN==1&&INFO.AF<0.03"

Or with just biallelic 1bp indels that are somewhat rare with sanity checking:

 vt peek mills.sites.bcf -f "PASS&&N_ALLELE==2&&LEN==1&&INFO.AC/INFO.AN<0.03"

which you will observe discrepancies due to rounding off in AF. So you should probably use INFO.AC/INFO.AN.

Normalization

Indel representation is not unique, you should normalize them and remove duplicates.

  Variant normalization is implemented in vt and this page explains the algorithm 
  and also provides a simple proof of correctness - Variant Normalization

The following table shows the number of variants that had to be normalized and the corresponding type of normalization performed and the ensuing number of duplicate variants found for some of the 1000 Genomes Trio High Coverage call sets. Although left alignment seems to be a trivial concept, it is easily overlooked and remain a common mistake.

Dataset	Freebayes	Haplotyecaller	PINDEL	Platypus	RTG	Samtools	SGA
Biallelic
Left trim	27069	1	0	0	0	0	15047
Left aligned	3	1	1	0	12262	2	1892
Multi-allelic
Left trim	40782	0	0	0	374
Left aligned	1892	0	0	0	1329	1	0
Right trimmed	0	0	0	25393	0	11	0
Duplicate variants	0	1	155	3143	286	8	7541

Another example is the Mills et al. data set which followed up with 10004 Indels for validation. Out of 9996 passed variants, it was found that after normalization, only 8904 distinct Indels remain - about a loss of 11% of variant thought distinct.

To normalize and remove duplicate variants:

 vt normalize  mills.genotypes.bcf -r ~/ref/vt/grch37/hs37d5.fa  | vt mergedups - -o mills.normalized.genotypes.bcf 

and you will observe that 3994 variants had to be left aligned and 1092 variants were removed.

 stats: biallelic
         no. left trimmed                      : 0
         no. left trimmed and left aligned     : 0
         no. left trimmed and right trimmed    : 0
         no. left aligned                      : 3994
         no. right trimmed                     : 0 

        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 

      no. variants observed                    : 9996 

 Time elapsed: 0.14s 
 

 stats: Total number of observed variants   9996
        Total number of unique variants     8904 

 Time elapsed: 0.13s

The following will be slight faster: + denotes using of uncompressed bcf stream.

 vt normalize  mills.genotypes.bcf -r ~/ref/vt/grch37/hs37d5.fa -o + | vt mergedups + -o mills.normalized.genotypes.bcf

Also remember to index this file and extract the sites.

Insertion/Deletion ratios, Coding Regions and Overlap analysis

You can obtain measure of insertion deletion ratios, coding region indels and sensitivity analysis by using the profile_indels analysis.

 vt profile_indels -g indel.reference.txt -r ~/ref/vt/grch37/hs37d5.fa mills.normalized.sites.bcf

The indel.reference.txt file contains the required reference to perform the overlap analysis.

 data set
   No Indels :       8904 [0.93]   //#variants in your data set [ins/del ratio]
      FS/NFS :       0.66 (67/35)  //Proportion of frameshift Indels. (#Frameshift Indels/#Nonframeshift Indels)
  
 dbsnp  //A represents the data set you input, B represents dbsnp
   A-B       2975 [1.06]  //#variants in A only [ins/del ratio]
   A&B       5929 [0.86]  //#variants in A and B
   B-A    2059845 [1.51]
   Precision    66.6%     //A&B/A this represents how novel your data set is in the variants represented.
   Sensitivity   0.3%     //A&B/B this represents sensitivity somewhat if dbsnp is considered a high quality Indel 
                          //set and the sample are the same in both data sets. (which they usually are not, this is still 
                          //nonetheless a useful indicator)

 

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.

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.

Overlap analysis: overlap analysis with other data sets is an indicator of sensitivity.

• 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.
• 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.
• Mills chip: This is a subset of the Mills data set. There are genotypes here that are useful for subsetting polymophic subsets of variants that are present in samples common with your data set, this can potentially provide a better estimate of sensitivity. In general not very useful unless you happen to be working on 1000 Genomes data or any data set who's individuals are commonly studied.
• Affy Exome Chip: This contains somewhat rare variants in exonic regions and is useful for exome chip analysis. You should subset your exome data to exome region Indels before comparing against this data set.

This analysis supports filters too.

to document

• Annotation of STRs is really important. Show example of a deceptive single base pair variant
• Mendelian analysis
• AFS
• Can check concordance of genotypes between callers - partitiion
• Type of Indels - homopolymer types and STR types and isolated, Adjacent SNPs ,Adjacent MNPs,Clumping variants
• genotype likelihood concordance
• concordance stratified by indel length or tract length
• mendelian concordance by tract length