Line 39: |
Line 39: |
| ##INFO=<ID=FIC,Number=1,Type=Float,Description="Genotype likelihood based Inbreeding Coefficient"> | | ##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"> | | ##INFO=<ID=AB,Number=1,Type=Float,Description="Genotype likelihood based Allele Balance"> |
− | ##FILTER=<ID=TPASS,Description="Temporary pass"> | + | ##FILTER=<ID=PASS,Description="Temporary pass"> |
| ##FILTER=<ID=overlap,Description="Overlapping variant"> | | ##FILTER=<ID=overlap,Description="Overlapping variant"> |
| | | |
Line 50: |
Line 50: |
| The columns are CHROM, POS, ID, REF, ALT, QUAL, FILTER, INFO, FORMAT, Genotype fields denoted by the sample name. | | 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; | + | 22 36990878 . GGT G 455 PASS AC=32;AN=116;AF=0.275862;GC=32,20,6;GN=58; |
| GF=0.551724,0.344828,0.103448;NS=58; | | GF=0.551724,0.344828,0.103448;NS=58; |
| HWEAF=0.275797;HWEGF=0.52447,0.399466,0.0760642; | | HWEAF=0.275797;HWEGF=0.52447,0.399466,0.0760642; |
Line 58: |
Line 58: |
| GT:PL:DP:AD:GQ 0/0:0,9,108:9:3,0,6:10 | | GT:PL:DP:AD:GQ 0/0:0,9,108:9:3,0,6:10 |
| | | |
− | Let's look at the first 5 records. | + | Let's look at the record's fields. |
| | | |
| 22 : chromosome | | 22 : chromosome |
− | 36990877 : genome position | + | 36990878 : genome position |
| . : this is the ID field that is left blank. | | . : this is the ID field that is left blank. |
− | GGT : the reference sequence that is replaced by the alternative sequence below. | + | GGT : the reference sequence that is replaced by the alternative sequence below. |
− | G : so this is basically a deletion of GT | + | G : so this is basically a deletion of GT |
− | . : QUAL field which is left missing. | + | 455 : QUAL field denoting validity of this variant, higher the better. |
− | TPASS : a temporary passed variant. | + | PASS : a passed variant. |
− | INFO : fields containing information about the variant. | + | INFO : fields containing information about the variant. |
| FORMAT : format field labels for the genotype columns. | | FORMAT : format field labels for the genotype columns. |
| 0/0:0,9,108:9:3,0,6:10 : genotype information. | | 0/0:0,9,108:9:3,0,6:10 : genotype information. |
Line 75: |
Line 75: |
| The information field are as follows: | | The information field are as follows: |
| | | |
− | AC=32 : alternate allele count | + | AC=32 : alternate allele count |
− | AN=116 : total number of alleles | + | AN=116 : total number of alleles |
− | AF=0.275862 : allele frequency based on AC/AN | + | AF=0.28 : allele frequency based on AC/AN |
− | GC=32,20,6 : genotype counts for 0/0, 0/1, 1/1 | + | GC=32,20,6 : genotype counts for 0/0, 0/1, 1/1 |
− | GN=58; : total number of genotypes | + | GN=58; : total number of genotypes |
− | GF=0.551724,0.344828,0.103448 : genotype frequencies | + | GF=0.55,0.34,0.10 : genotype frequencies based on GC |
− | NS=58 : no. of samples. | + | NS=58 : no. of samples |
− | HWEAF=0.275797: Genotype likelihood based estimation of the allele frequency assuming Hardy Weinberg equilibrium. | + | HWEAF=0.28 : genotype likelihood based estimation of the allele frequency assuming Hardy Weinberg equilibrium |
− | HWEGF=0.52447,0.399466,0.0760642 : genotype frequency derived from HWEAF | + | HWEGF=0.52,0.40,0.08 : genotype frequency derived from HWEAF |
− | MLEAF=0.27366: genotype likelihood based estimation of the genotype frequency | + | MLEAF=0.27 : genotype likelihood based estimation of the genotype frequency |
− | MLEGF=0.494275,0.464129,0.0415952 : genotype frequency derived from MLEAF | + | MLEGF=0.49,0.46,0.04 : genotype frequency derived from MLEAF |
− | HWE_LLR=-0.453098 : log likelihood ratio of HWE test | + | HWE_LLR=-0.45 : log likelihood ratio of HWE test |
− | HWE_LPVAL=-1.0755 : log p value of HWE test | + | HWE_LPVAL=-1.08 : log p value of HWE test |
− | HWE_DF=1 : degrees of freedom of the HWE test | + | HWE_DF=1 : degrees of freedom of the HWE test |
− | FIC=-0.0718807;AB=0.6129 | + | FIC=-0.07 : genotype likelihood based inbreeding coefficient |
| + | AB=0.61 : genotype likelihood based allele balance |
| | | |
| ===GENOTYPE field=== | | ===GENOTYPE field=== |
| | | |
− | 0/0: homozygous reference chosen based on in PL.
| + | The genotype fields are described as follows: |
− | 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.
| |
| | | |
− | =Tools=
| + | 0/0 : homozygous reference chosen based on 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 alleles respectively. |
| + | The last category might be due to insufficient |
| + | coverage of the read over the locus |
| + | or simply an allele that is not accounted for. |
| + | 10 : genotype quality |
| | | |
− | You can download [[vt|vt]] and have some working knowledge of PERL to do stuff that vt does not support.
| + | = Analysis = |
| | | |
− | =Analyses=
| + | The following section details some simple analyses we can perform. |
| | | |
− | ==File Preparation== | + | == Summary == |
| | | |
− | The VCF file you work with should preferably be BCF2.1 compatible. Here we provide an example in /net/fantasia/home/atks/indel_analysis_tutorial. \\
| + | First you want to know what is in the bcf file. |
| | | |
− | To convert to BCF format which will work fast with vt:
| + | vt peek all.genotypes.bcf |
| | | |
− | vt view mills.vcf -o mills.bcf
| + | stats: no. of samples : 62 |
| + | no. of chromosomes : 1 <br> |
| + | no. Indels : 720 |
| + | 2 alleles (ins/del) : 720 (0.84) [328/392] #(insertion deletion ratio) [#insertions, #deletions] |
| + | >=3 alleles (ins/del) : 0 (-nan) [0/0] <br> |
| + | no. of observed variants : 720 |
| | | |
− | You will encounter an error as the header does not contain contigs. To fix this, you should construct a complete header for mills.vcf. This is done for you in mills.with.alt.hdr.*
| + | The variants have filter labels PASS meaning a temporary pass and overlap, meaning that the variants are overlapping with another variant, implying multiallelicity. |
| + | We can count the number of variants with the following commands. |
| | | |
− | vt view mills.with.alt.hdr.vcf -o mills.genotypes.bcf
| + | vt peek all.genotypes.bcf -f "FILTER.PASS" |
| | | |
− | To index:
| + | stats: no. of samples : 62 |
| + | no. of chromosomes : 1 <br> |
| + | no. Indels : 584 |
| + | 2 alleles (ins/del) : 584 (0.69) [239/345] |
| + | >=3 alleles (ins/del) : 0 (-nan) [0/0] |
| | | |
− | vt index mills.genotypes.bcf | + | vt peek all.genotypes.bcf -f "FILTER.overlap" |
| | | |
− | To extract just the site list which is convenient for working with if you are not analysing the genotypes of the individuals
| + | stats: no. of samples : 62 |
| + | no. of chromosomes : 1 <br> |
| + | no. Indels : 136 |
| + | 2 alleles (ins/del) : 136 (1.89) [89/47] #notice the difference in insertion deletion ratios |
| + | >=3 alleles (ins/del) : 0 (-nan) [0/0] |
| | | |
− | vt view -s mills.genotypes.bcf -o mills.sites.bcf | + | #passed singletons only |
| + | vt peek all.genotypes.bcf -f "FILTER.PASS&&INFO.AC==1" |
| + | |
| + | #passed indels of length 1 only |
| + | vt peek all.genotypes.bcf -f "FILTER.PASS&&LEN==1" |
| + | |
| + | #passed indels of length >4 |
| + | vt peek all.genotypes.bcf -f "FILTER.PASS&&LEN>1" |
| + | |
| + | #passed singletons of length 4 or insertions of length 3 |
| + | vt peek all.genotypes.bcf -f "FILTER.PASS&&(LEN==4||DLEN==3)" |
| | | |
− | To index:
| + | == Comparison with other data sets == |
| | | |
− | vt index mills.sites.bcf
| + | It is usually useful to examine the call sets against known data sets for the passed variants. |
| | | |
− | You may also work with vcf.gz, just name the output as *.vcf.gz. But it will be slower with vt.
| + | vt profile_indels -g indel.reference.txt -r hs37d5.fa all.genotypes.bcf -i 22:36000000-37000000 -f "PASS" |
| | | |
| + | data set |
| + | No Indels : 613 [0.72] #613 passed variants with an insertion deletion ratio of 0.72 |
| + | 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 |
| + | Low complexity : 0.46 (283/613) #fraction of indels in low complexity region, the bracket gives the counts of the indels <br> |
| + | 1000G #1000 Genomes Phase 1 data set |
| + | A-B 371 [0.76] #variants found in call set only, square brackets contain insertion deletion ratio |
| + | A&B 242 [0.66] #variants found in both data sets |
| + | B-A 276 [0.89] #variants found in 1000G phase 1 data set only |
| + | Precision 39.5% #39.5% of the call set are previously known, so 60.5% are novel variants. |
| + | Sensitivity 46.7% #sensitivity of variant calling, 46,7% of known variants from 1000 Genomes were rediscovered <br> |
| + | mills #The gold standard Mills et al. indel set |
| + | A-B 542 [0.68] |
| + | A&B 71 [1.03] |
| + | B-A 31 [1.07] |
| + | Precision 11.6% |
| + | Sensitivity 69.6% <br> |
| + | dbsnp #Indels from dbSNP |
| + | A-B 405 [0.68] |
| + | A&B 208 [0.79] |
| + | B-A 494 [2.03] |
| + | Precision 33.9% |
| + | Sensitivity 29.6% |
| | | |
− | === A quick summary ===
| + | 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. |
| | | |
− | atks@1000g:~/dev/vt/comparisons/seq_workshop$ vt peek run/final/all.genotypes.bcf
| + | 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. |
− | peek v0.5
| |
| | | |
− | options: input VCF file run/final/all.genotypes.bcf
| + | Complexity region analysis: Indels in regions marked by DUST - a low complexity region masker used in the NCBI pipeline. |
− | [o] output VCF file -
| |
| | | |
| + | Overlap analysis: overlap analysis with other data sets is an indicator of sensitivity. |
| | | |
− | stats: no. of samples : 62
| + | * 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. |
− | no. of chromosomes : 1
| + | * 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. |
− | | + | * 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. |
− | 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
| + | 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. |
− | no. of unclassified variants : 0
| |
| | | |
− | Time elapsed: 0.01s
| + | vt profile_indels -g indel.reference.txt -r hs37d5.fa all.genotypes.bcf -i 22:36000000-37000000 -f "~PASS" |
− | | |
− | == 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 | | data set |
− | No Indels : 720 [0.84] #720 indels, with and insertion deletion ratio of 0.84 | + | No Indels : 107 [2.06] |
− | FS/NFS : 0.50 (2/2) #only 4 variants overlap with coding regions, half of which are frameshift variants | + | FS/NFS : -nan (0/0) |
− | Low complexity : 0.47 (335/720) #47% of the variants are in low complexity regions <br> | + | Low complexity : 0.79 (85/107) <br> |
| 1000G | | 1000G |
− | A-B 719 [0.83] | + | A-B 107 [2.06] |
− | A&B 1 [inf] #only one variant overlaps with 1000 Genomes phase 1 data set.
| + | A&B 0 [-nan] |
− | B-A 517 [0.77]
| + | B-A 518 [0.77] |
− | Precision 0.1%
| |
− | Sensitivity 0.2% <br>
| |
− | 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% | | Precision 0.0% |
| Sensitivity 0.0% <br> | | Sensitivity 0.0% <br> |
| + | mills |
| + | A-B 105 [2.09] |
| + | A&B 2 [1.00] |
| + | B-A 100 [1.04] |
| + | Precision 1.9% |
| + | Sensitivity 2.0% <br> |
| dbsnp | | dbsnp |
− | A-B 720 [0.84] | + | A-B 102 [2.00] |
− | A&B 0 [-nan] #no variants overlaps with Mills et al. double hit variants. | + | A&B 5 [4.00] |
− | B-A 702 [1.52] | + | B-A 697 [1.51] |
− | Precision 0.0% | + | Precision 4.7% |
− | Sensitivity 0.0% | + | Sensitivity 0.7% |
| | | |
− | This discovery set appears to have many novel variants! (or false positives)
| |
| | | |
− | ==Peek==
| + | This analysis supports filters too. |
| | | |
− | You can see what you have in the file with:
| + | ==Normalization== |
− |
| |
− | vt peek mills.genotypes.bcf
| |
| | | |
− | You can also focus on a chromosome:
| + | A slight digression here, when analyzing indels, it is important to normalize it. While it is a simple concept, |
− | | + | it is hardly standardized. The call set here had already been normalized but we feel that this is an important |
− | vt peek mills.genotypes.bcf -i 20
| + | concept so we discuss this a bit here. |
− | | |
− | 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. | | Indel representation is not unique, you should normalize them and remove duplicates. |
Line 315: |
Line 282: |
| | 0 | | | 0 |
| | 374 | | | 374 |
− | | | + | | 0 |
− | | | + | | 0 |
| |- | | |- |
| | Left aligned | | | Left aligned |
Line 354: |
Line 321: |
| To normalize and remove duplicate variants: | | To normalize and remove duplicate variants: |
| | | |
− | vt normalize mills.genotypes.bcf -r ~/ref/vt/grch37/hs37d5.fa | vt mergedups - -o mills.normalized.genotypes.bcf | + | vt normalize mills.genotypes.bcf -r 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. | | and you will observe that 3994 variants had to be left aligned and 1092 variants were removed. |
Line 371: |
Line 338: |
| no. right trimmed : 0 <br> | | no. right trimmed : 0 <br> |
| no. variants observed : 9996 <br> | | no. variants observed : 9996 <br> |
− | Time elapsed: 0.14s <br> <br>
| + | <br> |
| stats: Total number of observed variants 9996 | | stats: Total number of observed variants 9996 |
| Total number of unique variants 8904 <br> | | Total number of unique variants 8904 <br> |
− | 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)<br>
| |
− | 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)<br>
| |
− |
| |
− |
| |
− | 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
| + | UMICH's algorithm for normalization has been adopted by Petr Danecek in bcftools and is also used in GKNO. |
− | * 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
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− | * mendelian concordance by tract length
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