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=Tools=
=Tools=
This walkthrough requires [[vt|vt]].
=Analyses=
=Analyses=
The file generated from the indel calling is a binary version [[http://www.1000genomes.org/wiki/analysis/variant-call-format/bcf-binary-vcf-version-2 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.
==File Preparation==
==File Preparation==
To convert to BCF format which will work fast with vt:
To convert to BCF format which will work fast with vt:
vt view mills.vcf -o mills.bcf
vt view mills.vcf -o mills.bcf
data set
data set
No Indels : 8904 [0.93]
No Indels : 8904 [0.93] //#variants in your data set [ins/del ratio]
FS/NFS : 0.66 (67/35)<br>
FS/NFS : 0.66 (67/35) //Proportion of frameshift Indels. (#Frameshift Indels/#Nonframeshift Indels)<br>
dbsnp
dbsnp //A represents the data set you input, B represents dbsnp
A-B 2975 [1.06]
A-B 2975 [1.06] //#variants in A only [ins/del ratio]
A&B 5929 [0.86]
A&B 5929 [0.86] //#variants in A and B
B-A 2059845 [1.51]
B-A 2059845 [1.51]
Precision 66.6%
Precision 66.6% //A&B/A this represents how novel your data set is in the variants represented.
Sensitivity 0.3% <br>
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>
mills
mills
A-B 5705 [0.81]
A-B 5705 [0.81]
Sensitivity 0.2%
Sensitivity 0.2%
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.
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.
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.
* 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.
* 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.
==Other useful evaluations==
==to document==
genotype likelihood concordance
* Annotation of STRs is really important. Show example of a deceptive single base pair variant
concordance stratified by indel length or tract length
* Mendelian analysis
mendelian concordance by tract length
* 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
