Next, we use a GATK tool called SplitNCigarReads developed specially for RNAseq,
which splits reads into exon segments (getting rid of Ns but maintaining grouping information) and hard-clip any sequences overhanging into the intronic regions.
In this example we will use Mutect2 to perform variant calling, which identifies somatic
SNVs and indels via local assembly of haplotypes.
For a more detailed description see https://github.com/gatk-workflows/gatk4-jupyternotebook-tutorials/blob/master/notebooks/Day3-Somatic/1-somatic-mutect2-tutorial.
ipynb.
10.2.4 Other Factors to Take into Account When Performing Variant
Calling
As we have seen, errors can be introduced at every step of the variant calling process. On
top of errors brought in during the base calling and read mapping and alignment steps, other
factors that can influence data quality are the preparation and storage of samples prior to
analysis. For example, it is known that samples stored as formalin-fixed paraffin embedded
(FFPE) tissue will have a higher bias toward C>T mutations due to deamination events
10 Identification of Genetic Variants and de novo Mutations Based on NGS
129
which splits reads into exon segments (getting rid of Ns but maintaining grouping information) and hard-clip any sequences overhanging into the intronic regions.
In this example we will use Mutect2 to perform variant calling, which identifies somatic
SNVs and indels via local assembly of haplotypes.
For a more detailed description see https://github.com/gatk-workflows/gatk4-jupyternotebook-tutorials/blob/master/notebooks/Day3-Somatic/1-somatic-mutect2-tutorial.
ipynb.
10.2.4 Other Factors to Take into Account When Performing Variant
Calling
As we have seen, errors can be introduced at every step of the variant calling process. On
top of errors brought in during the base calling and read mapping and alignment steps, other
factors that can influence data quality are the preparation and storage of samples prior to
analysis. For example, it is known that samples stored as formalin-fixed paraffin embedded
(FFPE) tissue will have a higher bias toward C>T mutations due to deamination events
10 Identification of Genetic Variants and de novo Mutations Based on NGS
129