Partek Flow Documentation

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Samtools1 (version 1.2) utilizes the mpileup command to look at observed bases for every genomic position surveyed in the aligned sequence data and subsequently calculate a Bayesian prior probability of each possible genotype.  Subsequently, bcftools utilizes the prior probability distributions to calculate actual genotypes and output detected genotype information in Variant Call Format (vcf).  This method can identify both single nucleotide variants and insertions/deletions.  General information about the underlying algorithm are detailed by Li. 2,3

Samtools dialog

Selecting Samtools from the context sensitive menu will bring up the Samtools task dialog, which contains three default sections: Variant detection method, Select Reference sequence, and Advanced options.

Variant detection Against reference will compare base composition for each sample against the reference sequence assembly, independently (Figure 1).  Selection of the Among samples, in the absence of sample attributes or when Paired analysis is not selected, utilizes information from all samples in the project in a joint variant detection.  Against reference typically performs well when samples have good coverage and may contain unique SNPs.  In instances where there is low coverage, Among samples may perform well for identifying SNPs shared between samples (but not for unique SNPs).

 

 

Figure 1. Selecting a variant detection method in the Samtools dialog
 

In the event paired samples exist within the project, Variant detection Among samples can be utilized to identify loci with differing genotypes between the paired samples once each sample has been compared to the reference sequence assembly.  To perform this analysis, sample attributes must be added in the Data tab (Figure 2).  Specifically, an attribute must be added for sample ID (shared between the paired samples) and an attribute may also be added sample type that differ between the paired samples.  Examples of the latter can include case and control or tumor and normal.  If these attributes are present, a section for Analysis options will be displayed below the Variant detection method (Figure 3).  To utilize this feature, select Paired analysis.  Match ID must then be specified and should correspond to the attribute that references the sample ID shared between the pair.   Selecting Case/control will allow for discriminating genotypes between paired samples in downstream tasks.  Attribute should correspond to the attribute that defines type within sample pairs, and Control can be specified for whatever category relates to the reference sample genotypes.

 

 

Figure 2. Example of attributes required for paired variant analysis
 

 

Figure 3. Specifying options for paired variant detection
 

Select Reference sequence will specify the reference assembly to utilize for variant detection.  If the alignment was generated in Partek® Flow®, the Assembly will be displayed as text in the section, and you do not have the option to change the reference.  In the event that alignment was performed outside of Partek® Flow®, you will need to select the appropriate Assembly utilized for alignment.  Assemblies previously added to library files (see Library File Management) will be available for selection or New assembly… in the menu can be utilized to import the reference sequence to library files within the task.

Advanced options provides a means to tune parameters in the variant detection for optimal performance.  Upon invoking the task dialog, Option set is set to Default, and these parameters are provided by the Samtools developers.  Clicking Configure button will open a window to tune advanced options (Figure 3).  Moving the mouse cursor over the info button will provide details for each parameter.  Please refer to the Samtools documentation for further details on any of these parameters.

 

 

Figure 4. Configuring advanced Samtools options
 

References

  1. Li H, Handsaker B, Wysoker A, et al. The Sequence Alignment/Map format and SAMtools. Bioinforma Oxf Engl. 2009;25(16):2078-2079.
  2. Li H. A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data. Bioinformatics. 2011;27(21):2987-2993.
  3. Li H. Improving SNP discovery by base alignment quality. Bioinformatics. 2011;27(8):1157-1158.

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