evaluateCovAndBQ {appreci8R} | R Documentation

## Evaluate coverage and base quality

### Description

appreci8R combines and filters the output of different variant calling tools according to the 'appreci8'-algorithm. In the 5th analysis step, all calls are evaluated with respect to coverage and basequality (using Rsamtools). Calls with insufficient coverage and/or basequality are filtered from further analysis. A GRanges object with all calls featuring sufficient coverage and basequality is returned.

### Usage

    evaluateCovAndBQ(output_folder, combined_calls_g, bam_folder,
                     dp = 50, nr_alt = 20, vaf = 0.01, bq = 15, bq_diff = 7)

### Arguments

`output_folder` | The folder to write the output files into. If an empty string is provided, no files are written out.
`combined_calls_g` | A GRanges object; necessary input object. The GRanges object contains one call per line. CombineOutput()-output can directly be taken as input.
`bam_folder` | The folder containing the bam- and bai-files for every sample. The file names have to match the sample names, i.e. Sample1.bam and Sample1.bai if Sample1.vcf (without any defined suffixes) was analyzed for caller1.
`dp` | Minimum depth that is required for a call to pass the filter (default: 50).
`nr_alt` | Minimum number of reads carrying the alternate allele that is required for a call to pass the filter (default: 20).
`vaf` | Minimum VAF that is required for a call to pass the filter (default: 0.01).
`bq` | Minimum base quality that is required for a call to pass the filter (default: 15; values above 44 not recommended).
`bq_diff` | Maximum difference in basequality between reference and alternative that is allowed for a call to pass the filter (default: 7).

### Details

The function `evaluateCovAndBQ` evaluates coverage and base quality of all calls and filteres them according to user-definable thresholds. Only those calls that feature sufficient coverage and base quality are reported. Filtration is performed in a 2-step procedure:

First, coverage is evaluated using Rsamtools `pileup` and a threshold of `min_base_quality=0`. If depth is below the defined threshold `dp` and/or the number of reads carrying the alternate allele is below the defined threshold `nr_alt` and/or VAF is below the defined threshold `VAF`, a call is excluded. However, if coverage is sufficient, base quality - which is more time-consuming - is evaluated.

To evaluate base quality, the threshold for `min_base_quality` is successively increased from 0 to 44 (thus, no values above 44 are recommended for `bq`). The average base quality for all reads carrying the reference and the alternate allele is calculated. For indels, which are always summed up as “+” by Rsamtools, no base quality can be determined. If the average base quality of the alternate allele is lower than `bq`, the call is filtered. If “average base quality reference allele” - “average base quality alternate allele” is higher than `bq_diff`, the call is filtered.

Coverage is reported with respect to the forward- and reverse reads separately. It is not yet evaluated. This is done in the 7th analysis step (`finalFiltration`).

### Value

A GRanges object is returned containing all calls with sufficient coverage and base quality. Reported metadata columns are: SampleID, Ref, Alt, Location, c. (position of variant on cDNA level), p. (position of variant on protein level), AA_ref, AA_alt, Codon_ref, Codon_alt, Consequence, Gene, GeneID, TranscriptID, Caller1 to CallerX (dependent on the number of callers that is evaluated), Nr_Ref, Nr_Alt, DP, VAF, BQ_REF, BQ_ALT, Nr_Ref_fwd, Nr_Alt_fwd, DP_fwd, VAF_rev, Nr_Ref_rev, Nr_Alt_rev, DP_rev, VAF_rev.

If an output folder is provided, the output is saved as Results_Frequency.txt.

### Author(s)

Sarah Sandmann <sarah.sandmann@uni-muenster.de>

### References

Rsamtools: http://bioconductor.org/packages/release/bioc/html/Rsamtools.html

### See Also

`appreci8R`, `appreci8Rshiny`, `filterTarget`, `normalize`, `annotate`, `combineOutput`, `determineCharacteristics`, `finalFiltration`

### Examples

    library("GenomicRanges")
    combined<-GRanges(seqnames = c("4","X"),
                      ranges = IRanges(start = c (106196951,15838366),
                                       end = c (106196951,15838366)),
                      SampleID = c("Sample2","Sample1"), Ref = c("A","C"),
                      Alt = c("G","A"), Location = c("coding,coding","coding"),
                      c. = c("5284,5347","864"), p. = c("1762,1783","288"),
                      AA_ref = c("I,I","N"), AA_alt = c("V,V","K"),
                      Codon_ref = c("ATA,ATA","AAC"),
                      Codon_alt = c("GTA,GTA","AAA"),
                      Consequence = c("nonsynonymous,nonsynonymous","nonsynonymous"),
                      Gene = c("TET2,TET2","ZRSR2"),
                      GeneID = c("54790,54790","8233"),
                      TranscriptID = c("18308,18309","75467"),
                      GATK = c(1,1), VarScan = c(NA,1))
    bam_folder <- system.file("extdata", package = "appreci8R")
    bam_folder <- paste(bam_folder, "/", sep="")

    filtered<-evaluateCovAndBQ("", combined, bam_folder)

[Package appreci8R version 1.6.0 Index]