CoNVaDING (Copy Number Variation Detection In Next-generation sequencing Gene panels) was designed for small (single-exon) copy number variation (CNV) detection in high coverage next-generation sequencing (NGS) data, such as obtained by analysis of smaller targeted gene panels.
CoNVaDING makes use of a group of (at least 30) possible control samples from which the samples with the most similar overall pattern are selected as control samples. These samples are then used for read-depth normalization on all (autosomal) targets and on all targets per gene. CNV prediction is based on a combination of ratio scores and Z-scores of the sample of interest compared to the selected controlsamples.
Quality (QC) metrics are calculated per sample and per analyzed target. Output is generated on three levels:
- longlist: This list contains all calls, disregarding the target quality.
- shortlist: This list contains a subset of the longlist, filtered on within sample target QC metrics.
- final list: This list contains a subset of the shortlist, filtered on target QC metrics obtained from other samples.
CoNVaDING has been written for use of CNV detection in high coverage NGS data (at least ~200x). With lower coverages it might still work, but more targets will fail QC metrics.
The program is written in perl and has dependencies on specific perl libraries as well as on samtools.
2. Installation
The latest version of CoNVaDING can be downloaded from DOWNLOADSITE[here].
CoNVaDING has several dependencies:
- Perl
- The Statistics::Normality package for perl
- Samtools
After installation the samtools executable has to be added to your local environment using the $PATH variable.
This version is known to be compatible with SAMtools version 0.1.18 and 0.1.19
3. General comments
Targets, or the region of interest, usually consist of an exon with some flanking bases, but can be anything that you specify, for instance part of an exon.
Because of the fact that the average coverage of the specified targets is used for the calculations, the resolution of the calls is also dependent on those targets. This means that the exact break point can not be determined. It might be within the last target of the call, or in the intron flanking this exon. Sub-exonic CNV’s can also be missed if they if they do not have enough effect on the average coverage of the target.
4. Analysis
The analysis consists of three steps that have to be run separately and is based on a list of targets in a bed format.
CoNVaDING can be started using the following command:
perl ./CoNVaDING-0.1.4.17.pl
If no options are used the help menu will appear.
Usage: ./countCNV-v0.1.4.17.pl <mode> <parameters>
-h This manual.
-mode Mode to run in, one of the following required:
StartWithBam :
Start with BAM files as input, to enable duplicate
removal use the rmdup variable.
REQUIRED:
[-inputDir, -outputDir, -bed, -controlsDir]
OPTIONAL:
[-rmDup, -useSampleAsControl]
StartWithAvgCount :
Start with Average Count files as input. This is a five column text file
with predefined column names. Please read the manual for instructions.
REQUIRED:
[-inputDir, -outputDir, -bed, -controlsDir]
OPTIONAL:
[-useSampleAsControl]
StartWithMatchScore :
Start with Normalized Coverage files as input.
REQUIRED:
[-inputDir, -outputDir, -controlsDir]
OPTIONAL:
[-controlSamples, -sexChr]
StartWithBestScore :
Best score analysis using Match score files as input.
REQUIRED:
[-inputDir, -outputDir, -controlsDir]
OPTIONAL:
[-regionThreshold, -sexChr, -ratioCutOffLow, -ratioCutOffHigh, -zScoreCutOffLow, -zScoreCutOffHigh]
GenerateTargetQcList :
Generate a target QC list to use as input for finallist creation.
REQUIRED:
[-inputDir, -outputDir, -controlsDir]
OPTIONAL:
[-controlSamples, -regionThreshold, -ratioCutOffLow, -ratioCutOffHigh, -zScoreCutOffLow, -zScoreCutOffHigh, -sampleRatioScore]
CreateFinalList :
Creates the final list using the target QC list for filtering.
REQUIRED:
[-inputDir, -targetQcList, -outputDir]
OPTIONAL:
[-percentageLessReliableTargets]
PARAMETERS:
-inputDir Input directory, depending on the analysis mode this contains
BAM, AvgCount, normalized coverage or match score files.
-bed Input file specifying regions to analyze in BED format.
-outputDir Output directory to write results to.
-controlsDir Directory containing control samples.
-targetQcList Path to file containing target QC values.
-controlSamples Number of samples to use in Match score analysis. DEFAULT: 30
-regionThreshold Percentage of all control samples differing more than 3
standard deviations from mean coverage of a region in the specified
BED file to exlude from sample ratio calculation. DEFAULT: 20
-rmDup Switch to enable duplicate removal when using BAM files as input.
-sexChr Switch to include sex chromosomes in analysis.
-useSampleAsControl Switch to use samples as control. Example: when using BAM
files to create count files and subsequentially use the
generated count files as controls.
-ratioCutOffLow Lower ratio cutoff value. Region ratio values below this
threshold are marked as deletion. DEFAULT: 0.65
-ratioCutOffHigh Higher ratio cutoff value. Region ratio values above this
threshold are marked as duplication. DEFAULT: 1.4
-zScoreCutOffLow Lower Z-score cutoff value. Regions with a Z-score below
this threshold are marked as deletion. DEFAULT: -3
-zScoreCutOffHigh Higher Z-score cutoff value. Regions with a Z-score above
this threshold are marked as duplication. DEFAULT: 3
-sampleRatioScore Sample ratio z-score cutoff value. Sample with a ratio
score below this value are excluded from analysis. DEFAULT: 0.09
-percentageLessReliableTargets Target labelled as less reliable in percentage
of control samples. DEFAULT: 20
Create normalized count files
The first step in the analysis is to create normalized count files. This can be done in two ways, from a bam file or from a text file including mean coverage per target.
StartWithBam
If a bam file is used CoNVaDING will use samtools to calculate the mean coverage for each target. For this type of analysis the StartWithBam mode has to be selected.
The basic analysis starts as follows:
perl ./CoNVaDING-0.1.4.17.pl \ -mode StartWithBam \ -inputDir /PATH/TO/INPUTDIR \ -controlsDir /PATH/TO/CONTROLSDIR \ -outputDir /PATH/TO/OUTPUTDIR \ -bed /PATH/TO/DIR/target_bedfile.bed
All bamfiles should be stored in the same folder, which can be specified with the inputDir option The outputDir option should specify the path to the folder in which the normalized coverage files .aligned.only.normalized.coverage.txt should be stored.
The bed file should contain the regions of interest seperated in four columns specifying the chromosome, start position, stop position and the gene. No headers should be included.
It is important that the gene column has the exact same gene name for every target of the same gene, because these names are used to cluster targets for a normalization based on the targets belonging to the same gene. The bedfile should be sorted on chromosome and start position.
Bed file example:
2 96919506 96919893 TMEM127 2 96920531 96920775 TMEM127 2 96930836 96931159 TMEM127 2 215593360 215593772 BARD1 2 215595095 215595272 BARD1 2 215609751 215609923 BARD1 2 215610406 215610618 BARD1 2 215617131 215617319 BARD1 2 215632166 215632418 BARD1 2 215633916 215634076 BARD1 2 215645244 215646273 BARD1 2 215656981 215657209 BARD1 2 215661745 215661881 BARD1 2 215674096 215674333 BARD1 3 10183492 10183911 VHL 3 10188158 10188360 VHL 3 10191431 10191689 VHL
The analysis options can be further extended:
If a control set is not yet present, or if the samples that are analyzed have to be added to the control set the following options should be added:
-useSampleAsControl -controlsDir /PATH/TO/CONTROLSDIR
The useSampleAsControl option specifies that the samples have to be used as a control sample later on. The controlsDir is the location where the normalized coverage files of the control samples will be stored.
If duplicates have to be removed before coverage calculations use the following option:
-rmdup
This is advisable for capturing data, but should not be done for amplicon data.
StartWithAvgCount
If no bam files are present the analysis can also start with a text file specifying average counts per target. This file shoud contain the headers as shown in the example below. This enables the use of alternative analysis software.
An example of a text file that can be used in this mode:
CHR START STOP GENE REGION_COV 2 96919506 96919893 TMEM127 209.606 2 96920531 96920775 TMEM127 230.959 2 96930836 96931159 TMEM127 127.735 2 215593360 215593772 BARD1 273.726 2 215595095 215595272 BARD1 297.522 2 215609751 215609923 BARD1 230.191 2 215610406 215610618 BARD1 224.822 2 215617131 215617319 BARD1 204.979 2 215632166 215632418 BARD1 211.352 2 215633916 215634076 BARD1 240.627 2 215645244 215646273 BARD1 281.97 2 215656981 215657209 BARD1 137.293 2 215661745 215661881 BARD1 264.81 2 215674096 215674333 BARD1 127.689 3 10183492 10183911 VHL 174.233 3 10188158 10188360 VHL 230.704 3 10191431 10191689 VHL 226.012
When this mode is used the analysis is started as follows:
perl ./CoNVaDING-0.1.4.17.pl \ -mode StartWithAvgCount \ -inputDir /PATH/TO/INPUTDIR \ -outputDir /PATH/TO/OUTPUTDIR \ -bed /PATH/TO/DIR/target_bedfile.bed
Also here the following options can be used if the samples should be used as a control set in later steps:
-useSampleAsControl -controlsDir /PATH/TO/CONTROLSDIR
The -rmdup option is not available in this mode. If necessary the duplicates should have been removed before calculating the mean coverage per target.
Selecting the most informative control samples
StartWithMatchScore
The next step in the analysis is selecting the control samples estimated to be the most informative.
If a bam file is used CoNVaDING will use samtools to calculate the mean coverage for each target. For this type of analysis the StartWithBam mode has to be selected.
The basic analysis starts as follows:
perl ./CoNVaDING-0.1.4.17.pl \ -mode StartWithMatchScore \ -inputDir /PATH/TO/INPUTDIR \ -outputDir /PATH/TO/OUTPUTDIR \ -controlsDir /PATH/TO/CONTROLSDIR
The inputDir option should specify the path to the folder in which the normalized coverage files .aligned.only.normalized.coverage.txt are be stored (the outputfolder of the previous step).
The outputDir option should specify the path to output folder. The script will produce two types of output files:
.best.match.score.txt shows the matchscore and the paths to the selected control samples.
.normalized.autosomal.coverage.all.controls.txt show the normalized coverage for all possible control samples
The controlsDir option should show the directory in which the control samples that have to be used are stored.
The analysis options can be further extended:
On default only targets located on autosomal chromosomes will be analyzed. If some targets are located on the sex chromosomes the following option should be added:
-sexChr
Note that for this option only samples of the same sex as the sample of interest can be used as possible control samples.
On default 30 samples are selected to create the control group. If you wish to use a different number of control samples this can be indicated with the option:
-controlSamples 40
to select for instance the 40 best matching samples.
CNV Detection
StartWithBestScore
The last step in the analysis is the CNV detection itself.
The basic analysis starts as follows:
perl ./CoNVaDING-0.1.4.17.pl \ -mode StartWithBestScore \ -inputDir /PATH/TO/INPUTDIR \ -outputDir /PATH/TO/OUTPUTDIR \ -controlsDir /PATH/TO/CONTROLSDIR
The inputDir option should specify the path to the folder in which the .best.match.score.txt files are stored (the outputfolder of the previous step).
The outputDir option should specify the path to output folder. The script will produce four types of output files:
.best.score.log show the used control samples, the sample ratio score and the omitted regions for the sample ratio score calculation.
.best.score.longlist.txt contains all calls, regardless of the target quality
.best.score.shortlist.txt contains the high quality calls based on within sample target QC
.best.score.totallist.txt contains information about all targets (ratio scores, Z-scores, QC)
The analysis options can be further extended:
The sample ratio calculation is based on calculation the variation coefficient of the normalized targets of the sample of interest. In this calculation highly variable targets are excluded. On default a target is considered highly variable if after transforming the normalized target ratio’s of all samples in the possible control group 20 percent or more of the samples have a Z-score outside the -3 to 3 range. This percentage can be altered using the regionTreshold option. For a threshold of 30 percent of the samples for instance the following option can be used:
-regionThreshold 30
To alter the ratio thresholds when making a call for a deletion of duplication for a region during the analysis, the ratioCutOffLow and ratioCutOffHigh parameters can be used. To apply a threshold of ratio score below 0.65 for a deletion and above 1.4 for duplication use:
-ratioCutOffLow 0.65 -ratioCutOffHigh 1.4
The same thresholds for calling a deletion or duplication can also be applied using the Z-score value cutoff. To call a deletion when the Z-score is below -3 or duplication when the Z-score is above 3 use:
-zScoreCutOffLow -3 -zScoreCutOffHigh 3
To finetune the variant list one can generate a list of targets which in general are of lower quality in all possible controlsamples and apply this as a filter to generate a final list of high quality calls. This can be done by executing two steps:
GenerateTargetQcList
To generate the list of targets and corresponding quality thresholds run:
perl ./CoNVaDING-0.1.4.17.pl \ -mode GenerateTargetQcList \ -inputDir /PATH/TO/CONTROLSDIR \ -outputDir /PATH/TO/OUTPUTDIR \ -controlsDir /PATH/TO/CONTROLSDIR
For this analysis, the same region threshold, ratio cutoffs and Z-score cutoffs as explained above can be altered using their corresponding parameters.
CreateFinalList
To apply the generated list of sample target QCs to the .best.score.shortlist.txt files execute:
perl ./CoNVaDING-0.1.4.17.pl \ -mode CreateFinalList \ -inputDir /PATH/TO/BESTSCOREOUTPUT \ -targetQcList /PATH/TO/TARGETQCLISTFILE \ -outputDir /PATH/TO/OUTPUTDIR
To change the percentage of samples in which a target can be labelled as less reliabe, for example in 20 percent of the samples, use the option:
-percentageLessReliableTargets 20
This produces the following output file:
.finallist.txt contains all final calls, basically a filtered shortlist file.
5. Test dataset
Not yet available
6. QC Thresholds
If the following thresholds are exceeded using default settings the CNV calling is less reliable. The target ratio is also used to filter calls for the shortlist. Both QC metrics are used for filtering the final list.
Sample ratio: 0.09 Target ratio: 0.10
7. Contact
Mailto:
Lennart Johansson <l.johansson@umcg.nl>
Freerk van Dijk <f.van.dijk02@umcg.nl>