Background

After extensively comparing different (shallow) whole-genome sequencing-based copy number detection tools, including WISECONDOR, QDNAseq, CNVkit, Control-FREEC, BIC-seq2 and cn.MOPS, WISECONDOR appeared to normalize sequencing data in the most consistent way, as shown by our paper. Nevertheless, WISECONDOR has limitations: Stouffer's Z-score approach is error-prone when dealing with large amounts of aberrations, the algorithm is extremely slow (24h) when operating at small bin sizes (15 kb), and sex chromosomes are not part of the analysis. Here, we present WisecondorX, an evolved WISECONDOR that aims at dealing with previous difficulties, resulting in overall superior results and significantly lower computing times, allowing daily diagnostic use. WisecondorX is meant to be applicable not only to NIPT, but also gDNA, PGT, FFPE, LQB, ... etc.

Manual

Mapping

We found superior results through WisecondorX when using bowtie2 as a mapper. Note that it is important that no read quality filtering is executed prior to running WisecondorX: this software requires low-quality reads to distinguish informative bins from non-informative ones.

WisecondorX

Installation

Stable releases can be installed through pip install. This option ascertains the latest version is downloaded, however, it does not install R dependencies.

pip install -U git+https://github.com/CenterForMedicalGeneticsGhent/WisecondorX

Alternatively, Conda additionally installs all necessary depedencies, however, the latest version might not be downloaded.

conda install -f -c conda-forge -c bioconda wisecondorx

Running WisecondorX

There are three main stages (converting, reference creating and predicting) when using WisecondorX:

• Convert aligned reads to .npz files (for both reference and test samples)
• Create a reference (using reference .npz files)
  • Important notes
    • Automated gender prediction, required to consistently analyze sex chromosomes, is based on a Gaussian mixture model. If few samples (<20) are included during reference creation, or not both male and female samples (for NIPT, this means male and female feti) are represented, this process might not be accurate. Therefore, alternatively, one can manually tweak the --yfrac parameter.
    • It is of paramount importance that the reference set consists of exclusively negative control samples that originate from the same sequencer, mapper, reference genome, type of material, ... etc, as the test samples. As a rule of thumb, think of all laboratory and in silico steps: the more sources of bias that can be omitted, the better.
    • Try to include at least 50 samples per reference. The more the better, yet, from 500 on it is unlikely to observe additional improvement concerning normalization.
• Predict copy number alterations (using the reference file and test .npz cases of interest)

Stage (1) Convert aligned reads (bam/cram) to .npz

WisecondorX convert input.bam/cram output.npz [--optional arguments]

Optional argument	Function
--reference x	Fasta reference to be used with cram inputs
--binsize x	Size per bin in bp; the reference bin size should be a multiple of this value. Note that this parameter does not impact the resolution, yet it can be used to optimize processing speed (default: x=5e3)
--normdup	Use this flag to avoid duplicate removal

→ Bash recipe at docs/include/pipeline/convert.sh

Stage (2) Create reference

WisecondorX newref reference_input_dir/*.npz reference_output.npz [--optional arguments]

Optional argument	Function
--nipt	Always include this flag for the generation of a NIPT reference
--binsize x	Size per bin in bp, defines the resolution of the output (default: x=1e5)
--refsize x	Amount of reference locations per target; should generally not be tweaked (default: x=300)
--yfrac x	Y read fraction cutoff, in order to manually define gender. Setting this to 1 will treat all samples as female
--plotyfrac x	plots Y read fraction histogram and Gaussian mixture fit to file x, can help when setting --yfrac manually; software quits after plotting
--cpus x	Number of threads requested (default: x=1)

→ Bash recipe at docs/include/pipeline/newref.sh

Stage (3) Predict copy number alterations

WisecondorX predict test_input.npz reference_input.npz output_id [--optional arguments]

Optional argument	Function
--minrefbins x	Minimum amount of sensible reference bins per target bin; should generally not be tweaked (default: x=150)
--maskrepeats x	Bins with distances > mean + sd * 3 in the reference will be masked. This parameter represents the number of masking cycles and defines the stringency of the blacklist (default: x=5)
--zscore x	Z-score cutoff to call segments as aberrations (default: x=5)
--alpha x	P-value cutoff for calling circular binary segmentation breakpoints (default: x=1e-4)
--beta x	When beta is given, --zscore is ignored. Beta sets a ratio cutoff for aberration calling. It's a number between 0 (liberal) and 1 (conservative) and, when used, is optimally close to the purity (e.g. fetal/tumor fraction)
--blacklist x	Blacklist for masking additional regions; requires headerless .bed file. This is particularly useful when the reference set is too small to recognize some obvious loci (such as centromeres; examples at ./example.blacklist/)
--gender x	Force WisecondorX to analyze this case as male (M) or female (F). Useful when e.g. dealing with a loss of chromosome Y, which causes erroneous gender predictions (choices: x=F or x=M)
--bed	Outputs tab-delimited .bed files (trisomy 21 NIPT example at ./example.bed), containing all necessary information (*)
--plot	Outputs custom .png plots (trisomy 21 NIPT example at ./example.plot), directly interpretable (*)
--ylim [a,b]	Force WisecondorX to use y-axis interval [a,b] during plotting, e.g. [-2,2]
--cairo	Some operating systems require the cairo bitmap type to write plots
--seed	Random seed for segmentation algorithm (default:None)

^{(*) At least one of these output formats should be selected}

→ Bash recipe at docs/include/pipeline/predict.sh

Additional functionality

WisecondorX gender test_input.npz reference_input.npz

Returns gender according to the reference.

Parameters

The default parameters are optimized for shallow whole-genome sequencing data (0.1x - 1x coverage) and reference bin sizes ranging from 50 to 500 kb.

Underlying algorithm

To understand the underlying algorithm, I highly recommend reading Straver et al (2014); and its update shortly introduced in Huijsdens-van Amsterdam et al (2018). Numerous adaptations to this algorithm have been made, yet the central normalization principles remain. Changes include e.g. the inclusion of a gender prediction algorithm, gender handling prior to normalization (ultimately enabling X and Y predictions), between-sample Z-scoring, inclusion of a weighted circular binary segmentation algorithm and improved codes for obtaining tables and plots.

Interpretation results

Plots

Every dot represents a bin. The dots range across the X-axis from chromosome 1 to X (or Y, in case of a male). The vertical position of a dot represents the ratio between the observed number of reads and the expected number of reads, the latter being the 'normal' state. As these values are log2-transformed, copy neutral dots should be close-to 0. Importantly, notice that the dots are always subject to Gaussian noise. Therefore, segments, indicated by horizontal white lines, cover bins of predicted equal copy number. The size of the dots represents the variability at the reference set. Thus, the size increases with the certainty of an observation. The same goes for the line width of the segments. Vertical grey bars represent the blacklist, which matches mostly hypervariable loci and repeats. Finally, the horizontal colored dotted lines show where the constitutional 1n and 3n states are expected (when constitutional DNA is at 100% purity). Often, an aberration does not reach these limits, which has several potential causes: depending on your type of analysis, the sample could be subject to tumor fraction, fetal fraction, a mosaicism, ... etc. Sometimes, the segments do surpass these limits: here it's likely you are dealing with 0n, 4n, 5n, 6n, ...

Tables

ID_bins.bed

This file contains all bin-wise information. When data is 'NaN', the corresponding bin is included in the blacklist. The Z-scores are calculated as default using the within-sample reference bins as a null set.

ID_segments.bed

This file contains all segment-wise information. A combined Z-score is calculated using a between-sample Z-scoring technique (the test case vs the reference cases).

ID_aberrations.bed

This file contains aberrant segments, defined by the --beta or --zscore parameters.

ID_statistics.bed

This file contains some interesting statistics (per chromosome and overall). The definition of the Z-scores matches the one from the 'ID_segments.bed'. Particularly interesting for NIPT.

Dependencies

• R (v4.3.3) packages
  • jsonlite (v1.8.8)
• R Bioconductor packages
  • DNAcopy (v1.76.0)
• Python (> v3.6) libraries
  • scipy (v1.13.0)
  • scikit-learn (v1.4.2)
  • pysam (v0.22.0)
  • numpy (v1.26.4)
  • matplotlib (v3.8.4)
  • pandas (2.2.2)

And of course, other versions are very likely to work as well.