SavvyCNV: Genome-wide CNV calling from off-target reads.

Identifying copy number variants (CNVs) can provide diagnoses to patients and provide important biological insights into human health and disease. Current exome and targeted sequencing approaches cannot detect clinically and biologically-relevant CNVs outside their target area. We present SavvyCNV, a tool which uses off-target read data from exome and targeted sequencing data to call germline CNVs genome-wide. Up to 70% of sequencing reads from exome and targeted sequencing fall outside the targeted regions. We have developed a new tool, SavvyCNV, to exploit this 'free data' to call CNVs across the genome. We benchmarked SavvyCNV against five state-of-the-art CNV callers using truth sets generated from genome sequencing data and Multiplex Ligation-dependent Probe Amplification assays. SavvyCNV called CNVs with high precision and recall, outperforming the five other tools at calling CNVs genome-wide, using off-target or on-target reads from targeted panel and exome sequencing. We then applied SavvyCNV to clinical samples sequenced using a targeted panel and were able to call previously undetected clinically-relevant CNVs, highlighting the utility of this tool within the diagnostic setting. SavvyCNV outperforms existing tools for calling CNVs from off-target reads. It can call CNVs genome-wide from targeted panel and exome data, increasing the utility and diagnostic yield of these tests. SavvyCNV is freely available at https://github.com/rdemolgen/SavvySuite.

This is a PLOS Computational Biology Software paper.

Introduction

Copy number variants (CNVs) are an important class of genetic variant. They can cause monogenic disease [1,2], are associated with polygenic traits [3] and may exert pharmacogenetic effects [4]. CNVs are structural rearrangements where bases are gained (duplication) or lost (deletion) from the genome causing an altered copy number compared to the reference.

The importance of CNVs is highlighted by the role they play in many diseases, including cancers [5], autism [6], developmental disorders [7], and heart disease [8]. Single or partial gene deletions can cause disease where haploinsufficiency would result in the disease phenotype. For example, both single nucleotide variants and whole gene deletions of PKD1 cause polycystic kidney disease [1]. Duplications can also cause disease as a result of gene disruption at the site of insertion or through increased gene expression. For example, paternal duplication of the chromosome 6q24 region causes neonatal diabetes by overexpression of the imprinted gene PLAGL1 [2,9]. Larger CNVs are likely to cause syndromic disease as they affect multiple genes. An extreme case is Down syndrome where duplication of chromosome 21 results in characteristic facial features and intellectual disability [10].

CNVs can be detected by a range of methods. In the clinical setting DNA microarrays are routinely used to detect larger rearrangements whilst multiplex ligation-dependent probe amplification (MLPA) is often used to detect single or partial gene CNVs [11]. With next generation sequencing (NGS) increasingly employed to investigate genetic variation, the detection of CNVs from NGS data has become increasingly important. While genome sequencing is the optimal method to capture all sequence variation across the genome, due to speed and cost exome sequencing and targeted NGS panels are still the most commonly used testing methods, particularly as a first line test in clinical diagnostic laboratories.

Many methods have been published to call CNVs from exome and targeted gene panel data [12]. These are designed to detect CNVs within the genes which are targeted by the assay, however biologically interesting and disease causing CNVs will often fall outside of the targeted regions. Even where existing methods are able to identify that a particular gene is deleted/duplicated, they will not necessarily be able to map the extent of the CNV, as the breakpoints will often be located outside the targeted regions.

Current approaches to gene targeting for NGS are imperfect. Samuels et al reported that between 40% and 60% of sequence reads generated map outside the target regions [13]. This in effect produces ultra-low depth whole genome sequence data. While there is insufficient information (<1X coverage) to call single nucleotide variants over the untargeted region, this ‘off-target’ data can be exploited to call large CNVs. The very low average read depth across the off-target regions is also why split reads (where reads map to either side of a breakpoint) cannot be used to detect CNVs. Instead they must be detected using read depth changes over a wide area. The ability to use off-target reads to call CNVs across the genome increases the diagnostic utility of targeted next-generation sequencing panels and also allows for more accurate mapping of CNVs where breakpoints fall outside of the targeted regions. Previous tools have been designed to detect CNVs in off-target reads from exome data [14] and large targeted panels [15,16]. As such sequencing panels target a larger region of the genome they require a large amount of sequencing and thus produce a relatively large number of off-target reads. However, no tool has been designed to call CNVs using off-target reads from small targeted panels (<100 genes targeted).

We have developed a new tool, SavvyCNV, for calling germline CNVs from off-target reads. It is able to call off-target CNVs from small targeted panels as well as having improved performance on exome data compared to previous tools. We benchmarked the utility of SavvyCNV by comparing it to the current tools for calling CNVs in off-target regions in both targeted sequencing and whole exome sequencing (using a truth set derived from genome sequencing), and in on-target regions (using a truth set derived from MPLA). We then used SavvyCNV in a patient cohort tested with a small targeted gene panel (75 genes) to perform a genome-wide analysis to detect CNVs of clinical relevance.

Results

How much off-target read data is there?

For our small targeted panel [17] of 75 genes, 3.4 (SD 1.6) million reads are sequenced on average per sample. 55% (SD 10%) of these map to off-target regions of the genome. This gives a mean read depth in off-target regions of 0.065 (SD 0.044). In the exome samples that we used as a benchmarking data set there are an average of 76 (SD 20) million reads per sample with 20.3% (SD 6.6%) off-target, equating to mean read depth of 0.52 (SD 0.20) in off-target regions. This compares to a typical genome sequencing experiment where sufficient reads are sequenced to give >30X mean coverage across the genome.

SavvyCNV can call off-target CNVs from targeted panels

To evaluate SavvyCNV’s ability to call off-target CNVs accurately from targeted panel data we benchmarked its performance against a truth set of deletions and duplications generated using genome sequencing of the same samples (see Materials and Methods) and compared it to five other tools for calling CNVs: GATK gCNV [18], DeCON [19], EXCAVATOR2 [14], CNVkit [15], and CopywriteR [16]. To prevent bias due to software configuration tuning, we ran all six tools with multiple configurations, and plotted the best results for each tool on a precision-recall graph (Fig 1). The best recall (sensitivity) where precision is at least 50% is shown in Table A in S1 Text.

Fig 1

Benchmarking off-target CNV calling from targeted panel data.

The data points on the plot are generated by a parameter sweep for each tool and show the precision and recall that can be achieved with each tool. The f statistic is the harmonic mean of precision and recall (see Materials and Methods for details).

All tools called all of the CNVs larger than 5Mb (although not necessarily with precision of at least 50%), however only SavvyCNV did so without any false positive calls. All CNVs larger than 1Mb were called by SavvyCNV, GATK gCNV, and DeCON (all with precision less than 50%), although SavvyCNV called the most (97.6%) at a precision of at least 50% (as in Table A in S1 Text). For CNVs of any size, SavvyCNV had the highest recall (25.5%) with precision of at least 50%. For all three CNV size categories, SavvyCNV had the greatest detection power. SavvyCNV can call CNVs that are larger than 1Mb from off-target reads from a targeted panel with good recall (97.6%) and precision (78.8%).

Of the 68 CNVs detected by SavvyCNV (all sizes, precision at least 50%), 17 overlapped with targeted regions. This shows that while the increased read depth across a targeted region is used by SavvyCNV to help detect CNVs, most of the CNV detections are truly in off-target regions. Off-target calling is also able to map the boundaries of on-target CNVs where they extend outside of the targeted regions (see Fig A in S1 Text for an example of this).

We analysed the accuracy of the locations of the boundaries of the CNVs as detected by all six tools. SavvyCNV, GATK gCNV, and DeCON all had a similar accuracy, which scaled proportionately to the analysis bin size. With a bin size of 200kbp, all three tools found the CNV boundaries with a mean absolute error in location of 90kbp. Excavator2, CopywriteR, and CnvKit had higher location errors of 200kbp, 400kbp, and 600kbp respectively, although they detected very few CNVs and the results may not be reliable.

We conducted a sensitivity analysis where an alternative CNV caller was used to call the truthset from the genome sequencing data and results were similar (see Fig B in S1 Text).

SavvyCNV can call on-target CNVs from targeted panels

To evaluate the performance of SavvyCNV at calling CNVs from on-target data we used the ICR96 validation series [20] and compared its performance to GATK gCNV, DeCON, and CNVkit. ICR96 is a set of 96 samples sequenced using a small targeted sequencing panel (TruSight Cancer Panel v2, 100 genes), with exon CNVs detected independently using MLPA (25 single-exon CNVs, 43 multi-exon CNVs, and 1752 normal copy number genes). SavvyCNV had the highest recall (for precision > = 50%) though GATK gCNV and DeCON also performed well—these 3 tools had a recall >95% (Table B in S1 Text). Precision can only be compared between tools if recall is identical. To give an example, GATK gCNV achieves its highest recall of 97.1% with 85.7% precision, at the same level of recall SavvyCNV has a precision of 93.0% (this is shown in Fig 2). DeCON was the next-best performing tool after SavvyCNV and GATK gCNV while CnvKit did not call the majority of CNVs. Excavator2 did not run on this data set, and CopywriteR does not call on-target CNVs by design. Fig 2 shows the recall and precision of the four tools. SavvyCNV was the only tool capable of detecting all the CNVs although only with a precision of 29.1%.

Fig 2

Benchmarking on-target CNV calling from targeted panel data.

The data points on the plot are generated by a parameter sweep for each tool and show the precision and recall that can be achieved with each tool. The f statistic is the harmonic mean of precision and recall (see Materials and Methods for details).

Two of the CNVs within the ICR96 dataset cover less than a complete exon and have one breakpoint within the targeted region. These two CNVs are the hardest to detect by read-depth methods, as the read depth is only altered over a fraction of the exon area. Both CNVs are detected only by SavvyCNV, even when the highest sensitivity settings are used with the other CNV callers. If a structural variant caller is used to search for targeted breakpoints, then 7 of the 68 CNVs can be found in this data set. Adding a structural variant caller to the analysis therefore increases the power to detect CNVs, even on targeted sequencing data. Of the remaining 61 CNVs, only SavvyCNV is capable of detecting them all (precision 59.2%) (Table B in S1 Text).

Multi-exon CNVs are easier to detect than single-exon CNVs. SavvyCNV, GATK gCNV, and DeCON can detect all 43 multi-exon CNVs, although only SavvyCNV and GATK gCNV did this with a precision of at least 50%.

SavvyCNV can call off-target CNVs from exome data

To assess SavvyCNV’s ability to call CNVs from off-target reads generated by exome sequencing we benchmarked it against a truth set (see Materials and Methods) and compared its performance to GATK gCNV, DeCON, EXCAVATOR2, CNVkit, and CopywriteR. The best recall where precision is at least 50% is shown in Table C in S1 Text for two different size categories, and recall/precision is shown in Fig 3 for all CNVs.

Fig 3

Benchmarking off-target CNV calling from exome data.

The data points on the plot are generated by a parameter sweep for each tool and show the precision and recall that can be achieved with each tool. The f statistic is the harmonic mean of precision and recall (see Materials and Methods for details).

SavvyCNV was the best performing tool on this data set, able to call 86.7% of the CNVs with at least 50% precision, while the next best tool (DeCON) called 46.7% of CNVs with at least 50% precision. The chief difference between the performances of the tools is SavvyCNV’s ability to call CNVs smaller than 200kpb. SavvyCNV is able to call an additional 30 CNVs that are smaller than 200kpb at > = 50% precision while GATK gCNV, EXCAVATOR2, CNVkit, and CopywriteR call no true CNVs smaller than 200kB, and DeCON calls 10.

We conducted a sensitivity analysis where an alternative CNV caller was used to call the truthset from the genome sequencing data and results were similar (see Fig C in S1 Text).

SavvyCNV can detect clinically relevant CNVs

Having validated the ability of SavvyCNV to call CNVs from off-target reads we proceeded to screen for CNVs in our cohort of targeted panel samples from patients referred for genetic testing to identify the cause of their diabetes or hyperinsulinism [17]. We were able to detect 6 clinically relevant CNVs both within and outside of the targeted regions (Table 1). Of these, 3 provided a new genetic diagnosis for diabetes/hyperinsulinism (rows 1–3 in Table 1), providing information which will guide clinical management and allow accurate counselling on recurrence risk in family members and future offspring. The remaining 3 CNVs (rows 4–6 in Table 1) confirmed clinically-reported diagnoses unrelated to the diabetes/hyperinsulinism. These findings demonstrate the ability of SavvyCNV to detect clinically relevant CNVs and aneuploidies from off-target data from a small targeted panel.

Table 1

Clinically-relevant CNVs detected.

Row	CNV detected (GRCh37)	CNV size	Clinical confirmation	Reason for referral	Clinical implications
1	Chr8:6,800,000–11,800,000 deletion	5Mb	Deletion includes GATA4, causative of the patient’s neonatal diabetes and additional features.	Diabetes	Genetic diagnosis of monogenic diabetes [21].
2	Chr8:8,000,000–10,400,000 duplication8:10,600,000–12,000,000 deletion	2.4Mb and 1.4Mb	Deletion includes GATA4, causative of the patient’s neonatal diabetes and additional features.	Diabetes	Genetic diagnosis of monogenic diabetes [21).
3	Chr18:19,400,000–21,800,000 deletion	2.4Mb	Deletion includes GATA6, causative of the patient’s neonatal diabetes and additional features.	Diabetes	Genetic diagnosis of monogenic diabetes [22].
4	Chr21:14,400,000–48,200,000 duplication	Chromosome	Patient known to have Down Syndrome at referral.	Hyperinsulinism	Confirms the diagnosis of Down syndrome.
5	Chr22:18,800,000–21,600,000 deletion	1.8Mb	Confirmed by array CGH.	Diabetes	Provided the diagnosis of DiGeorge syndrome.
6	ChrX:0–155,400,000 duplication	Chromosome	Patient known to have XXX syndrome at referral.	Diabetes	Confirms the diagnosis of XXX syndrome.

Discussion

SavvyCNV can detect CNVs genome-wide from off-target reads

We benchmarked SavvyCNV on its ability to call germline off-target and on-target CNVs from targeted panel and exome sequencing data. This new tool outperformed five existing tools, three of which (CNVkit [15], Excavator2 [14], and CopywriteR [16]) were specifically designed to call off-target CNVs. GATK gCNV performed similarly to SavvyCNV in the on-target (ICR96) analysis. However, SavvyCNV considerably outperforms all other tools in the off-target analyses.

SavvyCNV finds the greatest number of true positive CNVs in all data sets while other tools did not call certain CNVs. For example, the two partial exon CNVs in the on-target (ICR96) data set are detected only by SavvyCNV. This is likely because of the improved error correction and error modelling that is incorporated into SavvyCNV over existing tools. SavvyCNV uses singular vector decomposition to reduce noise. CNVkit, EXCAVATOR2 and CopywriteR only correct for GC content, while GATK gCNV uses Bayesian principle component analysis (https://www.broadinstitute.org/videos/scalable-bayesian-model-copy-number-variation-bayesian-pca), and DeCON uses sample matching (it searches for samples in the control set that have a similar noise profile). Unlike the other tools tested, CopywriteR does not normalise against other samples but excludes on-target reads to make read counts representative of the true copy number. CNVkit was primarily designed for somatic CNV calling and thus it is perhaps not surprising that when used for germline calling it demonstrates limited performance.

SavvyCNV had a higher precision than other tools when calling off-target CNVs, an important consideration in diagnostic and research laboratories as if false positives are reduced, fewer CNVs will require orthogonal testing to identify the true positive results. Many of the false positives produced by DeCON, CNVKit, and EXCAVATOR2 have a read depth ratio close to 1, where a true full deletion should be close to 0.5 and a true full duplication should be close to 1.5. This indicates that these tools are picking up either mosaic CNVs or noise. The prior probability is overwhelmingly that these are noise. This is why the default for SavvyCNV and GATK gCNV is to call only non-mosaic CNVs as this hugely reduces the number of false positives called. Mosaic CNV calling can be enabled in SavvyCNV for projects where it is applicable.

SavvyCNV can also be used to call CNVs from on-target sequencing and, while it is not demonstrated in this study, it can be used to call CNVs from genome sequencing data and has already been used to identify a novel disease-causing deletion [23].

Estimates of precision and recall rely on the quality of the truth set

On-target CNV calling from a targeted panel was tested on the ICR96 data set in which the truth set was verified by MLPA. The truth sets for the off-target CNV calling from targeted panel and exome sequence data were generated from CNV calls from genome sequencing data. Genome sequencing has a much higher coverage than that generated from only off-target reads which allows CNVs to be called more accurately enabling them to be used as a truth set. GenomeStrip [24] was used to call the truth set as it was designed to call CNVs from genome data and was not one of the tools under examination in this study. We also conducted a sensitivity analysis where Canvas was used to call the truthset and results were similar. However, it is possible that there could be some false positive and negative calls in the truth set. This would lower the precision and recall of the tools under examination but should not bias the results in favour of a particular tool. Furthermore, as the majority of the truthset was called using a read depth method, and SavvyCNV and the tools it was compared to also use read depth methods, it is possible that they would share technical artifacts which could artificially inflate estimates of performance.

Sensitivity depends on the size of the CNV

Smaller CNVs are harder for all software to detect. For all tools tested the larger the CNV the better the precision and recall, however SavvyCNV performs better than the other tools tested. SavvyCNV detects CNVs larger than 1Mb with 100% recall in off-target data from both targeted panel and exome data.

Another important factor determining performance is the number of off-target reads. The number of off-target reads will vary depending on the depth of sequencing, size of panel and the particular capture method used. We have created an additional tool, CoverageOffTarget (available at https://github.com/rdemolgen/SavvySuite), which calculates the number of off-target reads in a set of samples.

CNV calling can be optimised for precision or recall by adjusting configuration

When calling CNVs, precision and recall are a trade-off; high recall will maximise the number of true CNVs that are called, with the consequence that it also reduces precision resulting in a large number of false positive CNV calls. Precision and recall can be adjusted using the bin size and transition probability parameters—for full documentation on how to use the software see the github page: https://github.com/rdemolgen/SavvySuite. The CoverageOffTarget tool can be run on the set of samples to be analysed to provide a recommendation for an appropriate bin size.

Different precision levels are appropriate in different situations, influenced both by the experimental methodology and the aims of the project. When calling CNVs on-target on a small gene panel there will be fewer false positive calls generated due to the smaller target area thus it may be preferable to adjust settings to enable a higher recall at the cost of a lower precision. This could also be true in a clinical context where the most important aim is to not miss a true causative variant. In contrast, when calling CNVs genome-wide in a gene-agnostic approach such as exome or genome sequencing, a higher precision is likely to be desirable to avoid generating an unmanageably long list of CNVs. A large bin size may also be preferable when the aim is to detect large CNVs as this will improve the precision for detecting these with the trade-off of potentially missing smaller CNVs. The user can choose their preferred settings for SavvyCNV for different project requirements.

Off-target CNV calling is ‘free’ data and increases diagnostic yield

SavvyCNV utilises data already generated by targeted panel and exome tests. These tests are carried out in order to detect single nucleotide variants and small insertions or deletions (<50 base pairs). In some laboratories CNVs are also detected within the targeted regions using CNV calling software while other laboratories use array-CGH or MLPA to detect CNVs. Using SavvyCNV allows CNVs to be detected not just within the targeted regions but allows genome-wide CNV calling. This will provide a genetic diagnosis for more patients, increasing the diagnostic yield of these tests. We have demonstrated the ability to find relevant genetic diagnoses using off-target CNV calling from our small targeted panel. Existing data can be reanalysed with our method to reveal additional CNVs. As an illustration of this, two of the CNVs in the ICR96 data set were found to actually be large CNVs (15Mb and 56Mb), which may have clinical implications beyond the targeted gene.

SavvyCNV calls germline CNVs from off-target reads from exomes and small targeted gene panels with high precision and recall, and performs better than existing tools including those designed for off-target CNV calling. Calling CNVs from off-target reads is exploiting ‘free’ data to increase the diagnostic yield of targeted panel and exome sequencing tests and reveal important biological findings.

Materials and methods

Ethics statement

For the patients referred to the molecular genetics department at the Royal Devon and Exeter Hospital for genetic testing, informed written consent was obtained from the probands or their parents/guardians, and the study was approved by the North Wales ethics committee. The study was conducted in accordance with the Declaration of Helsinki.

Overview of how SavvyCNV calls CNVs

SavvyCNV and the commands to run it are freely available from https://github.com/rdemolgen/SavvySuite.

SavvyCNV calls CNVs by looking at read depth over the genome. The genome is split into bins and each bin is assessed for statistical divergence from normal copy number. The bin size is user specified. If there is targeted sequencing with three million reads and approximately 50% off-target reads, then a bin size of 200kbp is appropriate.

SavvyCNV normalises the read depths by first dividing the read count by the mean relative read depth of the sample across all genomic locations, and then subsequently dividing the read count by the mean read depth of the genomic location across all samples. SavvyCNV then uses singular vector decomposition (SVD) to reduce noise. This identifies biases common to multiple samples, which can be caused by differences in sample handling or chemistry. The SVD is performed on the logarithm of the normalised read depth, and by default the first five singular vectors are discarded. Fig D in S1 Text shows the effect of using SVD on precision and recall. SavvyCNV estimates the error in each genomic location in each sample by modelling the error using the average error in each sample across all genomic locations and the average error in each genomic location across all samples. The total number of reads available is also analysed, and the error estimation is increased if it would be lower than the error calculated using the Poisson distribution. The effect on CNV calling accuracy is shown in Fig E in S1 Text while the design of the error modelling is described in Fig F in S1 Text. The error estimate is used to determine whether the read depth in a genomic location is significantly outside the range expected for normal copy number. The significance probability of the read depth representing a deletion, duplication, or normal dosage is calculated using an approximation of the cumulative normal distribution (P(d< = 0.5) for deletions, P(d> = 1.5) for duplications, and P(d< = 1.0) or P(d> = 1.0) for normal dosage). These probabilities are used in a hidden Markov model (HMM) with three states (deletion, duplication, and normal) to identify CNVs.

SavvyCNV assumes CNVs are non-mosaic by default. The HMM state probability calculations assume that the relative dosage is either < = 0.5, 1, or > = 1.5. Dosage levels must cross the mid-point between 1 and 0.5 or 1.5 (so the probability of a deletion or duplication is calculated as greater than the probability of normal dosage) before they become evidence of a CNV. This increases specificity at the cost of being able to detect mosaic CNVs (see Fig G in S1 Text). Mosaic CNV calling can be enabled in SavvyCNV for projects where it is applicable, which changes the probability calculations to use just the probability of the read depth being not 1.

Computational requirements

Run time is proportional to the number of active bins in the analysis, and proportional to the number of samples to the power of 2.3. Calling on-target CNVs on 150 exomes takes around half an hour, but 300 exomes would take five times as long. Calling off-target CNVs on 1700 targeted samples with a bin size of 200kpb takes around 4 hours.

If only a small number of samples are available, then CNV detection performance is lower. Fig H in S1 Text shows how the CNV detection ability of SavvyCNV depends on the number of samples that are analysed in a single batch.

If a large number of samples must be processed, then they can be split into groups and processed separately. Additional software is provided alongside SavvyCNV that allows a selection of similar samples to be selected from a large pool to use as a control for a set of samples, to reduce run time. RAM usage depends on the bin size and the number of samples. Calling on-target CNVs on 1700 samples with a bin size of 400 base-pairs requires 170GB of RAM, and calling off-target CNVs on 1700 samples with a bin size of 200kpb requires around 1GB of RAM.

Targeted panel data

2591 patients were referred to the molecular genetics department at the Royal Devon and Exeter Hospital for genetic testing for maturity onset diabetes of the young (MODY), neonatal diabetes (NDM) or hyperinsulinemic hypoglycemia (HH). Samples were sequenced on a targeted gene panel test for monogenic diabetes and HH using a custom Agilent SureSelect panel of 75 genes, targeting 200kpb and obtaining 3.4 million reads on average per sample (standard deviation 1.6 million)[17], using an Illumina HiSeq 2500 or an Illumina NextSeq 500. Based on the GATK best practice guidelines [18] reads were aligned to the hg19/GRCh37 human reference genome with BWA mem [25], duplicates were removed using Picard (https://broadinstitute.github.io/picard/) and GATK IndelRealigner was used for local re-alignment. CNV analysis was carried out on these BAM files.

Exome sequencing data

Following testing using the targeted panel, samples from 86 patients underwent exome sequencing with Agilent SureSelect Whole Exome versions 1, 3, 4, and 5, obtaining 76 million reads on average (standard deviation 20 million). Sequencing, alignment and variant calling was as above for the targeted panel.

Truth set for targeted and exome data

The truth set was based on genome sequencing of the same samples. 170 of the targeted panel samples and 42 of the exome samples were subsequently genome sequenced on an Illumina HiSeq 2500 or an Illumina HiSeq X10. These were used to create a truth set of CNVs for testing the off-target CNV calling from targeted panel or exome data. The CNVs in the truth set were called by GenomeStrip [24] from the genome sequencing data. In order to remove false positive calls CNVs were filtered based on their allele balance ratios–whether the allele balance of the variants within the called CNV was consistent with it being a true call. We used the X chromosome in males to calibrate the expected allele ratio for a deletion and used the allele ratio of normal, two copy regions to evaluate if the allele ratio for duplications fell above that. In addition 37 CNVs were added to the targeted panel truth set as they were validated by other methods such as MLPA or were aneuploidies reported by the clinician at time of referral for genetic testing. There were 45 duplications and 30 deletions tested in the exomes, and 171 duplications and 96 deletions tested in the targeted samples.

We conducted a sensitivity analysis where Canvas [26] was used to call the truthsets from the genome sequencing data. The ICR96 data set [20] was used to benchmark on-target CNV calling. This data set consists of 96 samples sequenced on a targeted panel where the truth set of CNVs is based on 68 positive and 1752 negative MLPA tests.

Calling clinically-relevant CNVs

The remaining 2479 targeted panel samples from unsolved patients with MODY, NDM and HH were analysed with SavvyCNV to look for off-target CNVs which might explain their phenotype. For clinical evidence of the CNVs, see Table 1.

CNV tool comparisons

A CNV was deemed to have been detected if there was any overlap between the call made by the CNV caller and the truthset. To ensure a fair comparison between the different tools, for each data set all tools were run with a variety of configurations. The size of genomic regions that were analysed was varied for all six tools (targeted panel: 150kbp to 300kbp or 50kbp to 2Mbp for CopywriteR; exomes: 6kbp to 50kbp or 20kbp to 2Mbp for CopywriteR; ICR96: 200bp to 600bp). The hidden Markov model transition probability was varied for DeCON and SavvyCNV (10−10 to 0.1). All six tools provide quality metrics for the CNV calls. These metrics were used to filter the CNV calls to reject false positive calls and retain true positive calls. All possible quality cut-off values were tried. The best precision achieved for each possible recall was then selected for each tool from all the generated results, and plotted in precision-recall graphs. GATK gCNV produces a probability of CNV in each individual genomic location, but does not estimate the boundaries of detected CNVs. The output was processed to group together locations with probable CNVs to determine the size and boundaries of the CNVs, in order to produce results that were comparable to the other CNV callers. EXCAVATOR2 [14] did not run on the ICR96 data set—we contacted the authors of the tool but did not receive a response. CopywriteR was not run on the ICR96 data set, as it is not designed to use on-target data. The code used to run the benchmarking comparisons is available at https://github.com/exeter-matthew-wakeling/SavvyCNV_benchmarking.

Statistics

We defined recall as the percentage of true positive CNVs that were found by the tool. We defined precision as the percentage of the total CNVs called by the tool that were true. Several figures use the f statistic to compare tools; this is the harmonic mean of precision and recall.

Supplementary information.

Table A. Benchmarking off-target CNV calling from targeted panel data. The table shows the performance of the different CNV calling software based on the size of the CNV. The tools were run with multiple different parameters. For this comparison, we have selected the configuration for each tool that provides the highest recall with a precision of at least 50%. More variants may be detected by each tool with different configuration, but with precision less than 50%. Table B. Benchmarking on-target CNV calling from the ICR96 targeted panel data. The table shows the performance of the different CNV calling software based on the size of the CNV. The tools were run with multiple different parameters. For this comparison, we have selected the configuration for each tool that provides the highest recall with a precision of at least 50%. Table C. Benchmarking on-target CNV calling from the exome data. The table shows the performance of the different CNV calling software based on the size of the CNV. The tools were run with multiple different parameters. For this comparison, we have selected the configuration for each tool that provides the highest recall with a precision of at least 50%. Fig A. An example of a single large CNV called by SavvyCNV in chromosome 8. The CNV is a heterozygous deletion of 8:6,800,000–12,400,000, which is a 5.6Mbp deletion. The CNV analysis uses a bin size of 200kbp, so the CNV covers 28 bins. The normalised read depth of each bin is shown as an error bar, where the error is the estimated error calculated by SavvyCNV. Bins either side of the deletion have a normal normalised read depth near 1, whereas bins inside the deletion have a normalised read depth near 0.5. This CNV overlaps a targeted gene in the sequencing capture, and this targeted data covers two separate bins, which are marked. Note that the error estimate for these two bins is slightly smaller than the other bins, partly because of the increased read count contributed by the targeted region of the capture, however the difference is small–the majority of evidence for this CNV comes from off-target data. Some of the bins have a large estimated error–this is caused by large tandem repeat regions which mean the read depth is highly variable between samples. Fig B. Benchmarking off-target CNV calling from targeted panel data. This figure shows the equivalent of Fig 1, but using a truth set called using Canvas on whole genome sequenced samples. The truth set used in Fig 1 was produced by analysing whole genome samples with GenomeStrip2 and filtered using variant allele fraction data. The data points on the plot are generated by a parameter sweep for each tool and show the precision and recall that can be achieved with each tool. The f statistic is the harmonic mean of precision and recall (see Materials and Methods for details). Fig C. Benchmarking off-target CNV calling from exome data. This figure shows the equivalent of Fig 3, but using a truth set called using Canvas on whole genome sequenced samples. The truth set used in Fig 3 was produced by analysing whole genome samples with GenomeStrip2 and filtered using variant allele fraction data. The data points on the plot are generated by a parameter sweep for each tool and show the precision and recall that can be achieved with each tool. The f statistic is the harmonic mean of precision and recall (see Materials and Methods for details). Fig D. Shows the improvement in precision/recall due to the error correction strategy used by SavvyCNV, for all three data sets. By default, SavvyCNV uses singular vector decomposition (SVD), which identifies biases common to multiple samples, which can be caused by differences in sample handling or chemistry. The "normal" line shows the default configuration, while the "No SVD" line is with SVD switched off. Fig E. Shows the improvement in precision/recall due to the error modelling strategy used by SavvyCNV. By default, SavvyCNV estimates the error in normalised read depth in each genomic location for a sample by calculating the standard deviation for that location across all samples, then scaling by the standard deviation for that sample across all locations. An alternative is to add the two error values. Other software assumes that the error is Poisson in nature, and can therefore be calculated from the read depth. The error estimate is used to determine whether the read depth in a genomic location is significantly outside the range expected for normal copy number. In reality, the actual error is larger than the Poisson method in some genomic locations. Modelling the error allows SavvyCNV to avoid making false CNV calls in these highly variable regions. Fig F. Shows the error estimation accuracy of three strategies investigated during the development of SavvyCNV. The CNV detection ability of these three strategies is shown in Fig E in S1 Text. SavvyCNV was used to analyse the read depths of around a thousand targeted samples with a bin size of 200kbp. The estimated error was calculated using the three strategies and listed alongside the actual normalised read depth value for each bin in each sample, giving a total of 32 million data points. These data points were then grouped into 100 sets with similar estimated error, and the standard deviation of the normalised read depth was calculated—this is the actual error. The Poisson error model calculates the estimated error by using the number of reads in the analysis bin, and it usually underestimates the actual error, however it does represent a theoretical minimum random error that the normalised read depth could have. The additive error is formed by adding the standard deviation of the genomic location across all samples with the standard deviation of the sample across all genomic locations. This usually overestimates the error, but this cannot be corrected by scaling. The normal error model multiplies the two error estimates together and divides by the mean error of all samples, and is highly effective at estimating the error in the normalised read depth. As a check, if this calculation yields an error lower than the Poisson error, then the Poisson error is used instead. Fig G. Shows the improvement in precision/recall due to the non-mosaic assumption used by SavvyCNV. By default, SavvyCNV assumes that CNVs are not mosaic, although this is a configurable setting. This allows it to reduce the number of false positive CNV calls. In mosaic mode (and DeCON, Excavator2, and CNVKit), a CNV call is produced when the normalised read depth is significantly away from 1.0. In SavvyCNV’s default mode (and GATK gCNV), the read depth must also be closer to 0.5 or 1.5 (representing a whole heterozygous deletion or duplication) than 1.0 in order for a CNV to be called. Fig H. Shows how the CNV detection ability of SavvyCNV depends on the number of samples that are analysed in a single batch. SavvyCNV detects regions where the read depth of a sample is higher or lower than expected, and so it must have other samples to compare the test sample to. A larger number of available samples improves the ability of SavvyCNV to correct for noise and determine the expected read depth for each sample, to better detect deviations from that expected read depth. We divided our set of targeted sequencing samples into randomly assigned batches sized between 7 and 400 samples, and ran SavvyCNV with multiple configuration settings as described for the main experiment that produced Fig 1 for each group size. The maximum f statistic was calculated for each group size. This process was repeated 25 times with randomly-assigned groups. This shows that the detection power of SavvyCNV for this set of samples is lower when fewer samples are analysed. The power to detect all CNVs is fully reached when there are at least 50 samples analysed. The power to detect larger CNVs continues to increase with larger sample batches up to a maximum with 200 in each batch.

(DOCX)

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14 Oct 2021

Dear Dr Laver,

Thank you very much for submitting your manuscript "SavvyCNV: genome-wide CNV calling from off-target reads" for consideration at PLOS Computational Biology.

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Reviewer's Responses to Questions

Comments to the Authors:

Please note here if the review is uploaded as an attachment.

Reviewer #1: The paper by Laver et al presents SavvyCNV, a tool for CNV calling from hybrid capture sequencing data which can make use of off-targets reads. While plenty of tools have been developed for CNV calling from exome and panel sequencing data, CNV calling cannot be considered as a solved problem: concordance between calls from different tools is low, and false negatives and false positives are common. Hence there is still a high (and with the increasing use of panel and exome sequencing for diagnostics growing) need for improved CNV calling methods.

The authors benchmark SavvyCNV against five other tools in three settings: off-target calling from exome data, off-target calling from panel data, and on-target calling from panel data. For benchmarking calling using off-target reads, an in-house data set with panel sequencing data from 2591 patients and exome sequencing data from 86 patients. The truth set for these benchmarks was obtained from deep whole genome sequencing of 170 panel-sequenced and 42 exome-sequenced samples. As truth, the authors considered CNV calls obtained by GenomeStrip 2.0 from this genome sequencing data. On-target calling was benchmarked using the previously published IRC96 dataset. For all tools, parameter sweep was performed and performance was evaluated in precision recall curves. SavvyCNV had the best overall performance in all shown comparisons. In the supplement, the authors add results which demonstrate the impact of three approaches used in the CNV calling process of SavvyCNV.

Overall, the authors have performed a comprehensive comparison to demonstrate the superiority of their tool in the tested scenarios. Based on these results, SavvyCNV appears to be a considerable improvement compared to the other callers included in the benchmark and thus might be of high value for a broader community.

However, there are still some issues which should be addressed:

Major issues:

1. The truth set for off-target calling can be expected to contain false negatives and false positives. The statement of the authors, that these will affect all tools to the same amount is not necessarily true: tools with a similar error profile as GenomeStrip 2.0 will have an advantage in this benchmark and might even appear to have a better performance than a perfect caller. Although one could hypothesize that calling from whole genome data is sufficiently more powerful to make the errors of GenomeStrip negligible, an additional comparison using an independent truth set would make the manuscript stronger. The authors could for example use the recently compiled validation set on NA12878 published by Gordeeva et al. (Scientific Reports 2021).

2. The methods descriptions in the manuscript are incomplete. This affects both the savvyCNV algorithm and the comparison. For example, it is not described at all how savvyCNV performs calling. There are only rather general descriptions of noise handling and the error model, but no in-depth methods; obviously, SavvyCNV employs a hidden Markov Model (mentioned in the CNV tools comparison section), but there is no description how it is implemented. Similarly, more details should be given about the CNV calling benchmark. How exactly have CNVs been compared? Was a CNV call considered as correct if it was partially overlapping with a reference call but had shifted start and/or end coordinates? Was the classification just gain or loss, or was there a discrimination of hemi- and homozygous deletions and different levels of gains.

3. More information should be included to help potential users to decide if savvyCNV could be the right tool for their application. In particular, it would be important to know how many samples are needed for an analysis and how similar these samples have to be. Many users won’t have access to such a big collection of samples as used here for exome and in particular panel off-target calling.

4. The code and in particular the exact parameters used for each tool in the benchmark should be provided.

Minor issues:

1. Page 11, discussing mosaic or random noise. I agree that it is very likely noise, but most likely not random.

2. CNVkit was primarily designed for somatic CNV calling, and the documentation mentions that when used for germline calling it can be expected to show limited performance. This should be briefly added to the discussion.

3. It seems that SavvyCNV has been developed and evaluated for germline CNV calling and not for somatic CNV calling. This should be mentioned in the manuscript; it could even be added to the title.

Reviewer #2: # Overview

Detection of CNVs from targeted sequencing and exome data is an important problem in clinical

genetics and there have been a wide range of tools published over the last decade. As noted

by the authors however, most of these tools achieve mediocre precision when applied at acceptable

sensitivity for clinical use, and further they typically can only operate well over the specific

genes targeted by the specific assay, making it impossible to characterise the real span of

larger genomic events even when they are detected.

SavvyCNV offers an exciting addition to this ecosystem of tools because it enables detection of

larger events completely outside targeted regions and characterisation of the size and breakpoints

of events that would otherwise be detected only within targeted regions. This is a valuable

contribution and even while other tools do exist, SavvyCNV bring several novel additions to the

process along with a high quality, well performing implementation.

In addition to reviewing the manuscript, I evaluated SavvyCNV on my own data. Although it required

several iterations of tuning the parameters of the software, after doing that I

was impressed with the speed and accuracy. It detected all known multi-exon CNVs and

nearly all or all single-exon CNVs, which depended on the configuration

parameters I chose. The software was easy to download, compile and run : it was

refreshing to see a pure Java implementation with minimal dependencies,

making it highly portable and performant.

Despite my good impressions of the software, I think the manuscript itself

needs some additions before it is fit for scientific publication. The problems

include insufficient description of methods, lack of clarity around terms used,

missing essential details for reproducibility and failure to acknowledge

significant weaknesses and limitations.

I have added specific notes below about areas that I think require addressing.

# General Comments

- Overall it is a bit confusing that evaluation of off-target calling is

combined with with on-target calling in various respects.

- When referring to "off target" CNVs, does this imply that there are no target regions

overlapping the true CNV? Or only that off-target evidence is the primary signal for detection?

- If on-target regions are included, are the read counts included in SavvyCNV's

algorithm? How does this affect the statistics when some bins contain spikes of very

high coverage while others only contain low level coverage? It seems to me this would

heavily distort, eg: the noise estimation which utilises standard deviation averaged

across all the bins.

I note that the software does not appear to take the target regions of the genome as

a parameter; therefore it seems that in regular use both targeted regions and non-targeted

regions would be considered by the tool together.

This leads to a contradiction: the primary claim of the manuscript is that SavvyCNV utilises

off-target reads, but in reality it appears that most of its detections would be aided by

at least *some* on target reads. To substantiate that off-target reads are really being utilised

I think there needs to be more detail given about (a) whether the results shown actually

overlap on-target reads or not and (b) whether the off-target reads aided in characterisation

of the true size / location of the CNV.

Further explanation around all of these issues is required.

# Specific Comments

> CNV calling can be optimised for precision or recall by adjusting configuration

The section describes that SavvyCNV can be optimised for different requirements, however

it does not present any details on how this is achieved. A summary of which parameters

can be tuned and some idea of methodology of approach for doing so should be included.

Is it possible without a dedicated truth set for the data under

consideration? since each different type of data needs potentially different parameter values,

it is not clear how a user can go about tuning these parameters in practice.

## Results

> How much off-target read data is there?

How well SavvyCNV performs is critically dependent on how many off target reads

there are. However this is a highly variable aspect of sequencing and capture

methods. The manuscript should ideally give a bit more background explanation

that off-target reads are influenced by the chemistry and laboratory process

and may vary significantly between different contexts - this to some extent is an important

limitation of the method and that should be acknowledged. It would be ideal to

give a table showing a variety of exome captures and targeted panels with

different chemistries and number of genes. Since this parameter is critical to

operation of SavvyCNV, it would seem useful if there is some kind of QC

statistic that is produced as part of an analysis that can inform the user of

the abundance of off target reads that were observed.

> SavvyCNV can detect clinically relevant CNVs

In Table 1, presumably in some of these cases the span of the CNVs detected include

some of the target regions of the panel (for example, the whole chrX aneuploidy). In these

cases it should be clarified to what extent the off-target detection was aided by on-target

reads. Presumably, SavvyCNV has enabled characterisation of the size of these events well

beyond what would possible from the target regions alone. However, to substantiate this,

it would be good for the results to include details showing how accurately SavvyCNV

characterised the size / location of the CNV compared to orthogonal validation data for

the events.

# Assessment of Off-Target CNV Calling

The truth set was constructed using GenomeSTRiP. This seems a surprising choice given that there

are many more well established whole genome sequencing CNV and SV callers now and GenomeSTRIP is

designed for population scale data. A key issue with this approach is that GenomeSTRiP is also

heavily read depth based, so it is susceptible to many of the same artefactual biases and could

mean that many calls in the "truth" set are shared false positives between GenomeSTRiP and

SavvyCNV. Similarly, many regions where GenomeSTRiP is unable to call CNVs will also apply

to SavvyCNV, so use of GenomeSTRiP is questionable for characterising the false negative rate.

While text acknowledges these issues in a minor way, this is somewhat buried and insufficiently

highlighted (eg: in the conclusions).

# Assessment of On-Target CNV Calling

- The manuscript doesn't state how much overlap was required for a CNV to be considered as successfully

detected. I think this is quite important in the case of SavvyCNV, as it primarily detects larger

events from off-target reads, but if these are only partially identified or identified in a

fragmented way then it would affect the usability in many settings.

# Materials and Methods

> Overview of how SavvyCNV calls CNVs

The overview is extremely brief and leaves many critical details out. For example, it is clear from the

source code and alluded to in other parts of the manuscript that SavvyCNV applies a hidden markov model

style segmentation algorithm using viterbi optimisation. However the overview does not include any mention

of this part of the algorithm. Further, within the HMM the likelihood is evaluated by use of a

normal distribution to model state probabilities, ie: reflecting a normal assumption for the distribution

of read counts. These are commonly examined properties of CNV detection algorithms, and certain limitations

arise from their characteristics (for example, utilising a normal model usually performs suboptimally for

low read counts, and HMM based methods often have issues at the start / end points of the model because a

neutral state is assumed as a starting point).

These details should be explained as important elements of the algorithm.

> Dosage levels must cross the mid-point between 1 and 0.5 or 1.5 before they become evidence of a CNV.

This increases sensitivity

It doesn't seem possible that this could increase sensitivity because it acts purely to remove

CNVs from the result set. Presumably the intent here is that it increases sensitivity at a given

specificity?

Further, this strategy seems likely to produce false negatives as even slightly imperfect normalisation

can easily result in a CNV dosage estimate fractionally above 0.5, especially in the case of single-bin

CNVs that would have only a point estimate of the dosage. Did the authors examine the strategy of using

a statistical model for this element rather than a fixed threshold / cutoff?

> CNV tool comparisons

The manuscript states that a range of parameter settings were used for each tool and the best chosen

for each one.

The parameters used for analysis with each tool are insufficiently described (either here, or where results are

presented elsewhere). However I could not find either the ranges (or values) evaluated or the optimal values

chosen for most of the callers (including SavvyCNV). For example, the bin size is stated as a user selectable

parameter of SavvyCNV but the value chosen is not documented in the results, materials and methods or the

supplementary information. These details are important to ensure reproducibility of the work. Similarly for

the transition probability and number of SVD components - if these are left at their defaults then this could

simply be stated.

A particular problem seems to arise in the selection of bin size. I evaluated SavvyCNV on internal data sets

that we have used to validate CNV detection for exome sequencing. It was apparent in this process that choosing

different bin sizes could make deletions in our truth set appear and disappear from the results. This effect

primarily seems to be driven by the luck of how much of a bin (or how many bins) end up overlapping the event

of interest. If bins are too large, small events cannot be captured, while if they are too small, they become

vulnerable to noise and weak power.

# Software Suggestions

- The output file for SavvyCNV does not contain any headers. This makes it difficult to parse and

process in downstream tools (eg: Pandas, Excel, etc).

- The output file is referred to in the documentation as CSV format but is actually written as a tab

separated file

- The output file associates each call to the binned data file that the call came from. This required

me to add separate tracking outside of SavvyCNV to relate the files to each sample. Although it might

be normal to do this implicitly, eg: by naming the input files by sample, I feel it would be more

friendly if there was a way to identify the actual sample.

**********

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The PLOS Data policy requires authors to make all data and code underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data and code should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data or code —e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: No: Code and exact calls to tools used in the benchmark have not been made available. As specified in the data availability statement, also the in house dataset used in the benchmark could not be made available due to patient privacy issues.

Reviewer #2: No: The authors have explained that part of their validation data set is clinical data that cannot be made public for patient confidentiality reasons. I am comfortable with this personally but obviously journal policy should be applied according to your own discretion.

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Reviewer #1: Yes: Matthias Schlesner

Reviewer #2: Yes: Simon Sadedin

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11 Jan 2022

Submitted filename: SavvyCNV_reviewer_responses - final (1).docx

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19 Feb 2022

Dear Dr Laver,

We are pleased to inform you that your manuscript 'SavvyCNV: genome-wide CNV calling from off-target reads' has been provisionally accepted for publication in PLOS Computational Biology.

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Mihaela Pertea

Software Editor

PLOS Computational Biology

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Reviewer's Responses to Questions

Comments to the Authors:

Please note here if the review is uploaded as an attachment.

Reviewer #1: The authors have adressed all issues raised by the reviewers.

I have no further comments.

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Reviewer #1: None

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Reviewer #1: Yes: Matthias Schlesner

14 Mar 2022

PCOMPBIOL-D-21-01118R1

SavvyCNV: genome-wide CNV calling from off-target reads

Dear Dr Laver,

I am pleased to inform you that your manuscript has been formally accepted for publication in PLOS Computational Biology. Your manuscript is now with our production department and you will be notified of the publication date in due course.

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