Abstract
Somatic copy number variations (CNVs) play a crucial role in development of many human cancers. The broad availability of next-generation sequencing data has enabled the development of algorithms to computationally infer CNV profiles from a variety of data types including exome and targeted sequence data; currently the most prevalent types of cancer genomics data. However, systemic evaluation and comparison of these tools remains challenging due to a lack of ground truth reference sets. To address this need, we have developed Bamgineer, a tool written in Python to introduce user-defined haplotype-phased allele-specific copy number events into an existing Binary Alignment Mapping (BAM) file, with a focus on targeted and exome sequencing experiments. As input, this tool requires a read alignment file (BAM format), lists of non-overlapping genome coordinates for introduction of gains and losses (bed file), and an optional file defining known haplotypes (vcf format). To improve runtime performance, Bamgineer introduces the desired CNVs in parallel using queuing and parallel processing on a local machine or on a high-performance computing cluster. As proof-of-principle, we applied Bamgineer to a single high-coverage (mean: 220X) exome sequence file from a blood sample to simulate copy number profiles of 3 exemplar tumors from each of 10 tumor types at 5 tumor cellularity levels (20–100%, 150 BAM files in total). To demonstrate feasibility beyond exome data, we introduced read alignments to a targeted 5-gene cell-free DNA sequencing library to simulate EGFR amplifications at frequencies consistent with circulating tumor DNA (10, 1, 0.1 and 0.01%) while retaining the multimodal insert size distribution of the original data. We expect Bamgineer to be of use for development and systematic benchmarking of CNV calling algorithms by users using locally-generated data for a variety of applications. The source code is freely available at http://github.com/pughlab/bamgineer.
Highlights
The emergence and maturation of next-generation sequencing technologies, including whole genome sequencing, whole exome sequencing, and targeted sequencing approaches, has enabled researchers to perform increasingly more complex analysis of copy number variants (CNVs)[1]
We have developed Bamgineer, a tool written in Python to introduce user-defined haplotype-phased allelespecific copy number events into an existing Binary Alignment Mapping (BAM) file, with a focus on targeted and exome sequencing experiments
Reads were aligned to the hg19 build of the human genome reference sequence and processed using the Genome Analysis Toolkit (GATK) Best Practices pipeline
Summary
The emergence and maturation of next-generation sequencing technologies, including whole genome sequencing, whole exome sequencing, and targeted sequencing approaches, has enabled researchers to perform increasingly more complex analysis of copy number variants (CNVs)[1]. While genome sequencing-based methods have long been used for CNV detection, these methods can be confounded when applied to exome and targeted sequencing data due to non-contiguous and highly-variable nature of coverage and other biases introduced during enrichment of target regions[1,2,3,4,5]. In cancer, this analysis is further challenged by bulk tumor samples that often yield nucleic acids of variable quality and are composed of a mixture of celltypes, including normal stromal cells, infiltrating immune cells, and subclonal cancer cell populations. BAMSurgeon provides support for adjusting variant allele fractions (VAF) of engineered mutations based on prior knowledge of overlapping CNVs but does not currently support direct simulation of CNVs themselves
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