BAMSurgeon

[…] simulators with built-in or user-supplied error models such as art  and seqmaker . alternatively, hybrid datasets featuring real reads and simulated variants at arbitrary vafs can be generated using bamsurgeon . synthetic reads can be generated in large scale, at virtually no cost, and most importantly, contain known variants. however, synthetic data alone are generally considered inadequate […]

[…] of the synthetic tumours and their properties are fully detailed in ewing et al. []. briefly, high coverage binary alignment map (bam) files were obtained from cell lines hcc1143 and hcc1954 []. bamsurgeon [] was used to randomly ‘spike-in’ germline mutations into the bam files. each file was then split into two: one file representing a synthetic tumour and the other file representing […]

[…] somatic mutations were generated on chromosome 1 of individual “a0bw” from tcga []. the whole genome had 30x coverage and the whole exome had 150x coverage. somatic mutations were generated using bamsurgeon [] at different depths and different allele fractions on whole genome and whole exome platforms (see methods for details)., the somatic mutations that are generated by bamsurgeon […]

[…] true and the minimum percent id set to 0.70. the ability of these programs to detect new insertions of transposons relative to the reference genome was assessed by running the programs after using bamsurgeon (github.com/adamewing/bamsurgeon) to simulate 1,000 sequences of tes in an n2 bam file in regions without pre-existing transposons and without excessive coverage (>1,000× coverage). […]

[…] second technical replicate was aligned to the hybrid genome, and downsampled to 60× doc. to generate a virtual tumor, a completely known mutation profile was then introduced by bam replacement using bamsurgeon and the is3 mutation set from the icgc-tcga dream somatic mutation calling challenge at a range of variant allele frequencies []. wgss data from the nsg mouse strain generated […]

[…] to determine which type of single-nucleotide-variant (snv) caller would perform best on our dataset and to set appropriate filters, we simulated variation in the n2 background. we used bamsurgeon (http://github.com/adamewing/bamsurgeon), which modifies base calls to simulate variants at specific positions within aligned reads and then realigns reads to the reference genome using […]