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AGBT-2018-Andy-Pang.Pdf Efficient Structural Variation Detection and Annotation Using Bionano Genome Mapping A. W. C. Pang1, J. Lee1, A. Hastie1, E. Lam1, E. Chan2, V. Hayes2, H. Cao1, M. Borodkin1 1) Bionano Genomics, San Diego, California, USA; 2) Garvan Institute of Medical Research, Darlinghurst, Australia Abstract Background Structural variation (SV) detection is fundamental to understanding cancer workflow can compare against a control sample, and examines whether the Generating high-quality finished genomes replete with accurate genomes. While karyotyping and conventional molecular detection approaches cancer mutations are present in low fraction among the control’s molecules. identification of structural variation and high completion (minimal are robust, they can be manually intensive, biased towards targeted loci, and Using this pipeline, whose runtime is only a few hours, we can efficiently focus gaps) remains challenging using short read sequencing cannot determine the copy number of long repeats. on several dozen significant candidates for further analysis. technologies alone. Bionano mapping provides direct visualization of long DNA molecules in their native state, bypassing the statistical inference needed to align Bionano Genomics’ Saphyr System offers a sensitive method for detecting large We ran multiple solid and hematologic cancer samples. First, we generated a paired-end reads with an uncertain insert size distribution. These long labeled SVs. DNA molecules larger than 100 kbp are extracted, labelled at specific highly contiguous genome map assembly on a solid tumor sample. The ultra molecules are de novo assembled into physical maps spanning the whole genome. motifs, and linearized through NanoChannels arrays for subsequent long maps – with lengths encompassing entire chromosomal arms – were able The resulting order and orientation of sequence elements in the map can be used visualization. Molecule images are digitized and the genome de novo to capture large inversion events, and balanced and unbalanced translocations. for anchoring NGS contigs and structural variation detection. assembled, creating megabase long Bionano maps. While our calling algorithm In a separate study, we discovered known rearrangements such as the can sensitively detect all types of SVs in these long maps, we also use a variant t(9;22)(q34.12;q11.23) in chronic myeloid leukemia (CML). Moreover, we annotation workflow to specifically uncover rare and sample-specific mutations. uncovered a large megabases long deletion on chr13 by using our copy number To determine variant frequency in a tumor normal experimental design, it detection tool. In conclusion, Bionano maps can discover functional SVs and compares the cancer sample’s calls against over 600,000 SVs from >160 improve our understanding of the mechanisms of diseases. humans with no observable diseases. To identify somatic mutations, the Methods 1 2 3 4 5 6 Label DNA at specific sequence Saphyr Chip linearizes DNA in Saphyr automates imaging of single Molecules and labels detected in Bionano Access software Extraction of long DNA molecules motifs NanoChannel arrays molecules in NanoChannel arrays images by instrument software assembles optical maps Free DNA Solution DNA in a Microchannel DNA in a Nanochannel Blood Cell Tissue Microbes Gaussian Coil Partially Elongated Linearized Position (kbp) (1) Long molecules of DNA are labeled with Bionano reagents by (2) incorporation of fluorophores at a specific sequence motif throughout the genome. (3) The labeled genomic DNA is then linearized in the Saphyr Chip using NanoChannel arrays (4) Single molecules are imaged by Saphyr and then digitized. (5) Molecules are uniquely identifiable by distinct distribution of sequence motif labels (6) and then assembled by pairwise alignment into de novo genome maps. Study 1: Direct labeling experiment of a solid tumor sample Study 2: SV results on samples with hematological malignancy Cancer SV DLE1 labeled sample Solid tumor 135.5 Mbp chr10 Data collected (molecules >150 kb) 367 Gbp Step 1 Tumor • Compare with > 160 human control samples SV from genome mapping maps 38.8 Mbp 91.7 Mbp • Examine the coverage and chimeric quality scores Assembly size (haplotype-aware) 5.87 Gbp • Overlap with gene annotation 78.1 Mbp 133,653,967 Genome map N50 71.0 Mbp chr18 hg19 chr9 ABL1 Step 2 Tumor • Compare the cancer sample calls with a match control sample calls SV called against hg19 maps 15.6 Mbp 59.5 Mbp Map 23,631,928 Insertions 3,894 Two examples of ultra long genome maps spanning entire arms of chromosomes 10 and 18. The maps hg19 chr22 BCR are only separated by centromeres. Step 3 • Align the match control sample molecules to the cancer assembly Deletions 1,799 • Re-align the cancer molecules to the cancer assembly • Determine the number of molecules supporting the cancer variant call Inversion breakpoints 95 er. chromosomal er. fragments RBM6 d chr22 chr9 Genes Annotated cancer Interchromosomal translocations 33 SV A Philadelphia translocation t(9;22) was detected in leukemia cancer cells. The A flowchart illustrating the variant annotation pipeline for chr3 Intrachromosomal translocations 21 map was aligned to the public reference assembly hg19, and the resulting alignments cancer-normal studies. show a conjoined junction between chr9 and chr22, creating a fusion gene called BCR- PKD1P1 XYLT1 NPIP Tumor ABL1. Genes map chr17:26.6 M chr16 chrX 50.6 M chr17 fusion BspQI map Copy number chr3 Tumor map Tumor map Molecule pileup Molecule chrX 1 3 5 7 9 11 13 15 17 19 21 X pileup A whole genome depth of coverage shoring a large chr13 deletion. The green dots represent raw copy number count, while the blue dots represent smoothed copy (Top) A 596.9 kbp genome map captures a t(3;X) translocation. The translocation breakpoint interrupts number count after the removal of local noise the gene RBM6 on chr3. RBM6 codes for a RNA-binding protein 6, and mutations and copy number mutations have been found in many types of cancer. A 2.3 Mbp inversion on chr16 captures by a 32.2 Mbp de novo assembled tumor genome map. Note (Bottom) An alternate map is able to capture the reciprocal translocation event. that the breakpoints contain some additional sequences that are novel to the reference genome hg19. The An intra-chromosomal fusion event detected in chr17. The two genes presence of overlapping alignments indicates homology (in opposite direction) between the two inversion located at the junctions are TAOK1 and HOXB3. The red arrows indicate the breakpoints. variant junction on the genome map. Conclusions Reference 1) Cao, H., et al., Rapid detection of structural variation in a human genome using NanoChannel- based genome mapping technology. Gigascience (2014); 3(1):34 We demonstrate that the Saphyr system can be used to accurately detect genetic mutation hallmarks in samples with solid tumor and 2) Hastie, A.R., et al. Rapid Genome Mapping in NanoChannel Arrays for Highly Complete and hematologic malignancies. These includes large rearrangements ranging from interchromosomal translocations, within chromosome Accurate De Novo Sequence Assembly of the Complex Aegilops tauschii Genome. PLoS ONE fusions, to copy number alterations. Researchers can perform mapping experiments to uncover somatic variants by comparing with (2013); 8(2): e55864. our control sample database and a matching non-tumor sample. Our results shown here indicate that the Saphyr system can capture a 3) Lam, E.T., et al. Genome mapping on NanoChannel arrays for structural variation analysis and broad spectrum of variation with functional importance, and can provide easy solutions for cancer studies. sequence assembly. Nature Biotechnology (2012); 10: 2303 4) Das, S. K., et al. Single molecule linear analysis of DNA in nano-channel labeled with sequence specific fluorescent probes. Nucleic Acids Research (2010); 38: 8 5) Xiao, M et. al. Rapid DNA mapping by fluorescent single molecule detection. Nucleic Acids ©2018 Bionano Genomics. All ©2018 All rights reserved. Bionano Genomics. Research (2007); 35:e16..
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