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Machine Learning Applied to Single-Molecule Electronic DNA Mapping for Structural Variant Verification in Human Genomes

Bready, B.; Davis, J.; Grinberg, B.; Kaiser, M.; Oliver, J.; Sage, J.; Seward, L. Nabsys 2.0 LLC, Providence, RI 02903

Using SV-Verify, thousands of hypotheses can be queried in a single analysis. In the human genome NA24385, a set of 9,000 putative deletion calls ≥300 bp with varying levels of support from different technologies, was evaluated. The distributions of resulting posterior probability values for all considered deletions asserted by 1 (n = 4443), 2 (n = 600), 3 (n = 691), and 4 (n = 244) technologies are shown in the left panel. The tailed ends of the distributions indicate that SV-Verify is able to discriminate between accurate calls (enriched in 4 Tech set) and inaccurate calls (prevalent in 1 Tech set). ROC curves were used to convert the posterior probability output from each SVM to a specificity value for each putative call. Here we employed a specificity threshold of 0.9 (sp90) to confirm a putative deletion. The percent of evaluated deletions confirmed at sp90, filtered by putative deletion size and number of technologies making the call, is shown in the right panel. SV-Verify demonstrated good sensitivity across all considered putative deletion size ranges, as low as 300 bp.

SV-Verify training and workflow The Nabsys SV-Verify software package provides an efficient, robust pipeline for the systematic and automated evaluation of putative SVs. SVM training was accomplished using reference material from the National Institute of Standards and Technology (NIST) Genome in a Bottle (GIAB) consortium for a well-characterized human genome, NA12878. Deletion calls ≥300 bp asserted by multiple technologies were parsed into four classes and used to train four distinct SVMs. The relationship between specificity and sensitivity for each respective SVM is graphed as a receiver operating characteristic (ROC) curve shown to the right. To evaluate hypothesized SVs, Nabsys single-molecule reads, a base reference map, and variant reference maps are used as inputs to HD-Mapping. SV-Verify utilizes the four unique SVMs tailored to different classes of structural variation (i.e. size, type and complexity) to output a posterior probability for each putative variant. Posterior probabilities and ROC curves are then used to determine calls at a given specificity threshold.

In order to construct whole genome maps, high molecular weight DNA is isolated using solution phase, commercially available kits. The genomic DNA is tagged in a sequence-specific manner through an enzymatic nicking reaction. As single molecules pass through the detector the presence of the DNA backbone and attached tags are sensed as changes in the resistance of the detector. The resulting data indicate the time between tagged sites on each DNA backbone. The temporal events are converted to distance-based events where the distances between tags (termed an “interval”) are reported in base pairs.

Single-molecule electronic detection

The accuracy of Nabsys single-molecule read mapping is central to the effective detection of SVs across a range of sizes using SV-Verify. To demonstrate the mapping accuracy of the Nabsys platform, single-molecule data were collected for E. coli MG1655 nicked with Nt.BspQI and mapped to the high quality reference. As shown in the plot above, there is a high degree of agreement between the expected reference interval sizes and the consensus interval sizes generated through Nabsys read mapping. The linear relationship observed (R2 was found to be 0.9999) extended down to intervals as small as 300 bp, well below the diffraction limit of optical mapping approaches. De novo assembly of these data resulted in 3 maps that spanned 99.4% of the reference with 0 false positives and 0 false negatives for intervals >500 bp. These results highlight the accuracy and resolution of Nabsys HD-Mapping that forms the foundation of the SV-Verify pipeline.

Underlying technology biases can significantly impact the accuracy of SV calls, particularly as the size of a given SV exceeds read length. To investigate this phenomenon, we determined the percentages of evaluated putative deletion calls made using Illumina or PacBio data (underlying data type; may include several data sets and several callers) that were confirmed at sp90 or refuted using a posterior probability threshold of ≤0.1 by SV-Verify in various size ranges (see below). The number of calls per category is indicated above each bar. As expected based on read length, the percentage of Illumina deletion calls confirmed by SV-Verify decreased as a function of deletion size, while the longer PacBio read lengths translated to more consistent calls across a range of deletion sizes, highlighting the importance of long-range information for large SV call accuracy.

Conclusions

ASHG 2017 Booth 753

The Nabsys HD-Mapping platform combined with the SV-Verify software package provides a high throughput, fully automated tool for the evaluation of structural variation in human genomes. SV-Verify can clearly distinguish between accurate and inaccurate deletion calls as small as 300 bp in size. Results here highlight the critical need for orthogonal technologies with a broad effective size range for accurate characterization of SVs on a genome-wide scale. We thank Justin Zook, Ph.D. and Marc Salit, Ph.D., of the NIST Genome-Scale Measurement Group for welcoming our participation in GIAB.

NA24385 putative deletion call set evaluation using SV-Verify

Illumina PacBio

Evaluation of Illumina and PacBio calls

Mapping of single-molecule reads to evaluate thousands of putative SVs simultaneously

ROC curves resulting from each support vector machine

The importance of structural variation in human disease and the difficulty of detecting structural variants larger than 50 base pairs has led to the development of several long-read sequencing technologies and optical mapping platforms. Frequently, multiple technologies and ad hoc methods are required to obtain a consensus regarding the location, size and nature of a structural variant, with no single approach able to reliably bridge the gap of variant sizes between those readily detected using NGS technologies and the largest rearrangements observed with optical mapping. Often, structural variants larger than 10 kilobases are not detected.

To address this unmet need, we have developed a new software package, SV-VerifyTM, which utilizes data collected with the Nabsys High Definition Mapping (HD-MappingTM) system, to perform hypothesis-based verification of putative deletions. We demonstrate that whole genome maps, constructed from data generated by electronic detection of tagged DNA,

hundreds of kilobases in length, can be used effectively to facilitate calling of structural variants ranging in size from 300 base pairs to hundreds of kilobase pairs. SV-Verify implements hypothesis-based verification of putative structural variants using supervised machine learning. Machine learning is realized using a set of support vector machines, capable of concurrently testing several thousand independent hypotheses. We describe support vector machine training, utilizing 1089 deletions and 4637 negative controls from a well-characterized human genome. Plots delineating the specificity versus sensitivity of each of the support vector machines will be presented. We subsequently applied the trained classifiers to another human genome, evaluating > 5000 putative deletions, demonstrating high sensitivity and specificity for deletions from 300 base pairs to hundreds of kilobases. Over 78% of deletions called by three or more technologies were confirmed by SV-Verify.

Single-molecule tag detection at a velocity of >1 Mbp/s.

Tagged sample introduction into instrument

High molecular weight DNA isolation

Sequence-specific tag attachment

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Deletions 300 – 499 bp Deletions 500 – 999 bp Deletions spanning multiple intervals Deletions ≥1000 bp

Advantages of Nabsys electronic detection: •  Long-range information •  Easy to multiplex without cross-talk •  Highly scalable •  High resolution, direct detection of 300 bp intervals •  Low, stochastic single-molecule false-positive and

false-negative rates •  Electrophoretic and hydrodynamic control of access

to detector •  Highly sensitive detection enables tag detection

during translocation •  Wider range of useful DNA lengths as compared to

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