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. 2018 Jun;15(6):461-468.
doi: 10.1038/s41592-018-0001-7. Epub 2018 Apr 30.

Accurate detection of complex structural variations using single-molecule sequencing

Fritz J Sedlazeck  1 , Philipp Rescheneder  2 , Moritz Smolka  2 , Han Fang  3 , Maria Nattestad  3 , Arndt von Haeseler  2   4 , Michael C Schatz  5   6
Affiliations

Accurate detection of complex structural variations using single-molecule sequencing

Fritz J Sedlazeck et al. Nat Methods. 2018 Jun.

Abstract

Structural variations are the greatest source of genetic variation, but they remain poorly understood because of technological limitations. Single-molecule long-read sequencing has the potential to dramatically advance the field, although high error rates are a challenge with existing methods. Addressing this need, we introduce open-source methods for long-read alignment (NGMLR; https://github.com/philres/ngmlr ) and structural variant identification (Sniffles; https://github.com/fritzsedlazeck/Sniffles ) that provide unprecedented sensitivity and precision for variant detection, even in repeat-rich regions and for complex nested events that can have substantial effects on human health. In several long-read datasets, including healthy and cancerous human genomes, we discovered thousands of novel variants and categorized systematic errors in short-read approaches. NGMLR and Sniffles can automatically filter false events and operate on low-coverage data, thereby reducing the high costs that have hindered the application of long reads in clinical and research settings.

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Conflict of interest statement

Competing interests

M.C.S. and F.J.S. have participated in PacBio sponsored meetings over the past few years and have received travel reimbursement and honoraria for presenting at these events. Since the initial submission, P.R. is an employee of Oxford Nanopore. PacBio and Oxford Nanopore had no role in decisions relating to the study/work to be published, data collection or analysis of data.

Figures

Figure 1
Figure 1
Overview of the main steps implemented in NGMLR (left) and Sniffles (right). For details see Supplementary Notes 1 and 2 for NGMLR and Sniffles, respectively.
Figure 2
Figure 2
Alignment improvements using NGMLR shown for a 228 bp deletion (left) and a 150 bp inversion (right) shown in IGV37. Upper track shows BWA-MEM alignments that indicate these events but is not able to localize the precise event and breakpoints. With the improved alignments of NGMLR, Sniffles can precisely pinpoint the location and type of the SV.
Figure 3
Figure 3
Evaluation of NGMLR, Sniffles and related tools using simulated data with 840 SVs. X axis is showing the size of the simulated SVs. For read alignments (top), we simulated PacBio-like (left) and Oxford Nanopore-like reads (right), and distinguish between: precise (green), indicated (yellow), forced (red), unaligned reads (white), or trimmed but not aligned through the SV (grey). The SV analysis (bottom) used the same alignments as before, and distinguishes between.
Figure 4
Figure 4
Systematic error in short-read based SV calling. A) An example of a putative translocation identified in the short-read data (top alignments) that overlaps an insertion detected by both PacBio (middle) and Oxford Nanopore sequencing (bottom). B) An example of a putative inversion identified in the short-read data (top) that overlaps an insertion detected by both PacBio (middle) and Oxford Nanopore reads (bottom)
Figure 5
Figure 5
Nested SVs in SKBR3 cancer cell line. A: Evaluation of Sniffles + NGMLR using simulated data to identify nested SVs. B: A 3kb region including two deletions flanking an inverted sequence clearly visible and detected by Sniffles using NGMLR (above) and not detected by the Illumina methods (below). C: The start of an inverted duplication. The breakpoints were reported by Sniffles as the start of an inverted duplication (above) and not correctly detected by short-read methods (below).
Figure 6
Figure 6
Analysis of SV detection accuracy with different amounts of coverage. A: Theoretical assessment of recall vs coverage for different read lengths requiring a 50bp overlap of each breakpoints for SV events. B: Subsampling experiment of the 55× PacBio NA12878 data; C: Subsampling experiment using 28× Oxford Nanopore NA12878 data; D: Subsampling experiment of the 70× PacBio SKBR3 breast cancer cell line dataset. For plots B–D, Sniffles and NGMLR were run on subsampled data (rate indicated by lines) and using different thresholds for Sniffles (s: 1–10 indicated in symbols and colors). In every data set we could show the success for Sniffles using NGMLR with only 10× to 30× coverage that recovers around 80% of the calls with a precision ~80% or higher.
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