June 1, 2021  |  

Highly accurate read mapping of third generation sequencing reads for improved structural variation analysis

Characterizing genomic structural variations (SV) is vital for understanding how genomes evolve. Furthermore, SVs are known for playing a role in a wide range of diseases including cancer, autism, and schizophrenia. Nevertheless, due to their complexity they remain harder to detect and less understood than single nucleotide variations. Recently, third-generation sequencing has proven to be an invaluable tool for detecting SVs. The markedly higher read length not only allows single reads to span a SV, it also enables reliable mapping to repetitive regions of the genome. These regions often contain SVs and are inaccessible to short-read mapping. However, current sequencing technologies like PacBio show a raw read error rate of 10% or more consisting mostly of insertions and deletions. Especially in repetitive regions the high error rate causes current mapping methods to fail finding exact borders for SVs, to split up large deletions and insertions into several small ones, or in some cases, like inversions, to fail reporting them at all. Furthermore, for complex SVs it is not possible to find one end-to-end alignment for a given read. The decision of when to split a read into two or more separate alignments without knowledge of the underlying SV poses an even bigger challenge to current read mappers. Here we present NextGenMap-LR for long single molecule PacBio reads which addresses these issues. NextGenMap-LR uses a fast k-mer search to quickly find anchor regions between parts of a read and the reference and evaluates them using a vectorized implementation of the Smith-Waterman (SW) algorithm. The resulting high-quality anchors are then used to determine whether a read spans an SV and has to be split or can be aligned contiguously. Finally, NextGenMap-LR uses a banded SW algorithm to compute the final alignment(s). In this last step, to account for both the sequencing error and real genomic variations, we employ a non-affine gap model that penalizes gap extensions for longer gaps less than for shorter ones. Based on simulated as well as verified human breast cancer SV data we show how our approach significantly improves mapping of long reads around SVs. The non-affine gap model is especially effective at more precisely identifying the position of the breakpoint, and the enhanced scoring scheme enables subsequent variation callers to identify SVs that would have been missed otherwise.


June 1, 2021  |  

Detection of structural variants using third generation sequencing

Structural Variants (SVs), which include deletions, insertions, duplications, inversions and chromosomal rearrangements, have been shown to effect organism phenotypes, including changing gene expression, increasing disease risk, and playing an important role in cancer development. Still it remains challenging to detect all types of SVs from high throughput sequencing data and it is even harder to detect more complex SVs such as a duplication nested within an inversion. To overcome these challenges we developed algorithms for SV analysis using longer third generation sequencing reads. The increased read lengths allow us to span more complex SVs and accurately assess SVs in repetitive regions, two of the major limitations when using short Illumina data. Our enhanced open-source analysis method Sniffles accurately detects structural variants based on split read mapping and assessment of the alignments. Sniffles uses a self-balancing interval tree in combination with a plane sweep algorithm to manage and assess the identified SVs. Central to its high accuracy is its advanced scoring model that can distinguish erroneous alignments from true breakpoints flanking SVs. In experiments with simulated and real genomes (e.g human breast cancer), we find that Sniffles outperforms all other SV analysis approaches in both the sensitivity of finding events as well as the specificity of those events. Sniffles is available at: https://github.com/fritzsedlazeck/Sniffles


June 1, 2021  |  

Comprehensive genome and transcriptome structural analysis of a breast cancer cell line using PacBio long read sequencing

Genomic instability is one of the hallmarks of cancer, leading to widespread copy number variations, chromosomal fusions, and other structural variations. The breast cancer cell line SK-BR-3 is an important model for HER2+ breast cancers, which are among the most aggressive forms of the disease and affect one in five cases. Through short read sequencing, copy number arrays, and other technologies, the genome of SK-BR-3 is known to be highly rearranged with many copy number variations, including an approximately twenty-fold amplification of the HER2 oncogene. However, these technologies cannot precisely characterize the nature and context of the identified genomic events and other important mutations may be missed altogether because of repeats, multi-mapping reads, and the failure to reliably anchor alignments to both sides of a variation. To address these challenges, we have sequenced SK-BR-3 using PacBio long read technology. Using the new P6-C4 chemistry, we generated more than 70X coverage of the genome with average read lengths of 9-13kb (max: 71kb). Using Lumpy for split-read alignment analysis, as well as our novel assembly-based algorithms for finding complex variants, we have developed a detailed map of structural variations in this cell line. Taking advantage of the newly identified breakpoints and combining these with copy number assignments, we have developed an algorithm to reconstruct the mutational history of this cancer genome. From this we have discovered a complex series of nested duplications and translocations between chr17 and chr8, two of the most frequent translocation partners in primary breast cancers, resulting in amplification of HER2. We have also carried out full-length transcriptome sequencing using PacBio’s Iso-Seq technology, which has revealed a number of previously unrecognized gene fusions and isoforms. Combining long-read genome and transcriptome sequencing technologies enables an in-depth analysis of how changes in the genome affect the transcriptome, including how gene fusions are created across multiple chromosomes. This analysis has established the most complete cancer reference genome available to date, and is already opening the door to applying long-read sequencing to patient samples with complex genome structures.


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