This clustering method is used in Sniffles:
"Clustering and nested SVs.
To enable the study of closely positioned or nested SVs, Sniffles optionally clusters SVs that are supported by the same set of reads. Note that Sniffles does not fully phase the haplotypes, as it does not consider single-nucleotide polymorphisms or small indels, but rather identifies SVs that occur together. If this option is enabled, Sniffles stores the name of each read that supports an SV in a hash table keyed by the read name, with the list of SVs associated with that read name as the value. The hash table is used to find reads that span more than one event, and later to cluster reads that span one or more of the same variants. In this way Sniffles can cluster two or more events, even if the distance between the events is larger than the read length. Future work will include a full phasing of hapolotypes including SVs, single-nucleotide polymorphisms, and other small variants. Details are presented in Supplementary Note 2."
Source: Sedlazeck, F.J., Rescheneder, P., Smolka, M. et al. Accurate detection of complex structural variations using single-molecule sequencing. Nat Methods 15, 461–468 (2018). https://doi.org/10.1038/s41592-018-0001-7
Supplementary Note 2
"All SVs found during step 1 [detect smaller (<1kb) insertions, deletions and regions with an increased number of mismatches and very short (1-5bp) indels] and 2 [large indels, inversions, duplications, translocations] are stored in a self-balancing binary tree. [...] Sniffles traverses the binary tree to merge SV calls that were caused by the same SV." (Supplementary Note 2, page 14)
"2.2.3 Storing/Clustering of SVs
Sniffles use a self-balancing binary tree to store and merge SV calls. Each node in the tree represents a single SV. The SVs are sorted based on the start coordinate of each SV.
Each time Sniffles detects a read that supports a SV, Sniffles traverses the binary tree to see if that particular SV has been observed before. The current SV call is merged with an already known one if their types (e.g. deletion) are the same and their breakpoints are within the maximum distance D. [...]
In the tree, each SV is represented by the coordinates that it was first found at. However, the coordinates from other reads supporting the same SV are stored as well. To store the SV type Sniffles uses a set of bit flags to enable a fast comparison between different SVs. Furthermore, the bit flags allow Sniffles to assign multiple types and additional information to a single SV, especially for nested SVs. For complex types, we allow inversions or deletions to be merged with a candidate SV as long as they agree on the coordinates. Furthermore, we allow insertions and tandem duplications to be merged since a tandem duplication is an insertion of the same element next to itself.
To account for multiple overlapping SVs or SVs in close proximity, especially if the genome is polyploid in this region as commonly observed in human cancers or plant genomes, Sniffles implements a more thorough tree search to assess whether the current SV has already been observed. Here, Sniffles starts at the current parental node and walks using an in-order traversal search through the sub tree to identify an already stored SV that would match the current one. Note that this does not significantly increase the runtime, since this procedure will generally only be performed on a very small subtree.
If Sniffles does not find the current SV in the tree, it adds it as a new leaf node. Each SV is stored together with the name of the read it was observed in, the strands, the start and stop position of the genome, the start and stop position on the read, the bit-flag for the type and information about the source (split reads, alignment event, noisy region)." (Supplementary Note 2, page 19)
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent