Accurate and fast detection of complex and nested ... - Michael Schatz
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Accurate and fast detection of complex and nested ... - Michael Schatz
Available: github.com/fritzsedlazeck/SURVIVOR. Sniffles: ⢠SVs detecCon for long reads. ⢠Nested SV. ⢠Manuscript
Accurate and fast detection of complex and nested structural variations using long read technologies Fritz Sedlazeck
Friday, Oct 28 CSHL
Structural varia+on
Weischenfeldt et. al. (2013)
Long Read Technologies • (+) SVs in repe++ve regions • (+) Can iden+fy nested SVs
• (-) Higher error rate • (-) Hard to align
Hard to align
BWA-MEM
Human genome: 1kb Inversion
Improving long read alignment
1. Split the reads: – Transloca+ons – Inversions – Duplica+ons
2. Improve alignment: – Inser+ons – Dele+ons
Philipp Rescheneder
NGM-LR workflow
NGM-LR reconcile Dot plot of 500bp segments from a read spanning a inversion
NGM-LR inversion
BWA-MEM
NGM-LR
Convex Pairwise Alignment
AAAGAATTCA A-A-A-T-CA
vs.
• Linear: gap cost always the same • Affine: separate penal+es for opening and extending a gap • Convex: ini+ally similar to affine, but becomes propor+onally less costly for larger gaps
Analyzing noisy regions • Extract the differences in the alignment • Detect the noisy regions: Plane sweep algorithm
• Store poten+al regions in a self balancing binary tree.
Simula+on/Evalua+on 1. Simulate 20 SVs of each type using SURVIVOR 2. Simulate Pacbio and illumina paired end reads 3. Evalua+on using SURVIVOR Found +-10bp + type Found +-1kbp + ignore type Not found Not simulated
• Short read version: github.com/cibiv/NextGenMap • Long read mapper: github.com/philres/nextgenmap-lr • Self detec+on of SVs • Manuscript in prepara+on
• Toolkit for SV detec+on on short reads • Simula+on/Evalua+on of current methods • Consensus approach • Accepted Nature Communica+ons • Available: github.com/fritzsedlazeck/SURVIVOR
• • • •
• • • •
SVs detec+on for long reads Nested SV Manuscript in prepara+on Available: github.com/fritzsedlazeck/Sniffles
How much coverage is needed? Nanopore support Analysis of nested SVs Applica+on to Cancer Genomes (See Maria Na@estad's Poster #79)
Acknowledgments
Maria Naaestad Han Fang Srividya Ramakrishnan
Philipp Rescheneder Moritz Smolka Arndt von Haeseler
