GRIDSS: sensitive and specific genomic rearrangement detection using positional de Bruijn graph assembly
By Daniel L. Cameron, Jan Schröder, J. Penington, H. Do, R. Molania, A. Dobrovic, Terence P. Speed, A. Papenfuss. bioRxiv, 2017
323 citations recorded by the source corpus.
The identification of genomic rearrangements, particularly in cancers, with high sensitivity and specificity using massively parallel sequencing remains a major challenge. Here, we describe the Genome Rearrangement IDentification Software Suite (GRIDSS), a high-speed structural variant (SV) caller that performs efficient genome-wide break-end assembly prior to variant calling using a novel positional de Bruijn graph assembler. By combining assembly, split read and read pair evidence using a probabilistic scoring, GRIDSS achieves high sensitivity and specificity on simulated, cell line and patient tumour data, recently winning SV sub-challenge #5 of the ICGC-TCGA DREAM Somatic Mutation Calling Challenge. On human cell line data, GRIDSS halves the false discovery rate compared to other recent methods. GRIDSS identifies non-template sequence insertions, micro-homologies and large imperfect homologies, and supports multi-sample analysis. GRIDSS is freely available at https://github.com/PapenfussLab/gridss.
Subfields: Genetics, Molecular Biology, Plant Science
View Paper: https://www.semanticscholar.org/paper/76ff0befd0a49b7b6a62751270252094441c445c
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Citations in the corpus
- Structural variant calling: the long and the short of it (2019, background): “Regarding precision (the proportion of inferred SVs that are correct), GRIDSS ’ s authors show similar performance to LUMPY, with an estimated precision rate of 90% (evaluated from 1000 previously validated deletions) [44].”
- Comprehensive evaluation of structural variation detection algorithms for whole genome sequencing (2019, background): “…highlights a subset of algorithms that can call many SVs with a high level of precision and re-call for both simulated and real datasets, which include 1-2-3-SV [39], DELLY [32], GRIDSS [40], inGAP-sv [41], Lumpy [35], Manta [42], MetaSV [43], Pindel [34], SoftSV [44], SvABA [45], and Wham [46].”
- Next-Generation Sequencing Technology: Current Trends and Advancements (2023, methodology): “MultiQC [92] Whole-Genome Sequencing/Whole-Exome Sequencing/Targeted Panel Removal of duplicate reads Picard [99], Sambamba [100] Variant calling (single-nucleotide polymorphisms and indels) GATK [101], freeBayes [102], Platypus [103], VarScan [104], DeepVariant [105], Illumina Dragen [106] Filter a”