LUMPY: a probabilistic framework for structural variant discovery

By Ryan M. Layer, Colby Chiang, Aaron R. Quinlan, Ira M. Hall. Genome Biology, 2012

1,443 citations recorded by the source corpus.

Comprehensive discovery of structural variation (SV) from whole genome sequencing data requires multiple detection signals including read-pair, split-read, read-depth and prior knowledge. Owing to technical challenges, extant SV discovery algorithms either use one signal in isolation, or at best use two sequentially. We present LUMPY, a novel SV discovery framework that naturally integrates multiple SV signals jointly across multiple samples. We show that LUMPY yields improved sensitivity, especially when SV signal is reduced owing to either low coverage data or low intra-sample variant allele frequency. We also report a set of 4,564 validated breakpoints from the NA12878 human genome. https://github.com/arq5x/lumpy-sv.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/ba72433527b6027c56635972c398b29e573b4561

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Citations in the corpus

SvABA: genome-wide detection of structural variants and indels by local assembly (2018, methodology): “Some tools like LUMPY perform inference directly from multipart alignments (Layer et al. 2014).”
Next-Generation Sequencing Technology: Current Trends and Advancements (2023, neutral): “Cont.”
Structural variant calling: the long and the short of it (2019, background): “For example, LUMPY [48] does a joint analysis of the read depth, paired-end read discord-ance, and split-reads.”
Integrated Proteogenomic Characterization of Clear Cell Renal Cell Carcinoma (2019, methodology): “We used an independent structural variant calling method LUMPY to validate the translocation events (Layer et al., 2014), selecting the LUMPY Express mode for automated breakpoint detection by using the default parameters.”
SVScore: an impact prediction tool for structural variation (2016, methodology): “SVScore supports weighting possible breakpoint scores using probability distributions calculated by tools such as LUMPY (Layer et al., 2014), as recorded in the INFO column.”
Nanopore sequencing and assembly of a human genome with ultra-long reads (2017, positive): “Previously identified high-confidence GM12878 SVs, validated with Moleculo and/or PacBio long reads, were used to determine genotyping sensitivity 29 .”
Comprehensive evaluation of structural variation detection algorithms for whole genome sequencing (2019, methodology): “…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].”
High-coverage whole-genome sequencing of the expanded 1000 Genomes Project cohort including 602 trios (2021, neutral): “The workflow combines per-sample variant discovery with lumpy (Layer et al., 2014) and manta (Chen et al.”
A framework for incorporating evolutionary genomics into biodiversity conservation and management (2015, background): “Such variation is still difficult to identify using short-read technology, but improved methodologies are under development [79,80].”
VarDict: a novel and versatile variant caller for next-generation sequencing in cancer research (2016, background): “VarDict has similar sensitivity to Lumpy and higher than Manta, although Manta has higher specificity for DEL and DUP.”
Best practices for variant calling in clinical sequencing (2020, methodology): “Structural variants DELLY [51], Lumpy [52], Manta [53], Pindel [54], SVMerge [55]”