Combinatorial Algorithms for Structural Variation Detection in High Throughput Sequenced Genomes

By Fereydoun Hormozdiari, Can Alkan, Evan E. Eichler, Süleyman Cenk Sahinalp. Annual International Conference on Research in Computational Molecular Biology, 2009

331 citations recorded by the source corpus.

Recent studies show that along with single nucleotide polymorphisms and small indels, larger structural variants among human individuals are common. The Human Genome Structural Variation Project aims to identify and classify deletions, insertions, and inversions (>5 Kbp) in a small number of normal individuals with a fosmid-based paired-end sequencing approach using traditional sequencing technologies. The realization of new ultra-high-throughput sequencing platforms now makes it feasible to detect the full spectrum of genomic variation among many individual genomes, including cancer patients and others suffering from diseases of genomic origin. Unfortunately, existing algorithms for identifying structural variation (SV) among individuals have not been designed to handle the short read lengths and the errors implied by the "next-gen" sequencing (NGS) technologies. In this paper, we give combinatorial formulations for the SV detection between a reference genome sequence and a next-gen-based, paired-end, whole genome shotgun-sequenced individual. We describe efficient algorithms for each of the formulations we give, which all turn out to be fast and quite reliable; they are also applicable to all next-gen sequencing methods (Illumina, 454 Life Sciences [Roche], ABI SOLiD, etc.) and traditional capillary sequencing technology. We apply our algorithms to identify SV among individual genomes very recently sequenced by Illumina technology.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/06b6faa8f378d1dec5b1a8131d7b49de168fff73

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Citations in the corpus

Computational tools for copy number variation (CNV) detection using next-generation sequencing data: features and perspectives (2013, methodology): “Using maximum parsimony, VariationHunter-SC can minimize the total number of genomic clusters by mapping each paired-end read to a particular SV. VariationHunter-Pr c The file including the detected structure variations using other tools. a RD: read depth-based approach; PEM: paired-end mapping appr”
deFuse: An Algorithm for Gene Fusion Discovery in Tumor RNA-Seq Data (2011, background): “in the context of genomic structural variation [18], is termed the maximum parsimony solution.”
Assembly Algorithms for Next-Generation Sequencing Data (2010, neutral): “Mapping-based variant discovery algorithms include MoDIL [82], VariationHunter [83] and BreakDancer [84].”
Aptamers as therapeutics (2010, background): “Although it is possible that microelectromechanical systems 91 , deep sequencing 97 or computational prediction 98 will accelerate the identification of aptamers, it seems likely that ‘analogue’ predictions (selection itself) will greatly outperform such efforts for some time to come.”
Copy number variation detection and genotyping from exome sequence data (2012, background): “2009), read-pair alignment (Korbel et al. 2007, 2009; Hormozdiari et al. 2009), or splitread mapping (Ye et al.”
Repetitive DNA and next-generation sequencing: computational challenges and solutions (2011, methodology): “With this caveat, one of the first algorithms to incor‑ porate both read‑depth and read‑pair data for accu‑ rate CNV discovery was VariationHunter 13 , which has been updated to allow it to find transposons 46 .”
Comprehensive evaluation of structural variation detection algorithms for whole genome sequencing (2019, methodology): “Examples of such good “ team players ” include CREST and VariationHunter in the DEL category and BASIL-ANISE [73] and BreakSeek in the INS category, each of which showed over twofold increase in combination with another algorithm.”