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Login to your account. The assembly of large, repeat-rich eukaryotic genomes represents a significant challenge in genomics. While long-read technologies have made the high-quality assembly of small, microbial genomes increasingly feasible, data generation can be expensive for larger genomes. OPERA-LG is a scalable, exact algorithm for the scaffold assembly of large, repeat-rich genomes, out-performing state-of-the-art programs for scaffold correctness and contiguity. It provides a rigorous framework for scaffolding of repetitive sequences and a systematic approach for combining data from different second-generation and third-generation sequencing technologies. OPERA-LG provides an avenue for systematic augmentation and improvement of thousands of existing draft eukaryotic genome assemblies. The field of sequence assembly has witnessed a significant amount of mathematical and algorithmic study of the problem 1 — 4. As there is a wide array of heuristics and parameter choices to try, the right combination that works well across a range of datasets may not always be apparent and new assembly tools run the risk of being tuned for the datasets on which they are benchmarked. Recent assembly competitions such as GAGE 8, Assemblathon 9, Assemblathon2 10, and a recent scaffolder benchmark 11 have thus played an important role in galvanizing the community and in highlighting the drawbacks of existing tools. The prevalence of heuristic choices in assembly owes its origins partly to several well-known early results regarding its computational opera laachir v3 zhu 1opera laachir v3 zhu and further confirmed by recent studies 34 which suggest that most formal definitions of various assembly problems such as contiging and scaffolding are computationally intractable NP-hard. Notably, though, most complexity results have been limited to worst-case analysis and relatively little has been said about average-case or parametric complexity of various assembly problems 1412. For example, while the problem of constructing contigs from read data typically formulated as a path-finding problem has been shown to be NP-hard in terms of worst-case complexity 34, in practice, the problem is usually under-constrained in the absence of ultra-long reads and trivially computable, fragmented contig assemblies are the best we can do 412.



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