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Fast-SG: an alignment-free algorithm for hybrid assembly

Abstract : Background: Long-read sequencing technologies are the ultimate solution for genome repeats, allowing near reference-level reconstructions of large genomes. However, long-read de novo assembly pipelines are computationally intense and require a considerable amount of coverage, thereby hindering their broad application to the assembly of large genomes. Alternatively, hybrid assembly methods that combine short-and long-read sequencing technologies can reduce the time and cost required to produce de novo assemblies of large genomes. Results: Here, we propose a new method, called Fast-SG, that uses a new ultrafast alignment-free algorithm specifically designed for constructing a scaffolding graph using lightweight data structures. Fast-SG can construct the graph from either short or long reads. This allows the reuse of efficient algorithms designed for short-read data and permits the definition of novel modular hybrid assembly pipelines. Using comprehensive standard datasets and benchmarks, we show how Fast-SG outperforms the state-of-the-art short-read aligners when building the scaffolding graph and can be used to extract linking information from either raw or error-corrected long reads. We also show how a hybrid assembly approach using Fast-SG with shallow long-read coverage (5X) and moderate computational resources can produce long-range and accurate reconstructions of the genomes of Arabidopsis thaliana (Ler-0) and human (NA12878). Conclusions: Fast-SG opens a door to achieve accurate hybrid long-range reconstructions of large genomes with low effort, high portability, and low cost.
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Contributor : Marie-France Sagot Connect in order to contact the contributor
Submitted on : Wednesday, July 18, 2018 - 12:21:58 PM
Last modification on : Tuesday, October 25, 2022 - 4:20:14 PM
Long-term archiving on: : Friday, October 19, 2018 - 5:38:04 PM


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Alex Di Genova, Gonzalo A Ruz, Marie-France Sagot, Alejandro Maass. Fast-SG: an alignment-free algorithm for hybrid assembly. GigaScience, 2018, 7 (5), pp.1 - 15. ⟨10.1093/gigascience/giy048⟩. ⟨hal-01842462⟩



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