Alex Di Genova,
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 which combine short and long read sequencing technologies can reduce the time and cost required to produce de novo assemblies of large genomes. In this paper, we propose a new method, called FAST-SG, which uses a new ultra-fast alignment- free algorithm specifically designed for constructing a scaffolding graph using light-weight 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).
Alex Di Genova is a bioinformatician interested in Genome assembly, third generation sequencing and population genetics.
He studied Bioinformatics at University of Talca (2003-2007). Then, he did a Ph.D. in Complex system at University Adolfo Ibañez (2013-2017) under the supervision of Alejandro Maass, Gonzalo Ruz, and Marie-France Sagot.
Alex worked as a research engineer in Mathomics (2008-2017) on several applied genomics projects (denovo genome assembly of Sultanina, Salmo salar, and Chinook salmon). Currently, he’s working as Postdoc in the ERABLE team (Lyon, France) directed by Marie-France Sagot on alignment-free algorithms.
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