Efficient de novo assembly of large genomes using compressed data structures.

By Jared T. Simpson, Richard Durbin. Genome Research, 2012

805 citations recorded by the source corpus.

Subfields: Molecular Biology, Plant Science

View Paper: https://www.semanticscholar.org/paper/3db83bc91d0aa801301a9c063bbfd9d4202f89f2

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Citations in the corpus

Sequence assembly demystified (2013, methodology): “Read de Bruijn graph approaches to avoid computing over-laps for large sets of reads (a computationally intensive task using the algorithms available at the time), the use of efficient search data structures (in particular, the FM index 25 ) has dramatically improved the scalability of overlap-based”
Gaining comprehensive biological insight into the transcriptome by performing a broad-spectrum RNA-seq analysis (2017, neutral): “23 and 24).”
Structural variant calling: the long and the short of it (2019, methodology): “SGVar [32] is a more recent string graph-based (see Table 2 for definition) de novo assembly pipeline based on the SGA assembler [75] that also uses short-read sequencing data.”
The MaSuRCA genome assembler (2013, methodology): “…et al., 2011) 2671 The MaSuRCA genome assembler Bambus2 (Koren et al., 2011) CABOG (Miller et al., 2008) MSR-CA (now renamed MaSuRCA 1.0) SGA (Simpson and Durbin, 2012) SOAPdenovo (Luo et al., 2012) Velvet (Zerbino and Birney, 2008) The best performers in GAGE were AllPaths-LG and SOAPdenovo.”
ABySS 2.0: resource-efficient assembly of large genomes using a Bloom filter (2016, positive): “To assess the performance of ABySS 2.0, we compared it with other leading assemblers for large genomes: ABySS 1.0 (Simpson et al. 2009), BCALM 2 (Chikhi et al. 2016), DISCOVAR de novo, MEGAHIT (Li et al. 2016), Minia (Chikhi and Rizk 2013), SGA (Simpson and Durbin 2011), and SOAPdenovo2 (Luo et al. ”
Informed and automated k-mer size selection for genome assembly (2013, methodology): “…methods were developed, there have been numerous improvements and the ﬁeld is now rich with tools that provide biologists several options [Ribeiro et al., 2012, Luo et al., 2012, Bankevich et al., 2013, Chikhi and Rizk, 2012, Peng et al., 2012, Simpson and Durbin, 2011, Zerbino and Birney, 2008].”
Computational solutions for omics data (2013, methodology): “…evaluations of numerous popular genome assemblers, including de Bruijn-graph-based 21–24 and FM-index-based (see BOX 2 and ‘Read mapping in next-generation sequencing’) assemblers 26 , have been carried out on four genomes with a wide phylogenetic range and varying degrees of difficulty 27–29 .”
Assemblathon 2: evaluating de novo methods of genome assembly in three vertebrate species (2013, methodology): “Despite the apparent pre-eminence of the SGA assembler it should still be noted that this assembly only ranked 1st in one of the ten key metrics that was used and ranked 7th in another (the amount of gene-sized scaffolds).”
GAGE: A critical evaluation of genome assemblies and assembly algorithms. (2012, neutral): “htm) • SGA (Simpson and Durbin 2012) • SOAPdenovo (Li et al.”
The Harvest suite for rapid core-genome alignment and visualization of thousands of intraspecific microbial genomes (2014, methodology): “Genome assembly [4,68-75], followed by whole-genome alignment [38,76,77], is the original method for variant detection between closely related bacterial genomes [78] and has been shown to perform well across multiple sequencing platforms [79].”
Musket: a multistage k-mer spectrum-based error corrector for Illumina sequence data (2013, methodology): “Furthermore, some recent assemblers, such as SGA (Simpson and Durbin, 2012) and Fermi (Li, 2012), have used string graphs.”