Assemblathon 1: a competitive assessment of de novo short read assembly methods.

By Dent Earl, Keith R Bradnam, John St. John, Aaron E. Darling, D. Lin, Joseph N. Fass, H. O. Yu, Vince Buffalo, Daniel R. Zerbino, Mark Diekhans and 61 more. Genome Research, 2011

544 citations recorded by the source corpus.

Low-cost short read sequencing technology has revolutionized genomics, though it is only just becoming practical for the high-quality de novo assembly of a novel large genome. We describe the Assemblathon 1 competition, which aimed to comprehensively assess the state of the art in de novo assembly methods when applied to current sequencing technologies. In a collaborative effort, teams were asked to assemble a simulated Illumina HiSeq data set of an unknown, simulated diploid genome. A total of 41 assemblies from 17 different groups were received. Novel haplotype aware assessments of coverage, contiguity, structure, base calling, and copy number were made. We establish that within this benchmark: (1) It is possible to assemble the genome to a high level of coverage and accuracy, and that (2) large differences exist between the assemblies, suggesting room for further improvements in current methods. The simulated benchmark, including the correct answer, the assemblies, and the code that was used to evaluate the assemblies is now public and freely available from http://www.assemblathon.org/.

Subfields: Genetics, Molecular Biology, Plant Science

View Paper: https://www.semanticscholar.org/paper/540cb7e61b8b6000ee11e0ee1def93e7ecf66934

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Citations in the corpus

QUAST: quality assessment tool for genome assemblies (2013, unknown): “The Assemblathon competition (Earl et al. , 2011) compared 41 de novo assemblies on 4 100 evaluation metrics.”
The Harvest suite for rapid core-genome alignment and visualization of thousands of intraspecific microbial genomes (2014, background): “This has made it very affordable to sequence, yet extremely challenging to produce finished genomes [47,48].”
SOAPdenovo2: an empirically improved memory-efficient short-read de novo assembler (2012, methodology): “This method could potentially make for an incorrect selection at inconsistent locations Contig and scaffold path NG50 were defined in Assemblathon1 [1].”
Reconstructing mitochondrial genomes directly from genomic next-generation sequencing reads—a baiting and iterative mapping approach (2013, background): “Sequence assembly algorithms are continuously being developed [e.g. (25–29)], improved and assessed (30,31).”
Sequence assembly demystified (2013, methodology): “Even if correctly used, however, N50 values rarely correlate with the actual quality of assembly, as demonstrated by recent assembly competitions 42,43 .”
Identifying conserved genomic elements and designing universal bait sets to enrich them (2017, background): “…downstream analysis or selection of conserved loci based on desirable properties derived from annotation or positional information (exon, intron, intergenic, unlinked, etc.), although how, exactly, to identify the bestassembled genome is a matter of debate (Earl et al. 2011; Bradnam et al. 2013).”
WhatsHap: Weighted Haplotype Assembly for Future-Generation Sequencing Reads (2015, methodology): “Using the read simulator SimSeq (Earl et al., 2011), we simulated current-generation sequencing reads using HiSeq and MiSeq error proﬁles, to generate a 2 · 100 bp and a 2 · 250 bp paired-end data set, respectively.”
Assemblathon 2: evaluating de novo methods of genome assembly in three vertebrate species (2013, methodology): “As in Assemblathon 1 [28], we find that N50 remains highly correlated with our overall rankings (Figure 20).”
Evaluation of GRCh38 and de novo haploid genome assemblies demonstrates the enduring quality of the reference assembly (2016, unknown): “…contribute to the reference assembly, that were sequenced and assembled with new technologies are candidates for use in assembly improvement (Earl et al. 2011; Vezzi et al. 2012; Bradnam et al. 2013; English et al. 2015; Pendleton et al. 2015; Seo et al. 2016; Shi et al. 2016; Zook et al. 2016).”
Finding the Genomic Basis of Local Adaptation: Pitfalls, Practical Solutions, and Future Directions (2016, unknown): “Multiple methods should be implemented in parallel and compared to achieve the best assembly (Earl et al. 2011; Bradnam et al. 2013).”
GapFiller: a de novo assembly approach to fill the gap within paired reads (2012, methodology): “The reference genomes used for simulated experiments were downloaded from NCBI website [27] and we used SimSeq, the reads simulator employed in Assemblathon 1 [7], to generate paired reads coverages.”
Metagenomics: Tools and Insights for Analyzing Next-Generation Sequencing Data Derived from Biodiversity Studies (2015, background): “49,50 A recent comparison of the latest line of assembly tools shows that IDBA-UD can reconstruct longer contigs with higher accuracy.”
Applications of next-generation sequencing to the study of biological invasions (2015, methodology): “, 2012), dnGASP (de novo Genome Assembly Assessment Project) (CNAG, 2014) and the Assemblathon (Earl et al., 2011; Bradnam et al., 2013) have assessed and compared homology-based and de novo assembly programs.”
Computational solutions for omics data (2013, background): “…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 .”
Identification and assembly of genomes and genetic elements in complex metagenomic samples without using reference genomes (2014, methodology): “If we consider unaligned scaffold with similarity to an alternative genus as a potential contaminant, the mean contamination rate was estimated to 1.00 scaffold per HQ assembly.”