MEGAHIT v1.0: A fast and scalable metagenome assembler driven by advanced methodologies and community practices.
By Dinghua Li, Ruibang Luo, Chi-Man Liu, Chi-Ming Leung, H. Ting, Kunihiko Sadakane, H. Yamashita, T. T. Lam. Methods, 2016
1,798 citations recorded by the source corpus.
The study of metagenomics has been much benefited from low-cost and high-throughput sequencing technologies, yet the tremendous amount of data generated make analysis like de novo assembly to consume too much computational resources. In late 2014 we released MEGAHIT v0.1 (together with a brief note of Li et al. (2015) [1]), which is the first NGS metagenome assembler that can assemble genome sequences from metagenomic datasets of hundreds of Giga base-pairs (bp) in a time- and memory-efficient manner on a single server. The core of MEGAHIT is an efficient parallel algorithm for constructing succinct de Bruijn Graphs (SdBG), implemented on a graphical processing unit (GPU). The software has been well received by the assembly community, and there is interest in how to adapt the algorithms to integrate popular assembly practices so as to improve the assembly quality, as well as how to speed up the software using better CPU-based algorithms (instead of GPU). In this paper we first describe the details of the core algorithms in MEGAHIT v0.1, and then we show the new modules to upgrade MEGAHIT to version v1.0, which gives better assembly quality, runs faster and uses less memory. For the Iowa Prairie Soil dataset (252Gbp after quality trimming), the assembly quality of MEGAHIT v1.0, when compared with v0.1, has a significant improvement, namely, 36% increase in assembly size and 23% in N50. More interestingly, MEGAHIT v1.0 is no slower than before (even running with the extra modules). This is primarily due to a new CPU-based algorithm for SdBG construction that is faster and requires less memory. Using CPU only, MEGAHIT v1.0 can assemble the Iowa Prairie Soil sample in about 43h, reducing the running time of v0.1 by at least 25% and memory usage by up to 50%. MEGAHIT v1.0, exhibiting a smaller memory footprint, can process even larger datasets. The Kansas Prairie Soil sample (484Gbp), the largest publicly available dataset, can now be assembled using no more than 500GB of memory in 7.5days. The assemblies of these datasets (and other large metgenomic datasets), as well as the software, are available at the website https://hku-bal.github.io/megabox.
Subfields: Ecology, Molecular Biology
View Paper: https://www.semanticscholar.org/paper/112951589280877b7b5d01acf5174fabce921438
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Citations in the corpus
- Petabase-scale sequence alignment catalyses viral discovery (2020, neutral): “2.9) 81 , and filtered to remove contigs smaller than 1 kb in size.”
- SKESA: strategic k-mer extension for scrupulous assemblies (2018, methodology): “Benchmark set N50 measure SKESA SPAdes MegaHit IDBA ABySS = 10 Kb 14 69 19 20 12 10001 − 50 Kb 40 41 46 67 43 50001 − 100 Kb 41 56 67 100 111 100001 − 250 Kb 191 169 197 154 185 250001 − 500 Kb 77 43 48 38 26 > 500 Kb 18 3 4 2 4 Median 170647 117340 124833 101678 107730 Minimum 1832 364 687 659 210”
- MitoFinder: Efficient automated large‐scale extraction of mitogenomic data in target enrichment phylogenomics (2019, background): “De novo assembly of 501 UCE capture sequencing libraries was performed with four different assemblers: iDbA-UD , MEGAHiT and M ETA SPA DES via MitoFinder and T riniTy via PHyLUCE .”
- ABySS 2.0: resource-efficient assembly of large genomes using a Bloom filter (2016, neutral): “MEGAHIT utilizes a data structure called a succinct de Bruijn graph (Bowe et al. 2012) to reduce the memory requirements for de Bruijn graph assembly.”
- MetaWRAP—a flexible pipeline for genome-resolved metagenomic data analysis (2018, methodology): “The reads from all given samples can then be assembled with the metaWRAP-Assembly module using MegaHit [35] or metaSPAdes [36], which also produces an assembly report.”