Abstract
Abstract Summary: MEGAHIT is a NGS de novo assembler for assembling large and complex metagenomics data in a time- and cost-efficient manner. It finished assembling a soil metagenomics dataset with 252 Gbps in 44.1 and 99.6 h on a single computing node with and without a graphics processing unit, respectively. MEGAHIT assembles the data as a whole, i.e. no pre-processing like partitioning and normalization was needed. When compared with previous methods on assembling the soil data, MEGAHIT generated a three-time larger assembly, with longer contig N50 and average contig length; furthermore, 55.8% of the reads were aligned to the assembly, giving a fourfold improvement. Availability and implementation: The source code of MEGAHIT is freely available at https://github.com/voutcn/megahit under GPLv3 license. Contact: rb@l3-bioinfo.com or twlam@cs.hku.hk Supplementary information: Supplementary data are available at Bioinformatics online.
Keywords
Affiliated Institutions
Related Publications
IDBA-UD: a <i>de novo</i> assembler for single-cell and metagenomic sequencing data with highly uneven depth
Abstract Motivation: Next-generation sequencing allows us to sequence reads from a microbial environment using single-cell sequencing or metagenomic sequencing technologies. How...
Velvet: Algorithms for de novo short read assembly using de Bruijn graphs
We have developed a new set of algorithms, collectively called “Velvet,” to manipulate de Bruijn graphs for genomic sequence assembly. A de Bruijn graph is a compact representat...
De novo assembly of human genomes with massively parallel short read sequencing
Next-generation massively parallel DNA sequencing technologies provide ultrahigh throughput at a substantially lower unit data cost; however, the data are very short read length...
SHARCGS, a fast and highly accurate short-read assembly algorithm for de novo genomic sequencing
The latest revolution in the DNA sequencing field has been brought about by the development of automated sequencers that are capable of generating giga base pair data sets quick...
Publication Info
- Year
- 2015
- Type
- article
- Volume
- 31
- Issue
- 10
- Pages
- 1674-1676
- Citations
- 8475
- Access
- Closed
External Links
Social Impact
Social media, news, blog, policy document mentions
Citation Metrics
Cite This
Identifiers
- DOI
- 10.1093/bioinformatics/btv033