MEGAHIT statistics

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Citations per year

Number of citations per year for the bioinformatics software tool MEGAHIT
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Tool usage distribution map

This map represents all the scientific publications referring to MEGAHIT per scientific context
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Associated diseases

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Protocols

MEGAHIT specifications

Information


Unique identifier OMICS_07234
Name MEGAHIT
Software type Package/Module
Interface Command line interface
Restrictions to use None
Output format FASTG
Operating system Unix/Linux, Mac OS
Programming languages C, C++, Python
Parallelization CUDA
License GNU General Public License version 3.0
Computer skills Advanced
Version 1.1.2
Stability Stable
Requirements
zlib, G++, CUDA
Maintained Yes

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Versioning


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Documentation


Maintainers


  • person_outline Tak-Wah Lam
  • person_outline MEGAHIT Team

Publications for MEGAHIT

MEGAHIT citations

 (91)
library_books

Identifying Group Specific Sequences for Microbial Communities Using Long k mer Sequence Signatures

2018
Front Microbiol
PMCID: 5943621
PMID: 29774017
DOI: 10.3389/fmicb.2018.00872

[…] metagenomic clusters as features () and 0.785 ± 0.104 using the presence of 83,456 strain-specific markers as features (). For further comparison, we implemented metagenome-wide de novo assembly with MegaHIT () and then binned the contigs with MetaGen (). The relative abundances of bins were used as features to separate the patient and control groups. The total of 96 samples were too large for rea […]

library_books

Ancient DNA study reveals HLA susceptibility locus for leprosy in medieval Europeans

2018
Nat Commun
PMCID: 5931558
PMID: 29717136
DOI: 10.1038/s41467-018-03857-x

[…] g runs was pooled after pre-processing for the samples G34, G154, G404, G427, G507, G533, G722, G749, G1083, and G1149. The pooled reads were used to carry out a metagenomic de novo assembly with the Megahit assembler v1.0.3-8-g4b5271e. The following command line was used:megahit --presets meta -r IN -o OUT, where IN is a file containing the pre−processed reads, OUT is the output directory.For sub […]

library_books

Human Activity Determines the Presence of Integron Associated and Antibiotic Resistance Genes in Southwestern British Columbia

2018
Front Microbiol
PMCID: 5938356
PMID: 29765365
DOI: 10.3389/fmicb.2018.00852

[…] short sequences (<75 nt) were discarded. Sequences were then assembled using PandaSeq (); unassembled pairs were retained. De novo assembly was conducted on the assembled and unassembled pairs using MEGAHIT () and contigs shorter than 200 nt were discarded. Nucleotide sequences were aligned against the comprehensive antibiotic resistance database (CARD; ) and Integrall () using BLAST (). […]

library_books

Microbiome Dynamics in a Large Artificial Seawater Aquarium

2018
Appl Environ Microbiol
PMCID: 5930379
PMID: 29523545
DOI: 10.1128/AEM.00179-18

[…] mpled to the same number of reads as that for sample 0805 (8,963,441 reads).Two coassemblies were generated from the quality-trimmed metagenomic reads of both samples (samples 0805 and 0928) by using Megahit v.1.0 (), with the default parameters for paired-end assembly. One coassembly was generated with a minimum contig size of 2,500 bp (for metagenome analysis), and the other was generated with a […]

library_books

Draft Genome Sequence of the Extremely Desiccation Tolerant Cyanobacterium Gloeocapsopsis sp. Strain AAB1

2018
Genome Announc
PMCID: 5920166
PMID: 29700137
DOI: 10.1128/genomeA.00216-18

[…] Raw reads were quality trimmed with Prinseq Lite v0.20.4 (). The resulting 5,830,860 pairs were processed with the SqueezeM pipeline (J. Tamames, unpublished data). Briefly, they were assembled using MEGAHIT v1.1.2 (), and open reading frames (ORFs) and rRNA coding sequences were predicted with Prodigal v2.6.2 () and barrnap v0.9-dev (https://github.com/tseemann/barrnap), respectively. ORFs were a […]

library_books

Genome Reduction in Psychromonas Species within the Gut of an Amphipod from the Ocean’s Deepest Point

2018
mSystems
PMCID: 5893861
PMID: 29657971
DOI: 10.1128/mSystems.00009-18

[…] hose with a length of <70 after trimming were removed. Reads with an average quality score of <20 and with sequencing adaptors were also removed by filtering. The paired-end reads were assembled with MegaHit (version 1.0.2) () by using kmer values of 21 to 111 (step size, 10). The parameters used were --kmin-1pass (1pass mode to build SdBG of k_min) and --mem-flag 2. The coverage of the contigs wa […]


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MEGAHIT institution(s)
HKU-BGI Bioinformatics Algorithms Research Laboratory & Department of Computer Science, University of Hong Kong, Hong Kong; L3 Bioinformatics Limited, Hong Kong; National Institute of Informatics, Tokyo, Japan
MEGAHIT funding source(s)
Supported by Hong Kong GRF (General Research Fund) HKU713512E and ITF (Innovation and Technology Fund) GHP/011/12.

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