MEGAN Community Edition - Interactive Exploration and Analysis of Large-Scale Microbiome Sequencing Data

By Daniel H. Huson, Sina Beier, I. Flade, A. Górska, M. El-Hadidi, S. Mitra, H. Ruscheweyh, Rewati Tappu. PLoS Comput. Biol., 2016

1,575 citations recorded by the source corpus.

There is increasing interest in employing shotgun sequencing, rather than amplicon sequencing, to analyze microbiome samples. Typical projects may involve hundreds of samples and billions of sequencing reads. The comparison of such samples against a protein reference database generates billions of alignments and the analysis of such data is computationally challenging. To address this, we have substantially rewritten and extended our widely-used microbiome analysis tool MEGAN so as to facilitate the interactive analysis of the taxonomic and functional content of very large microbiome datasets. Other new features include a functional classifier called InterPro2GO, gene-centric read assembly, principal coordinate analysis of taxonomy and function, and support for metadata. The new program is called MEGAN Community Edition (CE) and is open source. By integrating MEGAN CE with our high-throughput DNA-to-protein alignment tool DIAMOND and by providing a new program MeganServer that allows access to metagenome analysis files hosted on a server, we provide a straightforward, yet powerful and complete pipeline for the analysis of metagenome shotgun sequences. We illustrate how to perform a full-scale computational analysis of a metagenomic sequencing project, involving 12 samples and 800 million reads, in less than three days on a single server. All source code is available here: https://github.com/danielhuson/megan-ce

Subfields: Ecology, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/642db283c90b2752c10b3c5ad372152190b18a55

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Citations in the corpus

SILVA, RDP, Greengenes, NCBI and OTT — how do these taxonomies compare? (2017, methodology): “In a typical taxonomy guided approach [1], sequencing reads are first binned into taxonomic units and then the microbial composition of samples is analyzed and compared in detail (see Fig.”
A review of methods and databases for metagenomic classification and assembly (2019, methodology): “MEGAN6/MEGAN-CE [73] and taxator-tk [79] both use the output of a local sequence aligner such as BLAST [61, 71], DIAMOND [70] or LAST [91].”
Species-level functional profiling of metagenomes and metatranscriptomes (2018, positive): “Using the MEGAN GUI EggNOG viewer, we exported COG abundances to a text file for downstream analysis.”
InterPro in 2017—beyond protein family and domain annotations (2016, methodology): “InterPro’s data and analysis software are increasingly used in the analysis of metagenomic data: in addition to underpinning EMBL-EBI’s in-house EBI Metagenomics resource (33), the MEGAN metagenomics analysis tool (34) now has the ability to process InterPro-derived data and GO terms.”
KrakenUniq: confident and fast metagenomics classification using unique k-mer counts (2018, methodology): “Blast and Diamond results were post-processed by Megan [17, 31], which assigns reads to the lowest common ancestor (LCA), but ignores coverages when computing the resulting taxonomic profile.”
InterPro in 2019: improving coverage, classification and access to protein sequence annotations (2018, methodology): “InterPro data are also used by a variety of other annotation pipelines, including Ensembl (21), Ensembl Genomes (22), PDBe (23), BLAST2GO (24), Genome Properties (https://www.ebi.ac. uk/interpro/genomeproperties/), PhytoPath (25), MEGAN (26) and MGnify (previously known as EBI Metagenomics) ( 27).”
Linking metagenomics to aquatic microbial ecology and biogeochemical cycles (2019, background): “Many computational tools exist to produce such profiles (Meyer et al. 2008; Huson et al. 2016), and once generated the phylogenetic and functional profiles can be statistically compared among different microbial communities (Fasching et al.”
Current challenges and best-practice protocols for microbiome analysis (2019, methodology): “nomic profiles and novel genome identification can be optimally performed using the above-described Kraken [114] and Diamond algorithms [121] with or without the Megan pipeline [117].”