MEGAN analysis of metagenomic data.

By Daniel H. Huson, Alexander F. Auch, Ji Qi, Stephan C. Schuster. Genome Research, 2007

994 citations recorded by the source corpus.

Metagenomics is the study of the genomic content of a sample of organisms obtained from a common habitat using targeted or random sequencing. Goals include understanding the extent and role of microbial diversity. The taxonomical content of such a sample is usually estimated by comparison against sequence databases of known sequences. Most published studies use the analysis of paired-end reads, complete sequences of environmental fosmid and BAC clones, or environmental assemblies. Emerging sequencing-by-synthesis technologies with very high throughput are paving the way to low-cost random "shotgun" approaches. This paper introduces MEGAN, a new computer program that allows laptop analysis of large metagenomic data sets. In a preprocessing step, the set of DNA sequences is compared against databases of known sequences using BLAST or another comparison tool. MEGAN is then used to compute and explore the taxonomical content of the data set, employing the NCBI taxonomy to summarize and order the results. A simple lowest common ancestor algorithm assigns reads to taxa such that the taxonomical level of the assigned taxon reflects the level of conservation of the sequence. The software allows large data sets to be dissected without the need for assembly or the targeting of specific phylogenetic markers. It provides graphical and statistical output for comparing different data sets. The approach is applied to several data sets, including the Sargasso Sea data set, a recently published metagenomic data set sampled from a mammoth bone, and several complete microbial genomes. Also, simulations that evaluate the performance of the approach for different read lengths are presented.

Subfields: Ecology, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/dfe199440352079a9ebeb0851a2322c9ff0627dc

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Citations in the corpus

A Bioinformatician's Guide to Metagenomics (2008, methodology): “and visualization tools such as BLAST and MEGAN (64) can”
Succession of microbial consortia in the developing infant gut microbiome (2010, methodology): “Filtered nucleotide metagenomic sequences were compared with the September 27th, 2009 version of the National Center for Biotechnology Information nonredundant database (nr) using BLASTX (11), and results were visualized in MEGAN (39) to determine the taxonomic distribution of genes in each library ”
Computational meta'omics for microbial community studies (2013, methodology): “…and provide accurate taxonomic characterization of the metagenomic reads; phylogenetic approaches typically rely on assigning reads to the lowest common ancestor of the taxa with hits (MEGAN (Huson et al , 2007)) or other parsimonious evolutionary principles (PaPaRa (Berger and Stamatakis, 2011)).”
Statistical Methods for Detecting Differentially Abundant Features in Clinical Metagenomic Samples (2009, methodology): “More recently, the software MEGAN of Huson et al. [13] provides a graphical interface that allows users to compare the taxonomic composition of different environments.”
Metagenomic analysis of a permafrost microbial community reveals a rapid response to thaw (2011, methodology): “For comparisons between the pyrotag and metagenome data, phylogenetic information was extracted from the metagenomes using MEGA”
Community cyberinfrastructure for Advanced Microbial Ecology Research and Analysis: the CAMERA resource (2010, methodology): “A taxonomy binning workflow, which will assign a taxonomy path to a read using tools like MEGAN (18), RDP Classifier (19) and the Silva database (20) will be released at the end of 2010.”
The human microbiome (2017, methodology): “MetaPhlAn [5] uses clade-specific marker genes to estimate relative abundances of different taxa, while MEGAN [6] relies on BLAST searches of sequences against microbial reference databases and employs a lowest common ancestor algorithm for classification.”
PhyloSift: phylogenetic analysis of genomes and metagenomes (2014, methodology): “Examples of current identity-based classification tools include MEGAN (a Lowest Common Ancestor algorithm that summarizes BLAST outputs to assign taxonomy) (Huson et al., 2007), SORT-Items (reciprocal BLAST approach to detect significant orthology) (Haque et al.”
Metagenomic biomarker discovery and explanation (2011, methodology): “Huson DH, Auch AF, Qi J, Schuster SC: MEGAN analysis of metagenomic data.”
Evaluation of Filtration and DNA Extraction Methods for Environmental DNA Biodiversity Assessments across Multiple Trophic Levels (2017, methodology): “9.2 (Zhang et al., 2014), quality filtering with USEARCH (Edgar, 2010), demultiplexing by primer tag or library barcode with the programming language AWK, primer removal with cutadapt v.1.4.2 (Martin, 2011) allowing for no mismatches in the primer sequence, de-replication, singleton removal, Operati”
Composition and energy harvesting capacity of the gut microbiota: relationship to diet, obesity and time in mouse models (2010, methodology): “The resulting BLAST output was parsed using MEGAN.(32) MEGAN assigns reads to NCBI taxonomies by employing the Lowest Common Ancestor algorithm which assigns each RNA-tag to the lowest common ancestor in the taxonomy from a subset of the best scoring matches in the BLAST result.”
Metagenomic reconstruction of nitrogen cycling pathways in a CO2-enriched grassland ecosystem (2017, methodology): “For taxonomic assignment, lowest common ancestors were assigned based on the best hits within 1/10 top evalue using the MEGAN program (Huson et al., 2007).”
Diet drives convergence in gut microbiome functions across mammalian phylogeny and within humans (2011, neutral): “When shotgun readswere assigned to phylogenetic bins using the programMEGAN (6), the results revealed that fecal microbiomes were dominated by members of Bacteria, had low levels of Eukarya (0.15 to 5.35% of identifiable reads), and archaeons were variably represented (0 to 1.77% of assignable reads”