1. Metagenomic Analysis Using MEGAN4
Peter R. Hoyt
Director, OSU Bioinformatics Graduate Certificate Program
Matthew Vaughn
iPlant, University of Texas Super Computing Center

2. Introduction
In METAGENOMICS, the aim is to understand the composition and operation of complex microbial consortia in environmental samples through sequencing and analysis of their DNA.
Similarly, metatranscriptomics and metaproteomics target the RNA and proteins obtained from such samples.
Technological advances in next-generation sequencing methods are fueling a rapid increase in the number and scope of environmental sequencing projects. In consequence, there is a dramatic increase in the volume of sequence data to be analyzed.

3. The Importance of Metagenomics is Driven by Sequencing Costs
The $100 Human Genome

4. Basic Computational Metagenomics
The first three basic computational tasks for such data are:
taxonomic analysis (“who is out there?”)
functional analysis (“what are they doing?”)
comparative analysis. (“how do different samples compare?”)
This is an immense conceptual and computational challenge that MEGAN is designed to address.

5. Function/Gene Ontology (SEED) Metabolomics/Pathway Analyses (KEGG)
Algorithms in Bioinformatics
MEGAN-4 USES
Taxonomic
Metagenomic
Metatranscriptomic
Metaproteomic
16S rRNA sequences
Function/Gene Ontology (SEED)
Metabolomics/Pathway Analyses (KEGG)
Comparative Genomics

6. Getting started Prepare a dataset for use with MEGAN:
1. First compare reads against a database of reference sequences,
e.g. BLASTX search against the NCBI-NR database.
2. Reads file & resulting BLAST file can be directly imported into MEGAN*
Automatic taxonomic classification or functional classification,
Uses SEED or KEGG classification, or both.
3. Multiple datasets can be opened simultaneously for comparative views
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Comparative Data
Raw Digital Data
Metagenomic
sample
MEGAN4
DNA-RNA-Protein
BLAST nr nt
Ref
seq
pdb
rdb

7. Taxonomic analysis
MEGAN can be used to interactively explore the dataset. Figure shows assignment of reads to the NCBI taxonomy.
Each node is labeled by a taxon and the number of reads assigned to the taxon,
The size of a node is scaled logarithmically to represent the number of assigned reads.
Tree display options allow you to interactively drill down to the individual BLAST hits and to export all reads
One can select a set of taxa and then use MEGAN to generate different types of charts

8. Multiple Chart Options are Available

9. Functional analysis using the SEED classification
SEED1 is a comparative genomics environment of curated genomic data. The following figure shows a part of the SEED analysis of a marine metagenome sample.
MEGAN attempts to map each read to a SEED functional role by the highest scoring BLAST protein match with a known functional role.
SEED rooted trees are “multi-labeled” because different leaves may represent the same functional role (if it occurs in different types of subsystems)
The current complete SEED tree has about 13,000 nodes.
1http://

10. Functional analysis using the KEGG classification
To perform a KEGG analysis, MEGAN attempts to match each read to a KEGG orthology (KO) accession number, using the best hit to a reference sequence
Reads are then assigned to enzymes and pathways. The KEGG classification is represented by a rooted tree whose leaves represent pathways. See:
Each pathway can also be inspected visually, for example the citric acid cycle (shown). These provide inferences regarding the cellular activities of a sample.
KEGG displays different participating enzymes by numbered rectangles. MEGAN shades each such rectangle is so as to indicate the number of reads assigned to the corresponding enzyme.

11. KEEG Pathways and examples
KEGG (Kyoto Encyclopedia of Genes and Genomes) “is a database resource for understanding high-level functions and utilities of the biological system, such as the cell, the organism and the ecosystem, from molecular-level information, especially large-scale molecular datasets generated by genome sequencing and other high-throughput experimental technologies”
KEGG is used to observe patterns in metabolic pathways, functional hierarchies, Diseases, Ortholog Groups, Genes and Genomes.
KEGG is heavily used by the metabolism community, and for comparative transcriptomics.
Here are some examples of the KEGG results from our metabolic samples. Do they suggest anything to you?

12. Comparitive analysis using the SEED classification
MEGAN also supports the simultaneous analysis and comparison of the SEED functional content of multiple metagenomes, or multiple timepoints/samples (shown)
A comparative view of assignments to a KEGG pathway is also possible.

13. Computational comparison of metagenomes
MEGANs analysis window compares multiple datasets.
This enables creating distance matrices for a collection of datasets using different ecological indices.
MEGAN supports a number of different methods for calculating a distance matrix,
These can be visualized either using a split network calculated using the neighbor-net algorithm, or using a multi-dimensional scaling plot. NeighborNet[1] is an algorithm that computes unrooted phylogenetic networks from molecular sequence data.
The figure we shows a comparison of eight marine datasets based on the taxonomic content of the datasets and computed using Goodall’s index.
1Bryant and Moulton : Neighbor-net, an agglomerative method for the construction of phylogenetic networks - Molecular Biology and Evolution 21 (2003)

14. Comparative Taxonomic Visualization
MEGAN provides a comparison view that is based on a tree in which each node shows the number of reads assigned to it for each of the datasets.
This can be done either as a pie chart, a bar chart or as a heat map.
Once the datasets are all individually opened MEGAN provides a “compare” dialog.
The following figure shows the taxonomic comparison of all eight marine datasets.
Here, each node in the NCBI taxonomy is shown as a bar chart indicating the number of reads (normalized, if desired) from each dataset assigned to the node.