Metagenomic Analysis Using MEGAN4

Peter R. HoytDirector, OSU Bioinformatics Graduate Certificate Program

Matthew VaughniPlant, University of Texas Super Computing Center

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.

http://ab.inf.uni-tuebingen.de/software/megan/welcome.html

The $100 Human Genome

The Importance of Metagenomics is Driven by Sequencing Costs

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.

http://ab.inf.uni-tuebingen.de/software/megan/welcome.html

Taxonomic Metagenomic Metatranscriptomic Metaproteomic 16S rRNA sequences

Function/Gene Ontology (SEED) Metabolomics/Pathway Analyses (KEGG) Comparative Genomics

http://ab.inf.uni-tuebingen.de/software/megan/welcome.html

Algorithms in Bioinformatics

MEGAN-4 USES

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 

http://ab.inf.uni-tuebingen.de/software/megan/welcome.html

Getting started

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nr nt Refseq pdb rdb

Comparative Data

MEGAN4Metagenomic sample

DNA-RNA-Protein

Raw Digital Data

BLAST

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

http://ab.inf.uni-tuebingen.de/software/megan/welcome.html

Taxonomic analysis

Multiple Chart Options are Available

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.

http://ab.inf.uni-tuebingen.de/software/megan/welcome.html

Functional analysis using the SEED classification

1http://www.theseed.org/wiki/Main_Page

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.

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:http://www.kegg.jp/kegg/pathway.html

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.

http://ab.inf.uni-tuebingen.de/software/megan/welcome.html

Functional analysis using the KEGG classification

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?

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.

http://ab.inf.uni-tuebingen.de/software/megan/welcome.html

Comparitive analysis using the SEED classification

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.

http://ab.inf.uni-tuebingen.de/software/megan/welcome.html

MEGANs analysis window compares multiple datasets. This enables creating distance matrices for a collection of datasets using different ecological indices.

Computational comparison of metagenomes

1Bryant and Moulton : Neighbor-net, an agglomerative method for the construction of phylogenetic networks - Molecular Biology and Evolution 21 (2003)

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.

http://ab.inf.uni-tuebingen.de/software/megan/welcome.html

Comparative Taxonomic Visualization

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.