In Silico Approach to Study the Regulatory Mechanisms and Pathways of Microorganisms - Applied Environmental Biotechnology: Present Scenario and Future Trends - page 38

Fig. 3.5 Construction and screening of metagenomic libraries. (Reproduced from Handlesman 2004 )

3.4 MEGAN (MetaGenome Analyzer)

signs a taxon ID to each sequence based on the
National Center for Biotechnology Information
(NCBI) taxonomy. This produces a MEGAN file
that contains all information needed for analyz-
ing and generating graphical and statistical out-
put. Fourth, the user interacts with the program
to run the lowest common ancestor (LCA) algo-
rithm to analyze the data, inspect the assignment
of individual reads to taxa based on their hits and
to produce summaries of the results at different
levels of the NCBI taxonomy (Fig. 3.6 ) (Huson
et al. 2007 )
MEGAN can be used to interactively explore
the dataset in the following manner:

MEGAN is the first stand-alone tool for metage-
nome analysis. It allows the investigation of
very large datasets from environmental samples
using shotgun sequencing techniques in particu-
lar, and is designed to sample and investigate the
unknown biodiversity of environmental samples
where more precise techniques with smaller,
better known samples cannot be used. In ME-
GAN's processing pipeline, it initially performs
the analysis of the metagenomic sample. First,
reads are collected from the sample using any
random shotgun protocol. Second, a sequence
comparison of all reads against one or more
databases of known reads is performed, using
basic local alignment search tool (BLAST) or
a similar comparison tool. Third, MEGAN pro-
cesses the results of the comparison to collect all
hits of reads against known sequences, and as-

a. Comparative visualization
To compare a collection of different datasets vi-
sually, 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