Abstract
Metagenomics has become a part of the standard toolkit for scientists interested in studying microbes in the environment. Compared to 16S rDNA sequencing, which allows coarse taxonomic profiling of samples, shotgun metagenomic sequencing provides a more detailed analysis of the taxonomic and functional content of samples. Long read technologies, such as developed by Pacific Biosciences or Oxford Nanopore, produce much longer stretches of informative sequence, greatly simplifying the difficult and time-consuming process of metagenomic assembly. MEGAN6 provides a wide range of analysis and visualization methods for the analysis of short and long read metagenomic data. A simple and efficient analysis pipeline for metagenomic analysis consists of the DIAMOND alignment tool on short reads, or the LAST alignment tool on long reads, followed by MEGAN. This approach performs taxonomic and functional abundance analysis, supports comparative analysis of large-scale experiments, and allows one to involve experimental metadata in the analysis.
Highlights
Introduction to the Analysis of EnvironmentalSequences: Metagenomics with MEGANCaner Bagcı, Sina Beier, Anna Gorska, and Daniel H
Metagenomic sequencing allows the study of microorganisms found in environmental samples without relying on culturing methods or prior knowledge of the composition of the community
Functional classification of long reads does not necessarily assign each read into one functional class, instead reads are assigned to the functional class of best-scoring alignment in each segment, each segment is assigned to one function and one read can be assigned to multiple different functional classes
Summary
Metagenomics is the study of microbiome samples, such as obtained from ocean water, soil, plant matter, or feces, say, using high-throughput DNA sequencing [1]. Long read technologies have been considered too expensive, difficult, or error-prone for application in metagenomics. This is changing and computational analysis methods designed for processing short reads need to be modified to work well on long. A major computational challenge in metagenomics is the alignment of sequencing reads against a comprehensive reference database. Long reads require frame-shift aware alignment tools, such as LAST [3, 4], because insertions or deletions due to sequencing errors impact long reads, as discussed in Subheading 2. We will first discuss how to perform basic alignment and analysis of short reads in Subheading 2.1 and long reads in Subheading 2.2. In Subheading 4.1 we describe some additional resources available for using MEGAN 6
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