Article

A Bioinformatician's Guide to Metagenomics

Microbial Ecology Program, DOE Joint Genome Institute, 2800 Mitchell Drive, Walnut Creek, CA, USA.
Microbiology and molecular biology reviews: MMBR (Impact Factor: 14.61). 01/2009; 72(4):557-78, Table of Contents. DOI: 10.1128/MMBR.00009-08
Source: PubMed

ABSTRACT

As random shotgun metagenomic projects proliferate and become the dominant source of publicly available sequence data, procedures for the best practices in their execution and analysis become increasingly important. Based on our experience at the Joint Genome Institute, we describe the chain of decisions accompanying a metagenomic project from the viewpoint of the bioinformatic analysis step by step. We guide the reader through a standard workflow for a metagenomic project beginning with presequencing considerations such as community composition and sequence data type that will greatly influence downstream analyses. We proceed with recommendations for sampling and data generation including sample and metadata collection, community profiling, construction of shotgun libraries, and sequencing strategies. We then discuss the application of generic sequence processing steps (read preprocessing, assembly, and gene prediction and annotation) to metagenomic data sets in contrast to genome projects. Different types of data analyses particular to metagenomes are then presented, including binning, dominant population analysis, and gene-centric analysis. Finally, data management issues are presented and discussed. We hope that this review will assist bioinformaticians and biologists in making better-informed decisions on their journey during a metagenomic project.

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    • "However, due to the lack of whole genome information from the individual members of the community, details on the metabolic pathways and the relationships among members of the community are not well understood. In metagenomic analysis, grouping sequences from a particular genome from microbial community sequencing data is an important step referred to as binning (Kunin et al., 2008; Mande et al., 2012). The binning process can greatly reduce the complexity of metagenomics data by grouping similar sequences together followed by assembly and annotation to the individual genome bins. "
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    • "Metagenome assembly is a critical step, since researchers are dealing with an unknown number of different genomes, and the possibility of assembling a chimeric sequence is real. It is well known that NGS platforms produce shorter reads than traditional dideoxynucletide sequencing, and short reads are more difficult to assemble, especially for metagenomics (Raes et al., 2007; Kunin et al., 2008). In order to minimize the effect of this sequence mosaic, bioinformaticians have been dedicated to discovering new assembly algorithms and pipelines, which will now be discussed. "
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    • "In contrast to whole genome sequencing, metagenomes comprise a variety of differentially abundant species, and there can be substantial interpopulation diversity within a single species. Whether two reads originate from the same gene as an entity is depending on many factors; abundance of the organism in the sample, size and copy number of the gene in the original sample [8], [9], effectiveness of enrichment strategies [10], amplification biases introduced during random amplification [11]–[13], biases inherent to next-generation sequencing protocols [14], and depth of sequencing and read lengths [15]. In theory, two reads of the same taxonomic unit should be assembled into a single contig if they have sufficient overlap. "
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