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Next Generation Sequencing (NGS)/Metagenomics

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Metagenomics

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Metagenomics methods use genomic DNA from many different organisms, usually within a microbiome. Metagenomic analyses present different challenges than single species studies as microbiome samples can contain thousands of species, often novel and closely related.[1]

Metagenomics is another useful bioinformatic tool to access the genetic information from entire community of organisms. It is also a powerful tool for generating novel findings about microbial functions. For example, new finding of proteorhodopsin-based photoheterophy or ammonia-oxidizing Archaea using metagenomics were amazing.[2] Using metagenomics, functional gene composition of microbial communities can be accessed.[3] Thomas et al.'s "Metagenomics - A guide from sampling to data analysis" gives us a flow diagram of a typical metagenome project, containing the following steps: experimental design, sampling, sample fractionation, DNA extraction, DNA sequencing, assembly, binning, annotation, statistical analysis, data storage, metadata and data sharing.[1]

Metagenomics is useful in studying DNA of uncultured organisms. More than 99% of all microbes cannot be cultured. A metagenome is the entire genetic information of a group of organisms. Metagenomics can be done on samples collected from soil, sea water, sea bed, air, animal waste, etc. For example, Venter et al. show how environmental genome shotgun sequencing of the Sargasso sea water samples was done.[4] About 1.2 million new genes were identified. The two main questions are how many microbes exist in the given sample and what are the functions provided by them.

The metagenomic processing pipeline involves sample collection; DNA read sequencing, sequence comparison to the reference genome, comparison file and interactive analysis and visualization. Sequence comparison is done using BLAST, Megablast, BLAT and SSAHA. Identification of species by DNA by using BLAST. Then analysis based on NCBI taxonomy is done. Megan metagenome analyzer is the functional analysis using the SEED classification. IMG/M and MG-RAST are different metagenomic analysis tools. Various uses of metagenomic analysis researches are recently done. A core gut microbiome in obese and lean twins studies done using metagenomics indicates that there is phylogenetic diversity of microbiota of lean and obese individuals.[5] Metagenomics involves binning. Binning is process in which DNA sequences are sorted into groups that might represent an individual genome or genomes from closely related organisms. Binning can be composition based or similarity based. Metagenomic sequencing is more challenging in soil samples than water samples, primarily due to uneven distribution of microorganisms in soil.

References

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  1. ↑ a b Thomas, T.; Gilbert, J.; Meyer, F. (2012). "Metagenomics - A guide from sampling to data analysis". Microbial Informatics and Experimentation. 2 (1): 3. doi:10.1186/2042-5783-2-3. PMC 3351745. PMID 22587947.{{cite journal}}: CS1 maint: PMC format (link) CS1 maint: multiple names: authors list (link)
  2. ↑ Tringe, S.G.; von Mering, C.; Kobayashi, A.; et al. (2005). "Comparative metagenomics of microbial communities". Science. 308 (5721): 554–557. doi:10.1126/science.1107851. PMID 15845853. {{cite journal}}: Explicit use of et al. in: |author= (help)CS1 maint: multiple names: authors list (link)
  3. ↑ Qin, J.; Li, R.; Raes, J.; et al. (2009). "A human gut microbial gene catalogue established by metagenomic sequencing". Nature. 464 (7285): 59–65. doi:10.1038/nature08821. PMC 3779803. PMID 20203603. {{cite journal}}: Explicit use of et al. in: |author= (help)CS1 maint: PMC format (link) CS1 maint: multiple names: authors list (link)
  4. ↑ Venter, J.C.; Remington, K.; Heidelberg, J.F.; et al. (2004). "Environmental genome shotgun sequencing of the Sargasso Sea". Science. 304 (5667): 66–74. doi:10.1126/science.1093857. PMID 15001713. {{cite journal}}: Explicit use of et al. in: |author= (help)CS1 maint: multiple names: authors list (link)
  5. ↑ Turnbaugh, P.J.; Hamady, M.; Yatsunenko, T.; et al. (2009). "A core gut microbiome in obese and lean twins". Nature. 457 (7228): 480–4. doi:10.1038/nature07540. PMC 2677729. PMID 19043404. {{cite journal}}: Explicit use of et al. in: |author= (help)CS1 maint: PMC format (link) CS1 maint: multiple names: authors list (link)
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