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Chapter 2.4.4 : Metagenomics: Assigning Functional Status to Community Gene Content

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Metagenomics: Assigning Functional Status to Community Gene Content, Page 1 of 2

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Abstract:

Microbial metabolic processes, dynamics and interactions shape the biogeochemistry of the planet. An estimated > 1030 prokaryotic cells and ∼1030 phages inherited in their genomes, inhabit Earth and contain an estimated 350–550 Petagrams (1 Pg = 1015 g) of carbon, 85–130 Pg of nitrogen and 9–14 Pg of phosphorus. These nutrient data have come from nucleic acid based cultivation-independent surveys (CIS) of microbial communities sampled during the past two decades. Often, these communities have been surveyed using PCR-based sequencing approaches targeting organisms at the domain level. The term ‘metagenomics’ can refer to such broad amplicon surveys, but is more commonly used for shotgun sequencing approaches that do not use PCR to select either for specific genes or for specific organisms. Even after a decade of concerted effort of sequence and metadata generation, continuous improvement in sequencing technologies and computational frameworks, our understanding of the microbial dynamics (taxonomical, functional and evolutionary) is still limited in many respects. Specifically, our understanding of the functional potential of the key (eco-genetically adapted) and/or dominating taxa and their interdependencies with the ‘rare biosphere’ (i.e. lesser abundant but genetically diverge species) in complex microbial ecosystems is still largely unknown. In this review, the existing concepts, methodologies and approaches for metagenomic data analysis are outlined in order to highlight the potential of community genomics (metagenomics) to decipher the metabolic potential of microbial assemblages. Significance of closely coupled parameters like ‘individual read versus assembly based functional analysis’ and ‘cross validation (replicates) versus deep coverage’ is also explored.

Citation: Sangwan N, Lal R. 2016. Metagenomics: Assigning Functional Status to Community Gene Content, p 2.4.4-1-2.4.4-7. In Yates M, Nakatsu C, Miller R, Pillai S (ed), Manual of Environmental Microbiology, Fourth Edition. ASM Press, Washington, DC. doi: 10.1128/9781555818821.ch2.4.4
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Figures

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FIGURE 1

Flow chart for the downstream analysis of metagenome sequences, with a focus on functional analysis. Processes described here are sequence technology independent and can be used for assembled and individual (unassembled) reads. doi:10.1128/9781555818821.ch2.4.4.f1

Citation: Sangwan N, Lal R. 2016. Metagenomics: Assigning Functional Status to Community Gene Content, p 2.4.4-1-2.4.4-7. In Yates M, Nakatsu C, Miller R, Pillai S (ed), Manual of Environmental Microbiology, Fourth Edition. ASM Press, Washington, DC. doi: 10.1128/9781555818821.ch2.4.4
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Image of FIGURE 2
FIGURE 2

Relative potential of similarity-based methods used in metagenome functional annotation. Method_A: BLASTP-based comparison of protein sequences (predicted from contigs generated by assembly of raw reads) against a protein database. Method_B: Direct comparison (BLASTX; e value = 10) of individual metagenome reads against protein database. Ten thousand randomly selected metagenome reads (average read length = 300 bases) from hexachlorocyclohexane-contaminated soil (accession no: SRX0964712; [ ]) and ORFs predicted (minimum read length = 90 amino acid) from the metagenomic contigs were compared (BLASTX; e value = 10–5) against the STRING ( ) database using methods B and A, respectively. The number of unique protein families were compared against metagenome reads and ORFs and Pearson correlation coefficient (PCC) was calculated. Owing to its high coverage based characteristic (variation in each read gets compared individually) Method_B provides better annotation results with more number of unique protein families per read. doi:10.1128/9781555818821.ch2.4.4.f2

Citation: Sangwan N, Lal R. 2016. Metagenomics: Assigning Functional Status to Community Gene Content, p 2.4.4-1-2.4.4-7. In Yates M, Nakatsu C, Miller R, Pillai S (ed), Manual of Environmental Microbiology, Fourth Edition. ASM Press, Washington, DC. doi: 10.1128/9781555818821.ch2.4.4
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Tables

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TABLE 1

Computational resources for community metabolism reconstruction

Citation: Sangwan N, Lal R. 2016. Metagenomics: Assigning Functional Status to Community Gene Content, p 2.4.4-1-2.4.4-7. In Yates M, Nakatsu C, Miller R, Pillai S (ed), Manual of Environmental Microbiology, Fourth Edition. ASM Press, Washington, DC. doi: 10.1128/9781555818821.ch2.4.4

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