Dataset Information

Molecular Analysis of Methanogen Richness in Landfill and Marshland Targeting 16S rDNA Sequences.

ABSTRACT: Methanogens, a key contributor in global carbon cycling, methane emission, and alternative energy production, generate methane gas via anaerobic digestion of organic matter. The methane emission potential depends upon methanogenic diversity and activity. Since they are anaerobes and difficult to isolate and culture, their diversity present in the landfill sites of Delhi and marshlands of Southern Assam, India, was analyzed using molecular techniques like 16S rDNA sequencing, DGGE, and qPCR. The sequencing results indicated the presence of methanogens belonging to the seventh order and also the order Methanomicrobiales in the Ghazipur and Bhalsawa landfill sites of Delhi. Sequences, related to the phyla Crenarchaeota (thermophilic) and Thaumarchaeota (mesophilic), were detected from marshland sites of Southern Assam, India. Jaccard analysis of DGGE gel using Gel2K showed three main clusters depending on the number and similarity of band patterns. The copy number analysis of hydrogenotrophic methanogens using qPCR indicates higher abundance in landfill sites of Delhi as compared to the marshlands of Southern Assam. The knowledge about "methanogenic archaea composition" and "abundance" in the contrasting ecosystems like "landfill" and "marshland" may reorient our understanding of the Archaea inhabitants. This study could shed light on the relationship between methane-dynamics and the global warming process.

SUBMITTER: Yadav S

PROVIDER: S-EPMC4623359 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Molecular Analysis of Methanogen Richness in Landfill and Marshland Targeting 16S rDNA Sequences.

Yadav Shailendra S Kundu Sharbadeb S Ghosh Sankar K SK Maitra S S SS

Archaea (Vancouver, B.C.) 20151013

Methanogens, a key contributor in global carbon cycling, methane emission, and alternative energy production, generate methane gas via anaerobic digestion of organic matter. The methane emission potential depends upon methanogenic diversity and activity. Since they are anaerobes and difficult to isolate and culture, their diversity present in the landfill sites of Delhi and marshlands of Southern Assam, India, was analyzed using molecular techniques like 16S rDNA sequencing, DGGE, and qPCR. The ...[more]

PMID: 26568700

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Project description:BACKGROUND:Shotgun metagenomic sequencing reveals the potential in microbial communities. However, lower-cost 16S ribosomal RNA (rRNA) gene sequencing provides taxonomic, not functional, observations. To remedy this, we previously introduced Piphillin, a software package that predicts functional metagenomic content based on the frequency of detected 16S rRNA gene sequences corresponding to genomes in regularly updated, functionally annotated genome databases. Piphillin (and similar tools) have previously been evaluated on 16S rRNA data processed by the clustering of sequences into operational taxonomic units (OTUs). New techniques such as amplicon sequence variant error correction are in increased use, but it is unknown if these techniques perform better in metagenomic content prediction pipelines, or if they should be treated the same as OTU data in respect to optimal pipeline parameters. RESULTS:To evaluate the effect of 16S rRNA sequence analysis method (clustering sequences into OTUs vs amplicon sequence variant error correction into amplicon sequence variants (ASVs)) on the ability of Piphillin to predict functional metagenomic content, we evaluated Piphillin-predicted functional content from 16S rRNA sequence data processed through OTU clustering and error correction into ASVs compared to corresponding shotgun metagenomic data. We show a strong correlation between metagenomic data and Piphillin-predicted functional content resulting from both 16S rRNA sequence analysis methods. Differential abundance testing with Piphillin-predicted functional content exhibited a low false positive rate (< 0.05) while capturing a large fraction of the differentially abundant features resulting from corresponding metagenomic data. However, Piphillin prediction performance was optimal at different cutoff parameters depending on 16S rRNA sequence analysis method. Using data analyzed with amplicon sequence variant error correction, Piphillin outperformed comparable tools, for instance exhibiting 19% greater balanced accuracy and 54% greater precision compared to PICRUSt2. CONCLUSIONS:Our results demonstrate that raw Illumina sequences should be processed for subsequent Piphillin analysis using amplicon sequence variant error correction (with DADA2 or similar methods) and run using a 99% ID cutoff for Piphillin, while sequences generated on platforms other than Illumina should be processed via OTU clustering (e.g., UPARSE) and run using a 96% ID cutoff for Piphillin. Piphillin is publicly available for academic users (Piphillin server. http://piphillin.secondgenome.com/.).

Dataset Information

Molecular Analysis of Methanogen Richness in Landfill and Marshland Targeting 16S rDNA Sequences.

Publications

Molecular Analysis of Methanogen Richness in Landfill and Marshland Targeting 16S rDNA Sequences.

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