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Xander: employing a novel method for efficient gene-targeted metagenomic assembly.

Wang Q, Fish JA, Gilman M, Sun Y, Brown CT, Tiedje JM, Cole JR - Microbiome (2015)

Bottom Line: However, assembling metagenomic datasets has proven to be computationally challenging.We compared our method to a recently published bulk metagenome assembly method and a recently published gene-targeted assembler and found our method produced more, longer, and higher quality gene sequences.HMMs used for assembly can be tailored to the targeted genes, allowing flexibility to improve annotation over generic annotation pipelines.

View Article: PubMed Central - PubMed

Affiliation: Center for Microbial Ecology, Michigan State University, East Lansing, MI USA.

ABSTRACT

Background: Metagenomics can provide important insight into microbial communities. However, assembling metagenomic datasets has proven to be computationally challenging. Current methods often assemble only fragmented partial genes.

Results: We present a novel method for targeting assembly of specific protein-coding genes. This method combines a de Bruijn graph, as used in standard assembly approaches, and a protein profile hidden Markov model (HMM) for the gene of interest, as used in standard annotation approaches. These are used to create a novel combined weighted assembly graph. Xander performs both assembly and annotation concomitantly using information incorporated in this graph. We demonstrate the utility of this approach by assembling contigs for one phylogenetic marker gene and for two functional marker genes, first on Human Microbiome Project (HMP)-defined community Illumina data and then on 21 rhizosphere soil metagenomic datasets from three different crops totaling over 800 Gbp of unassembled data. We compared our method to a recently published bulk metagenome assembly method and a recently published gene-targeted assembler and found our method produced more, longer, and higher quality gene sequences.

Conclusion: Xander combines gene assignment with the rapid assembly of full-length or near full-length functional genes from metagenomic data without requiring bulk assembly or post-processing to find genes of interest. HMMs used for assembly can be tailored to the targeted genes, allowing flexibility to improve annotation over generic annotation pipelines. This method is implemented as open source software and is available at https://github.com/rdpstaff/Xander_assembler.

No MeSH data available.


Related in: MedlinePlus

Principal component analysis of rhizosphere samples (n = 7 per crop). The OTU abundances at 95 % aa identity were corrected using the mean kmer coverage of each contig. The OTU data were then standardized using the Wisconsin square root normalization as implemented in R. Ellipses represent 1 standard deviation of the points from the centroid. C corn, M Miscanthus, S switchgrass. Left: nitrite reductase (nirK). Right: ribosomal protein L2 (rplB)
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Fig4: Principal component analysis of rhizosphere samples (n = 7 per crop). The OTU abundances at 95 % aa identity were corrected using the mean kmer coverage of each contig. The OTU data were then standardized using the Wisconsin square root normalization as implemented in R. Ellipses represent 1 standard deviation of the points from the centroid. C corn, M Miscanthus, S switchgrass. Left: nitrite reductase (nirK). Right: ribosomal protein L2 (rplB)

Mentions: We used PCA to visualize the community structures among the 21 rhizosphere samples using either nirK or rplB representative contigs. The nifH contigs were not used due to the low number of contigs assembled. Both PCA plots showed that corn samples are distinct from the switchgrass and Miscanthus samples (Fig. 4).Fig. 4


Xander: employing a novel method for efficient gene-targeted metagenomic assembly.

Wang Q, Fish JA, Gilman M, Sun Y, Brown CT, Tiedje JM, Cole JR - Microbiome (2015)

Principal component analysis of rhizosphere samples (n = 7 per crop). The OTU abundances at 95 % aa identity were corrected using the mean kmer coverage of each contig. The OTU data were then standardized using the Wisconsin square root normalization as implemented in R. Ellipses represent 1 standard deviation of the points from the centroid. C corn, M Miscanthus, S switchgrass. Left: nitrite reductase (nirK). Right: ribosomal protein L2 (rplB)
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4526283&req=5

Fig4: Principal component analysis of rhizosphere samples (n = 7 per crop). The OTU abundances at 95 % aa identity were corrected using the mean kmer coverage of each contig. The OTU data were then standardized using the Wisconsin square root normalization as implemented in R. Ellipses represent 1 standard deviation of the points from the centroid. C corn, M Miscanthus, S switchgrass. Left: nitrite reductase (nirK). Right: ribosomal protein L2 (rplB)
Mentions: We used PCA to visualize the community structures among the 21 rhizosphere samples using either nirK or rplB representative contigs. The nifH contigs were not used due to the low number of contigs assembled. Both PCA plots showed that corn samples are distinct from the switchgrass and Miscanthus samples (Fig. 4).Fig. 4

Bottom Line: However, assembling metagenomic datasets has proven to be computationally challenging.We compared our method to a recently published bulk metagenome assembly method and a recently published gene-targeted assembler and found our method produced more, longer, and higher quality gene sequences.HMMs used for assembly can be tailored to the targeted genes, allowing flexibility to improve annotation over generic annotation pipelines.

View Article: PubMed Central - PubMed

Affiliation: Center for Microbial Ecology, Michigan State University, East Lansing, MI USA.

ABSTRACT

Background: Metagenomics can provide important insight into microbial communities. However, assembling metagenomic datasets has proven to be computationally challenging. Current methods often assemble only fragmented partial genes.

Results: We present a novel method for targeting assembly of specific protein-coding genes. This method combines a de Bruijn graph, as used in standard assembly approaches, and a protein profile hidden Markov model (HMM) for the gene of interest, as used in standard annotation approaches. These are used to create a novel combined weighted assembly graph. Xander performs both assembly and annotation concomitantly using information incorporated in this graph. We demonstrate the utility of this approach by assembling contigs for one phylogenetic marker gene and for two functional marker genes, first on Human Microbiome Project (HMP)-defined community Illumina data and then on 21 rhizosphere soil metagenomic datasets from three different crops totaling over 800 Gbp of unassembled data. We compared our method to a recently published bulk metagenome assembly method and a recently published gene-targeted assembler and found our method produced more, longer, and higher quality gene sequences.

Conclusion: Xander combines gene assignment with the rapid assembly of full-length or near full-length functional genes from metagenomic data without requiring bulk assembly or post-processing to find genes of interest. HMMs used for assembly can be tailored to the targeted genes, allowing flexibility to improve annotation over generic annotation pipelines. This method is implemented as open source software and is available at https://github.com/rdpstaff/Xander_assembler.

No MeSH data available.


Related in: MedlinePlus