MetAMOS: a metagenomic assembly and analysis pipeline for AMOS

Treangen, Todd J.; Koren, Sergey; Astrovskaya, Irina; Sommer, Dan; Liu, Bo; Pop, Mihai

doi:10.1186/gb-2011-12-s1-p25

Cited by 13 publications

(4 citation statements)

References 11 publications

(9 reference statements)

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“…Initial analysis by 16S sequencing detected over 70 bacterial families at relative abundance levels as low as 4×10 −6% (Supplementary Table 7). Analysis of marker genes in each partition by MetaPhyler 10 indicated statistically significant enrichment of microbial families across six orders of magnitude in relative abundance (Fig. 4, Supplementary Table 8).…”

Section: Resultsmentioning

confidence: 99%

“…Tools such as MetAMOS 10 , MetaVelvet 11 , Meta-IDBA 12 , Ray Meta 13 , and diginorm with khmer 14,15 relax the assumptions of single-genome de Bruijn assemblers to allow multiple coverage / multiple strain assembly and have produced improved results compared with standard de Bruijn assemblies such as those produced by Velvet 23 . However, early meta-assemblers cannot scale to terabyte data sets; in practice it can be a challenge to find the compute (RAM) resources to process even a single, 100Gb sample.…”

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confidence: 99%

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Detection of low-abundance bacterial strains in metagenomic datasets by eigengenome partitioning

et al. 2015

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Analyses of metagenomic datasets that are sequenced to a depth of billions or trillions of bases can uncover hundreds of microbial genomes, but naive assembly of these data is computationally intensive, requiring hundreds of gigabytes to terabytes of RAM. This is a bottleneck in many studies, especially when very deep sequencing is needed to detect low-abundance species and separate strains of the same species. We present Latent Strain Analysis (LSA), a scalable, de novo pre-assembly method that separates reads into biologically informed partitions and thereby enables assembly of individual genomes. LSA is implemented with a streaming calculation of unobserved variables that we call eigengenomes. Eigengenomes reflect covariance in the abundance of short, fixed length sequences, or “k-mers”. Since the abundance of each genome in a sample is reflected in the abundance of each k-mer in that genome, eigengenome analysis can be used to partition reads from different genomes. This partitioning can be done in fixed memory using tens of gigabytes of RAM, which makes assembly and downstream analyses of terabytes of data feasible on commodity hardware. Using LSA, we assemble partial and near-complete genomes of bacterial taxa present at relative abundances as low as 0.00001%. We also show that LSA is sensitive enough to separate reads from several strains of the same species.

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Section: Resultsmentioning

confidence: 99%

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confidence: 99%

Detection of low-abundance bacterial strains in metagenomic datasets by eigengenome partitioning

et al. 2015

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“…Consequently, scientists/bioinformaticians in this field need to operate, merge and interpret results from various tools, which for larger data sets can be a daunting task. In terms of pipelines, MetaAMOS [ 137 ] provides an integrated solution for the initial post-processing of mated read metagenome data that supports different assemblers, the BAMBUS 2 scaffolder and various gene prediction, annotation and taxonomic classification tools. In terms of data integration, CAMERA has so far developed the most comprehensive infrastructure for holistic metagenome analyses, and further tools and pipelines are currently developed in the GSC and Micro B3 project ( http://www.microb3.eu/ ) frameworks.…”

Section: Automatization Standardization and Contextual Datamentioning

confidence: 99%

Current opportunities and challenges in microbial metagenome analysis--a bioinformatic perspective

Teeling¹,

Glöckner²

2012

Briefings in Bioinformatics

190

130

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Metagenomics has become an indispensable tool for studying the diversity and metabolic potential of environmental microbes, whose bulk is as yet non-cultivable. Continual progress in next-generation sequencing allows for generating increasingly large metagenomes and studying multiple metagenomes over time or space. Recently, a new type of holistic ecosystem study has emerged that seeks to combine metagenomics with biodiversity, meta-expression and contextual data. Such ‘ecosystems biology’ approaches bear the potential to not only advance our understanding of environmental microbes to a new level but also impose challenges due to increasing data complexities, in particular with respect to bioinformatic post-processing. This mini review aims to address selected opportunities and challenges of modern metagenomics from a bioinformatics perspective and hopefully will serve as a useful resource for microbial ecologists and bioinformaticians alike.

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“…a). Although challenging in data sets from more complex microbial communities and for organisms with significant strain heterogeneity, this approach is expected to scale favorably with increased sequencing depth and advancements in assembly of metagenomic data (Mavromatis et al ., ; Namiki et al ., ; Peng et al ., ; Treangen et al ., ; Wrighton et al ., ). One way to enhance the targeting of organisms associated with a particular community function is through the use of enrichment culture under a condition designed to bloom organisms associated with a function of interest and/or to select against other organisms prior to the collection of a ‘targeted metagenomic’ data set (Hess et al ., ; Fig.…”

Section: Introductionmentioning

confidence: 99%

The future is now: single-cell genomics of bacteria and archaea

2013

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Interest in the expanding catalog of uncultivated microorganisms, increasing recognition of heterogeneity among seemingly similar cells, and technological advances in whole-genome amplification and single-cell manipulation are driving considerable progress in single-cell genomics. Here, the spectrum of applications for single-cell genomics, key advances in the development of the field, and emerging methodology for single-cell genome sequencing are reviewed by example with attention to the diversity of approaches and their unique characteristics. Experimental strategies transcending specific methodologies are identified and organized as a road map for future studies in single-cell genomics of environmental microorganisms. Over the next decade, increasingly powerful tools for single-cell genome sequencing and analysis will play key roles in accessing the genomes of uncultivated organisms, determining the basis of microbial community functions, and fundamental aspects of microbial population biology.

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MetAMOS: a metagenomic assembly and analysis pipeline for AMOS

Cited by 13 publications

References 11 publications

Detection of low-abundance bacterial strains in metagenomic datasets by eigengenome partitioning

Detection of low-abundance bacterial strains in metagenomic datasets by eigengenome partitioning

Current opportunities and challenges in microbial metagenome analysis--a bioinformatic perspective

The future is now: single-cell genomics of bacteria and archaea

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