Meta-IDBA: a <i>de Novo</i> assembler for metagenomic data

Yu, Peng; Leung, Ho-fung; Yiu, Siu-Ming; Chin, Francis Y. L.

doi:10.1093/bioinformatics/btr216

Cited by 281 publications

(194 citation statements)

References 23 publications

(40 reference statements)

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“…The MG-RAST quality control-passed reads were directly assembled using IDBA-ud (Peng et al, 2011) and only contigs longer than 200 bp were analyzed. Average coverage for each contig was calculated as the IDBA header designated read count multiplied by the average sequence length and divided by the length of the contig.…”

Section: Metagenome Assembly and Annotationmentioning

confidence: 99%

A metagenomic window into carbon metabolism at 3 km depth in Precambrian continental crust

et al. 2015

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Subsurface microbial communities comprise a significant fraction of the global prokaryotic biomass; however, the carbon metabolisms that support the deep biosphere have been relatively unexplored. In order to determine the predominant carbon metabolisms within a 3-km deep fracture fluid system accessed via the Tau Tona gold mine (Witwatersrand Basin, South Africa), metagenomic and thermodynamic analyses were combined. Within our system of study, the energy-conserving reductive acetyl-CoA (Wood-Ljungdahl) pathway was found to be the most abundant carbon fixation pathway identified in the metagenome. Carbon monoxide dehydrogenase genes that have the potential to participate in (1) both autotrophic and heterotrophic metabolisms through the reversible oxidization of CO and subsequent transfer of electrons for sulfate reduction, (2) direct utilization of H 2 and (3) methanogenesis were identified. The most abundant members of the metagenome belonged to Euryarchaeota (22%) and Firmicutes (57%)-by far, the highest relative abundance of Euryarchaeota yet reported from deep fracture fluids in South Africa and one of only five Firmicutes-dominated deep fracture fluids identified in the region. Importantly, by combining the metagenomics data and thermodynamic modeling of this study with previously published isotopic and community composition data from the South African subsurface, we are able to demonstrate that Firmicutes-dominated communities are associated with a particular hydrogeologic environment, specifically the older, more saline and more reducing waters.

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Section: Metagenome Assembly and Annotationmentioning

confidence: 99%

A metagenomic window into carbon metabolism at 3 km depth in Precambrian continental crust

et al. 2015

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“…This exploits a convenient biological feature of metagenomic samples: They contain many microbes that should not assemble together. Graph partitioning has been used to improve the quality of metagenome and transcriptome assemblies by adapting assembly parameters to local coverage of the graph (23)(24)(25). However, to our knowledge, partitioning has not been applied to scaling metagenome assembly.…”

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confidence: 99%

Scaling metagenome sequence assembly with probabilistic de Bruijn graphs

Pell

Hintze

Canino-Koning

et al. 2012

Proc. Natl. Acad. Sci. U.S.A.

220

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Deep sequencing has enabled the investigation of a wide range of environmental microbial ecosystems, but the high memory requirements for de novo assembly of short-read shotgun sequencing data from these complex populations are an increasingly large practical barrier. Here we introduce a memory-efficient graph representation with which we can analyze the k-mer connectivity of metagenomic samples. The graph representation is based on a probabilistic data structure, a Bloom filter, that allows us to efficiently store assembly graphs in as little as 4 bits per k-mer, albeit inexactly. We show that this data structure accurately represents DNA assembly graphs in low memory. We apply this data structure to the problem of partitioning assembly graphs into components as a prelude to assembly, and show that this reduces the overall memory requirements for de novo assembly of metagenomes. On one soil metagenome assembly, this approach achieves a nearly 40-fold decrease in the maximum memory requirements for assembly. This probabilistic graph representation is a significant theoretical advance in storing assembly graphs and also yields immediate leverage on metagenomic assembly. metagenomics | compression D e novo assembly of shotgun sequencing reads into longer contiguous sequences plays an important role in virtually all genomic research (1). However, current computational methods for sequence assembly do not scale well to the volume of sequencing data now readily available from next-generation sequencing machines (1, 2). In particular, the deep sequencing required to sample complex microbial environments easily results in datasets that surpass the working memory of available computers (3, 4).Deep sequencing and assembly of short reads is particularly important for the sequencing and analysis of complex microbial ecosystems, which can contain millions of different microbial species (5, 6). These ecosystems mediate important biogeochemical processes but are still poorly understood at a molecular level, in large part because they consist of many microbes that cannot be cultured or studied individually in the lab (5, 7). Ensemble sequencing ("metagenomics") of these complex environments is one of the few ways to render them accessible, and has resulted in substantial early progress in understanding the microbial composition and function of the ocean, human gut, cow rumen, and permafrost soil (3,4,8,9). However, as sequencing capacity grows, the assembly of sequences from these complex samples has become increasingly computationally challenging. Current methods for short-read assembly rely on inexact data reduction in which reads from low-abundance organisms are discarded, biasing analyses towards high-abundance organisms (3, 4, 9).The predominant assembly formalism applied to short-read sequencing datasets is a de Bruijn graph (10-12). In a de Bruijn graph approach, sequencing reads are decomposed into fixedlength words, or k-mers, and used to build a connectivity graph. This graph is then traversed to determine contiguous...

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“…The common k-mers were assembled using Minimo from the amos package (version 1.5; http://sourceforge.net/ projects/amos/ and Treangen et al, 2002) at 100% nucleotide identity and an overlap length of 50% and the adapter sequences were trimmed with Trimmomatic. Following trimming, DNA sequences were assembled using IDBA-UD (Peng et al, 2011(Peng et al, ,2012 using a k-mer range of 28-124 and default parameters.…”

Section: Sample Collection For Illumina Data Setsmentioning

confidence: 99%

Viral assemblage composition in Yellowstone acidic hot springs assessed by network analysis

et al. 2015

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Understanding of viral assemblage structure in natural environments remains a daunting task. Total viral assemblage sequencing (for example, viral metagenomics) provides a tractable approach. However, even with the availability of next-generation sequencing technology it is usually only possible to obtain a fragmented view of viral assemblages in natural ecosystems. In this study, we applied a network-based approach in combination with viral metagenomics to investigate viral assemblage structure in the high temperature, acidic hot springs of Yellowstone National Park, USA. Our results show that this approach can identify distinct viral groups and provide insights into the viral assemblage structure. We identified 110 viral groups in the hot springs environment, with each viral group likely representing a viral family at the sub-family taxonomic level. Most of these viral groups are previously unknown DNA viruses likely infecting archaeal hosts. Overall, this study demonstrates the utility of combining viral assemblage sequencing approaches with network analysis to gain insights into viral assemblage structure in natural ecosystems.

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Meta-IDBA: a de Novo assembler for metagenomic data

Cited by 281 publications

References 23 publications

A metagenomic window into carbon metabolism at 3 km depth in Precambrian continental crust

A metagenomic window into carbon metabolism at 3 km depth in Precambrian continental crust

Scaling metagenome sequence assembly with probabilistic de Bruijn graphs

Viral assemblage composition in Yellowstone acidic hot springs assessed by network analysis

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