Tackling soil diversity with the assembly of large, complex metagenomes

By R. Willette, J. Lepore, Joseph P. Marino, C. Schnackenberg, Jeremy P. Huynh, James E. Crowe Jr., B. Doranz, S. Malfatti, Susannah Tringe, James M. Tiedje and 5 more. 2014

308 citations recorded by the source corpus.

Subfields: Ecology, Molecular Biology, Computational Biology & Bioinformatics

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Citations in the corpus

MMseqs2: sensitive protein sequence searching for the analysis of massive data sets (2017, background): “of sequences are being performed, such as the genome 10K project [11] and metagenomic and metatranscriptomic studies with applications in medical, biotechnological, microbiological, and agricultural research [1, 3, 8, 13, 25].”
metaSPAdes: a new versatile metagenomic assembler. (2017, methodology): “For example, since all metagenomic assemblers available in 2014 failed to process a large soil dataset with 3.3 billion reads, Howe et al. (2014) preprocessed the data by performing digital normalization of coverage depth; constructing the probabilistic de Bruijn graph (Pell et al. 2012) based on…”
A meta-analysis of the effect of organic and mineral fertilizers on soil microbial diversity (2020, background): “…potential appears greater in many organic than conventional crop systems (Clark and Tilman, 2017) Soils are exceedingly complex and biologically-diverse ecosystems, varying in physico-chemical and biological composition across spatial scales (Howe et al., 2014; Thompson et al., 2017).”
Assembling metagenomes, one community at a time (2017, methodology): “Eight of the assemblers tested here use command-line interfaces (CLI) and are open-source freeware (Velvet, MetaVelvet, SPAdes, metaSPAdes, Ray Meta, IDBA-UD, MEGAHIT and Omega).”
Current challenges and best-practice protocols for microbiome analysis (2019, background): “and intragenomic repeats and uneven sequencing coverage [78, 79].”
The ecologist's field guide to sequence‐based identification of biodiversity (2016, unknown): “mental data sets we are able to generate (Howe et al. 2014).”
MEGAN Community Edition - Interactive Exploration and Analysis of Large-Scale Microbiome Sequencing Data (2016, background): “Metagenome assembly from Illumina reads is considered a difficult problem [30], although progress is being made [31].”
Computational pan-genomics: status, promises and challenges (2016, background): “Abundant metagenomic data are currently being generated from various environments such as human hosts [12, 31], the world’s oceans [32, 33] and soil [34].”
MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph (2014, background): “This step is, however, constrained by the heavy requirement of computational resources, especially for large and complex datasets encountered in environmental metagenomics (Howe, et al., 2014).”