Use of simulated data sets to evaluate the fidelity of metagenomic processing methods

By K. Mavromatis, Natalia N. Ivanova, Kerrie W. Barry, H. Shapiro, E. Goltsman, Alice Carolyn McHardy, Isidore Rigoutsos, A. Salamov, Frank Korzeniewski, M. Land and 5 more. Nature Methods, 2007

347 citations recorded by the source corpus.

Subfields: Ecology, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/cce1bf1c2490759eabf0ceabe9cb2929915e071d

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Citations in the corpus

Identification and assembly of genomes and genetic elements in complex metagenomic samples without using reference genomes (2014, background): “Of the 44 MGS-augmented assemblies, only 16 contained any scaffolds with similarity to an alternative genus.”
CLARK: fast and accurate classification of metagenomic and genomic sequences using discriminative k-mers (2015, background): “We also analyzed the set “simHC” of synthetic reads [19], which simulates high complexity communities lacking dominant populations.”
MetaSim—A Sequencing Simulator for Genomics and Metagenomics (2008, background): “In particular, it is very difficult to assemble reads from highly diverse ecologic systems [9].”
Clustering metagenomic sequences with interpolated Markov models (2010, methodology): “Because these reads come from a diverse population of microbial strains, assembly produces a large collection of small contigs (contiguous stretches of unambiguously overlapping reads) [13,14].”
A Bioinformatician's Guide to Metagenomics (2008, background): “A variety of assembly programs are publicly available, including Phrap (www.phrap.org), Arachne (13, 65), the Celera Assembler (97), PGA (http://www.paracel.com/), and CAP3 (60; for a description and history of these assemblers, we refer the reader to reference 79).”
Phymm and PhymmBL: Metagenomic Phylogenetic Classification with Interpolated Markov Models (2009, positive): “These sets were used to generate the per-group predictive accuracy results presented in Supplementary Figures 1–9.”
Chapter 12: Human Microbiome Analysis (2012, background): “Since microbial genomes typically contain few intergenic sequences, most fragments will contain pieces of one or more genes; these can be used to quantify enzymatic or pathway abundances directly as described below [1,58,59,60].”
MaxBin: an automated binning method to recover individual genomes from metagenomes using an expectation-maximization algorithm (2014, methodology): “For 100-species metagenomes, the settings used by [24] were mimicked, and 100 million paired-end reads were sampled to create three datasets: simLC+, simMC+, and simHC+.”
MetaBAT, an efficient tool for accurately reconstructing single genomes from complex microbial communities (2015, background): “Predicting draft genomes from assembled 27 metagenomic contigs by metagenome binning provides a substitute for full-length genomes 28 (Mande et al. 2012; Mavromatis et al. 2007).”
Assembly Algorithms for Next-Generation Sequencing Data (2010, neutral): “Simulations indicate high rates of chimera, especially in short contigs assembled from complex mixtures [21].”
Orphelia: predicting genes in metagenomic sequencing reads (2009, background): “(13) reported FGENESB to overlook 20% of the genes on single sequencing reads from annotated genomes, and that FGENESB ‘newly predicted’ 10% genes in the same reads.”
Benchmarking Metagenomics Tools for Taxonomic Classification. (2019, background): “Several groups have previously benchmarked metagenomic tools (Lindgreen et al., 2016; Mavromatis et al., 2007; McIntyre et al., 2017; Meyer et al., 2019; Sczyrba et al., 2017), but the continual introduction of newer tools requires ongoing evaluation to compare them against established tools.”