A simple, fast, and accurate method of phylogenomic inference

By Martin Wu, Jonathan A. Eisen. Genome Biology, 2008

513 citations recorded by the source corpus.

The explosive growth of genomic data provides an opportunity to make increased use of protein markers for phylogenetic inference. We have developed an automated pipeline for phylogenomic analysis (AMPHORA) that overcomes the existing bottlenecks limiting large-scale protein phylogenetic inference. We demonstrated its high throughput capabilities and high quality results by constructing a genome tree of 578 bacterial species and by assigning phylotypes to 18,607 protein markers identified in metagenomic data collected from the Sargasso Sea.

Subfields: Ecology, Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/f84ee8b21c0b32a524b85a529396c0de02ca7dde

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Ecology

In Genetics

In Molecular Biology

Citations in the corpus

454 Pyrosequencing analyses of forest soils reveal an unexpectedly high fungal diversity. (2009, background): “…MEGAN, including the lowest common ancestor (LCA) assignment, were kept at default values, except for the ‘min support’ option (regulating the minimum number of sequence reads that must be assigned to a taxon), which was set to either unity or five depending on the analysis (cf. Wu & Eisen, 2008).”
Resurrection and emendation of the Hypoxylaceae, recognised from a multigene phylogeny of the Xylariales (2017, methodology): “The best fitting substitution model for each single gene alignment and the MGAwas determined using jModeltest 2.1.7 (Darriba et al. 2012; Guindon and Gascuel 2003).”
The Road to Metagenomics: From Microbiology to DNA Sequencing Technologies and Bioinformatics (2015, methodology): “…using these approaches are MOCAT (Kultima et al., 2012), which uses the RefMG database (Ciccarelli et al., 2006) constituted by a collection of 40 single copy marker genes, and AMPHORA (Wu and Eisen, 2008), which includes a database containing around 31 single copy universal markers ( Table 2 ).”
Phylogeography of Xiphorhynchus fuscus (Passeriformes, Dendrocolaptidae): vicariance and recent demographic expansion in southern Atlantic forest (2007, background): “Lepidocolaptes squamatus and Lepidocolaptes falcinellus, Silva & Straube, 1996; Heliobletus contaminatus contaminatus and Heliobletus contaminatus camargoi; Silva & Stotz, 1992) and marsupials (i.e. Marmosops paulensis and Marmosops incanus; Mustrangi & Patton, 1997), and it separates mitochondrial ”
PhyloSift: phylogenetic analysis of genomes and metagenomes (2014, methodology): “A variety of computational approaches are now available to investigate115 the community composition of metagenome datasets, including: AMPHORA (bacterial protein markers116 and tree insertion via parsimony) [66] and AMPHORA2 (bacterial/archael protein and DNA markers117 and tree insertion via likeli”
Bayesian Inference of Species Trees from Multilocus Data (2009, background): “2010 doi:10.1093/molbev/msp274 Advance Access publication November 11, 2009 this common practice of concatenation can result in a well supported but incorrect tree, it is still a widely usedmethod (Rokas et al. 2003; Wu and Eisen 2008), largely because of a lack of alternatives.”
Incorporating 16S Gene Copy Number Information Improves Estimates of Microbial Diversity and Abundance (2012, methodology): “[20], PCR amplification of 16S genes is still the method of choice in many environmental surveys.”
Clustering metagenomic sequences with interpolated Markov models (2010, methodology): “In some cases, the presence of marker genes like 16 S rRNA, which is very highly conserved across species but has variable regions, can be used to assign a taxonomic class to sequence fragments [16,17], but this typically pertains to only a very small percentage of the reads.”
Metagenomic analysis of a permafrost microbial community reveals a rapid response to thaw (2011, unknown): “For the single-copy gene analysis, we selected 19 protein-encoding genes ( rpl1p , rpl2p , rpl3p , rpl4lp , rpl5p , rpl6p , rpl11p , rpl13 , rpl14p , rpl19e , rps2p , rps5p, rps9p , rps10p , rps11p , rps13p , dnaG , pyrG , nusA ) that were identified previously by Wu and Eise”