PhyloSift: phylogenetic analysis of genomes and metagenomes

By Aaron E. Darling, Guillaume Jospin, E. Lowe, Frederick Albert Matsen IV, Holly M. Bik, Jonathan A. Eisen. PeerJ, 2014

575 citations recorded by the source corpus.

Like all organisms on the planet, environmental microbes are subject to the forces of molecular evolution. Metagenomic sequencing provides a means to access the DNA sequence of uncultured microbes. By combining DNA sequencing of microbial communities with evolutionary modeling and phylogenetic analysis we might obtain new insights into microbiology and also provide a basis for practical tools such as forensic pathogen detection. In this work we present an approach to leverage phylogenetic analysis of metagenomic sequence data to conduct several types of analysis. First, we present a method to conduct phylogeny-driven Bayesian hypothesis tests for the presence of an organism in a sample. Second, we present a means to compare community structure across a collection of many samples and develop direct associations between the abundance of certain organisms and sample metadata. Third, we apply new tools to analyze the phylogenetic diversity of microbial communities and again demonstrate how this can be associated to sample metadata. These analyses are implemented in an open source software pipeline called PhyloSift. As a pipeline, PhyloSift incorporates several other programs including LAST, HMMER, and pplacer to automate phylogenetic analysis of protein coding and RNA sequences in metagenomic datasets generated by modern sequencing platforms (e.g., Illumina, 454).

Subfields: Ecology, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/a3da45151dfa3abc449aa3659ce0dbacaa04ae1c

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Citations in the corpus

Characterization of the Gut Microbiome Using 16S or Shotgun Metagenomics (2016, background): “For shotgun libraries, we compared BLAST top hits, the MEtaGenome ANalyzer MEGAN5, and Metagenomic Phylogenetic Analysis (MetaPhlAn) approaches; however, we do acknowledge that many other excellent tools have also been developed, including PhymmBL (Brady and Salzberg, 2009, 2011), PhyloSift (Darling”
Linking pangenomes and metagenomes: the Prochlorococcus metapangenome (2018, background): “0.1 ( Darling et al., 2014 ) with default parameters to quantify evolutionary distances between genomes.”
The ecologist's field guide to sequence‐based identification of biodiversity (2016, methodology): “For example, ubiquitous loci such as rRNA genes or conserved single-copy orthologs (representing ~ 1% of metagenomic sequence reads) can be mined and analysed using phylogenetic workﬂows (Sunagawa et al. 2013; Darling et al. 2014) and tree-based metrics such as ‘Edge PCA’ (Fig.”
KrakenUniq: confident and fast metagenomics classification using unique k-mer counts (2018, methodology): “[20], CLARK-S [21], Kraken [9], LMAT [22], and NBC [23]; and the marker-based methods GOTTCHA [24], MetaPhlAn2 [25], and PhyloSift [26].”
Tracking microbial colonization in fecal microbiota transplantation experiments via genome-resolved metagenomics (2017, background): “1 [46] to determine the phylogenomic relationships between our MAGs and a collection of 1758 reference genomes (with no redundancy at the species level) that we acquired from the Ensembl database [47] (Additional file 1: Table S3).”
DNA Sequencing as a Tool to Monitor Marine Ecological Status (2017, methodology): “, 2010), and using a combination of methods (Darling et al., 2014; Dröge et al., 2015).”
UBCG: Up-to-date bacterial core gene set and pipeline for phylogenomic tree reconstruction (2018, methodology): “More recently, two phylogenomic software tools, namely Phylosift (Darling et al., 2014) and bcgTree (Ankenbrand and Keller, 2016), used these gene sets respectively.”
CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes (2015, methodology): “Although this increasing breadth of draft genomes is providing key information regarding the evolutionary and functional diversity of microbial life, it has become impractical to finish all available reference genomes.”
Machine learning for metagenomics: methods and tools (2015, methodology): “pplacer is used as a component of the popular PhyloSift (Darling et al., 2014) suite, that uses a reference database of gene families, aligns all of the input reads against this database and then calculates the posterior probability that the read diverged from particular branches of the reference tr”
A review of methods and databases for metagenomic classification and assembly (2019, methodology): “The assignment can then be made with fast, sensitive aligners, such as Bowtie2 [85] used by MetaPhlAn and HMMER [86] used by Phylosift [87] and mOTU [82].”
Current challenges and best-practice protocols for microbiome analysis (2019, methodology): “Eventually, several fast aligners like Bowtie2 [108] and HMMER [109] are utilized by several other tools, such as MetaPhlAn [110], Phylosift [111] and mOTU [112].”