Accurate phylogenetic classification of variable-length DNA fragments

By Alice Carolyn McHardy, Héctor García Martín, Aristotelis Tsirigos, Philip Hugenholtz, Isidore Rigoutsos. 2007

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Citations in the corpus

MetaSim—A Sequencing Simulator for Genomics and Metagenomics (2008, methodology): “Common strategies for taxonomical binning are for example: (1) detecting phylogenetic markers like rRNA, RecA, heat shock protein (HSP70) and elongation factors (EF-Tu, EF-G) [11], (2) comparing reads against a reference database such as NCBI-nr [12] and then analyzing the matches to place the reads”
The Road to Metagenomics: From Microbiology to DNA Sequencing Technologies and Bioinformatics (2015, methodology): “Examples of software that perform sequence classiﬁcation by composition are TETRA (Teeling et al., 2004), PhylophytiaS (McHardy et al., 2007), and MetaclusterTA (Wang et al., 2014) ( Table 2 ).”
Metagenomic Analyses: Past and Future Trends (2010, methodology): “Tools such as PhyloPythia (76), TETRA (121, 122), and the taxonomic composition analysis method (TACOA) (23) allow direct classification of short single reads.”
Barcodes for genomes and applications (2008, background): “We have compared our binning performance with the published results by the best available algorithm PhyloPythia [5].”
WebMGA: a customizable web server for fast metagenomic sequence analysis (2011, methodology): “To address these problems, many meth-ods have been developed such as taxonomy binning [9-11], use of simulated datasets [12], diversity analysis [13], ORF calling [14,15], rRNA prediction [16], sequence clustering [17-20], assembly [21], statistical comparison [22], fragment recruitment [1,8,23]…”
Machine learning for metagenomics: methods and tools (2015, methodology): “In a similar fashion, PhyloPithia and PhyloPithiaS (McHardy et al., 2007; Patil et al., 2012) learn Support Vector Machine classifiers based on k-mer frequencies associated with different taxonomic groups.”
Clustering metagenomic sequences with interpolated Markov models (2010, methodology): “Composition-based classification methods typically train on the oligonucleotide frequencies of all known genomes, and then classify sequences using supervised machine learning such as kernelized nearest neighbor [34], support vector machines [35], self-organizing maps [36], and naive Bayesian classi”
pplacer: linear time maximum-likelihood and Bayesian phylogenetic placement of sequences onto a fixed reference tree (2010, background): “PhyloPythia [11], TACOA [12], and Phymm [13] use composition based methods to assign taxonomic information to metagenomic sequences.”
Chapter 12: Human Microbiome Analysis (2012, unknown): “Conversely, de novo methods have been developed to directly bin [67,68,69] and call genes within [61,62] metagenomic sequences using DNA features alone (GC content, codon usage, etc.”
NBC: the Naïve Bayes Classification tool webserver for taxonomic classification of metagenomic reads (2010, methodology): “Results: We introduce a webserver that implements the naïve Bayes classifier (NBC) to classify all metagenomic reads to their best taxonomic match.”
Large-scale machine learning for metagenomics sequence classification (2015, methodology): “1023 This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited”
Performance, Accuracy, and Web Server for Evolutionary Placement of Short Sequence Reads under Maximum Likelihood (2011, unknown): “This also hinders direct comparison with other previous phylogenetic classification methods like PhyloPythia (McHardy et al. 2007), which is the only phylogenetic classifier that has been compared with Phymm (Brady and Salzberg 2009 show that Phymm and BLAST substantially outperform PhyloPythia) and”
A Primer on Metagenomics (2010, methodology): “PhyloPythia [84] is a supervised method that trains a set of support vector machines (SVMs) to bin sequences of a length greater than 1 kb, and thus not suitable for binning second generation sequences.”
A Bioinformatician's Guide to Metagenomics (2008, background): “population (95), often increasing the amount of classified sam-”