HyPhy: hypothesis testing using phylogenies

By Sergei L. Kosakovsky Pond, Simon D. W. Frost, Spencer V. Muse. Bioinform., 2005

2,535 citations recorded by the source corpus.

Subfields: Genetics, Molecular Biology, Plant Science

View Paper: https://www.semanticscholar.org/paper/6ece27a1817be86fd7019fdeb503309917ebe2d5

How researchers see this paper

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

In Genetics

1. Alexei J. Drummond
2. Andrew Rambaut
3. Marc A. Suchard
4. Philippe Lemey
5. Sergei L. Kosakovsky Pond
6. Guangchuang Yu
7. Jesse D. Bloom
8. Simon D. W. Frost

In Molecular Biology

1. Guangchuang Yu
2. Alexei J. Drummond
3. Marc A. Suchard
4. Sergei L. Kosakovsky Pond
5. Simon D. W. Frost
6. Philippe Lemey
7. Andrew Rambaut
8. Jesse D. Bloom

In Plant Science

1. Sergei L. Kosakovsky Pond
2. Simon D. W. Frost
3. Art F. Y. Poon
4. Jesse D. Bloom
5. Fraser Iain Lewis
6. B. Thyagarajan
7. Ben Murrell
8. Pamela S. Soltis

Citations in the corpus

• Evolution of lacewings and allied orders using anchored phylogenomics (Neuroptera, Megaloptera, Raphidioptera) (2018, methodology): “Graphs of PI values were calculated using HyPhy (Kosakovsky-Pond et al., 2005) in the web application PhyDesign (López-Giráldez & Townsend, 2011),”
• New phylogenetic hypotheses for the core Chlorophyta based on chloroplast sequence data (2014, methodology): “The fast site removal procedure consisted of: (1) inferring a preliminary guide phylogeny with RAxML v.7.3.5 (Stamatakis, 2006) using a GTR+I+ model partitioned into codon positions for coding genes, (2) calculating site-specific evolutionary rates with the “site rates” standard analysis in HyPhy v.”
• Inference of higher-order conifer relationships from a multi-locus plastid data set (2008, methodology): “We used HyPhy (Kosakovsky Pond et al. 2005; and see Burleigh and Mathews 2004) to classify each alignment site into one of nine rate change classes (referred to as RC0-RC8, with RC0 being the zero-rate category and RC8 the fastest).”
• A random effects branch-site model for detecting episodic diversifying selection. (2011, methodology): “The model is implemented as a collection of HyPhy (Kosakovsky Pond et al. 2005) Batch Language scripts and is distributed as a part of HyPhy v2.”
• The inherent mutational tolerance and antigenic evolvability of influenza hemagglutinin (2014, methodology): “These comparisons were used made using HYPHY (Pond et al., 2005) and phyloExpCM (Bloom, 2014).”
• Gene-wide identification of episodic selection. (2015, methodology): “BUSTED is implemented in the HyPhy batch language (Kosakovsky Pond et al. 2005), which performs all likelihood calculations and parameter optimizations.”
• Prevalence of Epistasis in the Evolution of Influenza A Surface Proteins (2011, methodology): “To demonstrate this point, we applied the method of Poon et al [64], implemented in the HyPhy package [72], to the same data set of influenza sequences.”