Bio.Phylo: A unified toolkit for processing, analyzing and visualizing phylogenetic trees in Biopython

Talevich, Eric; Invergo, Brandon M.; Cock, Peter J. A.; Chapman, Brad

doi:10.1186/1471-2105-13-209

Cited by 142 publications

(119 citation statements)

References 32 publications

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“…Otherwise, using the implementation of the smoothed marginal-likelihood estimator of Newton and Raftery (27) in Tracer (28), Bayes factors between the strict and relaxed clock models were calculated, and results from the favored model for each respective data set were carried forward. The first 10% of the sampled trees were disregarded as burn-in, and the remainder imported into Python and converted to the standard Newick format using Biopython's Phylo module (29).…”

Section: Methodsmentioning

confidence: 99%

An Evaluation of Phylogenetic Methods for Reconstructing Transmitted HIV Variants using Longitudinal Clonal HIV Sequence Data

McCloskey

Liang

Harrigan

et al. 2014

J Virol

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A population of human immunodeficiency virus (HIV) within a host often descends from a single transmitted/founder virus. The high mutation rate of HIV, coupled with long delays between infection and diagnosis, make isolating and characterizing this strain a challenge. In theory, ancestral reconstruction could be used to recover this strain from sequences sampled in chronic infection; however, the accuracy of phylogenetic techniques in this context is unknown. To evaluate the accuracy of these methods, we applied ancestral reconstruction to a large panel of published longitudinal clonal and/or single-genome-amplification HIV sequence data sets with at least one intrapatient sequence set sampled within 6 months of infection or seroconversion (n ‫؍‬ 19,486 sequences, median [interquartile range] ‫؍‬ 49 [20 to 86] sequences/set). The consensus of the earliest sequences was used as the best possible estimate of the transmitted/founder. These sequences were compared to ancestral reconstructions from sequences sampled at later time points using both phylogenetic and phylogeny-naive methods. Overall, phylogenetic methods conferred a 16% improvement in reproducing the consensus of early sequences, compared to phylogeny-naive methods. This relative advantage increased with intrapatient sequence diversity (P < 10 ؊5 ) and the time elapsed between the earliest and subsequent samples (P < 10 ؊5 ). However, neither approach performed well for reconstructing ancestral indel variation, especially within indel-rich regions of the HIV genome. Although further improvements are needed, our results indicate that phylogenetic methods for ancestral reconstruction significantly outperform phylogeny-naive alternatives, and we identify experimental conditions and study designs that can enhance accuracy of transmitted/founder virus reconstruction. IMPORTANCEWhen HIV is transmitted into a new host, most of the viruses fail to infect host cells. Consequently, an HIV infection tends to be descended from a single "founder" virus. A priority target for the vaccine research, these transmitted/founder viruses are difficult to isolate since newly infected individuals are often unaware of their status for months or years, by which time the virus population has evolved substantially. Here, we report on the potential use of evolutionary methods to reconstruct the genetic sequence of the transmitted/founder virus from its descendants at later stages of an infection. These methods can recover this ancestral sequence with an overall error rate of about 2.3%-about 15% more information than if we had ignored the evolutionary relationships among viruses. Although there is no substitute for sampling infections at earlier points in time, these methods can provide useful information about the genetic makeup of transmitted/founder HIV.

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Section: Methodsmentioning

confidence: 99%

An Evaluation of Phylogenetic Methods for Reconstructing Transmitted HIV Variants using Longitudinal Clonal HIV Sequence Data

McCloskey

Liang

Harrigan

et al. 2014

J Virol

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“…This alignment was used to create a Maximum Likelihood tree using the RaxML commandline package in Biopython (Stamatakis 2006; Talevich et al 2012). The sequence alignments and phylogenetic trees were then used to calculate the evolutionary rate (dN/dS) using PAML codeml program (seqtype = 1, NSsites = [0], CodonFreq = 2, fix_alpha = 1, kappa = 4.54006, model = 0, RateAncestor = 0), which were generated under a neutral evolution model (Nei and Gojobori 1986; Yang 2007; Talevich et al 2012). When either the dN = 0 or the dS = 0 a value of 0.0001 was automatically assigned by the software; these cases were removed from our analyses, as the assigned value does not accurately reflect the evolutionary rate of the protein.…”

Section: Methodsmentioning

confidence: 99%

Complex Patterns of Association between Pleiotropy and Transcription Factor Evolution

et al. 2016

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Pleiotropy has been claimed to constrain gene evolution but specific mechanisms and extent of these constraints have been difficult to demonstrate. The expansion of molecular data makes it possible to investigate these pleiotropic effects. Few classes of genes have been characterized as intensely as human transcription factors (TFs). We therefore analyzed the evolutionary rates of full TF proteins, along with their DNA binding domains and protein-protein interacting domains (PID) in light of the degree of pleiotropy, measured by the number of TF–TF interactions, or the number of DNA-binding targets. Data were extracted from the ENCODE Chip-Seq dataset, the String v 9.2 database, and the NHGRI GWAS catalog. Evolutionary rates of proteins and domains were calculated using the PAML CodeML package. Our analysis shows that the numbers of TF-TF interactions and DNA binding targets associated with constrained gene evolution; however, the constraint caused by the number of DNA binding targets was restricted to the DNA binding domains, whereas the number of TF-TF interactions constrained the full protein and did so more strongly. Additionally, we found a positive correlation between the number of protein–PIDs and the evolutionary rates of the protein–PIDs. These findings show that not only does pleiotropy associate with constrained protein evolution but the constraint differs by domain function. Finally, we show that GWAS associated TF genes are more highly pleiotropic. The GWAS data illustrates that mutations in highly pleiotropic genes are more likely to be associated with disease phenotypes.

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“…Phylogenies were imported from the Newick tree strings produced by RAxML into Python using the Phylo module from the Biopython package [28], [29]. Branch lengths in each phylogeny were normalized by the mean branch length to facilitate comparisons between viruses with different overall rates of evolution.…”

Section: Methodsmentioning

confidence: 99%

Mapping the Shapes of Phylogenetic Trees from Human and Zoonotic RNA Viruses

et al. 2013

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A phylogeny is a tree-based model of common ancestry that is an indispensable tool for studying biological variation. Phylogenies play a special role in the study of rapidly evolving populations such as viruses, where the proliferation of lineages is constantly being shaped by the mode of virus transmission, by adaptation to immune systems, and by patterns of human migration and contact. These processes may leave an imprint on the shapes of virus phylogenies that can be extracted for comparative study; however, tree shapes are intrinsically difficult to quantify. Here we present a comprehensive study of phylogenies reconstructed from 38 different RNA viruses from 12 taxonomic families that are associated with human pathologies. To accomplish this, we have developed a new procedure for studying phylogenetic tree shapes based on the ‘kernel trick’, a technique that maps complex objects into a statistically convenient space. We show that our kernel method outperforms nine different tree balance statistics at correctly classifying phylogenies that were simulated under different evolutionary scenarios. Using the kernel method, we observe patterns in the distribution of RNA virus phylogenies in this space that reflect modes of transmission and pathogenesis. For example, viruses that can establish persistent chronic infections (such as HIV and hepatitis C virus) form a distinct cluster. Although the visibly ‘star-like’ shape characteristic of trees from these viruses has been well-documented, we show that established methods for quantifying tree shape fail to distinguish these trees from those of other viruses. The kernel approach presented here potentially represents an important new tool for characterizing the evolution and epidemiology of RNA viruses.

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Bio.Phylo: A unified toolkit for processing, analyzing and visualizing phylogenetic trees in Biopython

Cited by 142 publications

References 32 publications

An Evaluation of Phylogenetic Methods for Reconstructing Transmitted HIV Variants using Longitudinal Clonal HIV Sequence Data

An Evaluation of Phylogenetic Methods for Reconstructing Transmitted HIV Variants using Longitudinal Clonal HIV Sequence Data

Complex Patterns of Association between Pleiotropy and Transcription Factor Evolution

Mapping the Shapes of Phylogenetic Trees from Human and Zoonotic RNA Viruses

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