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CitationDetails.bib
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% Cite this if you use phyloscanner:
@article {Wymant157768,
author = {Wymant, Chris and Hall, Matthew and Ratmann, Oliver and Bonsall, David and Golubchik, Tanya and de Cesare, Mariateresa and Gall, Astrid and Cornelissen, Marion and Fraser, Christophe and STOP-HCV Consortium and The Maela Pneumococcal Collaboration and The BEEHIVE Collaboration},
title = {PHYLOSCANNER: Inferring Transmission from Within- and Between-Host Pathogen Genetic Diversity},
journal = {Molecular Biology and Evolution},
volume = {35},
number = {3},
pages = {719-733},
year = {2018},
doi = {10.1093/molbev/msx304},
URL = {http://dx.doi.org/10.1093/molbev/msx304},
eprint = {/oup/backfile/content_public/journal/mbe/35/3/10.1093_molbev_msx304/3/msx304.pdf}
}
% Cite this if you use phyloscanner_make_trees.py
@article{Li08062009,
author = {Li, Heng and Handsaker, Bob and Wysoker, Alec and Fennell, Tim and Ruan, Jue and Homer, Nils and Marth, Gabor and Abecasis, Goncalo and Durbin, Richard and {1000 Genome Project Data Processing Subgroup}},
title = {The Sequence Alignment/Map (SAM) Format and SAMtools},
year = {2009},
doi = {10.1093/bioinformatics/btp352},
URL = {http://bioinformatics.oxfordjournals.org/content/early/2009/06/08/bioinformatics.btp352.abstract},
journal = {Bioinformatics}
}
% Cite this if you use phyloscanner_make_trees.py
@article{biopython,
author = {Cock, Peter J. A. and Antao, Tiago and Chang, Jeffrey T. and Chapman, Brad A. and Cox, Cymon J. and Dalke, Andrew and Friedberg, Iddo and Hamelryck, Thomas and Kauff, Frank and Wilczynski, Bartek and de Hoon, Michiel J. L.},
title = "{Biopython: freely available Python tools for computational molecular biology and bioinformatics}",
journal = {Bioinformatics},
volume = {25},
number = {11},
pages = {1422-1423},
year = {2009},
month = {03},
issn = {1367-4803},
doi = {10.1093/bioinformatics/btp163},
url = {https://doi.org/10.1093/bioinformatics/btp163},
eprint = {https://academic.oup.com/bioinformatics/article-pdf/25/11/1422/48989335/bioinformatics\_25\_11\_1422.pdf},
}
% Cite this if you use phyloscanner_make_trees.py
@article{Katoh15072002,
author = {Katoh, Kazutaka and Misawa, Kazuharu and Kuma, Kei‐ichi and Miyata, Takashi},
title = {MAFFT: a novel method for rapid multiple sequence alignment based on fast Fourier transform},
volume = {30},
number = {14},
pages = {3059-3066},
year = {2002},
doi = {10.1093/nar/gkf436},
URL = {http://nar.oxfordjournals.org/content/30/14/3059.abstract},
journal = {Nucleic Acids Research}
}
% Cite this if you use RAxML NG as part of phyloscanner_make_trees.py:
@article{10.1093/bioinformatics/btz305,
author = {Kozlov, Alexey M and Darriba, Diego and Flouri, Tomáš and Morel, Benoit and Stamatakis, Alexandros},
title = "{RAxML-NG: a fast, scalable and user-friendly tool for maximum likelihood phylogenetic inference}",
journal = {Bioinformatics},
volume = {35},
number = {21},
pages = {4453-4455},
year = {2019},
month = {05},
abstract = "{Phylogenies are important for fundamental biological research, but also have numerous applications in biotechnology, agriculture and medicine. Finding the optimal tree under the popular maximum likelihood (ML) criterion is known to be NP-hard. Thus, highly optimized and scalable codes are needed to analyze constantly growing empirical datasets.We present RAxML-NG, a from-scratch re-implementation of the established greedy tree search algorithm of RAxML/ExaML. RAxML-NG offers improved accuracy, flexibility, speed, scalability, and usability compared with RAxML/ExaML. On taxon-rich datasets, RAxML-NG typically finds higher-scoring trees than IQTree, an increasingly popular recent tool for ML-based phylogenetic inference (although IQ-Tree shows better stability). Finally, RAxML-NG introduces several new features, such as the detection of terraces in tree space and the recently introduced transfer bootstrap support metric.The code is available under GNU GPL at https://github.com/amkozlov/raxml-ng. RAxML-NG web service (maintained by Vital-IT) is available at https://raxml-ng.vital-it.ch/.Supplementary data are available at Bioinformatics online.}",
issn = {1367-4803},
doi = {10.1093/bioinformatics/btz305},
url = {https://doi.org/10.1093/bioinformatics/btz305},
eprint = {https://academic.oup.com/bioinformatics/article-pdf/35/21/4453/50721688/bioinformatics\_35\_21\_4453.pdf},
}
% Cite this if you use RAxML-standard as part of phyloscanner_make_trees.py:
@article{doi:10.1093/bioinformatics/btu033,
author = {Stamatakis, Alexandros},
title = {RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies},
journal = {Bioinformatics},
volume = {30},
number = {9},
pages = {1312-1313},
year = {2014},
doi = {10.1093/bioinformatics/btu033},
URL = { + http://dx.doi.org/10.1093/bioinformatics/btu033},
eprint = {/oup/backfile/content_public/journal/bioinformatics/30/9/10.1093_bioinformatics_btu033/3/btu033.pdf}
}
% Cite this if you use IQtree as part of phyloscanner_make_trees.py:
@article{10.1093/molbev/msu300,
author = {Nguyen, Lam-Tung and Schmidt, Heiko A. and von Haeseler, Arndt and Minh, Bui Quang},
title = "{IQ-TREE: A Fast and Effective Stochastic Algorithm for Estimating Maximum-Likelihood Phylogenies}",
journal = {Molecular Biology and Evolution},
volume = {32},
number = {1},
pages = {268-274},
year = {2014},
month = {11},
abstract = "{Large phylogenomics data sets require fast tree inference methods, especially for maximum-likelihood (ML) phylogenies. Fast programs exist, but due to inherent heuristics to find optimal trees, it is not clear whether the best tree is found. Thus, there is need for additional approaches that employ different search strategies to find ML trees and that are at the same time as fast as currently available ML programs. We show that a combination of hill-climbing approaches and a stochastic perturbation method can be time-efficiently implemented. If we allow the same CPU time as RAxML and PhyML, then our software IQ-TREE found higher likelihoods between 62.2\\% and 87.1\\% of the studied alignments, thus efficiently exploring the tree-space. If we use the IQ-TREE stopping rule, RAxML and PhyML are faster in 75.7\\% and 47.1\\% of the DNA alignments and 42.2\\% and 100\\% of the protein alignments, respectively. However, the range of obtaining higher likelihoods with IQ-TREE improves to 73.3–97.1\\%. IQ-TREE is freely available at http://www.cibiv.at/software/iqtree.}",
issn = {0737-4038},
doi = {10.1093/molbev/msu300},
url = {https://doi.org/10.1093/molbev/msu300},
eprint = {https://academic.oup.com/mbe/article-pdf/32/1/268/13171186/msu300.pdf},
}
% Cite this if you make pdf tree images using phyloscanner_analyse_trees.R:
@article {MEE3:MEE312628,
author = {Yu, Guangchuang and Smith, David K. and Zhu, Huachen and Guan, Yi and Lam, Tommy Tsan-Yuk},
title = {ggtree: an r package for visualization and annotation of phylogenetic trees with their covariates and other associated data},
journal = {Methods in Ecology and Evolution},
volume = {8},
number = {1},
issn = {2041-210X},
url = {http://dx.doi.org/10.1111/2041-210X.12628},
doi = {10.1111/2041-210X.12628},
pages = {28--36},
keywords = {annotation, bioconductor, evolution, phylogeny, r package, visualization},
year = {2017},
}
% Cite this if you use the --multinomial option of phyloscanner_analyse_trees.R:
@ARTICLE{Ratmann2019-eg,
title = "Inferring {HIV-1} transmission networks and sources of epidemic
spread in Africa with deep-sequence phylogenetic analysis",
author = "Ratmann, Oliver and Grabowski, M Kate and Hall, Matthew and
Golubchik, Tanya and Wymant, Chris and Abeler-D{\"o}rner, Lucie
and Bonsall, David and Hoppe, Anne and Brown, Andrew Leigh and de
Oliveira, Tulio and Gall, Astrid and Kellam, Paul and Pillay,
Deenan and Kagaayi, Joseph and Kigozi, Godfrey and Quinn, Thomas
C and Wawer, Maria J and Laeyendecker, Oliver and Serwadda, David
and Gray, Ronald H and Fraser, Christophe and {PANGEA Consortium
and Rakai Health Sciences Program}",
abstract = "To prevent new infections with human immunodeficiency virus type
1 (HIV-1) in sub-Saharan Africa, UNAIDS recommends targeting
interventions to populations that are at high risk of acquiring
and passing on the virus. Yet it is often unclear who and where
these 'source' populations are. Here we demonstrate how viral
deep-sequencing can be used to reconstruct HIV-1 transmission
networks and to infer the direction of transmission in these
networks. We are able to deep-sequence virus from a large
population-based sample of infected individuals in Rakai
District, Uganda, reconstruct partial transmission networks, and
infer the direction of transmission within them at an estimated
error rate of 16.3\% [8.8-28.3\%]. With this error rate,
deep-sequence phylogenetics cannot be used against individuals in
legal contexts, but is sufficiently low for population-level
inferences into the sources of epidemic spread. The technique
presents new opportunities for characterizing source populations
and for targeting of HIV-1 prevention interventions in Africa.",
journal = "Nature Communications",
volume = 10,
number = 1,
pages = "1411",
month = mar,
year = 2019,
language = "en"
}
@ARTICLE{Romero-Severson2016-pb,
title = "Phylogenetically resolving epidemiologic linkage",
author = "Romero-Severson, Ethan O and Bulla, Ingo and Leitner, Thomas",
abstract = "Although the use of phylogenetic trees in epidemiological
investigations has become commonplace, their epidemiological
interpretation has not been systematically evaluated. Here, we
use an HIV-1 within-host coalescent model to probabilistically
evaluate transmission histories of two epidemiologically linked
hosts. Previous critique of phylogenetic reconstruction has
claimed that direction of transmission is difficult to infer, and
that the existence of unsampled intermediary links or common
sources can never be excluded. The phylogenetic relationship
between the HIV populations of epidemiologically linked hosts can
be classified into six types of trees, based on cladistic
relationships and whether the reconstruction is consistent with
the true transmission history or not. We show that the direction
of transmission and whether unsampled intermediary links or
common sources existed make very different predictions about
expected phylogenetic relationships: (i) Direction of
transmission can often be established when paraphyly exists, (ii)
intermediary links can be excluded when multiple lineages were
transmitted, and (iii) when the sampled individuals' HIV
populations both are monophyletic a common source was likely the
origin. Inconsistent results, suggesting the wrong transmission
direction, were generally rare. In addition, the expected tree
topology also depends on the number of transmitted lineages, the
sample size, the time of the sample relative to transmission, and
how fast the diversity increases after infection. Typically, 20
or more sequences per subject give robust results. We confirm our
theoretical evaluations with analyses of real transmission
histories and discuss how our findings should aid in interpreting
phylogenetic results.",
journal = "Proc. Natl. Acad. Sci. U. S. A.",
volume = 113,
number = 10,
pages = "2690--2695",
month = mar,
year = 2016,
language = "en"
}