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Variational Combinatorial Sequential Monte Carlo Methods for Bayesian Phylogenetic Inference [article]

Antonio Khalil Moretti, Liyi Zhang, Christian A. Naesseth, Hadiah Venner, David Blei, Itsik Pe'er
2021 arXiv   pre-print
Bayesian phylogenetic inference is often conducted via local or sequential search over topologies and branch lengths using algorithms such as random-walk Markov chain Monte Carlo (MCMC) or Combinatorial  ...  We introduce Variational Combinatorial Sequential Monte Carlo (VCSMC), a powerful framework that establishes variational sequential search to learn distributions over intricate combinatorial structures  ...  Acknowledgements We thank the reviewers for their helpful feedback. We acknowledge funding from NIH/NCI grant U54CA209997 and two NIH shared instrumentation grants, S10 OD012351 and S10 OD021764.  ... 
arXiv:2106.00075v2 fatcat:jnz5etloqzawxfcaqm5cwswvfq

Bayesian Phylogenetic Inference Using a Combinatorial Sequential Monte Carlo Method

Liangliang Wang, Alexandre Bouchard-Côté, Arnaud Doucet
2015 Journal of the American Statistical Association  
Sequential Monte Carlo (SMC) methods are approximate inference algorithms that have become very popular for time series models.  ...  Monte Carlo (MCMC) schemes.  ...  Sequential Monte Carlo (SMC) methods are another class of sampling algorithms, which have become popular for statespace models (Doucet, de Freitas, and Gordon 2001; Liu 2001) and are now increasingly  ... 
doi:10.1080/01621459.2015.1054487 fatcat:ycsuy4wrifgulfvpyzclpwsa2u

PHYML Online--a web server for fast maximum likelihood-based phylogenetic inference

S. Guindon, F. Lethiec, P. Duroux, O. Gascuel
2005 Nucleic Acids Research  
PHYML Online is a web interface to PHYML, a software that implements a fast and accurate heuristic for estimating maximum likelihood phylogenies from DNA and protein sequences.  ...  This tool provides the user with a number of options, e.g. nonparametric bootstrap and estimation of various evolutionary parameters, in order to perform comprehensive phylogenetic analyses on large data  ...  S.G. is supported by a postdoctoral fellowship from the Allan Wilson Centre for Molecular Ecology and Evolution, New Zealand.  ... 
doi:10.1093/nar/gki352 pmid:15980534 pmcid:PMC1160113 fatcat:me4eq57gkfftteaw3a72saebuu

Algorithmic approaches to clonal reconstruction in heterogeneous cell populations

Wazim Mohammed Ismail, Etienne Nzabarushimana, Haixu Tang
2019 Quantitative Biology  
We categorize these methods based on the type of input data that they use (space-resolved or time-resolved), and also based on their computational formulation as either combinatorial or probabilistic.  ...  We note that most of the available clonal inference algorithms were developed for elucidating tumor evolution whereas clonal reconstruction for unicellular genomes are less addressed.  ...  Megan Behringer and Michael Lynch for very inspiring discussions.  ... 
doi:10.1007/s40484-019-0188-3 pmid:32431959 pmcid:PMC7236794 fatcat:rcigo5p6evbylh2s2jh4xshpcu

Advances in Computational Methods for Phylogenetic Networks in the Presence of Hybridization [article]

R.A.L. Elworth, H.A. Ogilvie, J. Zhu, L. Nakhleh
2018 arXiv   pre-print
Most recently, Bayesian approaches for inferring phylogenetic networks directly from sequence data were developed and implemented.  ...  In particular, parsimony, likelihood, and Bayesian methods have been devised for estimating phylogenetic networks and associated parameters using estimated gene trees as data.  ...  The authors thank Matthew Hahn and Kelley Harris for their discussion of the definition of phylogenetic invariant methods.  ... 
arXiv:1808.08662v1 fatcat:par4fybfkrdu5m4bfiunqgs4xm

Interface '99

Arnold Goodman
2000 SIGKDD Explorations  
This personal overview of Interface '99 is intended to communicate its meaning and relevance to SIGKDD, as well as provide valuable information on trends within the Interface for data miners seeking to  ...  Monte Carlo tests are reported and data mining examples are discussed. "Hybrid and Sequential Modeling with Trees" by Thomas W.  ...  It considers classes of models that are subjected to a fully Bayesian analysis based on Markov Chain Monte Carlo methods, with the analysis of treatment effect being based on the posterior distribution  ... 
doi:10.1145/846183.846207 fatcat:ncoticeezndhpknzyx3huk66hu

Detecting recombination in evolving nucleotide sequences

Cheong Xin Chan, Robert G Beiko, Mark A Ragan
2006 BMC Bioinformatics  
Bayesian phylogenetic-based approaches showed high accuracy in detecting evidence of recombination event and in identifying recombination breakpoints.  ...  The best method for detecting recombined regions is not necessarily the most accurate in identifying recombination breakpoints.  ...  CXC is supported by a UQIPRS scholarship for his postgraduate study.  ... 
doi:10.1186/1471-2105-7-412 pmid:16978423 pmcid:PMC1592127 fatcat:yzf44ypscjcrtajum4u3pmzg6a

Approximating Model Probabilities in Bayesian Information Criterion and Decision-Theoretic Approaches to Model Selection in Phylogenetics

J. Evans, J. Sullivan
2010 Molecular biology and evolution  
Here, we extended the DT method by using reversible jump Markov chain Monte Carlo approaches to directly estimate model probabilities for an extended candidate pool of all 406 special cases of the general  ...  Model choice under DT differed between the BIC approximation and direct estimation methods for 45% of the data sets (113/250), and differing model choice resulted in significantly different sets of trees  ...  John Huelsenbeck provided source code for his allmodels2 program, which was useful for our preliminary analyses, and served as a great resource for understanding the methods described in Huelsenbeck et  ... 
doi:10.1093/molbev/msq195 pmid:20671039 pmcid:PMC3144157 fatcat:xvqttlaw4rddlome5rchrfx2sa

BayesMD: Flexible Biological Modeling for Motif Discovery

Man-Hung Eric Tang, Anders Krogh, Ole Winther
2008 Journal of Computational Biology  
The Bayesian inference is carried out using a combination of exact marginalization (multinomial parameters) and sampling (over the position of sites).  ...  Thereby, maximum a posteriori inference for the motifs is avoided and the marginal probabilities can be used directly to assess the significance of the findings.  ...  ACKNOWLEDGMENTS Thanks to Albin Sandelin for his valuable comments on the manuscript and Thomas Down for sharing his training and assessment datasets.  ... 
doi:10.1089/cmb.2007.0176 pmid:19040368 fatcat:yv6ppbvkkzhc3pomh5zszuq6mu

Statistical Challenges in Tracking the Evolution of SARS-CoV-2 [article]

Lorenzo Cappello and Jaehee Kim and Sifan Liu and Julia A. Palacios
2021 arXiv   pre-print
Here, we describe the models and methods currently used to monitor the spread of SARS-CoV-2, discuss long-standing and new statistical challenges, and propose a method for tracking the rise of novel variants  ...  The availability of SARS-CoV-2 molecular sequences isolated from infected individuals, coupled with phylodynamic methods, have provided insights into the origin of the virus, its evolutionary rate, the  ...  We centered our scalability discussion on Bayesian algorithms that either aim to replace MCMC by sequential Monte Carlo or variational inference, or to potentially improve the convergence of MCMC.  ... 
arXiv:2108.13362v1 fatcat:hf2yqrrilbhv5didcwqn6ps2ga

Efficient Bayesian inference of phylogenetic trees from large scale, low-depth genome-wide single-cell data [article]

Fatemeh Dorri, Sohrab Salehi, Kevin Chern, Tyler Funnell, Marc Williams, Daniel Lai, Mirela Andronescu, Kieran R Campbell, Andrew McPherson, Samuel Aparicio, Andrew Roth, Sohrab P Shah (+1 others)
2020 bioRxiv   pre-print
We propose a phylogenetic model and associated Bayesian inference procedure which exploits the specifics of scWGS data.  ...  This framework allows us to realistically consider routine Bayesian phylogenetic inference at the scale of scWGS data.  ...  We run the three inference methods described in Section 4.5 (MCMC, non-reversible parallel tempering, and adaptive sequential Monte Carlo) as well a maximum likelihood estimator approximated using the  ... 
doi:10.1101/2020.05.06.058180 fatcat:4s6zvqzygrazjol35mobvg7gcu

Network archaeology: phase transition in the recoverability of network history [article]

Jean-Gabriel Young, Guillaume St-Onge, Edward Laurence, Charles Murphy, Laurent Hébert-Dufresne, Patrick Desrosiers
2019 arXiv   pre-print
In this paper, we introduce a Bayesian formulation of network archaeology, with a generalization of preferential attachment as our generative mechanism.  ...  We use these methods to identify and characterize a phase transition in the quality of the reconstructed history, when they are applied to artificial networks generated by the model itself.  ...  The main causes are the path degeneracy and ESS problems characteristic of sequential Monte-Carlo methods.  ... 
arXiv:1803.09191v2 fatcat:35hfm54ee5eozdk733dvs4bdie

Blang: Bayesian declarative modelling of general data structures and inference via algorithms based on distribution continua [article]

Alexandre Bouchard-Côté, Kevin Chern, Davor Cubranic, Sahand Hosseini, Justin Hume, Matteo Lepur, Zihui Ouyang, Giorgio Sgarbi
2021 arXiv   pre-print
To perform inference at scale on such arbitrary state spaces, Blang leverages recent advances in sequential Monte Carlo and non-reversible Markov chain Monte Carlo methods.  ...  In principle, Bayesian inference should be particularly well-suited in such scenarios, as the Bayesian paradigm provides a principled way to obtain confidence assessment for random variables of any type  ...  Monte Carlo methods.  ... 
arXiv:1912.10396v2 fatcat:l76j43vqlzgq3kl74433iwshom

A Bayesian phylogenetic hidden Markov model for B cell receptor sequence analysis

Amrit Dhar, Duncan K. Ralph, Vladimir N. Minin, Frederick A. Matsen, Sergei L. Kosakovsky Pond
2020 PLoS Computational Biology  
In this paper, we introduce a novel approach to Bayesian phylogenetic inference for BCR sequences that is based on a phylogenetic hidden Markov model (phylo-HMM).  ...  Most current methods for BCR sequence analysis focus on separately modeling the above processes.  ...  Acknowledgments We thank Arman Bilge for many stimulating conversations about our phylo-HMM emission likelihood calculation, Andy Magee for answering all our questions about RevBayes, and Jean Feng for  ... 
doi:10.1371/journal.pcbi.1008030 pmid:32804924 fatcat:vy27vxq35ncxhdlkt5msnwb3pa

Evolutionary inference via the Poisson Indel Process

Alexandre Bouchard-Côté, Michael I. Jordan
2012 Proceedings of the National Academy of Sciences of the United States of America  
We present illustrative experiments in which Bayesian inference under the PIP model is compared to separate inference of phylogenies and alignments.  ...  Standard results for Poisson processes allow key computations to be decoupled, which yields the favorable computational profile of inference under the PIP model.  ...  We thank Bastien Boussau, Ian Holmes, Michael Newton, and Marc Suchard for their comments and suggestions.  ... 
doi:10.1073/pnas.1220450110 pmid:23275296 pmcid:PMC3557041 fatcat:jptxmwoszzcs7ermvzx5d3gc7q
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