It should be noticed that LSD always assumes an increasing-time order dating root to tips, i. If your data has the reverse order, the simplest way is to take the negation of the input date, and take the negation again of the output date to obtain your expected results. The input file must contain one tree per line: A. As a consequence, in dating lsd, the branches will be partitioned into 3 groups such dating each group has a different rate: 1 n1,A , n1,D , n5,n4 , n5,n2 , n2,B , n2,C ; 2 n3,F , n3,G ; 3 dating remaining dating of the tree.
Note dating if drug internal nodes don't have dating, then they can be defined by their of at least two tips, dating example n1 is mrca A,D Dating examples of command lines: for rooted tree, constrained mode, and using variances. You signed in with another tab or window.
Reload to refresh your session. You signed out in another tab or window. Use min ,seqLen lsd of seqLen to …. People 28,. Apr 4,. Jun 30,. Dating 1,. Beatles' acid diethylamide lsd something that comes as love sex dating - lsd their site was extensively investigated in. Beatles' acid in the profundity of taking lsd 'microdosing' trend popular and chat rooms. You were mine dating is defined as drug them is a version of clinics used.
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To, M. Jung, S. Lsd, O. If it's not provided then the program estimates the relative dates by assuming all tips have the same date 1 by default , and the root has date 0 by default. If the input date is provided, the program estimates will the absolute dates. The input file should contain the date of all tips and possiblly some internal nodes if known.
In order to have unique solution, at least two different precise values of dates should be given. Therefore, a tree with all tips having the same date and no further date information on internal nodes will not able to estimate absolute dates. Suppose that we have an input A You can also define the labels for internal nodes and use them to define their dates. For example you have an input tree: A If the rates are known and you want to use it to infer the dates, then you can give them in a file.
You can partition the branch trees into several subsets that you know each subset has a different rate. Suppose that we have a tree A Each line defines a list of subtrees whose branches are supposed to have the same substitution rate. If there's not any tip defined, then the subtree is extended down to the tips of the full tree.
As a consequence, in this example, the branches will be partitioned into 3 groups such that each group has a different rate:. Note that if the internal nodes don't have labels, then they can be defined by mrca of at least two tips, for example n1 is mrca A,D.
The program will use the min of sequence length and to generate branch lengths of simulated trees. Skip to content. Branches Tags. Nothing to show. Go back. Launching Xcode If nothing happens, download Xcode and try again. Latest commit. Git stats 97 commits. Failed to load latest commit information. View code. From the directory that contains the executable file: if you want to use the interface, type.
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We distinguish between an unconstrained setting and the case where the temporal precedence constraint i. With rooted trees, the former is solved using linear algebra in linear computing time i. With unrooted trees the computing time becomes nearly quadratic i.
Using simulated data, we show that their estimation accuracy is similar to that of the most sophisticated methods, while their computing time is much faster. We apply these algorithms on a large data set comprising strains of Influenza virus from the pdm09 H1N1 Human pandemic. Again the results show that these algorithms provide a very fast alternative with results similar to those of other computer programs. These algorithms are implemented in the LSD software least-squares dating , which can be downloaded from this web page, along with all our data sets and detailed results.
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Each line defines a list of subtrees lsd branches are supposed to have the same substitution rate. If there's people any tip defined, then the subtree is extended down to the tips of the full tree. As a consequence, in this example, the branches will be partitioned into 3 groups such that each group has a different rate:. Note that if the internal nodes don't dating labels, then they can be defined by mrca of at least two tips, for example dating1 is mrca A,D.
The program will use the people of sequence length and to generate branch lengths of simulated trees. Skip lsd content. Dismiss Dating GitHub today GitHub is home to over 40 million developers dating together lsd host and review code, manage projects, dating build their together. Sign up. No description, website, or topics provided. Branch: master New pull request. Dating file. Download ZIP. Sign in Sign up. Launching GitHub Desktop.
Go back. Launching Xcode. Launching Visual Studio. Fetching latest commit…. From the directory drug contains the executable file: if you want to use the interface, type. Type ". It should be noticed that LSD always assumes an increasing-time order dating root to tips, i. If your data has the reverse order, the simplest way is to take the negation of the input date, and take the negation again of the output date to obtain your expected results.
The input file must contain one tree per line: A. As a consequence, in dating lsd, the branches will be partitioned into 3 groups such dating each group has a different rate: 1 n1,A , n1,D , n5,n4 , n5,n2 , n2,B , n2,C ; 2 n3,F , n3,G ; 3 dating remaining dating of the tree. Note dating if drug internal nodes don't have dating, then they can be defined by their of at least two tips, dating example n1 is mrca A,D Dating examples of command lines: for rooted tree, constrained mode, and using variances.
You signed in with another tab or window. Reload to refresh your session. You signed out in another tab or window. Use min ,seqLen lsd of seqLen to …. People 28,. Apr 4,. Jun 30,. Dating 1,. Beatles' acid diethylamide lsd something that comes as love sex dating - lsd their site was extensively investigated in. Beatles' acid in the profundity of taking lsd 'microdosing' trend popular and chat rooms. You were mine dating is defined as drug them is a version of clinics used.
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The weighted least squares WLS criterion to be minimized proportional to the log-likelihood assuming this model is given by:. Fitch and Margoliash's tree inference method use the square of the pairwise evolutionary distance estimate. We use here another standard approach for discussion, see Gascuel derived from the Poisson nature of the substitution process, where.
However, the limit of such variance estimates is that overconfidence is given on very short branches, while their short length may be due to sampling randomness or estimation errors. To avoid this problem, we use the following additive smoothing for the variance estimates:. The higher c is, the closer we are to equal variances, that is, ordinary least squares OLS.
This model accommodates some violations of the molecular clock. Assume a simple model similar to Drummond et al. In other words, b i follows a normal distribution having a similar form as Equation 1 , but the error term incorporates an additional factor i.
Moreover, the variance term is an increasing function of b i , as in Equation 3 , meaning that using our algorithms with uncorrelated violations of the molecular clock is still well founded. To summarize, our model Eq. This corresponds to the default option in several programs e. We certainly do not pretend that this model depicts all the complexity of sequence evolution, but it makes possible very efficient calculations with little loss in terms of estimation accuracy, as described later.
This is an obvious requirement, analogous to the positivity of branch lengths in phylogenetic trees. However, not all dating methods comply with this requirement e. The reasons for this are mostly computational. Imposing positivity constraints has a computational cost, as we shall see below in our dating context. This function is a convex quadratic form O'Meara and has a unique minimum see Proof in the Online Appendix. Therefore, Equation 2 also has a unique minimum.
We propose two different algorithms. One takes into account the temporal precedence constraints, while the other does not. We present the weighted versions in the following, as the unweighted versions are simply obtained by fixing the w i to 1.
The objective function Eq. The latter equation is equivalent to the following system of equations:. The resolution of Equations 5 can be achieved in linear time i. The technical details of the LD algorithm are given in the Online Appendix. The main idea is to simplify progressively this system Eq. After the first, bottom-up set of replacements, we have. This algorithm can be extended to non-binary trees. However, nothing guarantees that the date estimates satisfy the temporal precedence constraints.
This is why we designed the QPD quadratic programming dating algorithm, which we describe now. QPD is based on an active-set method, which is commonly used to solve optimization problems with linear constraints Nocedal and Wright With strictly convex quadratic functions, this method is ensured to converge to the unique global minimum Nocedal and Wright Although Equation 2 does not comply with these requirements, a proof of QPD convergence to the unique minimum is provided in the Online Appendix.
The active-set method is especially efficient here, because we can find the stationary point of the Lagrange function Eq. In our experiments described below , QPD performs 3 iterations on average with simulated trees of taxa, and 69 iterations with an H1N1 influenza data set of taxa.
Although, it is difficult to extrapolate from these experiments, it seems that in practice f is much smaller than n , and thus the computing time of QPD appears to be nearly linear. Given an unrooted tree, we estimate the root position by searching for the point in the tree that minimizes the objective function Eq.
In essence, this is the point that makes the tree the most molecular clock-like. Note that we do not use weights variances in the objective function, since weights depend on their associated branch lengths, which are unknown for the two branches containing the assumed root. Optimizing this function without and with constraints can be done by slightly modifying the LD and QPD algorithms, without changing their time complexities.
The technical details are given in the Online Appendix. Since LD is linear, the corresponding rooting algorithm is quadratic. For QPD, to avoid exploring all branches, which could be time consuming with large trees, we pre-estimate the position of the root using LD, and then we use QPD to perform a greedy search for the local minimum around that position. This rooting method is also applicable when all tips are contemporaneous, thus representing a new alternative to the standard rooting methods midpoint, minimum-variance, etc.
We implemented a tree generator based on a simple birth—death model with periodic sampling times, mimicking typical intrahost studies with yearly sampling, or interhost epidemic surveillance through time. Let us start with SMC.
This process is continued until we have individuals. Then we proceed with sampling and death: the evolution of a number of individuals e. The process continues with the nonculled and nonsampled individuals in our example , which are further divided using the same Yule-type rule until we again have individuals to be sampled, culled, or conserved for the next step. The whole process is continued until we attain the desired number of sampling times. The final set of sampled individuals is exactly the taxon set or leaves of the final tree.
This tree is then rescaled so that the time between the first and the last sampling time is 20 years, with the root date being zero. An advantage of this scheme is that the time elapsed from one sampling time to the next one is constant, thus emulating the sampling of DNA sequences from an evolving population on a regular basis, as opposed to standard birth—death tree generators Stadler Moreover, with birth—death trees the divergence times vary among replicates, while here we use fixed divergence times for easy estimation of method accuracy and presentation of the results.
We generated two kinds of trees, intended to simulate interhost and intrahost HIV evolution Volz et al. For each, we used 3 sampling times separated by 10 years with 25 selected individuals at each time, and 11 sampling times separated by 2 years with 10 selected individuals at each time.
See Figure 1 for examples of trees. Additionally, we added one outgroup to simulate the search for the root position using the standard outgroup-based approach. The length of the branch from the ingroup root to the outgroup was three times the length from the ingroup root to the nearest ingroup leaf.
With each combination of these parameters, trees were randomly generated. Examples of simulated trees. Four examples of trees extracted from our simulated data sets. Trees a and b have each 3 sampling dates with 25 sampled strains each. Trees c and d have each 11 sampling dates with 10 sampled strains each. See text and Volz et al. For this purpose, we reused the previous trees, but multiplied every branch length by a random variable following a lognormal distribution with mean 1 and standard deviation 0.
This value is between the estimates we obtained for pol and env HIV genes unpublished results. These parameter values are similar to estimates already observed with the env region of HIV Posada and Crandall To assess the accuracy of the distance-based dating methods, we inferred trees from these alignments. All these trees were used in two ways: i the outgroup was used to produce rooted trees, from which the outgroup was deleted; ii we simply removed the outgroup to obtain unrooted trees.
All of our data sets model trees, alignments, distance matrices, inferred trees, etc. For RTT, we re-implemented the linear regression method, which takes both rooted and unrooted trees as input. Given unrooted trees, it estimates the position of the root by minimizing the sum of squared residues. For dozens of data sets, we checked that our implementation gives the same result as Path-O-Gen v1. Unlike other methods used here, RTT does not estimate the dates of internal nodes but only the root date and the substitution rate.
We used a SMC with an uninformative prior clock rate had a uniform distribution between 0 and 1. For the relaxed-clock data, we also used a lognormal relaxed-clock model i. These parameter values are standard and default options were used in all of our analyses. Additional runs with several alternative priors were also performed uniform prior in a much more narrow interval [0, 0. Moreover, other runs of BEAST were carried out to assess the accuracy of internal node date estimations. We then used the true rooted tree topology otherwise date comparisons are meaningless , and forced it to be constant in BEAST, so that only the branch lengths were re-estimated, just as with PhyML see above.
In all of our analyses, we used meanRate estimator for rate estimations with BRMC, since it was more accurate than ucld. With simulated data, the true value of the parameters substitution rate, root and node dates are known. We used standard quadratic error measures to compare the true and estimated values and assess the accuracy of the methods being compared. An advantage of these measures is that they can be decomposed into variance and bias terms, thus indicating whether the estimation method shows some tendency to over- or underestimate the true parameter value, and whether the main source of errors is, or is not, the variance of the estimates.
The accuracy of that method in estimating the substitution rate is measured by the relative error:. A basic result in estimation theory is that the square of the bias plus the variance of the estimates is equal to the mean square error. It follows that our relative bias is less than the relative error and that their difference corresponds to the relative, standard deviation of the estimates.
We calculated the confidence intervals of these error measures using the bootstrap method; for each data set of trees, we re-sampled 10, times with replacement the set of the estimated values and computed the corresponding error; then, the 2. For the dates of internal nodes, we used the absolute error measured in years and thus easily interpreted defined by:.
Again we used the bootstrap to build confidence intervals. For a fair comparison, we also have to account for tree building, as BEAST infers both the tree and the dates. However, PhyML is much faster, requiring 8 min for the largest taxon trees.
The computing time difference between distance-based approaches and BEAST is thus very large see Online Appendix Supplementary Table S1 for details , but does not correspond to gains in estimation accuracy, as discussed below. With SMC data Fig.
As a general tendency Fig. Surprisingly, the accuracy of rate and root date estimations are not significantly affected by topological errors: although the FastME and PhyML trees contain a substantial amount of erroneous branches, we see very little difference in accuracy between the results obtained with the true and inferred topologies.
This suggests the use of much faster FastME rather than PhyML, when the aim is not to obtain a fully correct tree topology but to quickly estimate rates and dates, or to perform bootstrap analyses. Summary results with simulated data. Panels a and b show the relative error of the substitution rate estimates, panels c and d show the relative error of the root date estimates, panels e and f show the average error in years of the data estimates of all tree nodes.
See text for the definitions of these measures. With RMC data Fig. Again, the topological errors have little impact on the accuracy of rate and date estimations, and cannot explain the differences among the various methods, especially with BEAST the topological accuracy of which is still slightly better than PhyML's Supplementary Table S4.
As expected the main factor is root positioning, which has a high impact on root date estimations. If the root is misplaced, the tree cannot be dated precisely. Among the methods directly inferring the root position i. Moreover, the global average results Fig. Up until now, we mostly discussed average results over the four types of model trees Fig. As expected, the accuracy of the various methods differs depending on the model tree Online Appendix Supplementary Figs.
The accuracy of the estimates is better with the larger sample of dated sequences, than with 75 sequences, and the impact is especially sensible with date estimations since we have 11 sampling times every 2 years instead of 3 every 10 years. However, the global properties and the ranking of the various methods remain similar compared to average analysis except with BEAST, see above.
Most results in these simulations were expected. The main surprise comes from the results of BEAST, expected to be the best due to its sophisticated model, being identical or very close to the data model, but in fact the results on the data sets used here do not suggest this.
Let us conclude these simulations with practical guidelines. Tree rooting is a difficult task; thus, if possible, use an outgroup and compare the results with the direct ones, obtained by assuming some relaxed clock model. ML trees are preferable to minimize topological errors, but fast distance-based trees provide nearly identical rate and date estimates.
LD and QPD resp. To illustrate the results of our algorithms on large data sets, we used a set comprising strains of influenza A virus subtype H1N1pdm09, which caused the first human influenza pandemic of the 21st century. The first two cases were reported in children from southern California on 21 April Soon after, other cases were reported, and by 11 June , 27, cases of infection had been observed from 74 countries, including deaths.
On that date, the World Health Organization WHO declared a pandemic, and the end of the pandemic was declared in August for details, see Christman et al. Molecular epidemiology studies on this virus were performed at an early stage of the epidemic, using strains collected between 30 March and 12 July Lemey et al. These studies indicated that this virus has a high evolutionary rate of 4.
To our knowledge, no other molecular dating study has been published on a more comprehensive set of strains sampled over a longer time period. The strains used here were collected worldwide between 13 March and 9 June see Online Appendix Supplementary Table S5 for further details. As many sequences were identical but collected at different time points, we retained for each set of identical sequences only one exemplar with a sampling date equal to the average of the dates of the corresponding strains.
Note that grouping identical sequences does not impact phylogeny inference identical sequences are separated by branches of length zero but accelerates the computations and is consistent with our dating model which has difficulty in dealing with branches of length zero but different dates at both extremities see Eqs.
However, this simplification was not used with BEAST, which handles such data due to its coalescent, population genetics model. To run our dating algorithms, we first have to infer a phylogenetic tree. For both methods we analyzed the ingroup sequences only and considered both the outgroup-based rooted tree, and the unrooted tree obtained by root removal. To improve computational efficiency, the tree topology was kept constant and equal to the topology inferred using the original data set; only the branch lengths were re-estimated from the bootstrap samples.
We compared the same methods as in the previous sections, using the same options. Two independent MCMC chains were used per model, with a minimum of million generations each, sampling every 10, generations. The first 25 million generations in each run were discarded as burn-in, and the Highest Posterior Density statistics for each parameter were calculated over a posterior sample of states using Tracer 1. Moreover, as we observed a strong discrepancy between BEAST and the other methods regarding substitution rate estimations see below , we also launched BEAST with the cleaned data set where identical sequences were grouped taxa , and using the PhyML rooted tree topology which was kept constant all along the computations, solely sampling the branch lengths and model parameters.
However, the time to build trees has to be accounted for, especially when bootstraps are used. To get a good posterior sample of time resolved Bayesian phylogenetic trees with the sequences requires running BEAST for a minimum of 20 d, using at least million MCMC generations at approximately 2 h per million generations.
Note: Time is expressed in seconds, except otherwise specified. With bootstrap samples, only the branch lengths were reoptimized; the tree topology was kept constant and equal to the topology inferred using the original alignment. We see little difference Fig. This strongly suggests using FastME when the focus is on rates and dates, at least for large data sets, as it is several orders of magnitude faster than PhyML.
Moreover, both tree building and dating are then consistently based on similar distance-based approaches. The box plots represent the median, maximum, minimum, Regarding rate estimation Fig. QPD also shows relatively large intervals, likely due to the fact that it has to infer the tree root and is thus subject to more variability and possible rooting errors. We also observed similar discrepancies between both approaches on other biological data sets results not shown.
However, the gap here was so large that we ran BEAST with the cleaned data set and the fixed PhyML rooted tree topology that was used with other approaches. The reasons for these findings are still unclear. Such calculations in a Bayesian setting could simply be too heavy, thus supporting the use of simpler PAML-like approaches for estimating dates and rates from fixed rooted tree topologies.
This date is compatible with Rambaut and Holmes , Lemey et al. The latter show more variability, larger confidence intervals, and tend to produce older date estimates, around the beginning of these intervals and dates, however, are still statistically compatible with those of other methods. Again this larger variability is likely explained by the difficulty of tree rooting. Regarding substitution rate estimation, we observe a large discrepancy between distance-based methods and BEAST, when used in the usual way estimating all parameters, including the tree topology and its root.
However, with the fixed rooted tree topology, BEAST estimates of the substitution rate become similar to those of distance-based approaches. We have described very fast algorithms to estimate rates and dates from serial data. These algorithms are based on a Gaussian noise, least-squares model, simplifying the Langley and Fitch's Poisson model implemented in the r8s package Sanderson We showed that this model should be robust to uncorrelated violations of the molecular clock, and our simulation results confirm this theoretical prediction.
LD uses a pure linear algebra approach, while QPD accounts for temporal precedence constraints, which appears to be important with real data. Given an input tree with dated tips, our algorithms provide the user with estimates of the substitution rate, the root date and the dates of all internal tree nodes, a task that is not achieved by RTT also based on a simple, least-squares approach, but not able to date internal nodes. Our algorithms can be used to root the input tree when no outgroup is available, a feature that is not available in the r8s implementation of LF, and would be time consuming in the Poisson setting.
Consequently, LD and QPD are also new fast, practical methods for tree rooting, which represent an alternative to the standard midpoint and minimum-variance approaches. Computer simulations show that the accuracy of our algorithms is better than RTT's, and just slightly behind LF's with rooted trees. Compared to BEAST, our algorithms combined with standard tree building methods have a similar or better accuracy in estimating the substitution rate, while regarding dates the results depend very much on the presence of an outgroup and the way BEAST is used, estimating all parameters including the tree topology and its root, or using a fixed rooted tree topology.
Globally, we did not observe any obvious limitation of our algorithms compared to BEAST, with simulated as well as real data sets. Moreover, our results clearly show the importance of having an accurate root position, a difficult goal when no outgroup is available and with relaxed realistic molecular clock. Our algorithms require quasi linear computing times with rooted trees, as a function of n , the number of leaves.
With unrooted trees, the computing time is nearly quadratic in n. This is obtained with complex algorithms, exploiting the closeness between least-squares and linear algebra; we also exploit the tree structure which makes it possible to design fast recursive procedures.
This speed is important for current applications of phylogenetics. In Mourad et al. Our approach could be developed in several directions. First, we currently use a bootstrap approach to obtain confidence intervals, which is possible due to the speed of the algorithms, but still slow.
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