Find research datasets worth reusing

opencc-by-4.0Oct 2016View details →

Figure 10. Maximum likelihood tree estimated using Garli 2.0 with 5 in Systematics and Phylogeny of the Australian Cicada Genus Pauropsalta Goding and Froggatt, 1904 and Allied Genera (Hemiptera: Cicadidae: Cicadettini)

Figure 10. Maximum likelihood tree estimated using Garli 2.0 with 5 loci (1 mtDNA and 4 nDNA). Branch support values are bootstrap percentages from 100 non parametric bootstrap replicates. Bootstrap support values ≥ 70 are shown. Molecular voucher numbers are adjacent to species names.

Figure 2 in The maximum likelihood identification method applied to insect morphometric data

Figure 2. Classification based on landmarks (and semilandmarks). Values in percent are the proportions of correct assignments averaged over the complete set of data, mixing the various species, but restricted to the landmark-based method (including here the combination of landmarks and semilandmarks). Each value is shown with an error bar which is its standard deviation. The left part shows the results from the maximum likelihood method applied to the centroid size and to the shape variables. The right part shows the average scores obtained with the Mahalanobis classification. Abbreviation: first PC—the shape variables used were the few first PC of shape variables that the MLi classification selected as the most discriminant set of PC; all PC—the maximum number of PC that could be used by the Mahalanobis classification.

Figure 4 in The maximum likelihood identification method applied to insect morphometric data

Figure 4. Principal component analysis of sandfly data. The factor map of the two first principal components (PC) describing the morphospace of female sandfly data: S. bailyi, S. barraudi and P. stantoni. Unidentified males show a convex hull covering both female S. bailyi and S. barraudi, leaving two specimens outside the female hulls.

Figure 1 in The maximum likelihood identification method applied to insect morphometric data

Figure 1. Measurements on Triatominae. A. Digitization of the contour of the eggs of Triatomini and Rhodniini. B. Landmarks as digitized on the wing of Panstrongylus chinai. C. Traditional measurement of eggs of the tribe Triatomini (Panstrongylus sp. and Triatoma sp., left) and the tribe Rhodniini (Rhodnius sp., right). Abbreviation: op.dim—dimensions of the operculum; max.leng— maximum length; max.dim—maximum diameter.

Figure 3 in The maximum likelihood identification method applied to insect morphometric data

Figure 3. Classification based on harmonics. Values in percent are the proportions of correct assignments averaged over the complete set of data, mixing the various species, but restricted to the outline-based method. Each value is shown with an error bar which is its standard deviation. The left part shows the results from the maximum likelihood method applied to the perimeter of the contour and to the shape variables. The right part shows the average scores obtained with the Mahalanobis classification. Abbreviation: first PC— the shape variables used were the few first PC of shape variables that the MLi classification selected as the most discriminant set of PC; all PC—the maximum number of PC that could be used by the Mahalanobis classification.

opencc-by-4.0Feb 2013View details →

FIG. 4. Maximum-likelihood phylogeny for 45 in Phylogenetic Relationships of New World Porcupines (Rodentia, Erethizontidae): Implications for Taxonomy, Morphological Evolution, and Biogeography

FIG. 4. Maximum-likelihood phylogeny for 45 ingroup (erethizontid) terminals; outgroup taxa are not shown. Labeling conventions and nodal support statistics are the same as in figure 3. Capital letters (A, B, C) indicate unnamed clades discussed in the text.

opencc-by-4.0May 2009View details →

Figure 11. Maximum likelihood phylogram. Bootstrap values greater than 50 in Evolution of cave living in Hawaiian Schrankia (Lepidoptera: Noctuidae) with description of a remarkable new cave species

Figure 11. Maximum likelihood phylogram. Bootstrap values greater than 50% are illustrated on tree as first value; Bayesian posterior probabilities are the second value. Values <50 are not labelled. Colours represent islands (black is Hawaii Island; red is Maui, blue is Oahu; green is Kauai). Asterisks denote flightless individuals. Black circles represent dark-zone morph Schrankia howarthi; grey circles represent twilight-zone individuals. Locality information for individual moths may be found in the Appendix.

opencc-by-4.0Mar 2002View details →

Figure 2. A, Tree resulting from maximum likelihood analysis from the 12S in A molecular perspective on the evolutionary affinities of an enigmatic neotropical frog, Allophryne ruthveni

Figure 2. A, Tree resulting from maximum likelihood analysis from the 12S data set using the Hasegawa–Kishino–Yano two parameter model (Hasegawa et al., 1985). Included for comparative purposes are bootstrap support from NJ analyses. Below each resolved branch are indicated percentage bootstrap support in excess of 50% for: ML (100 pseudoreplicates), NJ (1000 pseudoreplicates; Kimura 2-parameter), NJ (1000 pseudoreplicates; Tamura–Nei). B, Strict consensus of three most parsimonious trees (all substitutions weighted equally; tree length = 164 steps). Below each branch are indicated percentage bootstrap support (1000 pseudreplicates) in excess of 50% for: MP (unweighted), MP (stems weighted twice loops), MP (transversions weighted four times transitions). Bremer decay indices (DI) are the final value shown below each resolved branch (for MP unweighted only). A dash indicates bootstrap support of less than 50%.

opencc-by-4.0Oct 2008View details →

Figure 3. Likelihood trees generated from the X in Forest monkeys and Pleistocene refugia: a phylogeographic window onto the disjunct distribution of the Chlorocebus lhoesti species group

Figure 3. Likelihood trees generated from the X- and Y-chromosomal datasets. Bootstrap values of 50 and above (100 replicates, 'fast' stepwise addition) are included throughout the trees. The dashed ovals indicate the consistent recovery of a lhoesti group monophyly. One Chlorocebus solatus sample (CS026) is derived from a female (XX), and therefore is not represented in the Y-chromosomal tree.

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Data from: LoRaD: Marginal likelihood estimation with haste (but no waste)

The Lowest Radial Distance (LoRaD) method is a modification of the recently-introduced Partition-Weighted Kernel method for estimating the marginal likelihood of a model, a quantity important for Bayesian model selection. For analyses involving a fixed tree topology, LoRaD improves upon the Steppingstone or Thermodynamic Integration (Path Sampling) approaches now in common use in phylogenetics because it requires sampling only from the posterior distribution, avoiding the need to sample from a series of ad hoc power posterior distributions, and yet is more accurate than other fast methods such as the Generalized Harmonic Mean (GHM) method. We show that the method performs well in comparison to the Generalized Steppingstone method on an empirical fixed-topology example from molecular phylogenetics involving 180 parameters. The LoRaD method can also be used to obtain the marginal likelihood in the variable-topology case if at least one tree topology occurs with sufficient frequency in the posterior sample to allow accurate estimation of the marginal likelihood conditional on that topology.

opencc-zeroFeb 2023View details →

Fig. 2. Maximum Likelihood species tree from the concatenated 50 in Ultraconserved elements-based phylogenomic systematics of the snake superfamily Elapoidea, with the description of a new Afro-Asian family

Fig. 2. Maximum Likelihood species tree from the concatenated 50 % complete dataset consisting of 4561 loci. Values on the branch indicate Shimodaira Hasegawalike approximate likelihood ratio test and ultrafast bootstrap. Abbreviations as in Fig. 1.

opencc-zeroMar 2023View details →

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Machine learning can be as good as maximum likelihood when reconstructing phylogenetic trees and determining the best evolutionary model on four taxon alignments

Machine learning can be as good as maximum likelihood when reconstructing phylogenetic topologies and determining the best evolutionary model on four taxon alignments. Phylogenetic tree reconstruction with molecular data is important in many fields of life science research. The gold standard in this discipline is the Maximum Likelihood tree reconstruction method. Here we show that for quartet trees, Machine Learning using neural networks can be as good as the Maximum Likelihood method to infer the best tree topology and the best model of sequence evolution for nucleotide as well as amino acid sequences. For this purpose we simulated data sets for a wide range of branch lengths, evolutionary models and model parameters and compared the topologies and inferred models obtained with Machine learning with those obtained with the Maximum Likelihood and the Neighbour Joining method. Our results show that neural networks are a promising avenue for determining relatedness between taxa, which is likely to accelerate the construction of phylogenetic trees in the future, while maintaining a high accuracy.

opencc-by-4.0Apr 2023View details →

CherryML: Scalable Maximum Likelihood Estimation of Phylogenetic Models

Simulated datasets used in our paper "CherryML: Scalable Maximum Likelihood Estimation of Phylogenetic Models" to produce figures 1bc, 1d, and 2ab. The data provided in each folder is as follows: <ul> <li>rate_matrices contains the classical LG rate matrix, and our 400 x 400 estimated co-evolutionary model Q2.</li> <li>fig_1bc contains the simulated data used to estimate and evaluate rate matrices using the CherryML method and EM (with XRATE) as shown in Fig. 1b and c of our paper. The files and sub-directories here are: <ul> <li>fig_1bc_simulated_data_families_all.txt contains the list of protein family names used to train the model. When only K families are used in Fig. 1b and c, these are the first K families of this list.</li> <li>gt_tree_dir contains the phylogenetic tree used to simulate data for each protein family. There were originally estimated running FastTree on the MSAs from the trRosetta paper, as described in our paper in detail.</li> <li>msa_dir contains the simulated multiple sequence alignments (MSAs). These were simulated running the LG rate matrix down each tree, without site rate variation.</li> <li>gt_site_rates_dir contains the site rates used. In this case, they are all 1.</li> <li>gt_likelihood_dir contains the log-likelihood of the original phylogenetic trees used for each family (as given by FastTree). This is irrelevant for but provided for completeness; you can safely ignore this directory.</li> </ul> </li> <li>fig_1d folder contains the simulated data used to evaluate the effect of time quantization on the CherryML method as shown in Fig. 1d of our paper. The files and sub-directories here are: <ul> <li>gt_tree_dir contains the phylogenetic tree used to simulate data for each protein family. There were originally estimated running FastTree on the MSAs from the trRosetta paper, as described in our paper in detail.</li> <li>msa_dir contains the simulated multiple sequence alignments (MSAs). These were simulated running the LG rate matrix down each tree, with site rate variation.</li> <li>gt_site_rates_dir contains the site rates used.</li> <li>gt_likelihood_dir contains the log-likelihood of the original phylogenetic trees used for each family (as given by FastTree). This is irrelevant for but provided for completeness; you can safely ignore this directory.</li> </ul> </li> <li>fig_2ab contains the simulated data used to evaluate the effect of time quantization on the CherryML method as shown in Fig. 1d of our paper. The files and sub-directories here are: <ul> <li>gt_tree_dir contains the phylogenetic tree used to simulate data for each protein family. There were originally estimated running FastTree on the MSAs from the trRosetta paper, as described in our paper in detail.</li> <li>msa_dir contains the simulated multiple sequence alignments (MSAs). These were simulated running the LG rate matrix down each non-contacting tree, and using Q2 for the contacting sites, all without site rate variation.</li> <li>gt_site_rates_dir contains the site rates used, in this case all 1 (i.e. no site rate variation).</li> <li>gt_likelihood_dir contains the log-likelihood of the original phylogenetic trees used for each family (as given by FastTree). This is irrelevant for but provided for completeness; you can safely ignore this directory.</li> <li>contact_map_dir contains the simulated contact maps for each family. These were obtained by computing a maximal matching on the true contact maps derived from the trRosetta paper, as described in detail in out paper.</li> </ul> </li> </ul> The exact end-to-end code which generates these simulated datasets is provided in our Github repository: <a href="https://github.com/songlab-cal/CherryML">https://github.com/songlab-cal/CherryML</a> In fact, by default, when you try to reproduce the figures in our paper by running the `reproduce_all_figures.py` script in our repository, the data will automatically be simulated for you if it isn't already present. This can be bypassed by downloading the data here in Zenodo and changing the top of `reproduce_all_figures.py` to point to these files.

opencc-by-4.0May 2023View details →

CNETML: maximum likelihood inference of phylogeny from copy number profiles of multiple samples

This folder includes simulated and real data used in validating CNETML, a new maximum likelihood method designed to reconstruct the evolutionary history of multiple samples of a single patient which may be taken at different locations and/or times, which can take as input (relative) total integer copy numbers called from shallow whole genome sequencing data.

opencc-by-4.0Nov 2020View details →

Figure 3. Maximum Likelihood phylogenetic tree inferred from 695 in Genetic Relationships of Long-nosed Potoroos Potorous tridactylus (Kerr, 1792) from the Bass Strait Islands, with Notes on the Subspecies Potorous tridactylus benormi Courtney, 1963

Figure 3. Maximum Likelihood phylogenetic tree inferred from 695 bp of CO1 mtDNA sequence, including data from the Potorous tridactylus benormi Holotype (AM M.8319) and Paratype (AM M.8373). Bootstrap values for major lineages are shown. A similar tree topology was inferred from Bayesian inference.

Figure 4 in Pathway Analysis: Likelihood of Coffee Berry Borer (Hypothenemus hampei Ferrari) Introduction into the Hawaiian Islands by Air Passenger Travel

Figure 4. Probability for the estimated annual passenger entries with CBB-infested materials: (a) to Hawaii from CBB-occurring countries; (b) between Hawaii island and Oahu; (c) between Oahu and Maui; (d) between Oahu and Kauai; (e) between Hawaii island and Maui; (f) between Maui and Kauai; (g) between Hawaii island and Kauai; (h) between Oahu and Lanai.

Figure 1 in Pathway Analysis: Likelihood of Coffee Berry Borer (Hypothenemus hampei Ferrari) Introduction into the Hawaiian Islands by Air Passenger Travel

Figure 1. World coffee production with CBB world distribution: a) 2019 coffee produc- tion (FAO 2020); b) CBB distribution by introduction year (Vega et al. 2015).

Figure 2 in Pathway Analysis: Likelihood of Coffee Berry Borer (Hypothenemus hampei Ferrari) Introduction into the Hawaiian Islands by Air Passenger Travel

Figure 2. Number of months per year with optimal temperature conditions for CBB growth. The optimal temperature conditions were determined by evaluating the area where daily minimum temperature was above 18°C and daily maximum temperature was below 30°C.

Figure 3 in Pathway Analysis: Likelihood of Coffee Berry Borer (Hypothenemus hampei Ferrari) Introduction into the Hawaiian Islands by Air Passenger Travel

Figure 3. Mean annual number of air passengers traveling between the Hawaiian Islands. Dispersal pathways are shown for those islands that are confirmed to have coffee berry borer. Thicker blue lines indicate higher numbers of passengers.