Accurate Tree Research Articles

Domain adaptive tree crown detection using high-resolution remote sensing images

Rapid and accurate tree crown detection is significant to forestry management and precision forestry. However, the variability of the data is challenging for traditional deep learning-based methods in cross-regional scenarios. To solve the problem of low accuracy of existing methods due to the distribution characteristics of tree crowns and the special background in which they are located, we propose a multi-adversarial domain adaptive (DA) crown detection model, which saves much of labeling efforts needed for covering different features in different regions. First, we embed a multilevel transferable attention mechanism to extract the multiscale transferable features of the tree crown and combat the negative transfer caused by the background. Second, a multi-adversarial instance alignment module with entropy regularization and a weighting factor is designed to match the cross-regional data distributions to reduce the misdetection and misidentification of complex backgrounds. The above-mentioned adaptations are integrated into a two-stage object detector to realize end-to-end training. We conduct comprehensive experiments with four transfer tasks. Our method improves F1-score to 95.68%, 83.12%, 78.56%, and 66.89% and performs 3.61% and 15.38% better than the state-of-the-art domain adaptation approaches without extra inference costs. The results demonstrate the great potential of our method for DA tree crown detection, which provides a reference for further research on forestry detection.

Individual Tree Species Classification Based on a Hierarchical Convolutional Neural Network and Multitemporal Google Earth Images

Accurate and efficient individual tree species (ITS) classification is the basis of fine forest resource management. It is a challenge to classify individual tree species in dense forests using remote sensing imagery. In order to solve this problem, a new ITS classification method was proposed in this study, in which a hierarchical convolutional neural network (H-CNN) model and multi-temporal high-resolution Google Earth images were employed. In an experiment conducted in a forest park in Beijing, China, GE images of several significant phenological phases of broad-leaved forests, namely, before and after the mushrooming period, the growth period, and the wilting period, were selected, and ITS classifications based on these images along with several typical CNN models and the H-CNN model were conducted. In the experiment, the classification accuracy of the multitemporal images was higher by 7.08–12.09% than those of the single-temporal images, and the H-CNN model offered an OA accuracy 2.66–3.72% higher than individual CNN models, demonstrating that multitemporal images rich in the phenological features of individual tree species, together with a hierarchical CNN model, can effectively improve ITS classification.

Enhanced machine learning tree classifiers for lithology identification using Bayesian optimization

Lithology identification is a fundamental activity in oil and gas exploration. The application of artificial intelligence (AI) is currently being adopted as a state-of-the-art means of automating lithology identification. One aspect of this AI approach is the application of population search algorithms to optimise hyperparameters for enhanced prediction performance. For the first time, Bayesian optimization is deployed to determine the optimal learning parameters for more accurate tree and tree ensemble lithology classifiers. The aim is to rely on the ability of Bayesian optimization to consider previous classification results to improve the output of decision and ensemble tree lithology models using well logs as inputs. The proposed Bayesian optimised decision tree (BODT) generated the best classification accuracy of 89.8% as compared to 86.9%, 83.3% and 81.2% for fine, medium and coarse trees. For the ensembled trees, the Bayesian optimised AdaBoost (BO-AdaBoost) classifier generated the highest improved prediction accuracy of 94.2% while Bayesian optimised Bagged (BO-Bagged) and Bayesian optimised RUSBoost (BO-RUSBoost) had a lower accuracy rate of 94.0% and 77.1% respectively. Additionally, the performance of the Bayesian optimised classifiers offered higher reliability when compared with particle swarm optimization-based artificial neural networks (PSO-ANN). Hence, incorporating Bayesian optimization as a hyperparameter search algorithm will improve litholofacies recognition, leading to a higher accuracy rate and thereby provide an improved alternative for intelligent lithology identification.

Semantic Segmentation Guided Coarse-to-Fine Detection of Individual Trees from MLS Point Clouds Based on Treetop Points Extraction and Radius Expansion

Urban trees are vital elements of outdoor scenes via mobile laser scanning (MLS), accurate individual trees detection from disordered, discrete, and high-density MLS is an important basis for the subsequent analysis of city management and planning. However, trees cannot be easily extracted because of the occlusion with other objects in urban scenes. In this work, we propose a coarse-to-fine individual trees detection method from MLS point cloud data (PCD) based on treetop points extraction and radius expansion. Firstly, an improved semantic segmentation deep network based on PointNet is applied to segment tree points from the scanned urban scene, which combining spatial features and dimensional features. Next, through calculating the local maximum, the candidate treetop points are located. In addition, the optimized treetop points are extracted after the tree point projection plane was filtered to locate the candidate treetop points, and a distance rule is used to eliminate the pseudo treetop points then the optimized treetop points are obtained. Finally, after the initial clustering of treetop points and vertical layering of tree points, a top-down layer-by-layer segmentation based on radius expansion to realize the complete individual extraction of trees. The effectiveness of the proposed method is tested and evaluated on five street scenes in point clouds from Oakland outdoor MLS dataset. Furthermore, the proposed method is compared with two existing individual trees segmentation methods. Overall, the precision, recall, and F-score of instance segmentation are 98.33%, 98.33%, and 98.33%, respectively. The results indicate that our method can extract individual trees effectively and robustly in different complex environments.

Identification of tree species based on the fusion of UAV hyperspectral image and LiDAR data in a coniferous and broad-leaved mixed forest in Northeast China.

Rapid and accurate identification of tree species via remote sensing technology has become one of the important means for forest inventory. This paper is to develop an accurate tree species identification framework that integrates unmanned airborne vehicle (UAV)-based hyperspectral image and light detection and ranging (LiDAR) data under the complex condition of natural coniferous and broad-leaved mixed forests. First, the UAV-based hyperspectral image and LiDAR data were obtained from a natural coniferous and broad-leaved mixed forest in the Maoer Mountain area of Northeast China. The preprocessed LiDAR data was segmented using a distance-based point cloud clustering algorithm to obtain the point cloud of individual trees; the hyperspectral image was segmented using the projection outlines of individual tree point clouds to obtain the hyperspectral data of individual trees. Then, different hyperspectral and LiDAR features were extracted, respectively, and the importance of the features was analyzed by a random forest (RF) algorithm in order to select appropriate features for the single-source and multi-source data. Finally, tree species identification in the study area were conducted by using a support vector machine (SVM) algorithm together with hyperspectral features, LiDAR features and fused features, respectively. Results showed that the total accuracy for individual tree segmentation was 84.62%, and the fused features achieved the best accuracy for identification of the tree species (total accuracy = 89.20%), followed by the hyperspectral features (total accuracy = 86.08%) and LiDAR features (total accuracy = 76.42%). The optimal features for tree species identification based on fusion of the hyperspectral and LiDAR data included the vegetation indices that were sensitive to the chlorophyll, anthocyanin and carotene contents in the leaves, the partial components of the transformed independent component analysis (ICA), minimum noise fraction (MNF) and principal component analysis (PCA), and the intensity features of the LiDAR echo, respectively. It was concluded that the framework developed in this study was effective in tree species identification under the complex conditions of natural coniferous and broad-leaved mixed forest and the fusion of UAV-based hyperspectral image and LiDAR data can achieve enhanced accuracy compared the single-source UAV-based remote sensing data.

A Novel Algorithm Based on Geometric Characteristics for Tree Branch Skeleton Extraction from LiDAR Point Cloud

More accurate tree models, such as branch skeleton, are needed to acquire forest inventory data. Currently available algorithms for constructing a branch skeleton from a LiDAR point cloud have low accuracy with problems such as irrational connection near trunk bifurcation, excessive central deviation and topological errors. Using the C++ and PCL library, a novel algorithm of the incomplete simulation of tree transmitting water and nutrients (ISTTWN), based on geometric characteristics for tree branch skeleton extraction, was developed in this research. The algorithm is an incomplete simulation of tree transmitting water and nutrients. Improvements were made to improve the time and memory consumption. The result show that the ISTTWN algorithm without any improvements is quite time consuming but has consecutive output. After improvement with iteration, the process is faster and has more detailed output. Breakpoint connection is added to recover continuity. The ISTTWN algorithm with improvements can produce a more accurate skeleton and cost less time than a previous algorithm. The superiority and effectiveness of the method are demonstrated, which provides a reference for the subsequent study of tree modeling and a prospect of application in other fields, such as virtual reality, computer games and movie scenes.

ASSESSMENT AND MONITORING OF VILLAGE FOREST RESOURCES

The area of village forest (0.27 million hectare) is very limited in comparison with government forest land (2.14 million hectare). But the village woodlot area contributes 85% of the total consumption and the remaining 15% is contributed by government forest land. Need for correct assessment and monitoring is discussed. The main objective of this paper is to recommend appropriate sampling design for correct assessment of village forest resources. A case study was undertaken in the district of Chittagong located at the south-east corner of Bangladesh. A study population of 900 households was formed consisting of 14 rural thanas in the district. The seven parameters defined under five variables were calculated for complete enumeration. The parameters were estimated through Cluster Sampling, Two Stage Sampling and Stratified Two Stage Sampling (Self-Weight) taking 250 sample households in each method. The estimates were compared with the enumerated values on the basis of percentage standard error, percentage accuracy, percentage bias and 95% confidence interval. Stratified Two Stage Sampling (Self-Weighted) provided the best estimates for true total homestead area (78.4 ha for 97 ha), for true total compact tree garden area (49.6 ha for 49 ha), for true total volume of trees (9432.2 m3 for 9490 m3 ), for true total number of bamboos (53546 for 53229), for volume of compact trees per hectare (164.9m3 for 172 m3) and true number of households with bamboo plantation (413 for 407). Stratified Two Stage Sampling (Self-Weighted) was found to be a suitable technique and was recommended to apply as field sampling design for correct assessment of village forest resources. A continuous monitoring system was also suggested.

The large-sample asymptotic behaviour of quartet-based summary methods for species tree inference

Summary methods seek to infer a species tree from a set of gene trees. A desirable property of such methods is that of statistical consistency; that is, the probability of inferring the wrong species tree (the error probability) tends to 0 as the number of input gene trees becomes large. A popular paradigm is to infer a species tree that agrees with the maximum number of quartets from the input set of gene trees; this has been proved to be statistically consistent under several models of gene evolution. In this paper, we study the asymptotic behaviour of the error probability of such methods in this limit, and show that it decays exponentially. For a 4-taxon species tree, we derive a closed form for the asymptotic behaviour in terms of the probability that the gene evolution process produces the correct topology. We also derive bounds for the sample complexity (the number of gene trees required to infer the true species tree with a given probability), which outperform existing bounds. We then extend our results to bounds for the asymptotic behaviour of the error probability for any species tree, and compare these to the true error probability for some model species trees using simulations.

Scelestial: Fast and accurate single-cell lineage tree inference based on a Steiner tree approximation algorithm.

Single-cell genome sequencing provides a highly granular view of biological systems but is affected by high error rates, allelic amplification bias, and uneven genome coverage. This creates a need for data-specific computational methods, for purposes such as for cell lineage tree inference. The objective of cell lineage tree reconstruction is to infer the evolutionary process that generated a set of observed cell genomes. Lineage trees may enable a better understanding of tumor formation and growth, as well as of organ development for healthy body cells. We describe a method, Scelestial, for lineage tree reconstruction from single-cell data, which is based on an approximation algorithm for the Steiner tree problem and is a generalization of the neighbor-joining method. We adapt the algorithm to efficiently select a limited subset of potential sequences as internal nodes, in the presence of missing values, and to minimize cost by lineage tree-based missing value imputation. In a comparison against seven state-of-the-art single-cell lineage tree reconstruction algorithms—BitPhylogeny, OncoNEM, SCITE, SiFit, SASC, SCIPhI, and SiCloneFit—on simulated and real single-cell tumor samples, Scelestial performed best at reconstructing trees in terms of accuracy and run time. Scelestial has been implemented in C++. It is also available as an R package named RScelestial.

Tree Species Classification of Backpack Laser Scanning Data Using the PointNet++ Point Cloud Deep Learning Method

To investigate forest resources, it is necessary to identify the tree species. However, it is a challenge to identify tree species using 3D point clouds of trees collected by light detection and ranging (LiDAR). PointNet++, a point cloud deep learning network, can effectively classify 3D objects. It is important to establish high-quality individual tree point cloud datasets when applying PointNet++ to identifying tree species. However, there are different data processing methods to produce sample datasets, and the processes are tedious. In this study, we suggest how to select the appropriate method by designing comparative experiments. We used the backpack laser scanning (BLS) system to collect point cloud data for a total of eight tree species in three regions. We explored the effect of tree height on the classification accuracy of tree species by using different point cloud normalization methods and analyzed the effect of leaf point clouds on classification accuracy by separating the leaves and wood of individual tree point clouds. Five downsampling methods were used: farthest point sampling (FPS), K-means, random, grid average sampling, and nonuniform grid sampling (NGS). Data with different sampling points were designed for the experiments. The results show that the tree height feature is unimportant when using point cloud deep learning methods for tree species classification. For data collected in a single season, the leaf point cloud has little effect on the classification accuracy. The two suitable point cloud downsampling methods we screened were FPS and NGS, and the deep learning network could provide the most accurate tree species classification when the number of individual tree point clouds was in the range of 2048–5120. Our study further illustrates that point-based end-to-end deep learning methods can be used to classify tree species and identify individual tree point clouds. Combined with the low-cost and high-efficiency BLS system, it can effectively improve the efficiency of forest resource surveys.

3D modeling of laser-scanned trees based on skeleton refined extraction

Laser point cloud provides a high-precision data source for the 3D model reconstruction of existing trees. However, due to the complexity of tree structure and the data missing of point cloud, it is difficult to reconstruct complete, detailed and accurate 3D tree models from discrete points, especially the points are incomplete due to single-view scanning. To this end, a new point cloud driven method based on skeleton refined extraction is proposed in this paper. First, as the focus of refined modeling, the main (thick) branches are separated from the raw tree points. Then, the main branch points are over-segmented into some whorl-segments, and the corresponding raw points on the branch surfaces of each whorl-segment are shrunk to generate smooth, continuous and local detail preserved potential skeleton nodes of the main branches. Next, a new shortest path metric meeting the natural growth characteristics of trees is adopted to extract the complete, detailed and correctly connected tree skeleton from both of the shrunk main branch points and the non-main-branch points. After that, to ensure the skeleton centralization, especially that extracted from single-view scanned data, the extracted skeleton is translated by a combination of cylinder fitting and allometric theory. Finally, the complete, detailed and accurate 3D tree model is expressed by a series of cylinders based on the extracted refined skeleton. The proposed method is tested with 7 different tree point clouds, some of which are obtained by single-view scanning and suffer from serious data missing. Both qualitative and quantitative evaluations prove the effectiveness of this method for the refined reconstruction of trees from point clouds. Overall, the average modeling accuracy and its standard deviation of the used different trees are 2.58 cm and 3.20 cm respectively.

Iceberg-seabed interaction evaluation in clay seabed using tree-based machine learning algorithms

In Arctic offshore regions, the oil and gas hydrocarbons are transferred to the onshore basins through the subsea pipelines. However, the operational integrity of the subsea pipeline may be at risk of collision with traveling icebergs, which gouge the seabed in the Arctic shallow waters. Even though these sea bottom-founded structures are buried at a secure depth below the seafloor, the pipeline is still threatened by the ice scouring event extended much deeper than the ice tip due to the shear resistance of the seabed soil. Modeling the sub-gouge soil characteristics is a challenging problem that requires costly experimental and long-running finite element (FE) simulations. To overcome these challenges, in this paper, the reaction forces and sub-gouge soil deformations in clay were modeled using an advanced extra tree regression (ETR) algorithm, as a quick and cost-effective alternative for the early design phases of pipeline engineering projects. Eight ETR models, ETR 1 to ETR 8, were developed by using the input parameters governing the iceberg-seabed interaction problem. The collected data were randomly split into 70% for training the machine learning (ML) models and 30% for testing purposes. The most accurate ETR models and the most significant input parameters were identified by performing a sensitivity analysis. The comparison of the most accurate ETR models and decision tree regression (DTR), random forest regression (RFR), and gradient boosting regression (GBR) algorithms proved that the ETR models had better performance to simulate the ice keel seabed interaction in clay.

Xylem porosity, sapwood characteristics, and uncertainties in temperate and boreal forest water use

Sapwood characteristics, such as sapwood area as well as thermal and hydraulic conductivity, are linked to species-specific hydraulic function and resource allocation to water transport tissues (xylem). These characteristics are often unknown and thus a major source of uncertainty in sap flow data processing and transpiration estimates because bulk rather than species-specific values are usually applied. Here, we analyzed the sapwood characteristics of fifteen common tree species in eastern North America from different taxonomic (i.e., angiosperms and gymnosperms) and xylem porosity groups (i.e., tracheid-bearing, diffuse- or ring-porous species) and we assessed how uncertainties in sapwood characteristics involved in sap flow calculations are propagated in tree water use estimates. We quantified their sapwood area changes with stem diameter (allometric scaling) and thermal conductivity. We combined these measurements with species-specific values of wood density and hydraulic conductivity found in the literature and assessed the role of wood anatomy in orchestrating their covariation. Using an example sap flow dataset from tree species with different xylem porosity, we assessed the sensitivity of tree water use estimates to sapwood characteristics and their interactions. Angiosperms (ring- and diffuse-porous species), with specialized vessels for water transport, showed a steeper relationship (scaling) between tree stem diameter and sapwood area in comparison to gymnosperms (tracheid-bearing species). Gymnosperms (angiosperms) were characterized by lower (higher) wood density and higher (lower) sapwood moisture content, resulting in non-significant differences in sapwood thermal conductivity between taxonomic and xylem porosity groups. Clustering of species sapwood characteristics based on taxonomic or xylem porosity groups and constraining these parameters could facilitate more accurate sap flow calculations and tree water use estimates. When combined with an increasing number of sap flow observations, these findings should improve tree- and landscape-level transpiration estimates, leading to more robust partitioning of terrestrial water fluxes.

3D point cloud fusion from UAV and TLS to assess temperate managed forest structures

Light detection and ranging (LiDAR) technology has become one of the most dominant acquisition methods for modeling forest attributes, such as very accurate tree structure information, which is necessary for qualitative forest management and research activities. In this study, we evaluated the efficacy of standalone unmanned aerial vehicle-laser scanning (UAV-LS) and terrestrial laser scanning (TLS) data to accurately estimate forest tree metrics under differing management types. Furthermore, we combined the UAV-LS and TLS data to test whether fusion can improve the mapping of the three-dimensional (3D) structure of individual trees to favor accurate estimates of tree metrics. We initially calculated the percentage of point density per square meter aboveground in each height class at intervals of 1 m for the UAV-LS, TLS, and fusion datasets. This helped illustrate the vertical point density distribution that reflects the structural complexity between broadleaf and conifer trees. We then used tree-level point clouds to assess several tree metrics, such as diameter at breast height (DBH), total tree height (HT), crown projection area (PAC), crown width (WC), crown length (LC), 3D crown surface (SC), and 3D crown volume (VC). Our results indicated that LiDAR fusion can increase the estimation accuracy of DBH and HT, especially in broadleaves (97.8% accuracy). In addition, fusion significantly reshaped the modeled crown structures in both plots, which led to improved estimates for all crown metrics. The results show empirical evidence that LiDAR fusion can also have a determining role in supporting ecosystem services. For example, detailed 3D mapping of tree crowns can be used to assess the mitigation of rainfall`s kinetic energy by tree crowns concerning soil erosion and sedimentation near habitable zones.

Loss of Relict Oak Forests along Coastal Louisiana: A Multiyear Analysis Using Google Earth Engine

Coastal forests along the southeastern Gulf of Mexico are known to be diminishing at an alarming rate. The live-oak dominant chenier forests of southeast Louisiana are amongst those exhibiting the steepest declines. The remnant stands have experienced numerous hurricanes and intense storm events in recent years, calling into question the current status and immediate future of this imperiled natural resource. Despite their noted ecological and physiographic importance, there is a lack within national geographic data repositories of accurate representations of forest loss and wetland extent for this region. Supervised machine learning algorithms in the Google Earth Engine were used to classify and process high-resolution National Agricultural Image Product (NAIP) datasets to create accurate (>90%) tree cover maps of the Louisiana Chenier Plains in Cameron and Vermilion Parishes. Data from three different years (2003, 2007, and 2019) were used to map 2302 km2 along the southwestern coast of Louisiana. According to the analyses, there was a 35.73% loss of forest cover in this region between 2003 and 2019. A majority of the land-use change was from tree cover to saltmarsh, with losses in pastoral land also documented. We found variable rates of loss with respect to elevation. Forest cover losses corresponded strongly to rises in mean sea level. These findings deliver a baseline understanding of the rate of forest loss in this region, highlighting the reduction and potentially the eventual extirpation of this imperiled ecosystem.

Tree sketch: An accurate and memory-efficient sketch for network-wide measurement

Traffic measurement provides essential information for bandwidth management and Quality of Service (QoS), which is an enabler of network status discovery. However, with the explosive growth of network traffic, traditional network measurement methods face challenges in terms of memory, computation, accuracy, especially in the high-speed networks. Network devices have no enough CPU or memory resources for collecting, counting and analyzing data streams, which makes measurement results inaccurate and unreliable. To address the problem, we design an accurate and memory-efficient Tree sketch. It consists of three different structures for heavy flow detection, Distributed Denial-of-Service (DDoS) attack detection, super-spreaders detection, respectively. The proposed heavy cuckoo algorithm stores the flows in categories according to their flow sizes reducing hash collisions in the sketch. Besides, Tree sketch employs the Single Instruction Multiple Data (SIMD) instructions to speed up packet processing. Experimental results show that F1 score of Tree sketch is 0.997 with 30 KB memory usage in heavy hitter detection, which is 0.019–0.137 higher than the state-of-the-art sketch algorithms. It is an efficient tool of traffic measurement in the backbone network and provides a good trade-off among accuracy, speed and memory usage.

Species diversity and phylogenetic relationships within the tribe Mentheae (Lamiaceae) in Uzbekistan using ITS sequence data

Regular monitoring and comprehensive study of natural populations rare endangered species, both by classical taxonomy and molecular genetic analysis methods are of considerable importance all over the world. This work has focused at the research of species diversity flora of Uzbekistan using combinations of DNA markers. Currently, there are no universal DNA site for plant species identification. Pairwise sequence alignments and multiple sequence alignments are made using ClustalW program. DNA sequence data were used to verify the taxonomic identity and phylogenetic aspects of the studied representatives the tribe Mentheae (Lamiaceae) collected in Uzbekistan territory. The phylogenetic analyzes of this tribe was reconstructed for 44 species, representing 19 genera using nuclear ribosomal DNA internal transcribed spacer region (nrITS), containing ITS1 and ITS4 adopted as an important basis, since it can provide a high resolution of relationships. 15 consensus ITS gene sequences were obtained for studied species in this work and 29 published molecular sequence data from GenBank were used. Dendrograms were constructed using hierarchical clustering with the software Mega X. The parsimony analyses were resulted in an accurate consensus tree. Three major clades was identified within the tribe Mentheae. So, this paper demonstrates the successfully usefulness of DNA technology as a tool for genetic inventory at the species diversity assessment of rare endangered plants.

Predicting Above-Sentence Discourse Structure Using Distant Supervision from Topic Segmentation

RST-style discourse parsing plays a vital role in many NLP tasks, revealing the underlying semantic/pragmatic structure of potentially complex and diverse documents. Despite its importance, one of the most prevailing limitations in modern day discourse parsing is the lack of large-scale datasets. To overcome the data sparsity issue, distantly supervised approaches from tasks like sentiment analysis and summarization have been recently proposed. Here, we extend this line of research by exploiting distant supervision from topic segmentation, which can arguably provide a strong and oftentimes complementary signal for high-level discourse structures. Experiments on two human-annotated discourse treebanks confirm that our proposal generates accurate tree structures on sentence and paragraph level, consistently outperforming previous distantly supervised models on the sentence-to-document task and occasionally reaching even higher scores on the sentence-to-paragraph level.

QuCo: quartet-based co-estimation of species trees and gene trees.

MotivationPhylogenomics faces a dilemma: on the one hand, most accurate species and gene tree estimation methods are those that co-estimate them; on the other hand, these co-estimation methods do not scale to moderately large numbers of species. The summary-based methods, which first infer gene trees independently and then combine them, are much more scalable but are prone to gene tree estimation error, which is inevitable when inferring trees from limited-length data. Gene tree estimation error is not just random noise and can create biases such as long-branch attraction.ResultsWe introduce a scalable likelihood-based approach to co-estimation under the multi-species coalescent model. The method, called quartet co-estimation (QuCo), takes as input independently inferred distributions over gene trees and computes the most likely species tree topology and internal branch length for each quartet, marginalizing over gene tree topologies and ignoring branch lengths by making several simplifying assumptions. It then updates the gene tree posterior probabilities based on the species tree. The focus on gene tree topologies and the heuristic division to quartets enables fast likelihood calculations. We benchmark our method with extensive simulations for quartet trees in zones known to produce biased species trees and further with larger trees. We also run QuCo on a biological dataset of bees. Our results show better accuracy than the summary-based approach ASTRAL run on estimated gene trees.Availability and implementationQuCo is available on https://github.com/maryamrabiee/quco.Supplementary information Supplementary data are available at Bioinformatics online.

Reconstructing tumor clonal lineage trees incorporating single-nucleotide variants, copy number alterations and structural variations.

MotivationCancer develops through a process of clonal evolution in which an initially healthy cell gives rise to progeny gradually differentiating through the accumulation of genetic and epigenetic mutations. These mutations can take various forms, including single-nucleotide variants (SNVs), copy number alterations (CNAs) or structural variations (SVs), with each variant type providing complementary insights into tumor evolution as well as offering distinct challenges to phylogenetic inference.ResultsIn this work, we develop a tumor phylogeny method, TUSV-ext, which incorporates SNVs, CNAs and SVs into a single inference framework. We demonstrate on simulated data that the method produces accurate tree inferences in the presence of all three variant types. We further demonstrate the method through application to real prostate tumor data, showing how our approach to coordinated phylogeny inference and clonal construction with all three variant types can reveal a more complicated clonal structure than is suggested by prior work, consistent with extensive polyclonal seeding or migration.Availability and implementation https://github.com/CMUSchwartzLab/TUSV-ext.Supplementary information Supplementary data are available at Bioinformatics online.