Annotated Trees Research Articles

Genome microsatellite signature analysis in Adenoviridae family of viruses

Simple sequence repeats (SSRs) are present across both coding & non-coding regions of the genomes of all organisms with diversity in incidence, complexity, and repetition. The present study is focused on Adenoviridae which are non-enveloped viruses consisting of linear double-stranded DNA genome. The members of Adenoviridae are known to cause respiratory illnesses like the common cold (cough, runny nose, mild fever), pneumonia (occasionally), keratoconjunctivitis (infection in the eye known as pink eye), croup and cystitis (inflammation of the bladder). Our investigation aims to extract and analyse SSRs from 68 Adenoviridae genomes. Virus genome sequences were retrieved from NCBI and SSRs extracted from MISA. ETE3 and iTOL were used for the phylogenetic tree construction, annotation and visualization. The genome length of Adenoviridae genomes ranged from 26163 bp to 45667 bp while GC content varied from 33.6 % to 66.9 %. Genome wide analysis revealed a total incidence of 9861 SSRs and 793 cSSRs. The minimum and maximum range of SSR incidence is 112 (A67) to 203 (A61) respectively. The most prevalent mono, di and tri-SSR motif is “A”, “GC/CG” and “GAG/CTC” comprised of 1177, 1859 and 201 occurrences respectively. About 78 % SSRs are present in the coding region in the studied genomes. In terms of protein specific distribution, DNA polymerase enzyme had the highest incidence of 485 SSRs. The presence of mono-SSRs in the A/T region is a marker for host determination and divergence. The average mono-SSRs present in A/T region is 67.14 % and it ranged from 16.22 % to 99.11 %. The high prevalence of mono-SSRs in the A/T region was associated with human and related species as hosts. Further, the clustering of viruses as per their hosts was observed in the phylogenetic tree suggesting the role of host in viral evolution. The presence of unique and conserved cSSRs as genome markers has also been highlighted.

Individual tree detection in large-scale urban environments using high-resolution multispectral imagery

Systematic maps of urban forests are useful for regional planners and ecologists to understand the spatial distribution of trees in cities. However, manually-created urban forest inventories are expensive and time-consuming to create and typically do not provide coverage of private land. Toward the goal of automating urban forest inventory through machine learning techniques, we performed a comparative study of methods for automatically detecting and localizing trees in multispectral aerial imagery of urban environments, and introduce a novel method based on convolutional neural network regression. Our evaluation is supported by a new dataset of over 1,500 images and almost 100,000 tree annotations, covering eight cities, six climate zones, and three image capture years. Our method outperforms previous methods, achieving 73.6% precision and 73.3% recall when trained and tested in Southern California, and 76.5% precision 72.0% recall when trained and tested across the entire state. To demonstrate the scalability of the technique, we produced the first map of trees across the entire urban forest of California. The map we produced provides important data for the planning and management of California’s urban forest, and establishes a proven methodology for potentially producing similar maps nationally and globally in the future.

Interactive Tree of Life (iTOL) v6: recent updates to the phylogenetic tree display and annotation tool.

The Interactive Tree Of Life (https://itol.embl.de) is an online tool for the management, display, annotation and manipulation of phylogenetic and other trees. It is freely available and open to everyone. iTOL version 6 introduces a modernized and completely rewritten user interface, together with numerous new features. A new dataset type has been introduced (colored/labeled ranges), greatly upgrading the functionality of the previous simple colored range annotation function. Additional annotation options have been implemented for several existing dataset types. Dataset template files now support simple assignment of annotations to multiple tree nodes through substring matching, including full regular expression support. Node metadata handling has been greatly extended with novel display and exporting options, and it can now be edited interactively or bulk updated through annotation files. Tree labels can be displayed using multiple simultaneous font styles, with precise positioning, sizing and styling of each individual label part. Various bulk label editing functions have been implemented, simplifying large scale changes of all tree node labels. iTOL's automatic taxonomy assignment functions now support trees based on the Genome Taxonomy Database (GTDB), in addition to the NCBI taxonomy. The functionality of the optional user account pages has been expanded, simplifying the management, navigation and sharing of projects and trees. iTOL currently handles more than one and a half million trees from >130000 individual user accounts.

DINGO: Towards Diverse and Fine-Grained Instruction-Following Evaluation

Instruction-following is particularly crucial for large language models (LLMs) to support diverse user requests. While existing work has made progress in aligning LLMs with human preferences, evaluating their capabilities on instruction-following remains a challenge due to complexity and diversity of real-world user instructions. While existing evaluation methods focus on general skills, they suffer from two main shortcomings, i.e., lack of fine-grained task-level evaluation and reliance on singular instruction expression. To address these problems, this paper introduces DINGO, a fine-grained and diverse instruction-following evaluation dataset that has two main advantages: (1) DINGO is based on a manual annotated, fine-grained and multi-level category tree with 130 nodes derived from real-world user requests; (2) DINGO includes diverse instructions, generated by both GPT-4 and human experts. Through extensive experiments, we demonstrate that DINGO can not only provide more challenging and comprehensive evaluation for LLMs, but also provide task-level fine-grained directions to further improve LLMs.

Annotation method of risk data in a certain field based on pattern matching

With the development of information technology and the increasing complexity of industrial technology, there is an urgent need for a certain field to use big data and artificial intelligence to improve the management and decision-making level. In order to classify the field’s risk text data through intelligent algorithms, analysing the risk distribution and the major problems, this paper researches on the annotation methods of training data in this field. The proposed data annotation method is based on pattern matching, addressing the special problems of risk data annotation in this field (such as strong professionalism, small data volume, high accuracy requirement and timeliness requirements). A new matching pattern is generated through the steps of text segmentation, keyword extraction, pattern preliminary generation, pattern relation tree construction, pattern optimization, pattern generalization, pattern verification, classification and annotation, and final classification and annotation are performed after pattern matching. Performance tests in terms of accuracy, recall rate, and annotation time have shown that the overall performance of the proposed method outperforms that of traditional item-by-item manual annotation, and semi-automatic annotation methods through machine learning. The method described in this paper has strong application value for risk data annotation in this field, and also has certain reference significance for high-density, high-accuracy and high-timeliness data annotation in other fields.

EvoLaps 2: Advanced phylogeographic visualization.

EvoLaps is a user-friendly web application designed to visualize the spatial and temporal spread of pathogens. It takes an annotated tree as entry, such as a maximum clade credibility tree obtained through continuous phylogeographic inference. By following a 'Top-Down' reading of a tree recursively, transitions (latitude/longitude changes from a node to its children) are represented on a cartographic background using graphical paths. The complete set of paths forms the phylogeographic scenario. EvoLaps offers several features to analyze complex scenarios: (1) enhanced path display using multiple graphical variables with time-dependent gradients, (2) cross-highlighting and selection capabilities between the phylogeographic scenario and the phylogenetic tree, (3) production of specific spatio-temporal scales and synthetic views through dynamic and iterative clustering of localities into spatial clusters, (4) animation of the phylogeographic scenario using tree brushing, which can be done manually or automatically, gradually over time or at specific time intervals, and for the entire tree or a specific clade, and (5) an evolving library of additional tools. EvoLaps is freely available for use at evolaps.org.

Multi-GPA-Tree: Statistical approach for pleiotropy informed and functional annotation tree guided prioritization of GWAS results.

Genome-wide association studies (GWAS) have successfully identified over two hundred thousand genotype-trait associations. Yet some challenges remain. First, complex traits are often associated with many single nucleotide polymorphisms (SNPs), most with small or moderate effect sizes, making them difficult to detect. Second, many complex traits share a common genetic basis due to 'pleiotropy' and and though few methods consider it, leveraging pleiotropy can improve statistical power to detect genotype-trait associations with weaker effect sizes. Third, currently available statistical methods are limited in explaining the functional mechanisms through which genetic variants are associated with specific or multiple traits. We propose multi-GPA-Tree to address these challenges. The multi-GPA-Tree approach can identify risk SNPs associated with single as well as multiple traits while also identifying the combinations of functional annotations that can explain the mechanisms through which risk-associated SNPs are linked with the traits. First, we implemented simulation studies to evaluate the proposed multi-GPA-Tree method and compared its performance with existing statistical approaches. The results indicate that multi-GPA-Tree outperforms existing statistical approaches in detecting risk-associated SNPs for multiple traits. Second, we applied multi-GPA-Tree to a systemic lupus erythematosus (SLE) and rheumatoid arthritis (RA), and to a Crohn's disease (CD) and ulcertive colitis (UC) GWAS, and functional annotation data including GenoSkyline and GenoSkylinePlus. Our results demonstrate that multi-GPA-Tree can be a powerful tool that improves association mapping while facilitating understanding of the underlying genetic architecture of complex traits and potential mechanisms linking risk-associated SNPs with complex traits.

The Type IV Secretion System (T4SS) Mediates Symbiosis between Bradyrhizobium sp. SUTN9-2 and Legumes.

There has been little study of the type IV secretion system (T4SS) of bradyrhizobia and its role in legume symbiosis. Therefore, broad host range Bradyrhizobium sp. SUTN9-2 was selected for study. The chromosome of Bradyrhizobium sp. SUTN9-2 contains two copies of the T4SS gene, homologous with the tra/trb operons. A phylogenetic tree of the T4SS gene traG was constructed, which exemplified its horizontal transfer among Bradyrhizobium and Mesorhizobium genera. They also showed similar gene arrangements for the tra/trb operons. However, the virD2 gene was not observed in Mesorhizobium, except M. oppotunistum WSM2075. Interestingly, the orientation of copG, traG, and virD2 cluster was unique to the Bradyrhizobium genus. The phylogenetic tree of copG, traG, and virD2 demonstrated that copies 1 and 2 of these genes were grouped in different clades. In addition, the derived mutant and complementation strains of T4SS were investigated in representative legumes Genistoids, Dalbergioids, and Millettiods. When T4SS copy 1 (T4SS1) was deleted, the nodule number and nitrogenase activity decreased. This supports a positive effect of T4SS1 on symbiosis. In addition, delayed nodulation was observed 7 dpi, which was restored by the complementation of T4SS1. Therefore, T4SS plays an important role in the symbiotic interaction between Bradyrhizobium sp. SUTN9-2 and its leguminous hosts. IMPORTANCE SUTN9-2 is a broad host range strain capable of symbiosis with several legumes. Two copies of T4SS clusters belonging to the tra/trb operon are observed on chromosomes with different gene arrangements. We use phylogenetic tree and gene annotation analysis to predict the evolution of the tra/trb operon of rhizobia. Our finding suggests that the gene encoding the T4SS gene among Bradyrhizobium and Mesorhizobium may have coevolution. In addition, Bradyrhizobium has a uniquely arranged copG, traG, and virD2 gene cluster. The results of T4SS1 gene deletion and complementation revealed its positive effect on nodulation. Therefore, T4SS seems to be another determinant for symbiosis. This is the first report on the role of T4SS in Bradyrhizobium symbiosis.

The protein kinases of Dictyostelia and their incorporation into a signalome

Protein kinases are major regulators of cellular processes, but the roles of most kinases remain unresolved. Dictyostelid social amoebas have been useful in identifying functions for 30% of its kinases in cell migration, cytokinesis, vesicle trafficking, gene regulation and other processes but their upstream regulators and downstream effectors are mostly unknown. Comparative genomics can assist to distinguish between genes involved in deeply conserved core processes and those involved in species-specific innovations, while co-expression of genes as evident from comparative transcriptomics can provide cues to the protein complement of regulatory networks. Genomes and developmental and cell-type specific transcriptomes are available for species that span the 0.5 billion years of evolution of Dictyostelia from their unicellular ancestors. In this work we analysed conservation and change in the abundance, functional domain architecture and developmental regulation of protein kinases across the 4 major taxon groups of Dictyostelia. All data are summarized in annotated phylogenetic trees of the kinase subtypes and accompanied by functional information of all kinases that were experimentally studied. We detected 393 different protein kinase domains across the five studied genomes, of which 212 were fully conserved. Conservation was highest (71%) in the previously defined AGC, CAMK, CK1, CMCG, STE and TKL groups and lowest (26%) in the “other” group of typical protein kinases. This was mostly due to species-specific single gene amplification of “other” kinases. Apart from the AFK and α-kinases, the atypical protein kinases, such as the PIKK and histidine kinases were also almost fully conserved. The phylogeny-wide developmental and cell-type specific expression profiles of the protein kinase genes were combined with profiles from the same transcriptomic experiments for the families of G-protein coupled receptors, small GTPases and their GEFs and GAPs, the transcription factors and for all genes that upon lesion generate a developmental defect. This dataset was subjected to hierarchical clustering to identify clusters of co-expressed genes that potentially act together in a signalling network. The work provides a valuable resource that allows researchers to identify protein kinases and other regulatory proteins that are likely to act as intermediates in a network of interest.

Tree Visualization By One Table (tvBOT): a web application for visualizing, modifying and annotating phylogenetic trees.

tvBOT is a user-friendly and efficient web application for visualizing, modifying, and annotating phylogenetic trees. It is highly efficient in data preparation without requiring redundant style and syntax data. Tree annotations are powered by a data-driven engine that only requires practical data organized in uniform formats and saved as one table file. A layer manager is developed to manage annotation dataset layers, allowing the addition of a specific layer by selecting the columns of a corresponding annotation data file. Furthermore, tvBOT renders style adjustments in real-time and diversified ways. All style adjustments can be made on a highly interactive user interface and are available for mobile devices. The display engine allows the changes to be updated and rendered in real-time. In addition, tvBOT supports the combination display of 26 annotation dataset types to achieve multiple formats for tree annotations with reusable phylogenetic data. Besides several publication-ready graphics formats, JSON format can be exported to save the final drawing state and all related data, which can be shared with other users, uploaded to restore the final drawing state for re-editing or used as a style template for quickly retouching a new tree file. tvBOT is freely available at: https://www.chiplot.online/tvbot.html.

Synteny Identifies Reliable Orthologs for Phylogenomics and Comparative Genomics of the Brassicaceae.

Large genomic data sets are becoming the new normal in phylogenetic research, but the identification of true orthologous genes and the exclusion of problematic paralogs is still challenging when applying commonly used sequencing methods such as target enrichment. Here, we compared conventional ortholog detection using OrthoFinder with ortholog detection through genomic synteny in a data set of 11 representative diploid Brassicaceae whole-genome sequences spanning the entire phylogenetic space. Then, we evaluated the resulting gene sets regarding gene number, functional annotation, and gene and species tree resolution. Finally, we used the syntenic gene sets for comparative genomics and ancestral genome analysis. The use of synteny resulted in considerably more orthologs and also allowed us to reliably identify paralogs. Surprisingly, we did not detect notable differences between species trees reconstructed from syntenic orthologs when compared with other gene sets, including the Angiosperms353 set and a Brassicaceae-specific target enrichment gene set. However, the synteny data set comprised a multitude of gene functions, strongly suggesting that this method of marker selection for phylogenomics is suitable for studies that value downstream gene function analysis, gene interaction, and network studies. Finally, we present the first ancestral genome reconstruction for the Core Brassicaceae which predating the Brassicaceae lineage diversification ∼25 million years ago.

Topiary: Pruning the manual labor from ancestral sequence reconstruction.

Ancestral sequence reconstruction (ASR) is a powerful tool to study the evolution of proteins and thus gain deep insight into the relationships among protein sequence, structure, and function. A major barrier to its broad use is the complexity of the task: it requires multiple software packages, complex file manipulations, and expert phylogenetic knowledge. Here we introduce topiary, a software pipeline that aims to overcome this barrier. To use topiary, users prepare a spreadsheet with a handful of sequences. Topiary then: (1) Infers the taxonomic scope for the ASR study and finds relevant sequences by BLAST; (2) Does taxonomically informed sequence quality control and redundancy reduction; (3) Constructs a multiple sequence alignment; (4) Generates a maximum-likelihood gene tree; (5) Reconciles the gene tree to the species tree; (6) Reconstructs ancestral amino acid sequences; and (7) Determines branch supports. The pipeline returns annotated evolutionary trees, spreadsheets with sequences, and graphical summaries of ancestor quality. This is achieved by integrating modern phylogenetics software (Muscle5, RAxML-NG, GeneRax, and PastML) with online databases (NCBI and the Open Tree of Life). In this paper, we introduce non-expert readers to the steps required for ASR, describe the specific design choices made in topiary, provide a detailed protocol for users, and then validate the pipeline using datasets from a broad collection of protein families. Topiary is freely available for download: https://github.com/harmslab/topiary.

Generalization of U-Net Semantic Segmentation for Forest Change Detection in South Korea Using Airborne Imagery

Forest change detection is essential to prevent the secondary damage occurring by landslides causing profound results to the environment, ecosystem, and human society. The remote sensing technique is a solid candidate for identifying the spatial distribution of the forest. Even though the acquiring and processing of remote sensing images are costly and time- and labor-consuming, the development of open source data platforms relieved these burdens by providing free imagery. The open source images also accelerate the generation of algorithms with large datasets. Thus, this study evaluated the generalizability of forest change detection by using open source airborne images and the U-Net model. U-Net model is convolutional deep learning architecture to effectively extract the image features for semantic segmentation tasks. The airborne and tree annotation images of the capital area in South Korea were processed for building U-Net input, while the pre-trained U-Net structure was adopted and fine-tuned for model training. The U-Net model provided robust results of the segmentation that classified forest and non-forest regions, having pixel accuracies, F1 score, and intersection of union (IoU) of 0.99, 0.97, and 0.95, respectively. The optimal epoch and excluded ambiguous label contributed to maintaining virtuous segmentation of the forest region. In addition, this model could correct the false label images because of showing exact classification results when the training labels were incorrect. After that, by using the open map service, the well-trained U-Net model classified forest change regions of Chungcheong from 2009 to 2016, Gangwon from 2010 to 2019, Jeolla from 2008 to 2013, Gyeongsang from 2017 to 2019, and Jeju Island from 2008 to 2013. That is, the U-Net was capable of forest change detection in various regions of South Korea at different times, despite the training on the model with only the images of the capital area. Overall, this study demonstrated the generalizability of a deep learning model for accurate forest change detection.

Alien trees and shrubs of Latvia – evaluation of current status and invasiveness

Abstract This article presents an updated list of annotated invasive trees and shrubs in Latvia. This list includes 178 taxa about which extensive information has been collected and analysed: family, first records in the area, native range, invasiveness status, vector of introduction, and species distribution in Latvia. In this article, the authors, for the first time in Latvia, provided an invasiveness risk assessment and distinguished risk classes for trees and shrubs. The methodology previously developed by E. Weber and D. Gut was used to assess the risk of invasiveness. The most widely represented families in the list of invasive trees and shrubs are Rosaceae (51 taxa or 28.65%) and Salicaceae (20 taxa or 11.24%). Having assessed the invasiveness status, two species (Acer negundo L. and Sorbaria sorbifolia (L.) A. Braun) were identified as transformers, and 42 tree and shrub taxa were identified as invasive. After the assessment of the invasiveness risk, the species were divided into three risk classes. The highest risk class includes 32 taxa that are threatening or are likely to threaten natural habitats in Latvia in the future. The results of this study can be used for further dendrological studies in relation to global change and for practical nature conservation and landscape gardening.

Genome-wide identification of the rubber tree superoxide dismutase (SOD) gene family and analysis of its expression under abiotic stress.

BackgroundThe rubber tree (Hevea brasiliensis) is the only species capable of producing high-quality natural rubber for commercial use, and is often subjected to various abiotic stresses in non-traditional rubber plantation areas. Superoxide dismutase (SOD) is a vital metalloenzyme translated by a SOD gene family member and acts as a first-line of protection in plant cells by catalysing the disproportionation of reactive oxygen species (ROS) to produce H2O2 and O2. However, the SOD gene family is not reported in rubber trees.MethodsHere, we used hidden markov model (HMM) and BLASTP methods to identify SOD genes in the H. brasiliensis genome. Phylogenetic tree, conserved motifs, gene structures, cis elements, and gene ontology annotation (GO) analyses were performed using MEGA 6.0, MEME, TBtools, PlantCARE, and eggNOG database, respectively. HbSOD gene expression profiles were analysed using quantitative reverse transcription polymerase chain reaction (qRT-PCR).ResultsWe identified nine HbSOD genes in the rubber tree genome, including five HbCSDs, two HbFSDs, and two HbMSDs. Phylogenetic relationship analysis classified the SOD proteins from the rubber tree and other related species into three subfamilies. The results of gene structure and conserved motif analysis illustrated that most HbSOD genes have similar exon-intron numbers and conserved motifs in the same evolutionary branch. Five hormone-related, four stress-related, and light-responsive elements were detected in the HbSODs’ promoters. HbSODs were expressed in different tissues, gradually increased with leaf development, and were abundantly expressed in mature leaves. HbCSD2 and HbCSD4 was significantly upregulated under low and high temperatures, and salt stress, except for HbCSD2, by heat. Furthermore, most HbSOD genes were significantly upregulated by drought, except HbMSD2. These findings imply that these genes may play vital roles in rubber tree stress resistance. Our results provide a basis for further studies on the functions of HbSOD genes in rubber trees and stress response mechanisms.

Automatic detection of snow breakage at single tree level using YOLOv5 applied to UAV imagery

The assessment of forest abiotic damages such as snow breakage is important to ensure compensation to forest owners. Currently, information on the extent of snow breakage is gathered through time-consuming and potentially biased field surveys. In such situations where field surveys are still common practice, unmanned aerial vehicles (UAVs) are increasingly being used to provide a more cost-efficient and objective methods to answer forest information needs. Further, the advent of sophisticated computer vision techniques such as convolutional neural networks (CNNs) offers new ways to analyze image data more efficiently and accurately. We proposed an object detection method to automatically identify trees and classify them according to the damage by snow based on a YOLO CNN architecture. UAV imagery collected across 89 study areas and over the course of the entire year were manually annotated into a total of >55 K single trees classified as healthy, damaged, or dead. The annotated trees, along with the corresponding UAV imagery were used to train a YOLOv5 object detection model. Furthermore, we tested the effect of seasonality, and varying atmospheric and lighting conditions on the model’s performance. Based on an independent test set of data we found that the general model including all of the data (i.e. any seasons, atmospheric conditions, and time of the day) outperformed all other tested scenarios (i.e. precision = 62 %; recall = 61 %). Furthermore, we found that despite the fact that the snow damaged trees represented a minority class (i.e. 16 % of the annotated trees), they were detected with the largest precision (76 %) and recall (78 %). Finally, the general model transferred well across the variation in seasons, atmospheric and illumination conditions, making it suitable for usage for any new UAV image acquisition.

Unsupervised and few-shot parsing from pretrained language models

Pretrained language models are generally acknowledged to be able to encode syntax [46,16,13]. In this article, we propose UPOA, an Unsupervised constituent Parsing model that calculates an Out Association score solely based on the self-attention weight matrix learned in a pretrained language model as the syntactic distance for span segmentation. We further propose an enhanced version, UPIO, which exploits both inside association and outside association scores for estimating the likelihood of a span. Experiments with UPOA and UPIO disclose that the linear projection matrices for the query and key in the self-attention mechanism play an important role in parsing. We therefore extend the unsupervised models to few-shot parsing models (FPOA, FPIO) that use a few annotated trees to learn better linear projection matrices for parsing. Experiments on the Penn Treebank demonstrate that our unsupervised parsing model UPIO achieves results comparable to the state of the art on short sentences (length <= 10). Our few-shot parsing model FPIO trained with only 20 annotated trees outperforms a previous few-shot parsing method trained with 50 annotated trees. Experiments on cross-lingual parsing show that both unsupervised and few-shot parsing methods are better than previous methods on most languages of SPMRL [39].

Orthology Clusters from Gene Trees with Possvm.

Possvm (Phylogenetic Ortholog Sorting with Species oVerlap and MCL [Markov clustering algorithm]) is a tool that automates the process of identifying clusters of orthologous genes from precomputed phylogenetic trees and classifying gene families. It identifies orthology relationships between genes using the species overlap algorithm to infer taxonomic information from the gene tree topology, and then uses the MCL to identify orthology clusters and provide annotated gene families. Our benchmarking shows that this approach, when provided with accurate phylogenies, is able to identify manually curated orthogroups with very high precision and recall. Overall, Possvm automates the routine process of gene tree inspection and annotation in a highly interpretable manner, and provides reusable outputs and phylogeny-aware gene annotations that can be used to inform comparative genomics and gene family evolution analyses.

Interactive Tree Of Life (iTOL) v5: an online tool for phylogenetic tree display and annotation.

The Interactive Tree Of Life (https://itol.embl.de) is an online tool for the display, manipulation and annotation of phylogenetic and other trees. It is freely available and open to everyone. iTOL version 5 introduces a completely new tree display engine, together with numerous new features. For example, a new dataset type has been added (MEME motifs), while annotation options have been expanded for several existing ones. Node metadata display options have been extended and now also support non-numerical categorical values, as well as multiple values per node. Direct manual annotation is now available, providing a set of basic drawing and labeling tools, allowing users to draw shapes, labels and other features by hand directly onto the trees. Support for tree and dataset scales has been extended, providing fine control over line and label styles. Unrooted tree displays can now use the equal-daylight algorithm, proving a much greater display clarity. The user account system has been streamlined and expanded with new navigation options and currently handles >1 million trees from >70 000 individual users.

Re-purposing software for functional characterization of the microbiome

BackgroundWidespread bioinformatic resource development generates a constantly evolving and abundant landscape of workflows and software. For analysis of the microbiome, workflows typically begin with taxonomic classification of the microorganisms that are present in a given environment. Additional investigation is then required to uncover the functionality of the microbial community, in order to characterize its currently or potentially active biological processes. Such functional analysis of metagenomic data can be computationally demanding for high-throughput sequencing experiments. Instead, we can directly compare sequencing reads to a functionally annotated database. However, since reads frequently match multiple sequences equally well, analyses benefit from a hierarchical annotation tree, e.g. for taxonomic classification where reads are assigned to the lowest taxonomic unit.ResultsTo facilitate functional microbiome analysis, we re-purpose well-known taxonomic classification tools to allow us to perform direct functional sequencing read classification with the added benefit of a functional hierarchy. To enable this, we develop and present a tree-shaped functional hierarchy representing the molecular function subset of the Gene Ontology annotation structure. We use this functional hierarchy to replace the standard phylogenetic taxonomy used by the classification tools and assign query sequences accurately to the lowest possible molecular function in the tree. We demonstrate this with simulated and experimental datasets, where we reveal new biological insights.ConclusionsWe demonstrate that improved functional classification of metagenomic sequencing reads is possible by re-purposing a range of taxonomic classification tools that are already well-established, in conjunction with either protein or nucleotide reference databases. We leverage the advances in speed, accuracy and efficiency that have been made for taxonomic classification and translate these benefits for the rapid functional classification of microbiomes. While we focus on a specific set of commonly used methods, the functional annotation approach has broad applicability across other sequence classification tools. We hope that re-purposing becomes a routine consideration during bioinformatic resource development.CWJ3S1nYSzUqUEQpCfrTiKVideo abstract