scholarly journals PhyD3: a phylogenetic tree viewer with extended phyloXML support for functional genomics data visualization

2017 ◽  
Author(s):  
Łukasz Kreft ◽  
Alexander Botzki ◽  
Frederik Coppens ◽  
Klaas Vandepoele ◽  
Michiel Van Bel
Keyword(s):  
Phylogenetic Tree ◽  
Functional Genomics ◽  
Open Source ◽  
Web Sites ◽  
Phylogenetic Trees ◽  
Biological Data ◽  
Web Technologies ◽  
Link Type ◽  
Current Implementation ◽  
Tree Viewer

AbstractMotivation:Comparative and evolutionary studies utilise phylogenetic trees to analyse and visualise biological data. Recently, several web-based tools for the display, manipulation, and annotation of phylogenetic trees, such as iTOL and Evolview, have released updates to be compatible with the latest web technologies. While those web tools operate an open server access model with a multitude of registered users, a feature-rich open source solution using current web technologies is not available.Results:Here, we present an extension of the widely used PhyloXML standard with several new options to accommodate functional genomics or annotation datasets for advanced visualization. Furthermore, PhyD3 has been developed as a lightweight tool using the JavaScript library D3.js to achieve a state-of-the-art phylogenetic tree visualisation in the web browser, with support for advanced annotations. The current implementation is open source, easily adaptable and easy to implement in third parties’ web sites.Availability:More information about PhyD3 itself, installation procedures, and implementation links are available at http://phyd3.bits.vib.be and at http://github.com/vibbits/phyd3/.Contact:[email protected]

scholarly journals PhyD3: a phylogenetic tree viewer with extended phyloXML support for functional genomics data visualization

2017 ◽  
Vol 33 (18) ◽  
pp. 2946-2947 ◽  
Author(s):  
Łukasz Kreft ◽  
Alexander Botzki ◽  
Frederik Coppens ◽  
Klaas Vandepoele ◽  
Michiel Van Bel
Keyword(s):  
Phylogenetic Tree ◽  
Functional Genomics ◽  
Data Visualization ◽  
Tree Viewer

scholarly journals AGEpy: a Python package for computational biology

2018 ◽  
Author(s):  
Franziska Metge ◽  
Robert Sehlke ◽  
Jorge Boucas
Keyword(s):  
Computational Biology ◽  
Open Source ◽  
High Throughput ◽  
Biological Data ◽  
Command Line ◽  
High Throughput Analysis ◽  
Throughput Analysis ◽  
Link Type ◽  
Biological Meaning ◽  
Python Package

AbstractSummary:AGEpy is a Python package focused on the transformation of interpretable data into biological meaning. It is designed to support high-throughput analysis of pre-processed biological data using either local Python based processing or Python based API calls to local or remote servers. In this application note we describe its different Python modules as well as its command line accessible toolsaDiff,abed,blasto,david, andobo2tsv.Availability:The open source AGEpy Python package is freely available at:https://github.com/mpg-age-bioinformatics/AGEpy.Contact:[email protected]

scholarly journals NeuroPycon: An open-source Python toolbox for fast multi-modal and reproducible brain connectivity pipelines

2019 ◽  
Author(s):  
David Meunier ◽  
Annalisa Pascarella ◽  
Dmitrii Altukhov ◽  
Mainak Jas ◽  
Etienne Combrisson ◽  
...  
Keyword(s):  
Open Source ◽  
Brain Connectivity ◽  
Reproducible Research ◽  
Link Type ◽  
Large Sets ◽  
Current Implementation ◽  
Wide Range ◽  
Neuroimaging Software ◽  
Brain Data ◽  
Automatic Removal

AbstractRecent years have witnessed a massive push towards reproducible research in neuroscience. Unfortunately, this endeavor is often challenged by the large diversity of tools used, project-specific custom code and the difficulty to track all user-defined parameters. NeuroPycon is an open-source multi-modal brain data analysis toolkit which provides Python-based template pipelines for advanced multi-processing of MEG, EEG, functional and anatomical MRI data, with a focus on connectivity and graph theoretical analyses. Importantly, it provides shareable parameter files to facilitate replication of all analysis steps. NeuroPycon is based on the NiPype framework which facilitates data analyses by wrapping many commonly-used neuroimaging software tools into a common Python environment. In other words, rather than being a brain imaging software with is own implementation of standard algorithms for brain signal processing, NeuroPycon seamlessly integrates existing packages (coded in python, Matlab or other languages) into a unified python framework. Importantly, thanks to the multi-threaded processing and computational efficiency afforded by NiPype, NeuroPycon provides an easy option for fast parallel processing, which critical when handling large sets of multi-dimensional brain data. Moreover, its flexible design allows users to easily configure analysis pipelines by connecting distinct nodes to each other. Each node can be a Python-wrapped module, a user-defined function or a well-established tool (e.g. MNE-Python for MEG analysis, Radatools for graph theoretical metrics, etc.). Last but not least, the ability to use NeuroPycon parameter files to fully describe any pipeline is an important feature for reproducibility, as they can be shared and used for easy replication by others. The current implementation of NeuroPycon contains two complementary packages: The first, called ephypype, includes pipelines for electrophysiology analysis and a command-line interface for on the fly pipeline creation. Current implementations allow for MEG/EEG data import, pre-processing and cleaning by automatic removal of ocular and cardiac artefacts, in addition to sensor or source-level connectivity analyses. The second package, called graphpype, is designed to investigate functional connectivity via a wide range of graph-theoretical metrics, including modular partitions. The present article describes the philosophy, architecture, and functionalities of the toolkit and provides illustrative examples through interactive notebooks. NeuroPycon is available for download via github (https://github.com/neuropycon) and the two principal packages are documented online (https://neuropycon.github.io/ephypype/index.html. and https://neuropycon.github.io/graphpype/index.html). Future developments include fusion of multi-modal data (eg. MEG and fMRI or intracranial EEG and fMRI). We hope that the release of NeuroPycon will attract many users and new contributors, and facilitate the efforts of our community towards open source tool sharing and development, as well as scientific reproducibility.

scholarly journals HiGlass: Web-based Visual Exploration and Analysis of Genome Interaction Maps

2017 ◽  
Author(s):  
Peter Kerpedjiev ◽  
Nezar Abdennur ◽  
Fritz Lekschas ◽  
Chuck McCallum ◽  
Kasper Dinkla ◽  
...  
Keyword(s):  
Open Source ◽  
Visual Exploration ◽  
Visualization Tool ◽  
Data Types ◽  
Experimental Conditions ◽  
Web Based ◽  
Web Technologies ◽  
Link Type

AbstractWe present HiGlass, an open source visualization tool built on web technologies that provides a rich interface for rapid, multiplex, and multiscale navigation of 2D genomic maps alongside 1D genomic tracks, allowing users to combine various data types, synchronize multiple visualization modalities, and share fully customizable views with others. We demonstrate its utility in exploring different experimental conditions, comparing the results of analyses, and creating interactive snapshots to share with collaborators and the broader public. HiGlass is accessible online athttp://higlass.ioand is also available as a containerized application that can be run on any platform.

scholarly journals D-graph clusters flaviviruses and β-coronaviruses according to their hosts, disease type and human cell receptors

2020 ◽  
Author(s):  
Benjamin A. Braun ◽  
Catherine H. Schein ◽  
Werner Braun
Keyword(s):  
Phylogenetic Trees ◽  
Sequence Data ◽  
Biological Data ◽  
Supplementary Information ◽  
Disease Phenotypes ◽  
Link Type ◽  
Physico Chemical ◽  
Protein Sequence Data ◽  
Large Groups ◽  
Physico Chemical Property

AbstractMotivationThere is a need for rapid and easy to use, alignment free methods to cluster large groups of protein sequence data. Commonly used phylogenetic trees based on alignments can be used to visualize only a limited number of protein sequences. DGraph, introduced here, is a dynamic programming application developed to generate 2D-maps based on similarity scores for sequences. The program automatically calculates and graphically displays property distance (PD) scores based on physico-chemical property (PCP) similarities from an unaligned list of FASTA files. Such “PD-graphs” show the interrelatedness of the sequences, whereby clusters can reveal deeper connectivities.ResultsPD-Graphs generated for flavivirus (FV), enterovirus (EV), and coronavirus (CoV) sequences from complete polyproteins or individual proteins are consistent with biological data on vector types, hosts, cellular receptors and disease phenotypes. PD-graphs separate the tick- from the mosquito-borne FV, clusters viruses that infect bats, camels, seabirds and humans separately and the clusters correlate with disease phenotype. The PD method segregates the β-CoV spike proteins of SARS, SARS-CoV-2, and MERS sequences from other human pathogenic CoV, with clustering consistent with cellular receptor usage. The graphs also suggest evolutionary relationships that may be difficult to determine with conventional bootstrapping methods that require postulating an ancestral sequence.Availability and implementationDGraph is written in Java, compatible with the Java 5 runtime or newer. Source code and executable is available from the GitHub website (https://github.com/bjmnbraun/DGraph/releases). Documentation for installation and use of the software is available from the Readme.md file at (https://github.com/bjmnbraun/DGraph)[email protected] or [email protected] informationSupplementary information Table S1 and Fig. S1 are online available.

scholarly journals Phylostems: a new graphical tool to investigate temporal signal of heterochronous sequences at various evolutionary scales

2020 ◽  
Author(s):  
Anna Doizy ◽  
Amaury Prin ◽  
Guillaume Cornu ◽  
Frederic Chiroleu ◽  
Adrien Rieux
Keyword(s):  
Phylogenetic Tree ◽  
Dna Sequences ◽  
Phylogenetic Trees ◽  
A Priori ◽  
List Type ◽  
Current Implementation ◽  
Graphical Tool ◽  
User Friendly ◽  
Sampling Times ◽  
Rates Of Molecular Evolution

AbstractMolecular tip-dating of phylogenetic trees is a growing discipline that uses DNA sequences sampled at different points in time to co-estimate the timing of evolutionary events with rates of molecular evolution. Such inferences should only be performed when there is sufficient temporal signal within the analysed dataset. Hence, it is important for researchers to be able to test their dataset for the amount and consistency of temporal signal prior to any tip-dating inference. For this purpose, the most popular method considered to-date has been the “root-to-tip regression” which consist in fitting a linear regression of the number of substitutions accumulated from the root to the tips of a phylogenetic tree as a function of sampling times. The main limitation of the regression method, in its current implementation, relies in the fact that the temporal signal can only be tested at the whole-tree evolutionary scale.To fill this methodological gap, we introduce phylostems, a new graphical and user-friendly tool developed to investigate temporal signal at every evolutionary scale of a phylogenetic tree.Phylostems allows detecting without a priori whether any subset of a tree would contain sufficient temporal signal for tip-based inference to be performed. We provide a “how to” guide by running phylostems on empirical datasets and supply guidance for results interpretation. Phylostems is freely available at https://pvbmt-apps.cirad.fr/apps/phylostems.Considering the impressive increase in availability and use of heterochronous datasets, we hope the new functionality provided by phylostems will help biologists to perform thorough tip-dating inferences.

scholarly journals SPECTRE: a Suite of PhylogEnetiC Tools for Reticulate Evolution

2017 ◽  
Author(s):  
Sarah Bastkowski ◽  
Daniel Mapleson ◽  
Andreas Spillner ◽  
Taoyang Wu ◽  
Monika Balvočiūtė ◽  
...  
Keyword(s):  
Open Source ◽  
Data Structures ◽  
Phylogenetic Trees ◽  
High Performance ◽  
Reticulate Evolution ◽  
Supplementary Information ◽  
Link Type ◽  
Data Structures And Algorithms ◽  
User Friendly ◽  
Split Networks

ABSTRACTSummarySplit-networks are a generalization of phylogenetic trees that have proven to be a powerful tool in phylogenetics. Various ways have been developed for computing such networks, including split-decomposition, NeighborNet, QNet and FlatNJ. Some of these approaches are implemented in the user-friendly SplitsTree software package. However, to give the user the option to adjust and extend these approaches and to facilitate their integration into analysis pipelines, there is a need for robust, open-source implementations of associated data structures and algorithms. Here we present SPECTRE, a readily available, open-source library of data structures written in Java, that comes complete with new implementations of several pre-published algorithms and a basic interactive graphical interface for visualizing planar split networks. SPECTRE also supports the use of longer running algorithms by providing command line interfaces, which can be executed on servers or in High Performance Computing (HPC) environments.AvailabilityFull source code is available under the GPLv3 license at: https://github.com/maplesond/SPECTRESPECTRE’s core library is available from Maven Central at: https://mvnrepository.com/artifactuk.ac.uea.cmp.spectre/coreDocumentation is available at: http://spectre-suite-of-phylogenetic-tools-for-reticulate-evolution.readthedocs.io/en/latest/[email protected] Information (SI)Supplementary information is available at Bioinformatics online.

scholarly journals Computational Analysis of Distance based Phylogenetic Tree for Azotobacter Species

2019 ◽  
Vol 8 (10) ◽  
pp. 810-814
Keyword(s):  
Phylogenetic Tree ◽  
Phylogenetic Trees ◽  
Bacterial Species ◽  
Maximum Size ◽  
Biological Data ◽  
Pictorial Representation ◽  
Human Beings ◽  
Substitution Model ◽  
Branch Lengths ◽  
Phylogenetic Model

Phylogenetic tree is a pictorial representation of evolutionary relationships between organisms. It is important method to analyze the biological data. Phylogenetic trees are based on two methods : Distance based and Character based. Phylogenetic tree are used comparative analysis of any organism like human Beings, Animals, Bacteria, Viruses and Fungi’s etc. In this paper we compare 12 different nucleotide sequences of Azotobacter species having linear DNA of 999 BP as maximum size using substitution model and phylogenetic model. In this study two different models name P-Distance and Jukes cantor model are used and helped in finding UPGMA or Neighbour joining method efficiency in evaluating the similarity and dissimilarity of bacterial species. This paper gives influence in reconciliation of Azotobacter species to produce phylogram with informative branch lengths. This further leads to analyze and understand various expressive characters of Azotobacter in agriculture field.

scholarly journals Computing nearest neighbour interchange distances between ranked phylogenetic trees

2021 ◽  
Vol 82 (1-2) ◽  
Author(s):  
Lena Collienne ◽  
Alex Gavryushkin
Keyword(s):  
Cancer Research ◽  
Computational Complexity ◽  
Phylogenetic Tree ◽  
Shortest Path ◽  
Phylogenetic Trees ◽  
Shortest Paths ◽  
Nearest Neighbour ◽  
Tree Inference ◽  
Subtree Prune And Regraft ◽  
Comparison Algorithms

AbstractMany popular algorithms for searching the space of leaf-labelled (phylogenetic) trees are based on tree rearrangement operations. Under any such operation, the problem is reduced to searching a graph where vertices are trees and (undirected) edges are given by pairs of trees connected by one rearrangement operation (sometimes called a move). Most popular are the classical nearest neighbour interchange, subtree prune and regraft, and tree bisection and reconnection moves. The problem of computing distances, however, is $${\mathbf {N}}{\mathbf {P}}$$ N P -hard in each of these graphs, making tree inference and comparison algorithms challenging to design in practice. Although anked phylogenetic trees are one of the central objects of interest in applications such as cancer research, immunology, and epidemiology, the computational complexity of the shortest path problem for these trees remained unsolved for decades. In this paper, we settle this problem for the ranked nearest neighbour interchange operation by establishing that the complexity depends on the weight difference between the two types of tree rearrangements (rank moves and edge moves), and varies from quadratic, which is the lowest possible complexity for this problem, to $${\mathbf {N}}{\mathbf {P}}$$ N P -hard, which is the highest. In particular, our result provides the first example of a phylogenetic tree rearrangement operation for which shortest paths, and hence the distance, can be computed efficiently. Specifically, our algorithm scales to trees with tens of thousands of leaves (and likely hundreds of thousands if implemented efficiently).

scholarly journals Amphritea ceti sp. nov., isolated from faeces of Beluga whale (Delphinapterus leucas)

2014 ◽  
Vol 64 (Pt_12) ◽  
pp. 4068-4072 ◽  
Author(s):  
Young-Ok Kim ◽  
Sooyeon Park ◽  
Doo Nam Kim ◽  
Bo-Hye Nam ◽  
Sung-Min Won ◽  
...  
Keyword(s):  
Phylogenetic Trees ◽  
Sequence Similarity ◽  
Beluga Whale ◽  
Delphinapterus Leucas ◽  
Rrna Gene ◽  
Phenotypic Properties ◽  
Content Type ◽  
Link Type ◽  
Beluga Whale Delphinapterus Leucas ◽  
Type Strains

A Gram-stain-negative, aerobic, non-spore-forming, non-flagellated and rod-shaped or ovoid bacterial strain, designated RA1T, was isolated from faeces collected from Beluga whale (Delphinapterus leucas) in Yeosu aquarium, South Korea. Strain RA1T grew optimally at 25 °C, at pH 7.0–8.0 and in the presence of 2.0 % (w/v) NaCl. Neighbour-joining, maximum-likelihood and maximum-parsimony phylogenetic trees based on 16S rRNA gene sequences revealed that strain RA1T joins the cluster comprising the type strains of three species of the genus Amphritea , with which it exhibited 95.8–96.0 % sequence similarity. Sequence similarities to the type strains of other recognized species were less than 94.3 %. Strain RA1T contained Q-8 as the predominant ubiquinone and summed feature 3 (C16 : 1ω7c and/or C16 : 1ω6c), C18 : 1ω7c and C16 : 0 as the major fatty acids. The major polar lipids of strain RA1T were phosphatidylethanolamine, phosphatidylglycerol, two unidentified lipids and one unidentified aminolipid. The DNA G+C content of strain RA1T was 47.4 mol%. The differential phenotypic properties, together with the phylogenetic distinctiveness, revealed that strain RA1T is separated from other species of the genus Amphritea . On the basis of the data presented, strain RA1T is considered to represent a novel species of the genus Amphritea , for which the name Amphritea ceti sp. nov. is proposed. The type strain is RA1T ( = KCTC 42154T = NBRC 110551T).

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