Applications and Considerations of GToTree: A User-Friendly Workflow for Phylogenomics.
Ontology highlight
ABSTRACT:
SUBMITTER: Lee MD
PROVIDER: S-EPMC6659180 | biostudies-literature | 2019
REPOSITORIES: biostudies-literature
Publications
Applications and Considerations of GToTree: A User-Friendly Workflow for Phylogenomics.
Evolutionary bioinformatics online 20190725
Phylogenomics is the practice of attempting to infer evolutionary relationships at a genome-level. This is becoming a standard step in the characterization of newly recovered genomes and to direct/constrain further research; yet the process from start to finish of building a de novo phylogenomic tree that is specific to the organisms of interest can still be computationally intractable for many biologists. GToTree is a recently published user-friendly workflow for phylogenomics intended to give ...[more]
Similar Datasets
Project description:SummaryGenome-level evolutionary inference (i.e. phylogenomics) is becoming an increasingly essential step in many biologists' work. Accordingly, there are several tools available for the major steps in a phylogenomics workflow. But for the biologist whose main focus is not bioinformatics, much of the computational work required-such as accessing genomic data on large scales, integrating genomes from different file formats, performing required filtering, stitching different tools together etc.-can be prohibitive. Here I introduce GToTree, a command-line tool that can take any combination of fasta files, GenBank files and/or NCBI assembly accessions as input and outputs an alignment file, estimates of genome completeness and redundancy, and a phylogenomic tree based on a specified single-copy gene (SCG) set. Although GToTree can work with any custom hidden Markov Models (HMMs), also included are 13 newly generated SCG-set HMMs for different lineages and levels of resolution, built based on searches of ∼12 000 bacterial and archaeal high-quality genomes. GToTree aims to give more researchers the capability to make phylogenomic trees.Availability and implementationGToTree is open-source and freely available for download from: github.com/AstrobioMike/GToTree. It is implemented primarily in bash with helper scripts written in python.Supplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC6792077 | biostudies-literature
Project description:Pipeline tools are becoming increasingly important within the field of bioinformatics. Using a pipeline manager to manage and run workflows comprised of multiple tools reduces workload and makes analysis results more reproducible. Existing tools require significant work to install and get running, typically needing pipeline scripts to be written from scratch before running any analysis. We present Cluster Flow, a simple and flexible bioinformatics pipeline tool designed to be quick and easy to install. Cluster Flow comes with 40 modules for common NGS processing steps, ready to work out of the box. Pipelines are assembled using these modules with a simple syntax that can be easily modified as required. Core helper functions automate many common NGS procedures, making running pipelines simple. Cluster Flow is available with an GNU GPLv3 license on GitHub. Documentation, examples and an online demo are available at http://clusterflow.io.
| S-EPMC5310375 | biostudies-literature
Project description:Computer-assisted drug design (CADD) methods have greatly contributed to the development of new drugs. Among CADD methodologies, virtual screening (VS) can enrich the compound collection with molecules that have the desired physicochemical and pharmacophoric characteristics that are needed to become drugs. Many free tools are available for this purpose, but they are difficult to use and do not have a graphical user interface. Furthermore, several free tools must be used to carry out the entire VS process, requiring the user to process the results of one software program so that they can be used in another program, adding a potential source of human error. Moreover, some software programs require knowledge of advanced computational skills, such as programming languages. This context has motivated us to develop Molecular Architect (MolAr). MolAr is a workflow with a simple and intuitive interface that acts in an integrated and automated form to perform the entire VS process, from protein preparation (homology modeling and protonation state) to virtual screening. MolAr carries out VS through AutoDock Vina, DOCK 6, or a consensus of the two. Two case studies were conducted to demonstrate the performance of MolAr. In the first study, the feasibility of using MolAr for DNA-ligand systems was assessed. Both AutoDock Vina and DOCK 6 showed good results in performing VS in DNA-ligand systems. However, the use of consensus virtual screening was able to enrich the results. According to the area under the ROC curve and the enrichment factors, consensus VS was better able to predict the positions of the active ligands. The second case study was performed on 8 targets from the DUD-E database and 10 active ligands for each target. The results demonstrated that using the final ligand conformation provided by AutoDock Vina as an input for DOCK 6 improved the DOCK 6 ROC curves by up to 42% in VS. These case studies demonstrated that MolAr is capable conducting the VS process and is an easy-to-use and effective tool. MolAr is available for download free of charge at http: //www.drugdiscovery.com.br/software/.
| S-EPMC7114615 | biostudies-literature
Project description:MotivationMany diseases have a metabolic background, which is increasingly investigated due to improved measurement techniques allowing high-throughput assessment of metabolic features in several body fluids. Integrating data from multiple cohorts is of high importance to obtain robust and reproducible results. However, considerable variability across studies due to differences in sampling, measurement techniques and study populations needs to be accounted for.ResultsWe present Metabolite-Investigator, a scalable analysis workflow for quantitative metabolomics data from multiple studies. Our tool supports all aspects of data pre-processing including data integration, cleaning, transformation, batch analysis as well as multiple analysis methods including uni- and multivariable factor-metabolite associations, network analysis and factor prioritization in one or more cohorts. Moreover, it allows identifying critical interactions between cohorts and factors affecting metabolite levels and inferring a common covariate model, all via a graphical user interface.Availability and implementationWe constructed Metabolite-Investigator as a free and open web-tool and stand-alone Shiny-app. It is hosted at https://apps.health-atlas.de/metabolite-investigator/, the source code is freely available at https://github.com/cfbeuchel/Metabolite-Investigator.Supplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC8352501 | biostudies-literature
Project description:BackgroundHeterogeneity in the features, input-output behaviour and user interface for available bioinformatics tools and services is still a bottleneck for both expert and non-expert users. Advancement in providing common interfaces over such tools and services are gaining interest among researchers. However, the lack of (meta-) information about input-output data and parameter prevents to provide automated and standardized solutions, which can assist users in setting the appropriate parameters. These limitations must be resolved especially in the workflow-based solution in order to ease the integration of software.FindingsWe report a Taverna Workbench plugin: the XworX BIFI (Beautiful Interfaces for Inputs) implemented as a solution for the aforementioned issues. BIFI provides a Graphical User Interface (GUI) definition language used to layout the user interface and to define parameter options for Taverna workflows. BIFI is also able to submit GUI Definition Files (GDF) directly or discover appropriate instances from a configured repository. In the absence of a GDF, BIFI generates a default interface.ConclusionThe Taverna Workbench is an open source software providing the ability to combine various services within a workflow. Nevertheless, users can supply input data to the workflow via a simple user interface providing only a text area to enter the input in text form. The workflow may contain meta-information in human readable form such as description text for the port and an example value. However, not all workflow ports are documented so well or have all the required information.BIFI uses custom user interface components for ports which give users feedback on the parameter data type or structure to be used for service execution and enables client-side data validations. Moreover, BIFI offers user interfaces that allow users to interactively construct workflow views and share them with the community, thus significantly increasing usability of heterogeneous, distributed service consumption.
| S-EPMC4228112 | biostudies-literature
Project description:Metabolomics and proteomics, like other omics domains, usually face a data mining challenge in providing an understandable output to advance in biomarker discovery and precision medicine. Often, statistical analysis is one of the most difficult challenges and it is critical in the subsequent biological interpretation of the results. Because of this, combined with the computational programming skills needed for this type of analysis, several bioinformatic tools aimed at simplifying metabolomics and proteomics data analysis have emerged. However, sometimes the analysis is still limited to a few hidebound statistical methods and to data sets with limited flexibility. POMAShiny is a web-based tool that provides a structured, flexible and user-friendly workflow for the visualization, exploration and statistical analysis of metabolomics and proteomics data. This tool integrates several statistical methods, some of them widely used in other types of omics, and it is based on the POMA R/Bioconductor package, which increases the reproducibility and flexibility of analyses outside the web environment. POMAShiny and POMA are both freely available at https://github.com/nutrimetabolomics/POMAShiny and https://github.com/nutrimetabolomics/POMA, respectively.
| S-EPMC8279420 | biostudies-literature
Project description:This study evaluated the usability of a direct manipulation device (touchscreen) vs. indirect manipulation devices (mouse and touchpad) on the selected Microsoft (MS) Word tasks as per ISO-9241-11 standard. MS Word was taken as an example of a complex application. The tasks were evaluated in terms of touch-friendly or click-friendly using efficiency, effectiveness, and satisfaction parameters to propose a customized task menu. The experiment was conducted with fifty-four participants, divided into three MS Word usage-based expertise groups. Each participant performed fifty-six tasks using a mouse, a touchpad, and a touchscreen. To assess task-level usability, individual one-way ANOVAs were performed for each task to gauge both efficiency and effectiveness. It's worth noting that the touchscreen significantly outperformed other input methods in just one specific task regarding effectiveness. Consequently, an ANCOVA was employed, with task completion time as the independent variable and the number of errors as a covariate, to further investigate effectiveness. A total of 19 (34%) of the total tasks were found to be significantly efficient with a mouse, while 21 (37.5%) were significantly efficient with a touchscreen. Based on the results, a customized menu is recommended for MS Word-like applications that combine actions in touch-friendly tasks and mouse-friendly tasks separately.
| S-EPMC10843409 | biostudies-literature
Project description:The discovery of the small regulatory RNA populations has changed our vision of cellular regulations. Indeed, loaded on Argonaute proteins they formed ribonucleoprotein complexes that target complementary sequences and achieved widespread silencing mechanisms conserved in most eukaryotes. The recent development of deep sequencing approaches highly contributed to their detection. Small RNA isolation form cells and/or tissues remains a crucial stage to generate robust and relevant sequencing data. In 2006, a novel strategy based on anion-exchange chromatography has been purposed as an alternative to the standard size-isolation purification procedure. However, the eventual biases of such a method have been poorly investigated. Moreover, this strategy not only relies on advanced technical skills and expensive material but is time consuming and requires an elevated starting biological material amount. Using bioinformatic comparative analysis of six independent small RNA-sequencing libraries of Drosophila ovaries, we here demonstrate that anion-exchange chromatography purification prior to small RNA extraction unbiasedly enriches datasets in bona fide reads (small regulatory RNA reads) and depletes endogenous contaminants (ribosomal RNAs and degradation products). The resulting increase of sequencing depth provides a major benefit to study rare populations. We then developed a fast and basic manual procedure to purify loaded small non coding RNAs using anion-exchange chromatography at the bench. We validated the efficiency of this new method and used this strategy to purify small RNAs from various tissues and organisms. We moreover determined that our manual purification increases the output of the previously described anion-exchange chromatography procedure.
2013-06-13 | GSE40748 | GEO
Project description:BACKGROUND:In the past decade, transcriptome data have become an important component of many phylogenetic studies. They are a cost-effective source of protein-coding gene sequences, and have helped projects grow from a few genes to hundreds or thousands of genes. Phylogenetic studies now regularly include genes from newly sequenced transcriptomes, as well as publicly available transcriptomes and genomes. Implementing such a phylogenomic study, however, is computationally intensive, requires the coordinated use of many complex software tools, and includes multiple steps for which no published tools exist. Phylogenomic studies have therefore been manual or semiautomated. In addition to taking considerable user time, this makes phylogenomic analyses difficult to reproduce, compare, and extend. In addition, methodological improvements made in the context of one study often cannot be easily applied and evaluated in the context of other studies. RESULTS:We present Agalma, an automated tool that constructs matrices for phylogenomic analyses. The user provides raw Illumina transcriptome data, and Agalma produces annotated assemblies, aligned gene sequence matrices, a preliminary phylogeny, and detailed diagnostics that allow the investigator to make extensive assessments of intermediate analysis steps and the final results. Sequences from other sources, such as externally assembled genomes and transcriptomes, can also be incorporated in the analyses. Agalma is built on the BioLite bioinformatics framework, which tracks provenance, profiles processor and memory use, records diagnostics, manages metadata, installs dependencies, logs version numbers and calls to external programs, and enables rich HTML reports for all stages of the analysis. Agalma includes a small test data set and a built-in test analysis of these data. In addition to describing Agalma, we here present a sample analysis of a larger seven-taxon data set. Agalma is available for download at https://bitbucket.org/caseywdunn/agalma. CONCLUSIONS:Agalma allows complex phylogenomic analyses to be implemented and described unambiguously as a series of high-level commands. This will enable phylogenomic studies to be readily reproduced, modified, and extended. Agalma also facilitates methods development by providing a complete modular workflow, bundled with test data, that will allow further optimization of each step in the context of a full phylogenomic analysis.
| S-EPMC3840672 | biostudies-literature
Project description:The discovery of the small regulatory RNA populations has changed our vision of cellular regulations. Indeed, loaded on Argonaute proteins they formed ribonucleoprotein complexes that target complementary sequences and achieved widespread silencing mechanisms conserved in most eukaryotes. The recent development of deep sequencing approaches highly contributed to their detection. Small RNA isolation form cells and/or tissues remains a crucial stage to generate robust and relevant sequencing data. In 2006, a novel strategy based on anion-exchange chromatography has been purposed as an alternative to the standard size-isolation purification procedure. However, the eventual biases of such a method have been poorly investigated. Moreover, this strategy not only relies on advanced technical skills and expensive material but is time consuming and requires an elevated starting biological material amount. Using bioinformatic comparative analysis of six independent small RNA-sequencing libraries of Drosophila ovaries, we here demonstrate that anion-exchange chromatography purification prior to small RNA extraction unbiasedly enriches datasets in bona fide reads (small regulatory RNA reads) and depletes endogenous contaminants (ribosomal RNAs and degradation products). The resulting increase of sequencing depth provides a major benefit to study rare populations. We then developed a fast and basic manual procedure to purify loaded small non coding RNAs using anion-exchange chromatography at the bench. We validated the efficiency of this new method and used this strategy to purify small RNAs from various tissues and organisms. We moreover determined that our manual purification increases the output of the previously described anion-exchange chromatography procedure. Comparison of small regulatory RNA populations obtained after three different small RNA purification procedures
2013-06-13 | E-GEOD-40748 | biostudies-arrayexpress