ArticleBioData mining2010
A reference guide for tree analysis and visualization.
Article in BioData mining, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
37 citing papers in PubMed.
- Comparative phylotranscriptomics reveals glycolytic adaptation associated with prolonged larval development in the coastal slipper lobsterAnimal cells and systems · 2026Article
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- Molecular Evolution of Influenza A Viruses From Mauritius, 2017-2019.Influenza and other respiratory viruses · 2025Article
- Genetic analyses and functional validation of ruminant SLAMs reveal potential hosts for PPRV.Veterinary research · 2025Article
- An overview of fasciolosis in human and cattle populations in New Valley, Egypt.Frontiers in veterinary science · 2025Article
- Visualizing metagenomic and metatranscriptomic data: A comprehensive review.Computational and structural biotechnology journal · 2024Review
- Genetic Tools for Cell Lineage Tracing and Profiling Developmental Trajectories in the Skin.The Journal of investigative dermatology · 2024Review
- Tree visualizations of protein sequence embedding space enable improved functional clustering of diverse protein superfamilies.Briefings in bioinformatics · 2023Article
- Exploring microbial functional biodiversity at the protein family level-From metagenomic sequence reads to annotated protein clusters.Frontiers in bioinformatics · 2023Review
- Finding Superior Biocatalysts via Homolog Screening.Chem catalysis · 2022Article
- Molecular Epidemiology of Clinical Carbapenem-ResistantTropical medicine and infectious disease · 2022Article
- Structured Populations of Critically Endangered Yellow Water Lily (Plants (Basel, Switzerland) · 2022Article
- Caseinolytic Proteins (Clp) in the GenusMolecules (Basel, Switzerland) · 2021Article
- Biomolecule and Bioentity Interaction Databases in Systems Biology: A Comprehensive Review.Biomolecules · 2021Review
- Small pigmented eukaryote assemblages of the western tropical North Atlantic around the Amazon River plume during spring discharge.Scientific reports · 2021Article
- Network bioinformatics analysis provides insight into drug repurposing for COVID-19.Medicine in drug discovery · 2021Article
- Article
- One Health Approaches to TraceFrontiers in microbiology · 2021Review
- Evolutionary Diversity in the Intracellular Microsporidian Parasite Nosema sp. Infecting Wild Silkworm Revealed by IGS Nucleotide Sequence Diversity.Journal of molecular evolution · 2020Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The quantities of data obtained by the new high-throughput technologies, such as microarrays or ChIP-Chip arrays, and the large-scale OMICS-approaches, such as genomics, proteomics and transcriptomics, are becoming vast. Sequencing technologies become cheaper and easier to use and, thus, large-scale evolutionary studies towards the origins of life for all species and their evolution becomes more and more challenging. Databases holding information about how data are related and how they are hierarchically organized expand rapidly. Clustering analysis is becoming more and more difficult to be applied on very large amounts of data since the results of these algorithms cannot be efficiently visualized. Most of the available visualization tools that are able to represent such hierarchies, project data in 2D and are lacking often the necessary user friendliness and interactivity. For example, the current phylogenetic tree visualization tools are not able to display easy to understand large scale trees with more than a few thousand nodes. In this study, we review tools that are currently available for the visualization of biological trees and analysis, mainly developed during the last decade. We describe the uniform and standard computer readable formats to represent tree hierarchies and we comment on the functionality and the limitations of these tools. We also discuss on how these tools can be developed further and should become integrated with various data sources. Here we focus on freely available software that offers to the users various tree-representation methodologies for biological data analysis.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.