ReviewProteomics2015
Open source libraries and frameworks for biological data visualisation: a guide for developers.
Review in Proteomics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 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
23 citing papers in PubMed.
- Applying AI/ML for Analyzing Gene Expression Patterns.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Nightingale: web components for protein feature visualization.Bioinformatics advances · 2023Article
- GGDB: A Grameneae genome alignment database of homologous genes hierarchically related to evolutionary events.Plant physiology · 2022Article
- 2D-GolgiTrack-a semi-automated tracking system to quantify morphological changes and dynamics of the Golgi apparatus and Golgi-derived membrane tubules.Medical & biological engineering & computing · 2022Article
- Bioinformatic Analyses of Peroxiredoxins and RF-Prx: A Random Forest-Based Predictor and Classifier for Prxs.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Analysis, identification and visualization of subgroups in genomics.Briefings in bioinformatics · 2021Review
- Article
- Scalable Data Analysis in Proteomics and Metabolomics Using BioContainers and Workflows Engines.Proteomics · 2020Review
- ThermoRawFileParser: Modular, Scalable, and Cross-Platform RAW File Conversion.Journal of proteome research · 2020Article
- RF-MaloSite and DL-Malosite: Methods based on random forest and deep learning to identify malonylation sites.Computational and structural biotechnology journal · 2020Article
- A Single Visualization Technique for Displaying Multiple Metabolite-Phenotype Associations.Metabolites · 2019Article
- Systems Pharmacogenomic Landscape of Drug Similarities from LINCS data: Drug Association Networks.Scientific reports · 2019Article
- The PRIDE database and related tools and resources in 2019: improving support for quantification data.Nucleic acids research · 2019Article
- Article
- Functional Transcriptomics for Bacterial Gene Detectives.Microbiology spectrum · 2018Review
- Future Prospects of Spectral Clustering Approaches in Proteomics.Proteomics · 2018Article
- GC4S: A bioinformatics-oriented Java software library of reusable graphical user interface components.PloS one · 2018Article
- mzStudio: A Dynamic Digital Canvas for User-Driven Interrogation of Mass Spectrometry Data.Proteomes · 2017Article
- Article
- Ten Simple Rules for Developing Public Biological Databases.PLoS computational biology · 2016Article
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
Abstract
Recent advances in high-throughput experimental techniques have led to an exponential increase in both the size and the complexity of the data sets commonly studied in biology. Data visualisation is increasingly used as the key to unlock this data, going from hypothesis generation to model evaluation and tool implementation. It is becoming more and more the heart of bioinformatics workflows, enabling scientists to reason and communicate more effectively. In parallel, there has been a corresponding trend towards the development of related software, which has triggered the maturation of different visualisation libraries and frameworks. For bioinformaticians, scientific programmers and software developers, the main challenge is to pick out the most fitting one(s) to create clear, meaningful and integrated data visualisation for their particular use cases. In this review, we introduce a collection of open source or free to use libraries and frameworks for creating data visualisation, covering the generation of a wide variety of charts and graphs. We will focus on software written in Java, JavaScript or Python. We truly believe this software offers the potential to turn tedious data into exciting visual stories.
Indexed as
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.