ArticleBioinformatics (Oxford, England)2016
Integrated genome browser: visual analytics platform for genomics.
Article in Bioinformatics (Oxford, England), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 238 papers, 2 of them syntheses that pooled it.
What it found
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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.
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Who cites it
238 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Genome-wide association meta-analysis of spontaneous coronary artery dissection identifies risk variants and genes related to artery integrity and tissue-mediated coagulation.Nature genetics · 2023Pooled it
- Genetic investigation of fibromuscular dysplasia identifies risk loci and shared genetics with common cardiovascular diseases.Nature communications · 2021Pooled it
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- Track Hub Quickload Translator: Convert Track Hub or Quickload data for viewing in the UCSC Genome Browser or the Integrated Genome Browser.bioRxiv : the preprint server for biology · 2026Article
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178 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
motivationGenome browsers that support fast navigation through vast datasets and provide interactive visual analytics functions can help scientists achieve deeper insight into biological systems. Toward this end, we developed Integrated Genome Browser (IGB), a highly configurable, interactive and fast open source desktop genome browser.
resultsHere we describe multiple updates to IGB, including all-new capabilities to display and interact with data from high-throughput sequencing experiments. To demonstrate, we describe example visualizations and analyses of datasets from RNA-Seq, ChIP-Seq and bisulfite sequencing experiments. Understanding results from genome-scale experiments requires viewing the data in the context of reference genome annotations and other related datasets. To facilitate this, we enhanced IGB's ability to consume data from diverse sources, including Galaxy, Distributed Annotation and IGB-specific Quickload servers. To support future visualization needs as new genome-scale assays enter wide use, we transformed the IGB codebase into a modular, extensible platform for developers to create and deploy all-new visualizations of genomic data. AVAILABILITY AND IMPLEMENTATION: IGB is open source and is freely available from http://bioviz.org/igb CONTACT: aloraine@uncc.edu.
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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.