Evidence map›Paper›PMID 26329021›Full record

ArticleBMC bioinformatics2015

BactoGeNIE: a large-scale comparative genome visualization for big displays.

Jillian Aurisano, Khairi Reda, Andrew Johnson, Elisabeta G Marai, Jason Leigh

Abstract read
In one paragraph

Article in BMC bioinformatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Multi-View Design Patterns and Responsive Visualization for Genomics Data.IEEE transactions on visualization and computer graphics · 2023
    Review
  2. Visual Analysis and Detection of Contrails in Aircraft Engine Simulations.IEEE transactions on visualization and computer graphics · 2023
    Article
  3. Tasks, Techniques, and Tools for Genomic Data Visualization.Computer graphics forum : journal of the European Association for Computer Graphics · 2019
    Article
  4. Activity-Centered Domain Characterization for Problem-Driven Scientific Visualization.IEEE transactions on visualization and computer graphics · 2018
    Article
  5. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Jillian Aurisano
Khairi Reda
Andrew Johnson
Elisabeta G Marai
Jason Leigh

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe volume of complete bacterial genome sequence data available to comparative genomics researchers is rapidly increasing. However, visualizations in comparative genomics--which aim to enable analysis tasks across collections of genomes--suffer from visual scalability issues. While large, multi-tiled and high-resolution displays have the potential to address scalability issues, new approaches are needed to take advantage of such environments, in order to enable the effective visual analysis of large genomics datasets.

resultsIn this paper, we present Bacterial Gene Neighborhood Investigation Environment, or BactoGeNIE, a novel and visually scalable design for comparative gene neighborhood analysis on large display environments. We evaluate BactoGeNIE through a case study on close to 700 draft Escherichia coli genomes, and present lessons learned from our design process.

conclusionsBactoGeNIE accommodates comparative tasks over substantially larger collections of neighborhoods than existing tools and explicitly addresses visual scalability. Given current trends in data generation, scalable designs of this type may inform visualization design for large-scale comparative research problems in genomics.

Indexed as

Computer GraphicsGenome, BacterialSoftwareComputational BiologyEscherichia coliEscherichia coli ProteinsGenomicsEscherichia coli Proteins

Identifiers

PMID26329021
PMCPMC4547189

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.