GenoSets: Visual Analytic Methods for Comparative Genomics

Cain, Aurora; Kosara, Robert; Gibas, Cynthia

doi:10.1371/journal.pone.0046401

Cited by 13 publications

(13 citation statements)

References 36 publications

(35 reference statements)

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“…To illustrate the impact of workflow choice on interpretation of functional content, we performed a comparative analysis among the results of six assembly-to-annotation workflows applied to the genome of V. vulnificus CECT4866, refer to Table 5 . We used the GenoSets[ 33 ] analysis system to perform the comparison of analysis outcomes, treating the annotation set produced by each workflow as if it were an independent "genome".…”

Section: Resultsmentioning

confidence: 99%

“…Each workflow's gene set was assigned Gene Ontology (GO) terms [ 34 , 35 ]as described in Cain et al ., 2012[ 33 ]. GO categories and individual genes having functionality significant enrichment or depletion between the various annotation versions were identified using the Gene Ontologizer[ 36 ].…”

Section: Resultsmentioning

confidence: 99%

“…For comparison of assembly-to-annotation workflow outcomes and for comparisons of genomic content, we used the GenoSets software application[ 33 ]. The annotations produced by each workflow were loaded into the GenoSets application, which enables comparisons among multiple genomes.…”

Section: Methodsmentioning

confidence: 99%

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Impact of analytic provenance in genome analysis

et al. 2014

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BackgroundMany computational methods are available for assembly and annotation of newly sequenced microbial genomes. However, when new genomes are reported in the literature, there is frequently very little critical analysis of choices made during the sequence assembly and gene annotation stages. These choices have a direct impact on the biologically relevant products of a genomic analysis - for instance identification of common and differentiating regions among genomes in a comparison, or identification of enriched gene functional categories in a specific strain. Here, we examine the outcomes of different assembly and analysis steps in typical workflows in a comparison among strains of Vibrio vulnificus.ResultsUsing six recently sequenced strains of V. vulnificus, we demonstrate the "alternate realities" of comparative genomics, and how they depend on the choice of a robust assembly method and accurate ab initio annotation. We apply several popular assemblers for paired-end Illumina data, and three well-regarded ab initio genefinders. We demonstrate significant differences in detected gene overlap among comparative genomics workflows that depend on these two steps. The divergence between workflows, even those using widely adopted methods, is obvious both at the single genome level and when a comparison is performed. In a typical example where multiple workflows are applied to the strain V. vulnificus CECT 4606, a workflow that uses the Velvet assembler and Glimmer gene finder identifies 3275 gene features, while a workflow that uses the Velvet assembler and the RAST annotation system identifies 5011 gene features. Only 3171 genes are identical between both workflows. When we examine 9 assembly/ annotation workflow scenarios as input to a three-way genome comparison, differentiating genes and even differentially represented functional categories change significantly from scenario to scenario.ConclusionsInconsistencies in genomic analysis can arise depending on the choices that are made during the assembly and annotation stages. These inconsistencies can have a significant impact on the interpretation of an individual genome's content. The impact is multiplied when comparison of content and function among multiple genomes is the goal. Tracking the analysis history of the data - its analytic provenance - is critical for reproducible analysis of genome data.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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Impact of analytic provenance in genome analysis

et al. 2014

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“…Finally, the velocity, which is the increasing rate at which data is generated, follows a similar pattern as the volume. 14) In the domain of healthcare, Big Data sources and techniques include structured electronic health record (EHR) data, unstructured clinical notes, medical imaging data, 15) genetic data, 16) and the other data (epidemiology and behavioral data). 17) …”

Section: Big Data Analytics In Healthcarementioning

confidence: 99%

Status and Future Perspectives of Utilizing Big Data in Neurosurgical and Stroke Research

Nishimura

Kada

et al. 2016

Neurol. Med. Chir.(Tokyo)

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The management, analysis, and integration of Big Data have received increasing attention in healthcare research as well as in medical bioinformatics. The J-ASPECT study is the first nationwide survey in Japan on the real-world setting of stroke care using data obtained from the diagnosis procedure combination-based payment system. The J-ASPECT study demonstrated a significant association between comprehensive stroke care (CSC) capacity and the hospital volume of stroke interventions in Japan; further, it showed that CSC capabilities were associated with reduced in-hospital mortality rates. Our study aims to create new evidence and insight from ‘real world’ neurosurgical practice and stroke care in Japan using Big Data. The final aim of this study is to develop effective methods to bridge the evidence-practice gap in acute stroke healthcare. In this study, the authors describe the status and future perspectives of the development of a new method of stroke registry as a powerful tool for acute stroke care research.

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“…The visualization of research results has a growing importance in nearly all scientific domains (e.g., environmental research [ 9 , 14 , 15 ], genetics [ 16 , 17 ], biomedicine [ 18 ], and animal movement [ 19 , 20 ]). We provide an overview of related work with a focus on visualization applications for earth system sciences, visualization applications in general, using movie applications for presenting, and approaches for using computer-games engines for scientific visualization.…”

Section: Related Workmentioning

confidence: 99%

MEVA - An Interactive Visualization Application for Validation of Multifaceted Meteorological Data with Multiple 3D Devices

et al. 2015

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BackgroundTo achieve more realistic simulations, meteorologists develop and use models with increasing spatial and temporal resolution. The analyzing, comparing, and visualizing of resulting simulations becomes more and more challenging due to the growing amounts and multifaceted character of the data. Various data sources, numerous variables and multiple simulations lead to a complex database. Although a variety of software exists suited for the visualization of meteorological data, none of them fulfills all of the typical domain-specific requirements: support for quasi-standard data formats and different grid types, standard visualization techniques for scalar and vector data, visualization of the context (e.g., topography) and other static data, support for multiple presentation devices used in modern sciences (e.g., virtual reality), a user-friendly interface, and suitability for cooperative work.Methods and ResultsInstead of attempting to develop yet another new visualization system to fulfill all possible needs in this application domain, our approach is to provide a flexible workflow that combines different existing state-of-the-art visualization software components in order to hide the complexity of 3D data visualization tools from the end user. To complete the workflow and to enable the domain scientists to interactively visualize their data without advanced skills in 3D visualization systems, we developed a lightweight custom visualization application (MEVA - multifaceted environmental data visualization application) that supports the most relevant visualization and interaction techniques and can be easily deployed. Specifically, our workflow combines a variety of different data abstraction methods provided by a state-of-the-art 3D visualization application with the interaction and presentation features of a computer-games engine. Our customized application includes solutions for the analysis of multirun data, specifically with respect to data uncertainty and differences between simulation runs. In an iterative development process, our easy-to-use application was developed in close cooperation with meteorologists and visualization experts. The usability of the application has been validated with user tests. We report on how this application supports the users to prove and disprove existing hypotheses and discover new insights. In addition, the application has been used at public events to communicate research results.

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GenoSets: Visual Analytic Methods for Comparative Genomics

Cited by 13 publications

References 36 publications

Impact of analytic provenance in genome analysis

Impact of analytic provenance in genome analysis

Status and Future Perspectives of Utilizing Big Data in Neurosurgical and Stroke Research

MEVA - An Interactive Visualization Application for Validation of Multifaceted Meteorological Data with Multiple 3D Devices

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