Fastbreak: a tool for analysis and visualization of structural variations in genomic data

Bressler, Ryan; Lin, Jake; Eakin, Andrea; Robinson, Thomas; Kreisberg, Richard; Rovira, Hector; Knijnenburg, Theo; Boyle, John P.; Shmulevich, Ilya

doi:10.1186/1687-4153-2012-15

Cited by 6 publications

(8 citation statements)

References 11 publications

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“…If further visualization is needed, users can export the produced alignments as a bam-file and variant lists as a txt file (VCF format), and use other available visualization methods aimed at visualization of SVs (Meander [30] , fastbreak [31] , Gremlin [32] ), or experienced users can write an add-on to directly incorporate one of these in ViVar.…”

Section: Resultsmentioning

confidence: 99%

ViVar: A Comprehensive Platform for the Analysis and Visualization of Structural Genomic Variation

et al. 2014

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Structural genomic variations play an important role in human disease and phenotypic diversity. With the rise of high-throughput sequencing tools, mate-pair/paired-end/single-read sequencing has become an important technique for the detection and exploration of structural variation. Several analysis tools exist to handle different parts and aspects of such sequencing based structural variation analyses pipelines. A comprehensive analysis platform to handle all steps, from processing the sequencing data, to the discovery and visualization of structural variants, is missing. The ViVar platform is built to handle the discovery of structural variants, from Depth Of Coverage analysis, aberrant read pair clustering to split read analysis. ViVar provides you with powerful visualization options, enables easy reporting of results and better usability and data management. The platform facilitates the processing, analysis and visualization, of structural variation based on massive parallel sequencing data, enabling the rapid identification of disease loci or genes. ViVar allows you to scale your analysis with your work load over multiple (cloud) servers, has user access control to keep your data safe and is easy expandable as analysis techniques advance. URL: https://www.cmgg.be/vivar/

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Section: Resultsmentioning

confidence: 99%

ViVar: A Comprehensive Platform for the Analysis and Visualization of Structural Genomic Variation

et al. 2014

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“…We organized the displaying methods into ten major categories (Table 1; Fig. 1), namely, linear genome browser, Fastbreak [30], Gremlin [51], ViVar [52] Two-way view --…”

Section: View Modules For Visualizing Svmentioning

confidence: 99%

“…com/mskilab/gGnome) is an R library that provides graph view for displaying SVs with gene annotations and copy number alterations. Fastbreak [30] is an early work that employed graph view to display multiple distant genomic intervals and their connections (SVs) in a single view. Both haplotype X and haplotype Y have insertion sequences of different sizes against the international reference genome such as GRCh38.…”

Section: Graph Genome-based Sv Visualization Toolsmentioning

confidence: 99%

“…Chromosomes are vertically or horizontally placed, and lines or curves connect the two-end points of SVs. Linear coordinates plot that displays multiple chromosomes in parallel is often known as a parallel coordinates plot [ 30 ] in the context of SV visualization. This view module is also useful for viewing large intra-chromosomal SVs or inter-chromosomal SVs.…”

Section: Sv Visualizationmentioning

confidence: 99%

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Visualization tools for human structural variations identified by whole-genome sequencing

Yokoyama

Kasahara

2019

J Hum Genet

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Visualizing structural variations (SVs) is a critical step for finding associations between SVs and human traits or diseases. Given that there are many sequencing platforms used for SV identification and given that how best to visualize SVs together with other data, such as read alignments and annotations, depends on research goals, there are dozens of SV visualization tools designed for different research goals and sequencing platforms. Here, we provide a comprehensive survey of over 30 SV visualization tools to help users choose which tools to use. This review targets users who wish to visualize a set of SVs identified from the massively parallel sequencing reads of an individual human genome. We first categorize the ways in which SV visualization tools display SVs into ten major categories, which we denote as view modules. View modules allow readers to understand the features of each SV visualization tool quickly. Next, we introduce the features of individual SV visualization tools from several aspects, including whether SV views are integrated with annotations, whether long-read alignment is displayed, whether underlying data structures are graph-based, the type of SVs shown, whether auditing is possible, whether bird's eye view is available, sequencing platforms, and the number of samples. We hope that this review will serve as a guide for readers on the currently available SV visualization tools and lead to the development of new SV visualization tools in the near future.

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“…Specifically, it allows the analysis of structural variations of next-generation sequencing data. One of the visualizations that this tool provides is a parallel coordinates plot ( Figure 1), where each of the vertical axes represents the level of expression that a gene may have in each particular cancer [20]. On the other hand, the horizontal lines represent a different gene and they are colored according to their genetic expression -red for the most expressed genes and blue for the least expressed-, which allows us to explore gene expression similarities between different cancers ( Figure 1).…”

Section: B Visual Analytics Toolsmentioning

confidence: 99%

Exploring Breast Cancer Patterns for Different Outcomes using Artificial Intelligence

Larburu¹,

Arrue²,

Muro³

et al. 2018

2018 IEEE 20th International Conference on E-Health Networking, Applications and Services (Healthcom)

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Breast Cancer is a complex disease characterized by multiple variables obtained from several data-sources, such as clinical, genetic or image sources. Over the past decades, various studies have tried to predict the outcome of breast cancer with the support of these data, and big advances have been done in this direction. However, only a few reports describe the causal relationships among the variables and outcomes, such as adverse events and survival rate, and usually they are very limited to a specific dataset. This research work presents a novel system that using data mining and visual analytics tools depicts in an intuitive way the patterns associated with different outcomes, such as treatment response and adverse events related to a treatment. For that the system processes heterogenous data coming from a real setting for primary breast cancer. This way clinicians can explore in a dynamic, fast and intuitive way whether certain group of patients are prone to certain outcomes.

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Fastbreak: a tool for analysis and visualization of structural variations in genomic data

Cited by 6 publications

References 11 publications

ViVar: A Comprehensive Platform for the Analysis and Visualization of Structural Genomic Variation

ViVar: A Comprehensive Platform for the Analysis and Visualization of Structural Genomic Variation

Visualization tools for human structural variations identified by whole-genome sequencing

Exploring Breast Cancer Patterns for Different Outcomes using Artificial Intelligence

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