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

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/

[1]  Markus J. van Roosmalen,et al.  Constitutional chromothripsis rearrangements involve clustered double-stranded DNA breaks and nonhomologous repair mechanisms. , 2012, Cell reports.

[2]  Yu-ping Wang,et al.  Comparative Studies of Copy Number Variation Detection Methods for Next-Generation Sequencing Technologies , 2013, PloS one.

[3]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[4]  Yong-shu He,et al.  [Structural variation in the human genome]. , 2009, Yi chuan = Hereditas.

[5]  J. Mullikin,et al.  SSAHA: a fast search method for large DNA databases. , 2001, Genome research.

[6]  David H. Laidlaw,et al.  Online Submission ID : 1199 Gremlin : An Interactive Visualization Model for Analyzing Genomic Rearrangements , 2010 .

[7]  L. Feuk,et al.  Detection of large-scale variation in the human genome , 2004, Nature Genetics.

[8]  Chao Xie,et al.  CNV-seq, a new method to detect copy number variation using high-throughput sequencing , 2009, BMC Bioinformatics.

[9]  M. Wigler,et al.  Circular binary segmentation for the analysis of array-based DNA copy number data. , 2004, Biostatistics.

[10]  R. Wilson,et al.  BreakDancer: An algorithm for high resolution mapping of genomic structural variation , 2009, Nature Methods.

[11]  Philip M. Kim,et al.  Paired-End Mapping Reveals Extensive Structural Variation in the Human Genome , 2007, Science.

[12]  H. Brunner.  Annual Review of Genomics and Human Genetics , 2001, European Journal of Human Genetics.

[13]  Richard Durbin,et al.  Fast and accurate long-read alignment with Burrows–Wheeler transform , 2010, Bioinform..

[14]  Rodrigo Lopez,et al.  Clustal W and Clustal X version 2.0 , 2007, Bioinform..

[15]  Edwin Cuppen,et al.  Mate pair sequencing for the detection of chromosomal aberrations in patients with intellectual disability and congenital malformations , 2013, European Journal of Human Genetics.

[16]  Ilya Shmulevich,et al.  Fastbreak: a tool for analysis and visualization of structural variations in genomic data , 2012, EURASIP J. Bioinform. Syst. Biol..

[17]  Kai Ye,et al.  Pindel: a pattern growth approach to detect break points of large deletions and medium sized insertions from paired-end short reads , 2009, Bioinform..

[18]  David Haussler,et al.  The UCSC genome browser database: update 2007 , 2006, Nucleic Acids Res..

[19]  Paul Medvedev,et al.  Computational methods for discovering structural variation with next-generation sequencing , 2009, Nature Methods.

[20]  Manuel A. R. Ferreira,et al.  PLINK: a tool set for whole-genome association and population-based linkage analyses. , 2007, American journal of human genetics.

[21]  Kenny Q. Ye,et al.  Large-Scale Copy Number Polymorphism in the Human Genome , 2004, Science.

[22]  Gonçalo R. Abecasis,et al.  The Sequence Alignment/Map format and SAMtools , 2009, Bioinform..

[23]  Lars Feuk,et al.  The Database of Genomic Variants: a curated collection of structural variation in the human genome , 2013, Nucleic Acids Res..

[24]  Georgios A. Pavlopoulos,et al.  Meander: visually exploring the structural variome using space-filling curves , 2013, Nucleic acids research.

[25]  Yiping Shen,et al.  Next-generation sequencing strategies enable routine detection of balanced chromosome rearrangements for clinical diagnostics and genetic research. , 2011, American journal of human genetics.

[26]  Mary Goldman,et al.  The UCSC Genome Browser database: update 2011 , 2010, Nucleic Acids Res..

[27]  Cole Trapnell,et al.  Ultrafast and memory-efficient alignment of short DNA sequences to the human genome , 2009, Genome Biology.

[28]  E. Cuppen,et al.  Matepair sequencing for the detection of chromosomal aberrations in patients with intellectual disability and congenital malformations , 2012 .

[29]  Richard Durbin,et al.  Sequence analysis Fast and accurate short read alignment with Burrows – Wheeler transform , 2009 .

[30]  Martin Goodson,et al.  Stampy: a statistical algorithm for sensitive and fast mapping of Illumina sequence reads. , 2011, Genome research.

[31]  Lennart Martens,et al.  LNCipedia: a database for annotated human lncRNA transcript sequences and structures , 2012, Nucleic Acids Res..

[32]  M. DePristo,et al.  The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. , 2010, Genome research.