CViT (chromosome visualization tool) is a Perl utility for quickly generating images of features on a whole genome at once. It reads GFF3-formated data representing chromosomes (linkage groups or pseudomolecules) and sets of features on those chromosomes. It can display features on any chromosomal unit system, including genetic (centimorgan), cytological (centiMcClintock), and DNA unit (base-pair) coordinates. CViT has been used to track sequencing progress (status of genome sequencing, location and number of gaps), to visualize BLAST hits on a whole genome view, to associate maps with one another, to locate regions of repeat densities to display syntenic regions, and to visualize centromeres and knobs on chromosomes. 1. Introduction Visualizing features on a whole genome (all chromosomes together) can be informative for many reasons: for identifying genome-wide patterns such as gene or repeat densities, for viewing internal duplications or synteny, for assessing clustering of genes or repeats or other features, for comparing chromosomal structures such as centromeres and pericentromeric regions, or for looking for associations between different types of genomic features. Several very capable genome browsers enable visualization of single chromosomes or regions, but few visualization tools have been developed for whole-genome-at-a-time views. We present CViT (chromosome visualization tool), for viewing a wide range of genomic features on an arbitrary set of linear regions—typically, all of the chromosomes or linkage groups for a genome. CViT is a set of Perl scripts that generate a PNG (portable network graphics) image of features on chromosomes. It can be executed as a standalone Unix command line utility or wrapped in a web page for either static display or as part of an interactive online tool. The characteristics of the output images are highly configurable. A package containing the CViT code itself along with documentation, examples, supporting scripts, and sample web implementations can be freely downloaded from SourceForge at http://sourceforge.net/projects/cvit/. CViT was initially developed to support the Medicago truncatula sequencing project [1] where it was used to display the assembled bacterial artificial chromosomes (BACs) and the status of the sequencing for each BAC. CViT was also wrapped in web pages to create interactive tools: to display BLAST [2] hits on the whole genome (see http://www.medicagohapmap.org/advanced_search_page.php?seq) and to search where BACs of interest are anchored on the pseudomolecules. For other projects, it
References
[1]
N. D. Young, S. B. Cannon, S. Sato et al., “Sequencing the genespaces of Medicago truncatula and Lotus japonicus,” Plant Physiology, vol. 137, no. 4, pp. 1174–1181, 2005.
[2]
S. F. Altschul, T. L. Madden, A. A. Schaffer et al., “Gapped BLAST and PSI-BLAST: a new generation of protein database search programs,” Nucleic Acids Research, vol. 25, no. 17, pp. 3389–3402, 1997.
[3]
R. W. Innes, C. Ameline-Torregrosa, T. Ashfield et al., “Differential accumulation of retroelements and diversification of NB-LRR disease resistance genes in duplicated regions following polyploidy in the ancestor of soybean,” Plant Physiology, vol. 148, no. 4, pp. 1740–1759, 2008.
[4]
C. J. Lawrence, T. E. Seigfried, H. W. Bass, and L. K. Anderson, “Predicting chromosomal locations of genetically mapped loci in maize using the Morgan2McClintock Translator,” Genetics, vol. 172, no. 3, pp. 2007–2009, 2006.
[5]
S. B. Cannon, L. Sterck, S. Rombauts et al., “Legume genome evolution viewed through the Medicago truncatula and Lotus japonicus genomes,” Proceedings of the National Academy of Sciences of the United States of America, vol. 103, no. 40, pp. 14959–14964, 2006.
[6]
C. J. Lawrence, L. C. Harper, M. L. Schaeffer, et al., “MaizeGDB: the maize model organism database for basic, translational, and applied research,” International Journal of Plant Genomics, vol. 2008, Article ID 496957, 2008.
[7]
E. K. S. Cannon, S. M. Birkett, B. M. Braun, et al., “POPcorn: an online resource providing access to distributed and diverse maize project data,” submitted to. International Journal of Plant Genomics.
[8]
S. B. Cannon, G. D. May, and S. A. Jackson, “Three sequenced legume genomes and many crop species: rich opportunities for translational genomics,” Plant Physiology, vol. 151, no. 3, pp. 970–977, 2009.
[9]
C. Ameline-Torregrosa, B. B. Wang, M. S. O'Bleness et al., “Identification and characterization of nucleotide-binding site-leucine-rich repeat genes in the model plant Medicago truncatula,” Plant Physiology, vol. 146, no. 1, pp. 5–21, 2008.
[10]
D. J. Bertioli, M. C. Moretzsohn, L. H. Madsen et al., “An analysis of synteny of Arachis with Lotus and Medicago sheds new light on the structure, stability and evolution of legume genomes,” BMC Genomics, vol. 10, article 45, 2009.
[11]
S. B. Cannon and R. C. Shoemaker, “Evolutionary and comparative analyses of the soybean genome,” Breeding Science. In press.
[12]
M. Krzywinski, J. Schein, I. Birol et al., “Circos: an information aesthetic for comparative genomics,” Genome Research, vol. 19, no. 9, pp. 1639–1645, 2009.
[13]
L. D. Stein, C. Mungall, S. Shu et al., “The generic genome browser: a building block for a model organism system database,” Genome Research, vol. 12, no. 10, pp. 1599–1610, 2002.
[14]
P. Flicek, B. L. Aken, B. Ballester et al., “Ensembl's 10th year,” Nucleic Acids Research, vol. 38, supplement 1, pp. D557–D562, 2010.
[15]
J. W. Nicol, G. A. Helt, S. G. Blanchard Jr., A. Raja, and A. E. Loraine, “The integrated genome browser: free software for distribution and exploration of genome-scale datasets,” Bioinformatics, vol. 25, no. 20, pp. 2730–2731, 2009.
[16]
J. Zhu, J. Z. Sanborn, S. Benz et al., “The UCSC cancer genomics browser,” Nature Methods, vol. 6, no. 4, pp. 239–240, 2009.
[17]
P. S. Schnable, D. Ware, R. S. Fulton et al., “The B73 maize genome: complexity, diversity, and dynamics,” Science, vol. 326, no. 5956, pp. 1112–1115, 2009.
[18]
T. K. Wolfgruber, A. Sharma, K. L. Schneider et al., “Maize centromere structure and evolution: sequence analysis of centromeres 2 and 5 reveals dynamic loci shaped primarily by retrotransposons,” PLoS Genetics, vol. 5, no. 11, Article ID e1000743, 2009.
[19]
J. J. Doyle and A. N. Egan, “Dating the origins of polyploidy events,” New Phytologist, vol. 186, no. 1, pp. 73–85, 2010.
[20]
J. Schmutz, S. B. Cannon, J. Schlueter et al., “Genome sequence of the palaeopolyploid soybean,” Nature, vol. 463, no. 7278, pp. 178–183, 2010.
