Table of Contents
ISRN Bioinformatics
Volume 2012 (2012), Article ID 371718, 10 pages
http://dx.doi.org/10.5402/2012/371718
Research Article

CallSim: Evaluation of Base Calls Using Sequencing Simulation

Center for Biotechnology Education, Johns Hopkins University, Baltimore, MD 21218, USA

Received 17 October 2012; Accepted 5 November 2012

Academic Editors: A. Bolshoy, F. Pappalardo, and J. Wang

Copyright © 2012 Jarrett D. Morrow and Brandon W. Higgs. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. M. L. Metzker, “Sequencing technologies the next generation,” Nature Reviews Genetics, vol. 11, no. 1, pp. 31–46, 2010. View at Publisher · View at Google Scholar · View at Scopus
  2. J. M. Perkel, “Sanger Who? sequencing the next generation,” Science, vol. 324, no. 5924, pp. 275–279, 2009. View at Publisher · View at Google Scholar · View at Scopus
  3. M. Ronaghi, “Pyrosequencing sheds light on DNA sequencing,” Genome Research, vol. 11, no. 1, pp. 3–11, 2001. View at Publisher · View at Google Scholar · View at Scopus
  4. J. M. Rothberg, W. Hinz, T. M. Rearick et al., “An integrated semiconductor device enabling non-optical genome sequencing,” Nature, vol. 475, no. 7356, pp. 348–352, 2011. View at Publisher · View at Google Scholar · View at Scopus
  5. W. Brockman, P. Alvarez, S. Young et al., “Quality scores and SNP detection in sequencing-by-synthesis systems,” Genome Research, vol. 18, no. 5, pp. 763–770, 2008. View at Publisher · View at Google Scholar · View at Scopus
  6. R. Nielsen, “Genomics: in search of rare human variants,” Nature, vol. 467, no. 7319, pp. 1050–1051, 2010. View at Publisher · View at Google Scholar · View at Scopus
  7. M. Gerlinger, A. J. Rowan, S. Horswell et al., “Intratumor heterogeneity and branched evolution revealed by multiregion sequencing,” The New England Journal of Medicine, vol. 366, pp. 883–892, 2012. View at Google Scholar
  8. S. P. Shah, A. Roth, R. Goya et al., “The clonal and mutational evolution spectrum of primary triple-negative breast cancers,” Nature, vol. 486, pp. 395–399, 2012. View at Google Scholar
  9. M. R. Henn, C. L. Boutwell, P. Charlebois et al., “Whole genome deep sequencing of HIV-1 reveals the impact of early minor variants upon immune recognition during acute infection,” PLoS Pathogens, vol. 8, article e1002529, 2012. View at Google Scholar
  10. O. Zagordi, A. Bhattacharya, N. Eriksson, and N. Beerenwinkel, “ShoRAH: estimating the genetic diversity of a mixed sample from next-generation sequencing data,” BMC Bioinformatics, vol. 12, article no. 119, 2011. View at Publisher · View at Google Scholar · View at Scopus
  11. A. R. Quinlan, D. A. Stewart, M. P. Strömberg, and G. T. Marth, “Pyrobayes: an improved base caller for SNP discovery in pyrosequences,” Nature Methods, vol. 5, no. 2, pp. 179–181, 2008. View at Publisher · View at Google Scholar · View at Scopus
  12. F. Meacham, D. Boffelli, J. Dhahbi, D. I. Martin, M. Singer, and L. Pachter, “Identification and correction of systematic error in high-throughput sequence data,” BMC Bioinformatics, vol. 12, 451, 2011. View at Google Scholar
  13. L. Ilie, F. Fazayeli, and S. Ilie, “HiTEC: accurate error correction in high-throughput sequencing data,” Bioinformatics, vol. 27, no. 3, pp. 295–302, 2011. View at Publisher · View at Google Scholar
  14. P. Skums, Z. Dimitrova, D. Campo et al., “Efficient error correction for next-generation sequencing of viral amplicons,” BMC Bioinformatics, vol. 13, (Supplement 10):S6, 2012. View at Google Scholar
  15. C. Quince, A. Lanzén, T. P. Curtis et al., “Accurate determination of microbial diversity from 454 pyrosequencing data,” Nature Methods, vol. 6, no. 9, pp. 639–641, 2009. View at Publisher · View at Google Scholar · View at Scopus
  16. B. P. Howden, C. R. E. McEvoy, D. L. Allen et al., “Evolution of multidrug resistance during staphylococcus aureus infection involves mutation of the essential two component regulator WalKR,” PLoS Pathogens, vol. 7, e1002359, 2011. View at Google Scholar
  17. A. R. Macalalad, ZodyMC, P. Charlebois et al., “Highly sensitive and specific detection of rare variants in mixed viral populations from massively parallel sequence data,” PLoS Computational Biology, vol. 8, article e1002417, 2012. View at Google Scholar
  18. N. Metropolis and S. Ulam, “The Monte Carlo method,” Journal of the American Statistical Association, vol. 44, no. 247, pp. 335–341, 1949. View at Google Scholar · View at Scopus
  19. SRA Toolkit, http://www.ncbi.nlm.nih.gov/.
  20. JfreeChart library, http://www.jfree.org/jfreechart/.
  21. A. Mellmann, D. Harmsen, C. A. Cummings et al., “Prospective genomic characterization of the german enterohemorrhagic Escherichia coli O104:H4 outbreak by rapid next generation sequencing technology,” PLoS ONE, vol. 6, no. 7, Article ID e22751, 2011. View at Publisher · View at Google Scholar · View at Scopus
  22. Sequence Read Archive, http://sra.dnanexus.com/.
  23. MUMmer 3, “Ultra-fast alignment of large-scale DNA and protein sequences,” http://mummer.sourceforge.net/.
  24. H. Rohde et al., “Open-source genomic analysis of shiga-toxin-producing E. coli O104:H4,” The New England Journal of Medicine, vol. 365, pp. 718–724, 2011. View at Google Scholar
  25. D. Li, F. Xi, M. Zhao et al., “Escherichia coli O104:H4 TY-2482 isolate genome sequencing consortium (2011): genomic data from Escherichia coli O104:H4 isolate TY-2482. BGI Shenzhen,” GigaScience. In press. View at Publisher · View at Google Scholar
  26. Bowtie, http://bowtie-bio.sourceforge.net/index.shtml/.
  27. “Integrative genomics viewer,” http://www.broadinstitute.org/igv/.