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BioMed Research International
Volume 2015 (2015), Article ID 403497, 11 pages
http://dx.doi.org/10.1155/2015/403497
Research Article

DNAseq Workflow in a Diagnostic Context and an Example of a User Friendly Implementation

1University of Applied Sciences and Arts of Western Switzerland, Perolles 80, 1700 Fribourg, Switzerland
2University of Würzburg, Am Hubland, 97074 Würzburg, Germany
3Phenosystems SA, 137 Rue de Tubize, 1440 Braine le Chateau, Belgium

Received 13 March 2015; Revised 10 May 2015; Accepted 18 May 2015

Academic Editor: Hong Lu

Copyright © 2015 Beat Wolf et al. 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. E. L. van Dijk, H. Auger, Y. Jaszczyszyn, and C. Thermes, “Ten years of next-generation sequencing technology,” Trends in Genetics, vol. 30, no. 9, pp. 418–426, 2014. View at Publisher · View at Google Scholar
  2. B. Sikkema-Raddatz, L. F. Johansson, E. N. de Boer et al., “Targeted next-generation sequencing can replace sanger sequencing in clinical diagnostics,” Human Mutation, vol. 34, no. 7, pp. 1035–1042, 2013. View at Publisher · View at Google Scholar · View at Scopus
  3. H. P. J. Buermans and J. T. den Dunnen, “Next generation sequencing technology: advances and applications,” Biochimica et Biophysica Acta—Molecular Basis of Disease, vol. 1842, no. 10, pp. 1932–1941, 2014. View at Publisher · View at Google Scholar
  4. FastQC, “A quality control tool for high throughput sequence data,” Babraham Bioinformatics Website, http://www.bioinformatics.babraham.ac.uk/projects/fastqc/.
  5. W. R. Pearson, T. Wood, Z. Zhang, and W. Miller, “Comparison of DNA sequences with protein sequences,” Genomics, vol. 46, no. 1, pp. 24–36, 1997. View at Publisher · View at Google Scholar · View at Scopus
  6. H. Li and N. Homer, “A survey of sequence alignment algorithms for next-generation sequencing,” Briefings in Bioinformatics, vol. 11, no. 5, pp. 473–483, 2010. View at Publisher · View at Google Scholar · View at Scopus
  7. H. Li and R. Durbin, “Fast and accurate long-read alignment with Burrows-Wheeler transform,” Bioinformatics, vol. 26, no. 5, pp. 589–595, 2010. View at Publisher · View at Google Scholar · View at Scopus
  8. B. Langmead and S. L. Salzberg, “Fast gapped-read alignment with Bowtie 2,” Nature Methods, vol. 9, no. 4, pp. 357–359, 2012. View at Publisher · View at Google Scholar · View at Scopus
  9. S. F. Altschul, T. L. Madden, A. A. Schäffer 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. View at Publisher · View at Google Scholar · View at Scopus
  10. M. A. DePristo, E. Banks, R. Poplin et al., “A framework for variation discovery and genotyping using next-generation DNA sequencing data,” Nature Genetics, vol. 43, no. 5, pp. 491–498, 2011. View at Publisher · View at Google Scholar
  11. P. Carnevali, J. Baccash, A. L. Halpern et al., “Computational techniques for human genome resequencing using mated gapped reads,” Journal of Computational Biology, vol. 19, no. 3, pp. 279–292, 2012. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  12. H. Li, B. Handsaker, A. Wysoker et al., “The sequence alignment/map format and SAMtools,” Bioinformatics, vol. 25, no. 16, pp. 2078–2079, 2009. View at Publisher · View at Google Scholar · View at Scopus
  13. A. R. Quinlan and I. M. Hall, “BEDTools: a flexible suite of utilities for comparing genomic features,” Bioinformatics, vol. 26, no. 6, pp. 841–842, 2010. View at Publisher · View at Google Scholar · View at Scopus
  14. D. C. Koboldt, Q. Zhang, D. E. Larson et al., “VarScan 2: somatic mutation and copy number alteration discovery in cancer by exome sequencing,” Genome Research, vol. 22, no. 3, pp. 568–576, 2012. View at Publisher · View at Google Scholar · View at Scopus
  15. J. O'Rawe, T. Jiang, G. Sun et al., “Low concordance of multiple variant-calling pipelines: practical implications for exome and genome sequencing,” Genome Medicine, vol. 5, no. 3, article 28, 2013. View at Publisher · View at Google Scholar · View at Scopus
  16. S. T. Sherry, M.-H. Ward, M. Kholodov et al., “dbSNP: the NCBI database of genetic variation,” Nucleic Acids Research, vol. 29, pp. 308–311, 2001. View at Publisher · View at Google Scholar
  17. W. McLaren, B. Pritchard, D. Rios, Y. Chen, P. Flicek, and F. Cunningham, “Deriving the consequences of genomic variants with the Ensembl API and SNP Effect Predictor,” Bioinformatics, vol. 26, no. 16, Article ID btq330, pp. 2069–2070, 2010. View at Publisher · View at Google Scholar · View at Scopus
  18. P. N. Robinson and S. Mundlos, “The human phenotype ontology,” Clinical Genetics, vol. 77, no. 6, pp. 525–534, 2010. View at Publisher · View at Google Scholar · View at Scopus
  19. P. Danecek, A. Auton, G. Abecasis et al., “The variant call format and VCFtools,” Bioinformatics, vol. 27, no. 15, pp. 2156–2158, 2011. View at Publisher · View at Google Scholar · View at Scopus
  20. J. T. Robinson, H. Thorvaldsdóttir, W. Winckler et al., “Integrative genomics viewer,” Nature Biotechnology, vol. 29, no. 1, pp. 24–26, 2011. View at Publisher · View at Google Scholar · View at Scopus
  21. I. Milne, G. Stephen, M. Bayer et al., “Using tablet for visual exploration of second-generation sequencing data,” Briefings in Bioinformatics, vol. 14, no. 2, Article ID bbs012, pp. 193–202, 2013. View at Publisher · View at Google Scholar · View at Scopus
  22. T. Abeel, T. van Parys, Y. Saeys, J. Galagan, and Y. van de Peer, “GenomeView: a next-generation genome browser,” Nucleic Acids Research, vol. 40, no. 2, article e12, 2012. View at Publisher · View at Google Scholar · View at Scopus
  23. W. J. Kent, C. W. Sugnet, T. S. Furey et al., “The human genome browser at UCSC,” Genome Research, vol. 12, no. 6, pp. 996–1006, 2002. View at Publisher · View at Google Scholar · View at Scopus
  24. F. Cunningham, M. Ridwan Amode, D. Barrell et al., “Ensembl 2015,” Nucleic Acids Research, vol. 43, no. 1, pp. D662–D669, 2015. View at Publisher · View at Google Scholar
  25. NGS Data Analysis Software List, SEQanswers, 2012, http://seqanswers.com/wiki/Software/list.
  26. CLC Genomics Workbench, CLC Bio, Aarhus, Denmark, 2014, http://www.clcbio.com.
  27. NextGENe, Softgenetics, State College, Pa, USAhttp://www.softgenetics.com.
  28. J. Goecks, A. Nekrutenko, J. Taylor et al., “Galaxy: a comprehensive approach for supporting accessible, reproducible, and transparent computational research in the life sciences,” Genome Biology, vol. 11, no. 8, article R86, 2010. View at Publisher · View at Google Scholar · View at Scopus
  29. G. Lunter and M. Goodson, “Stampy: a statistical algorithm for sensitive and fast mapping of Illumina sequence reads,” Genome Research, vol. 21, no. 6, pp. 936–939, 2011. View at Publisher · View at Google Scholar · View at Scopus
  30. Online Mendelian Inheritance in Man, OMIM, McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University, Baltimore, Md, USA, 2015, http://omim.org/.
  31. Cafe Variome, http://www.cafevariome.org.
  32. The Genome Sequencing Consortium, “Initial sequencing and analysis of the human genome,” Nature, vol. 409, pp. 860–921, 2001. View at Publisher · View at Google Scholar
  33. P. Kumar, S. Henikoff, and P. C. Ng, “Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm,” Nature Protocols, vol. 4, no. 7, pp. 1073–1082, 2009. View at Publisher · View at Google Scholar · View at Scopus
  34. I. A. Adzhubei, S. Schmidt, L. Peshkin et al., “A method and server for predicting damaging missense mutations,” Nature Methods, vol. 7, no. 4, pp. 248–249, 2010. View at Publisher · View at Google Scholar · View at Scopus
  35. Alamut Visual, Interactive Biosoftware, Rouen, France, http://www.interactivebiosoftware.com/alamut-visual.
  36. S. Richards, N. Aziz, S. Bale et al., “Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology,” Genetics in Medicine, vol. 17, no. 5, pp. 405–423, 2015. View at Publisher · View at Google Scholar
  37. B. Wolf and P. Kuonen, “A novel approach for heuristic pairwise DNA sequence alignment,” in Proceedings of the International Conference on Bioinformatics & Computational Biology (BIOCOMP '13), Las Vegas, Nev, USA, July 2013.
  38. O. Gotoh, “An improved algorithm for matching biological sequences,” Journal of Molecular Biology, vol. 162, no. 3, pp. 705–708, 1982. View at Google Scholar · View at Scopus
  39. D. Smedley, S. Kohler, J. C. Czeschik et al., “Walking the interactome for candidate prioritization in exome sequencing studies of Mendelian diseases,” Bioinformatics, vol. 30, no. 22, pp. 3215–3222, 2014. View at Publisher · View at Google Scholar
  40. A. L. Semmler, S. Sacconi, J. Bach et al., “Unusual multisystemic involvement and a novel BAG3 mutation revealed by NGS screening in a large cohort of myofibrillar myopathies,” Orphanet Journal of Rare Diseases, vol. 9, no. 1, p. 121, 2014. View at Publisher · View at Google Scholar
  41. M. Larsen, S. Rost, N. El Hajj et al., “Diagnostic approach for FSHD revisited: SMCHD1 mutations cause FSHD2 and act as modifiers of disease severity in FSHD1,” European Journal of Human Genetics, vol. 23, no. 6, pp. 808–816, 2015. View at Publisher · View at Google Scholar
  42. S. Rost, E. Bach, C. Neuner et al., “Novel form of X-linked nonsyndromic hearing loss with cochlear malformation caused by a mutation in the type IV collagen gene COL4A6,” European Journal of Human Genetics, vol. 22, no. 2, pp. 208–215, 2014. View at Publisher · View at Google Scholar · View at Scopus
  43. M. Broman, I. Kleinschnitz, J. E. Bach, S. Rost, G. Islander, and C. R. Müller, “Next-generation DNA sequencing of a Swedish malignant hyperthermia cohort,” Clinical Genetics, 2014. View at Publisher · View at Google Scholar
  44. T. Low, S. vanHeesch, H. van den Toorn et al., “Quantitative and qualitative proteome characteristics extracted from in-depth integrated genomics and proteomics analysis,” Cell Reports, vol. 5, no. 5, pp. 1469–1478, 2013. View at Publisher · View at Google Scholar · View at Scopus