BACKGROUND: An avalanche of next generation sequencing (NGS) studies has generated an unprecedented amount of genomic structural variation data. These studies have also identified many novel gene fusion candidates with more detailed resolution than previously achieved. However, in the excitement and necessity of publishing the observations from this recently developed cutting-edge technology, no community standardization approach has arisen to organize and represent the data with the essential attributes in an interchangeable manner. As transcriptome studies have been widely used for gene fusion discoveries, the current non-standard mode of data representation could potentially impede data accessibility, critical analyses, and further discoveries in the near future. RESULTS: Here we propose a prototype, Gene Fusion Markup Language (GFML) as an initiative to provide a standard format for organizing and representing the significant features of gene fusion data. GFML will offer the advantage of representing the data in a machine-readable format to enable data exchange, automated analysis interpretation, and independent verification. As this database-independent exchange initiative evolves it will further facilitate the formation of related databases, repositories, and analysis tools. The GFML prototype is made available at http://code.google.com/p/gfml-prototype/. CONCLUSION: The Gene Fusion Markup Language (GFML) presented here could facilitate the development of a standard format for organizing, integrating and representing the significant features of gene fusion data in an inter-operable and query-able fashion that will enable biologically intuitive access to gene fusion findings and expedite functional characterization. A similar model is envisaged for other NGS data analyses.
BACKGROUND: An avalanche of next generation sequencing (NGS) studies has generated an unprecedented amount of genomic structural variation data. These studies have also identified many novel gene fusion candidates with more detailed resolution than previously achieved. However, in the excitement and necessity of publishing the observations from this recently developed cutting-edge technology, no community standardization approach has arisen to organize and represent the data with the essential attributes in an interchangeable manner. As transcriptome studies have been widely used for gene fusion discoveries, the current non-standard mode of data representation could potentially impede data accessibility, critical analyses, and further discoveries in the near future. RESULTS: Here we propose a prototype, Gene Fusion Markup Language (GFML) as an initiative to provide a standard format for organizing and representing the significant features of gene fusion data. GFML will offer the advantage of representing the data in a machine-readable format to enable data exchange, automated analysis interpretation, and independent verification. As this database-independent exchange initiative evolves it will further facilitate the formation of related databases, repositories, and analysis tools. The GFML prototype is made available at http://code.google.com/p/gfml-prototype/. CONCLUSION: The Gene Fusion Markup Language (GFML) presented here could facilitate the development of a standard format for organizing, integrating and representing the significant features of gene fusion data in an inter-operable and query-able fashion that will enable biologically intuitive access to gene fusion findings and expedite functional characterization. A similar model is envisaged for other NGS data analyses.
Authors: Scott A Tomlins; Bharathi Laxman; Saravana M Dhanasekaran; Beth E Helgeson; Xuhong Cao; David S Morris; Anjana Menon; Xiaojun Jing; Qi Cao; Bo Han; Jindan Yu; Lei Wang; James E Montie; Mark A Rubin; Kenneth J Pienta; Diane Roulston; Rajal B Shah; Sooryanarayana Varambally; Rohit Mehra; Arul M Chinnaiyan Journal: Nature Date: 2007-08-02 Impact factor: 49.962
Authors: Oliver A Hampton; Petra Den Hollander; Christopher A Miller; David A Delgado; Jian Li; Cristian Coarfa; Ronald A Harris; Stephen Richards; Steven E Scherer; Donna M Muzny; Richard A Gibbs; Adrian V Lee; Aleksandar Milosavljevic Journal: Genome Res Date: 2008-12-03 Impact factor: 9.043
Authors: Renzo Kottmann; Tanya Gray; Sean Murphy; Leonid Kagan; Saul Kravitz; Thierry Lombardot; Dawn Field; Frank Oliver Glöckner Journal: OMICS Date: 2008-06
Authors: Peter J Campbell; Philip J Stephens; Erin D Pleasance; Sarah O'Meara; Heng Li; Thomas Santarius; Lucy A Stebbings; Catherine Leroy; Sarah Edkins; Claire Hardy; Jon W Teague; Andrew Menzies; Ian Goodhead; Daniel J Turner; Christopher M Clee; Michael A Quail; Antony Cox; Clive Brown; Richard Durbin; Matthew E Hurles; Paul A W Edwards; Graham R Bignell; Michael R Stratton; P Andrew Futreal Journal: Nat Genet Date: 2008-04-27 Impact factor: 38.330
Authors: Dawn Field; George Garrity; Tanya Gray; Norman Morrison; Jeremy Selengut; Peter Sterk; Tatiana Tatusova; Nicholas Thomson; Michael J Allen; Samuel V Angiuoli; Michael Ashburner; Nelson Axelrod; Sandra Baldauf; Stuart Ballard; Jeffrey Boore; Guy Cochrane; James Cole; Peter Dawyndt; Paul De Vos; Claude DePamphilis; Robert Edwards; Nadeem Faruque; Robert Feldman; Jack Gilbert; Paul Gilna; Frank Oliver Glöckner; Philip Goldstein; Robert Guralnick; Dan Haft; David Hancock; Henning Hermjakob; Christiane Hertz-Fowler; Phil Hugenholtz; Ian Joint; Leonid Kagan; Matthew Kane; Jessie Kennedy; George Kowalchuk; Renzo Kottmann; Eugene Kolker; Saul Kravitz; Nikos Kyrpides; Jim Leebens-Mack; Suzanna E Lewis; Kelvin Li; Allyson L Lister; Phillip Lord; Natalia Maltsev; Victor Markowitz; Jennifer Martiny; Barbara Methe; Ilene Mizrachi; Richard Moxon; Karen Nelson; Julian Parkhill; Lita Proctor; Owen White; Susanna-Assunta Sansone; Andrew Spiers; Robert Stevens; Paul Swift; Chris Taylor; Yoshio Tateno; Adrian Tett; Sarah Turner; David Ussery; Bob Vaughan; Naomi Ward; Trish Whetzel; Ingio San Gil; Gareth Wilson; Anil Wipat Journal: Nat Biotechnol Date: 2008-05 Impact factor: 54.908