BACKGROUND: Parallel computing is frequently used to speed up computationally expensive tasks in Bioinformatics. RESULTS: Herein, a parallel version of the multi-alignment program DIALIGN is introduced. We propose two ways of dividing the program into independent sub-routines that can be run on different processors: (a) pair-wise sequence alignments that are used as a first step to multiple alignment account for most of the CPU time in DIALIGN. Since alignments of different sequence pairs are completely independent of each other, they can be distributed to multiple processors without any effect on the resulting output alignments. (b) For alignments of large genomic sequences, we use a heuristics by splitting up sequences into sub-sequences based on a previously introduced anchored alignment procedure. For our test sequences, this combined approach reduces the program running time of DIALIGN by up to 97%. CONCLUSIONS: By distributing sub-routines to multiple processors, the running time of DIALIGN can be crucially improved. With these improvements, it is possible to apply the program in large-scale genomics and proteomics projects that were previously beyond its scope.
BACKGROUND: Parallel computing is frequently used to speed up computationally expensive tasks in Bioinformatics. RESULTS: Herein, a parallel version of the multi-alignment program DIALIGN is introduced. We propose two ways of dividing the program into independent sub-routines that can be run on different processors: (a) pair-wise sequence alignments that are used as a first step to multiple alignment account for most of the CPU time in DIALIGN. Since alignments of different sequence pairs are completely independent of each other, they can be distributed to multiple processors without any effect on the resulting output alignments. (b) For alignments of large genomic sequences, we use a heuristics by splitting up sequences into sub-sequences based on a previously introduced anchored alignment procedure. For our test sequences, this combined approach reduces the program running time of DIALIGN by up to 97%. CONCLUSIONS: By distributing sub-routines to multiple processors, the running time of DIALIGN can be crucially improved. With these improvements, it is possible to apply the program in large-scale genomics and proteomics projects that were previously beyond its scope.
Authors: Berthold Göttgens; Linda M Barton; Michael A Chapman; Angus M Sinclair; Bjarne Knudsen; Darren Grafham; James G R Gilbert; Jane Rogers; David R Bentley; Anthony R Green Journal: Genome Res Date: 2002-05 Impact factor: 9.043
Authors: B Göttgens; L M Barton; J G Gilbert; A J Bench; M J Sanchez; S Bahn; S Mistry; D Grafham; A McMurray; M Vaudin; E Amaya; D R Bentley; A R Green; A M Sinclair Journal: Nat Biotechnol Date: 2000-02 Impact factor: 54.908
Authors: Burkhard Morgenstern; Oliver Rinner; Saïd Abdeddaïm; Dirk Haase; Klaus F X Mayer; Andreas W M Dress; Hans-Werner Mewes Journal: Bioinformatics Date: 2002-06 Impact factor: 6.937
Authors: Michael A Chapman; Fadi J Charchar; Sarah Kinston; Christine P Bird; Darren Grafham; Jane Rogers; Frank Grützner; Jennifer A Marshall Graves; Anthony R Green; Berthold Göttgens Journal: Genomics Date: 2003-03 Impact factor: 5.736
Authors: Tung T Nguyen; Richard R Almon; Debra C Dubois; William J Jusko; Ioannis P Androulakis Journal: BMC Bioinformatics Date: 2010-10-14 Impact factor: 3.169
Authors: Therese A Catanach; Andrew D Sweet; Nam-Phuong D Nguyen; Rhiannon M Peery; Andrew H Debevec; Andrea K Thomer; Amanda C Owings; Bret M Boyd; Aron D Katz; Felipe N Soto-Adames; Julie M Allen Journal: PeerJ Date: 2019-01-03 Impact factor: 2.984