Affiliations: Department of Biotechnology, Royal Institute of
Technology (KTH), Stockholm, Sweden | Department of Medicine, Atherosclerosis Research Unit,
King Gustaf V Research Institute, Karolinska Institutet, Karolinska University
Hospital, Stockholm, Sweden. E-mail: {jorge,mathias}@biotech.kth.se;
[email protected]
Note: [] Corresponding author: Department of Biotechnology, KTH, AlbaNova
University Center, SE 106 97 Stockholm, Sweden. Tel.: +46 70 208 75 71; +46 8
5537 8332; Fax: +46 8-5537 8481; E-mail: [email protected]
Abstract: For several applications and algorithms used in applied
bioinformatics, a bottle neck in terms of computational time may arise when
scaled up to facilitate analyses of large datasets and databases.
Re-codification, algorithm modification or sacrifices in sensitivity and
accuracy may be necessary to accommodate for limited computational capacity of
single work stations. Grid computing offers an alternative model for solving
massive computational problems by parallel execution of existing algorithms and
software implementations. We present the implementation of a Grid-aware model
for solving computationally intensive bioinformatic analyses exemplified by a
blastp sliding window algorithm for whole proteome sequence similarity
analysis, and evaluate the performance in comparison with a local cluster and a
single workstation. Our strategy involves temporary installations of the BLAST
executable and databases on remote nodes at submission, accommodating for
dynamic Grid environments as it avoids the need of predefined runtime
environments (preinstalled software and databases at specific Grid-nodes).
Importantly, the implementation is generic where the BLAST executable can be
replaced by other software tools to facilitate analyses suitable for
parallelisation. This model should be of general interest in applied
bioinformatics. Scripts and procedures are freely available from the
authors.
