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algorithm in guidance of contextual information. A multithreaded parallel implementation of
sequence comparison by a DP algorithm could well be employed. The algorithmic steps and data
space of the problem can be designed specifically for parallel implementation. The problem
might be solved in different set-ups to validate, consolidate and further improve the result. Time
and space complexity must well be balanced in the followed procedure, though.
Bioinformatics work is multi-disciplined in nature but, not complex. The way ahead, a
road map for a computer science expert in the field of bioinformatics might be to attend
(computational) molecular biology classes and workshops; to review genome data and existing
supporting computational tools; to examine the analysis requirements for genome data including
sequence, pattern, association structures and concepts; to study the latest work and literature to
determine the present technology; to join and exchange views with a team of different expertise.
These issues equally apply for other disciplines that are inherent in bioinformatics, as well. This
initiative lays the required groundwork to identify and solve the bioinformatics problems with
bioinformatics-specific frameworks.
One major goal of bioinformatics is the analysis of sequence, structure and function
relationships. In those studies, lab experiments and computational work must validate and
consolidate each other. The findings of both initiatives expedite each other's improvement. This
process requires experts who can both work at lab bench and in computer applications. Better
algorithms, improved scoring tables, solid semantic models will all emerge with better
understanding of huge experimental data residing in large annotated databases. This remains to
be the major challenge of our time.
References
[1]
Huelsenbeck, J. Ogihara, M. (2001). Lecture 7.
CS 120
.
[2]
Kraulis, P. (2000). Structural Biochemistry and Bioinformatics Lecture Notes.
Stockholm Bioinformatics
Center
.
[3]
Sabbiah, S. An Overview of the Computational Analysis of Biological Sequences.
Stanford Univ.
Bioinformatics Center, Singapore
.
[4]
Shamir, R. (2001). Algorithms for Molecular Biology. Lecture Notes.
Tel Aviv University School of
Computer Science.
