Year: 2001
Type: conference-paper
Abstract: With the ever-increasing genomic information pouring into the databases researchers start to look for pattern in genomes. Key questions are the identification of function. In the past function was mainly understood to be assigned to a single gene isolated from other cellular components or mechanisms. Sequence comparison fo single genes and their products (proteins) as well as of intergenic space are a consequence of a well established one-gene one-function interpretation. prediction of function solely by sequence similarity searches are powerful techniques that initiated the advent of bioinformatics and computational biology. Seminal work on sequence alignment by Temple Smith and Michael Waterman [33] and sequence searches with the BLAST algorithm by Altschul et al. [2] provide essential methods for sequence based determination of function. Similar outstanding contributions to determination of function have been archived in the area of structure prediction, molecular modeling and molecular dynamics. Techniques covering ab initio and homology modeling up to biophysical interpretation of long-run molecular dynamics simulations are mentioned ehre. With the ever-increasing number of information of different genetic/genomic origin, new aspect are looked for that deviate from the single gene at a time method. Especially with the identification of surprisingly few human genes the emerging perception in the scientific community that the concept of function has to be extended to include other sequence based as well as non-sequenced based information. A schema of determination of function by different concepts is shown in Figure 1. The tutorial is comprised of the following sections: The first two sections discuss the differences between genomic and non-genomic based context information, section three will cover combined methods. Finally, section four lsits web-resources and databases. All presented approaches extensively employ comparative methods.
Author: Forst, C. (Christian)
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W2021965367