3.3 Pharmacogenomics in Bioinformatics

3.3 Pharmacogenomics in Bioinformatics

Pharmacogenomics is defined as the study of how genes affect a person’s response to drugs. This relatively new field combines pharmacology and genomics to develop effective, safe medications and doses that will be specifically with the different genetic makeup. (Genetics Home Reference, 2015)

Many drugs that are currently available are “one size fits all,” but they don’t work the same way for everyone. It can be difficult to predict who will benefit from a medication, who will not respond at all, and who will get adverse drug. These genetic differences will be used to predict whether a medication will be effective for a particular person and to help prevent adverse drug reactions (Genetics Home Reference, 2015). In the future, pharmacogenomics will allow the development of tailored drugs to treat a wide range of health problems, including cardiovascular disease, cancer, HIV/AIDS, and asthma. Pharmacogenomics relatively used in the development of drug to prevent adverse effect and in order to make the drug more effective and safe.

With the help of bioinformatics tools, rational drug design can be done easily based on the pharmacogenomics where potential protein sequence or DNA or even RNA sequence can be determined. Example of such a software is called BLAST (basic local alignment search tool) dominantly used in the drug development process. Following are uses of BLAST according to Altschul, Stephen; Gish, Warren; Miller, Webb; Myers, Eugene; Lipman, David (1990) :

a)      Identifying species

With the use of BLAST, you can possibly correctly identify a species or find homologous species. This can be useful, for example, when you are working with a DNA sequence from an unknown species.

b)      Locating domains

When working with a protein sequence you can input it into BLAST, to locate known domains within the sequence of interest.

c)      Establishing phylogeny

Using the results received through BLAST you can create a phylogenetic tree using the BLAST web-page. Phylogenies based on BLAST alone are less reliable than other purpose-built computational phylogenetic methods, so should only be relied upon for "first pass" phylogenetic analyses.

d)     DNA mapping

When working with a known species, and looking to sequence a gene at an unknown location, BLAST can compare the chromosomal position of the sequence of interest, to relevant sequences in the database(s).

e)      Comparison

When working with genes, BLAST can locate common genes in two related species, and can be used to map annotations from one organism to another

BLAST used heuristic method to find similar sequences not by comparing either sequence in its entirety, but rather by locating short matches between the two sequences. To run, BLAST requires a query sequence to search for, and a sequence to search against (also called the target sequence) or a sequence database containing multiple such sequences. BLAST will find sub-sequences in the database which are similar to subsequences in the query. In typical usage, the query sequence is much smaller than the database, e.g., the query may be one thousand nucleotides while the database is several billion nucleotides.

The main idea of BLAST is that there are often high-scoring segment pairs (HSP) contained in a statistically significant alignment. BLAST searches for high scoring sequence alignments between the query sequence and sequences in the database using a heuristic approach that approximates the Smith-Waterman algorithm.(Altschul et al  (1990)). Based on the studies of Mount, D. W. (2004) overview of the BLASTP algorithm (a protein to protein search) is as follows :

o   Remove low-complexity region or sequence repeats in the query sequence.

o   Make a k-letter word list of the query sequence.

o   List the possible matching words.

o   Organize the remaining high-scoring words into an efficient search tree.

o   Repeat step 3 to 4 for each k-letter word in the query sequence.

o   Scan the database sequences for exact matches with the remaining high-scoring words.

o   Extend the exact matches to high-scoring segment pair (HSP).

o   List all of the HSPs in the database whose score is high enough to be considered.

o   Evaluate the significance of the HSP score.

o   Make two or more HSP regions into a longer alignment.

o   Show the gapped Smith-Waterman local alignments of the query and each of the matched database sequences.

o   Report every match whose expect score is lower than a threshold parameter E.