Bioinformatics
 | This page is incomplete. |
| Bioinformatics | |
|---|---|
| Type | Life Science |
| Category | Study |
| Description | Teams will complete a written exam on the theory behind bioinformatic methods and their applications in solving biological problems. |
| Event Information | |
| Participants | 2 |
| Approx. Time | 50 Minutes |
| Allowed Resources |
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| Official Resources | |
| Website | www |
Bioinformatics is a Division C trial event that is set to run in the 2022 season at the National Invitational as well as the BirdSO Invitational.
Biological Prerequisites
Central Dogma of Life
Every individual has a unique sequence of nucleobases. The distinct sequence is what leads to the diversity of life. DNA makes RNA, and RNA makes protein. DNA is stored inside of a cell's nucleus, and can't move. RNA transmits the information in DNA to make proteins in the cell. Proteins are what essentially every part of our body is made of.
Nucleic Acids and Proteins
There are five nucleobases: Adenine, Thymine (DNA only), Guanine, Cytosine, and Uracil (RNA only). Adenine always pairs with thymine in DNA and uracil in RNA, while guanine is always paired with cytosine in both DNA and RNA.
Types of Bioinformatic Databases
Sequence Alignment
Sequence alignment is arranging DNA, RNA, or proteins to identify regions of similarity. Gaps can be inserted between them so identical or similar characters are aligned in successful columns. Typically there are points assigned for mismatched or blanks, and either the highest or lowest score depending on the algorithm would be the best alignment. Dynamic programming is often utilized to solve these alignments as quickly as possible.
Biological Motivation
Sequence alignments are modeled after mutations where genes can be inserted/deleted. By trying to solve for these alignments, it helps assist with mutations or mistakes that might be fatal.
Scoring Matrices
Pairwise Sequence Alignment
This is comparing only two sequences together.
Needleman-Wunsch Algorithm
Sometimes this algorithm is referred to as the optimal matching or the global alignment technique because it checks the entire sequence rather than a part of it.
Smith-Waterman Algorithm
This algorithm focuses on local sequence alignment rather than globally, where it checks for similar regions between two sequences. The negative scoring matrix cells are set to zero, resulting in only positive scores. However, it can't be practically applied to large-scale problems due to poor efficiency (quadratic complexity in time and space).
Multiple Sequence Alignment
This is comparing more than two sequences, possibly a whole database of sequences with one another.