DNA Information

1. A: Adenosine
2. C: Cytosine
3. G: Guanine
4. U: Uracil

Typically, DNA is created from RNA, and this is done by faithfully copying the sequence of base pairs, with the only change converting T to U. Hence, an RNA copy of a DNA sequence encodes the identical information, though it uses a slightly different set of 4 substances. The differences between DNA and RNA are also many. The underlying sugar molecule that traps the 4 bases is different: deoxyribose in DNA, ribose in RNA. DNA is two strands wrapped in a double-helix, but RNA is a single strand.

• tRNA: transfer RNA
• rRNA: ribosome RNA

There are exactly 20 forms of tRNA, one each to transfer a particular amino acid. tRNA molecules contain about 75-80 bases. tRNA recognizes one of the 64 triplets, and matches it to one of the 20 amino acids. Since there are 20 tRNA types, and not 64, each tRNA molecule has to recognize more than one triplet ordering as a match. The DNA code contains multiple repetitions of the codes for tRNA and rRNA. About 280 copies are spread over 5 chromosomes. Presumably, this allows each cell to make multiple copies of tRNA and rRNA molecules at once from its single copy of the DNA.

Evolutionary biology is the study of the origin and descent of species, as well as their change over time. Informatics has assisted evolutionary biologists in several key ways; it has enabled researchers to:
1. trace the evolution of a large number of organisms by measuring changes in their DNA, rather than through physical taxonomy or physiological observations alone

2. more recently, compare entire genomes, which permits the study of more complex evolutionary events, such as gene duplication, horizontal gene transfer, and the prediction of factors important in bacterial speciation

3. build complex computational models of populations to predict the outcome of the system over time

4. track and share information on an increasingly large number of species and organisms.

Bioinformatics was applied in the creation and maintenance of a database to store biological information at the beginning of the "genomic revolution", such as nucleotide and amino acid sequences. Development of this type of database involved not only design issues but the development of complex interfaces whereby researchers could both access existing data as well as submit new or revised data.In order to study how normal cellular activities are altered in different disease states, the biological data must be combined to form a comprehensive picture of these activities. Therefore, the field of bioinformatics has evolved such that the most pressing task now involves the analysis and interpretation of various types of data, including nucleotide and amino acid sequences, protein domains, and protein structures. The actual process of analyzing and interpreting data is referred to as computational biology

Important sub-disciplines within bioinformatics and computational biology include

1. the development and implementation of tools that enable efficient access to, and use and management of, various types of information

2. the development of new algorithms and statistics with which to assess relationships among members of large data sets, such as methods to locate a gene within a sequence, predict protein structure and/or function, and cluster protein sequences into families of related sequences.

The area of research within computer science that uses genetic algorithms is sometimes confused with computational evolutionary biology but the two areas are not necessarily related.

The expression of many genes can be determined by measuring mRNA levels with multiple techniques including microarrays, expressed cDNA sequence tag (EST) sequencing, serial analysis of gene expression (SAGE) tag sequencing massively parallel signature sequencing (MPSS) or various applications of multiplexed in-situ hybridization. All of these techniques are extremely noise prone and or subject to bias in the biological measurement and a major research area in computational biology involves developing statistical tools to separate signal from noise in high-throughput gene expression studies. Such studies are often used to determine the genes implicated in a disorder: one might compare microarray data from cancerous epithelial cells to data from non-cancerous cells to determine the transcripts that are up-regulated and down-regulated in a particular population of cancer cells.

Protein microarrays and high throughput (HT) mass spectrometry (MS) can provide a snapshot of the proteins present in a biological sample. Bioinformatics is very much involved in making sense of protein microarray and HT MS data the former approach faces similar problems as with microarrays targeted at mRNA, the latter involves the problem of matching large amounts of mass data against predicted masses from protein sequence databases, and the complicated statistical analysis of samples where multiple, but incomplete peptides from each protein are detected.