within bioinformatics and computational relations between species (the use of to biology include: construct ). With the growing amount of data, it long ago became impractical to the development and analyze DNA sequences manually. implementation of tools that Today, such as are used daily to search enable efficient access to, and the of thousands of organisms, use and management of, various containing billions of . These programs types of information. can compensate for mutations the development of new (exchanged, deleted or inserted bases) in algorithms (mathematical the DNA sequence, in order to identify formulas) and statistics with sequences that are related, but not which to assess relationships identical. A variant of this is used in the among members of large data sequencing process itself. The so-called sets, such as methods to locate a technique (which was used, for example, gene within a sequence, predict by to sequence the first bacterial protein structure and/or function, genome, Haemophilus influenzae) does and cluster protein sequences not produce entire chromosomes, but into families of related instead generates the sequences of many sequences. thousands of small DNA fragments (ranging from 35 to 900 nucleotides Major research areas: long, depending on the sequencing technology). The ends of these Since the was in 1977, the of fragments overlap and, when aligned thousands of organisms have been properly by a genome assembly decoded and stored in databases. This program, can be used to reconstruct the sequence information is analyzed to complete genome. Shotgun sequencing determine genes that encode (), RNA yields sequence data quickly, but the genes, regulatory sequences, structural task of assembling the fragments can be motifs, and repetitive sequences. A quite complicated for larger genomes. comparison of genes within a or For a genome as large as the , it may between different species can show take many days of CPU time on similarities between protein functions, or
within bioinformatics and computational biology include: the development and implementation of tools that enable efficient access to, and use and management of, various types of information. the development of new algorithms (mathematical formulas) 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. Major research areas: Since the was in 1977, the of thousands of organisms have been decoded and stored in databases. This sequence information is analyzed to determine genes that encode (), RNA genes, regulatory sequences, structural motifs, and repetitive sequences. A comparison of genes within a or between different species can show similarities between protein functions, or relations between species (the use of to construct ). With the growing amount of data, it long ago became impractical to analyze DNA sequences manually. Today, such as are used daily to search the of thousands of organisms, containing billions of . These programs can compensate for mutations (exchanged, deleted or inserted bases) in the DNA sequence, in order to identify sequences that are related, but not identical. A variant of this is used in the sequencing process itself. The so-called technique (which was used, for example, by to sequence the first bacterial genome, Haemophilus influenzae) does not produce entire chromosomes, but instead generates the sequences of many thousands of small DNA fragments (ranging from 35 to 900 nucleotides long, depending on the sequencing technology). The ends of these fragments overlap and, when aligned properly by a genome assembly program, can be used to reconstruct the complete genome. Shotgun sequencing yields sequence data quickly, but the task of assembling the fragments can be quite complicated for larger genomes. For a genome as large as the , it may take many days of CPU time on