Computer software sequence searches

The number of unique phylogenetic trees increases exponentially with the number of taxa, becoming astronomical even for, say, 50 sequences (Swofford et al., 1996 Li, 1997). In most cases, computational limitations permit exploration of only a small fraction of possible trees. The exact number will depend mainly on the nmnber of taxa, the optimality criterion (e.g., MP is much faster than ML), the parameters (e.g., unweighted MP is much faster than weighted ML with fewer preset parameters is much faster than with more and/or simultaneously optimized parameters), computer hardware, and computer software (some algorithms are faster than others some software allows multiprocessing some software limits the number and kind of trees that can be stored in memory). The search procediue is also affected by data structure poorly resolvable data produce more nearly optimal trees that must be evaluated to find the most optimal. 

In addition to ChIP assays, there are open-source/web-accessible computational tools (e.g. Bailey et al., 2006) that allow researchers to find motifs in DNA or protein sequences that serve as binding sites for TF or NR. This approach could be helpful in analyzing microarray or proteomics data sets, which often uncover large numbers of seemin y co-regulated genes. Software tools can search for statistically-significant motifs within user-provided DNA sequences that may be present in some or all of the input sequences (Bailey et al, 2006). A recent study used this computational approach to search for putative TFBS in microarray data from bovine cardiac muscle (Zadissa et al, 2007). 

In databases built using bottom-up approaches, any computational representation believed to be common to all members of a particular domain family can be used. This representation, in conjunction with appropriate searching software, should optimally be able to distinguish all true family members from the background noise of unrelated proteins stored in sequence databases. This is a challenging problem, tackled with varying degrees of sophistication by different approaches. At the most basic level, the representation can consist of a simple pattern of amino acids common to a particular domain. Such an approach is found in... 

Entrez searches can be performed using one of two Internet-based interfaces. The first is a client-server implementation known as NetEntrez. This makes a direct connection to an NCBI computer. Because the client software resides on the user s machine, it is up to the user to obtain, install and maintain the software, downloading periodic updates as new features are introduced. The second implementation is over the World Wide Web and is known as WWW Entrez or WebEntrez (simply referred to as Entrez). This option makes use of available Web browsers (e.g., Netscape or Explorer) to deliver search results to the desktop. The Web allows the user to navigate by clicking on selected words in an entry. Furthermore, the Web implementation allows for the ability to link to external data sources. While the Web version is formatted as sequential pages, the Network version uses a series of windows with faster speed. The NCBI databases are, by far the most often accessed by biochemists and some of their searchable fields include plain text, author name, journal title, accession number, identity name (e.g., gene name, protein name, chemical substance name), EC number, sequence database keyword and medical subject heading. 

Whatever analytical approach is used for the characterization of proteins, the common last step is to use a computer algorithm for the evaluation of the data obtained [24]. The general role of bioinformatics in protein analysis, including database searches, sequence comparisons, and structural predictions, has been reviewed [25,26]. A concise review of the available software tools for database searching to interpret mass spectrometric data lists relevant original references [25]. 

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