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Motif-finding strategies in .NET Receive qrcode in .NET Motif-finding strategies




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10.3 Motif-finding strategies use visual .net qrcode implementation todraw qrcode on .net Office Word The biological problem .net vs 2010 QR Code of nding TFBSs directly translates into the computational problem of nding common sequence motifs. Motifs can generically be de ned as patterns in sequences, typically speci c sets of words.

A number of different biological problems are subsumed under the heading. 10.3 MOTIF-FINDING STRATEGIES of motif nding and hen .net vs 2010 QR Code 2d barcode ce various statistical and computational methods are used. In this chapter we focus only on the simplest problems, providing pointers to more detailed literature in Section 10.

6. We review here the main design choices to be made by the analyst, before illustrating all of them in Example 10.1.

Given a set of co-regulated genes, the rst thing we do is collect the set of DNA sequences surrounding these genes that we believe will contain the relevant binding sites. Generally the sequences collected will be some xed length sequence upstream of the coding region. The choice of how far upstream to search is always an arbitrary one, and depends on the speci c organism being analyzed; one common choice is to use 1000 bases upstream of the ORF.

Though regulatory elements can be found hundreds of thousands of bases upstream and downstream of coding regions (at least in the genomes of multicellular organisms), the highest concentration is generally found in the rst 1000 bases. The next choice in our analysis is the type of motif to search for, i.e.

the motif model. Is it going to be a gapped or an ungapped motif Is the gap going to be of xed or variable length How long will the motif be In this chapter we will concentrate on ungapped motifs of xed length (gapped motifs of variable length were discussed in 4, in the context of pro le HMMs). Ungapped motifs of xed length can be seen as words of length L that appear somewhere in the upstream regions and that are similar to each other.

As mentioned earlier, binding sites for the same transcription factor are not necessarily identical, only highly similar. One way to summarize any list of xed-length motifs that differ in their exact sequences is to report the consensus sequence: a new sequence formed by the most frequent letter used at each position (the consensus sequence does not necessarily need to appear in the data). When all (equal-length) motifs are aligned, we can easily nd the most common nucleotide for each position, and form a consensus motif from these.

Thus, from any alignment, we can easily obtain the consensus sequence. This can be a very useful representation of a set of sequences, and is described in Example 10.1.

Another way to summarize motifs (given a multiple alignment) is to report the frequency of each nucleotide used at every position, resulting in a position speci c scoring matrix (PSSM) or pro le (these are also sometimes called position speci c weight matrices, or PSWMs). The PSSM essentially represents a multinomial model of a sequence of length L, where one of the four bases (or 20 AAs) is chosen independently from a multinomial distribution for each position, and in which parameters are position speci c. In other words, a different loaded-die is rolled for each position, its bias represented in the PSSM.

The PSSM is a 4 L (or 20 L) matrix (4 rows, L columns), like the one shown in Example 10.1 . Note that a consensus motif can readily be obtained from a PSSM by taking the most frequent nucleotide in each position.

. Example 10.1 Fixed leng th, ungapped motifs. In this set of eight sequences we nd a similar 6-letter motif in each, appearing in various positions.

The start positions are.
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