Vertex Ce 115 Software Download

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For example, approaches based on Gibbs sampling randomly select a candidate subsequence of a fixed length from each sequence. A sequence is arbitrarily selected and each subsequence of the same length in the sequence is aligned to a profiling model obtained from the subsequences selected on other sequences, and each subsequence in the selected sequence is thus associated with a probability value which is the alignment score. One of the subsequences is then selected based on the distribution of the probability values of these subsequences, and a new set of subsequences is thus obtained. The procedure can be repeated until the maximum allowed number of iterations has been reached or a set of satisfying local optimal subsequences have been found [4, 5, 10]. Consensus used a greedy algorithm to align functionally related sequences and applied the algorithm to identify the binding sites for the E. coli CRP protein [11]. Bailey and Elkan [12, 13] used the Expectation maximization technique to fit a two-component mixture model to find binding sites and developed a software MEME+. MEME+ performed better than their previous MEME software [13]. However, its accuracy for identifying transcription factor binding sites is far from being satisfactory.

In this paper, we develop a new approach that can predict the transcription factor binding sites without using a sampling procedure to select subsequences. Our approach uses a graph to model all subsequences in the promoter regions of the homologous genes and the similarity between any pair of subsequences that are from different promoter regions. In particular, each subsequence is represented by a vertex in the graph, and two vertices are joined by an edge if the two corresponding subsequences are from different promoter regions and their similarity is higher than a threshold. The threshold can be determined using the base compositions of the promoter regions and is guaranteed to be statistically significant. Each edge in the graph is associated with a weight value which is the similarity of the two corresponding subsequences. We then compute the maximum weighted clique in the graph, and the subsequences represented by the vertices in the clique are the transcription factor binding sites.

We then construct a graph G such that each vertex of the graph represents a subsequence. Subsequences from the same sequence are placed in one column, and all vertices in G can thus be partitioned into k disjoint columns. A sample of randomly generated and normally distributed sequence comparison scores is used to calculate a threshold which is then used to determine which sequences are similar. The algorithm starts with the first column and selects a vertex in that column and aligns its corresponding subsequence to that of every other vertex that is from a different column. If the alignment score of two subsequences is higher than the threshold, we make their vertices adjacent in G. The algorithm repeats the above process for each vertex until all vertices in the graph have been processed.

After all the edges have been added to the graph, we proceed to preprocess the graph and remove the vertices that cannot be in a clique of size k. In particular, we examine the degree of each vertex, and if the degree of a vertex is less than k, we remove it from G. This procedure is applied iteratively to all vertices in the graph until the size of the graph cannot be further reduced.

The algorithm then starts enumerating all k-cliques in the graph and computes the weight of each clique. To this end, the algorithm assigns an integer id between 1 and k to each column in the graph and starts with columns 1 and 2. In particular, all edges that connect a vertex from column 1 and a vertex from column 2 are included in a set S. S is maintained by the algorithm to store cliques. Initially, S contains a set of edges, which are in fact cliques on two vertices. The algorithm then proceeds to examine vertices in columns 3 through k. After the algorithm completes processing column j, S contains a set of cliques of size j in G. For every vertex u in column j + 1, the algorithm checks each j-clique M in S to examine whether u and M can together form a j + 1-clique. In other words, the algorithm examines whether u is adjacent to every vertex in M or not. If it is the case, u and M are combined into a single j + 1-clique and included in S. After every vertex in column j + 1 has been processed, the algorithm examines the cliques in S and removes all j-cliques from S. It is not difficult to see that after all k columns have been processed, S contains a set of k-cliques in G. It then computes the weight value of each clique in S by adding up the weight values of all edges in the clique and outputs the clique with the largest weight value. 2b1af7f3a8