【正文】 ites on mRNA can be recovered from initiation plexes. They include the upstream ShineDalgarno sequence and the initiation codon. (From Gene VIII) 構(gòu)造刻畫原核基因 TIS的 4元統(tǒng)計(jì)模型 : P1: the correlation between translation terminate site and TIS of genes P2: the sequence content around the start codon P3: the sequence content of the consensus signal related to RBS P4: the correlation between TIS and the upstream consensus signal ATG ATG P1 P2 P3 P4 STP …CCC TCGAAGC… ATG …AACAGGAGGATT… …AGGATT… 自學(xué)習(xí)迭代系統(tǒng)MEDStart MEDStart算法的實(shí)現(xiàn) (1). Finding candidate motifs in upstream regions of predicted coding ORFs ? Motif (l, d): — Motif: a subsequence that is well preserved over several sequences, and the occurrences of the motif in those sequences are called instances. — The motifs in DNA or protein sequences may indicate functional connections, such as the transcription factor binding sites in noncoding regions of genes, as well as RBS in prokaryotes. — We use the term, (l, d) motif, to refer to the situation where a consensus string of length l, without wildcards, and the instances must differ in at most d positions from the consensus. ? Assume that the SD signal should be found in the upstream region of the leftmost start codons — The SD signal tends to be a preserved feature in the upstream regions of bacterial gene starts — Most of the start codons of the longest ORF are real gene starts. Reliable data set EcoGene dataset Link dataset Bsub1248 Number of genes 854 195 1248 Number of genes with 5’most start codons 537 (%) 133 (%) 786 (%) Table: Numbers of genes whose starts are leftmost start codon for a set of reliable data ?We first search for (l, d) string within L bps upstream of the start codon of the longest ORF in the original annotation (the default values are l=5, d=0, L=20) — In order to remove many false positive cases, the initial search is restricted to ORFs longer than 300bp. — For instance, a (5, 0) string is a word of 5 alphabets with zero variation that appears in many sequences within 20 bp upstream of the start codons. ?We select several strings with the highest frequency of occurrence as the candidate motifs. — In the next iteration step, the search for candidate motifs will be conducted within L bps upstream regions of the adjusted start sites that may not be the start codon of the longest ORFs. — The training sequences, . L bps long upstream regions of start sites of all the training ORFs are updated constantly until the iteration reaches convergence. (2). Determining hit motifs and their alignment weight matrix ? For each candidate motif, search for its (l, 1) instances. — They are regarded as candidates for SD signallike substring. ? Calculate the distribution of the location of the occurred instance to the start codon, which will be referred to as the spacer distribution. ? ?2( ) ( )1LkkiilppLl? ??????)(kip ?????Llikik plLp )()(11? Choose the one having highest ?, to be socalled ?hit motif ?. ? Use deviation ? of spacer distribution to characterize each candidate motif. ?If there exists more than one candidate motif having nearly the same ? to the highest one, the algorithm will select all of them, but at most three motifs, as the hit motifs. ?After hit motifs are determined, pute the positional weight matrix of each hit motif, by a multiple alignment of all its (l, 1) instances occurred within training sequences. — By the assumption that the hit motifs should be similar to a substring of SD sequence, the algorithm calculates the alignment weight matrix of [3+l+2] bp size of window around the hit motif. ? To detect the context feature of start codon fragments around starts. ? Calculate the positional probability within the alignment windows around start codon with length of (4+3+15) bp. ? We may represent the weight matrix by wSD(k)(bi, i) for bi?{A, C, G, T}, where (k) means the kth iterative step and i means position within these alignment windows and (4+3+15)? i ?1. ? Despite the difficulty of unknown true start codons, we can reach an approximation through this weight matrix, because nucleotides occur more randomly around the false start codons. (3). Weight matrix for start codon context (4). Weights for potential start codons behind the leftmost start codon ? Not all the start codons have equal possibility to be selected as true gene start, different weights should be assigned to different start codons when they are investigated whether to be true translation initiation sites ? Note m is the index of start codons, define wm(k) as the weight of the mth start codon being true gene start site, k is the iterative step. ? Describes the likelihood for a start codon of order m counting from the left most one to be a true start site. ? For k=1, . in the first iterative step, as the initial condition, we set an equal weight to each wm(k) , . w1(1) = w2(1) =…=. (5)