A Simple Algorithm for (l, d) Motif Search1

机译：（l，d）主题搜索的简单算法

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Extracting meaningful patterns from voluminous amount of biological data is a very big challenge. Motifs are biological patterns of great interest to biologists. There are different versions of the Motif Finding Problem. In this paper we concentrate on the Planted (l, d) Motif Search Problem. There have been numerous algorithms designed to solve this problem. Many instances of the Planted (l, d) Motif Problem have been identified as challenging instances. The algorithm proposed here is an extension of PMS3. It uses a very simple approach and solves challenging instances ((21, 8), for example) that have not been reported solved before in the literature. We also propose a new algorithm PMS3p. We expect PMS3p to be significantly faster than PMS3.

机译：从大量的生物学数据中提取有意义的模式是一个很大的挑战。主题是生物学家非常感兴趣的生物学模式。有不同版本的“主题查找问题”。在本文中，我们专注于种植（l，d）主题搜索问题。设计了许多算法来解决此问题。种植（l，d）母题的许多实例已被确定为具有挑战性的实例。这里提出的算法是PMS3的扩展。它使用一种非常简单的方法，并解决了以前在文献中尚未解决的具有挑战性的实例（例如，（（21，8））。我们还提出了一种新的算法PMS3p。我们希望PMS3p的速度明显快于PMS3。

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《Computational Intelligence in Bioinformatics and Computational Biology, 2009. CIBCB '09》|2009年|148-154|共7页
会议地点 Nashville TN(US);Nashville TN(US)
作者
Sharma D.; Rajasekaran S.;
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Comput. Sci. Eng., Univ. of Connecticut, Storrs, CT;

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genetics; molecular biophysics; (l, d) motif search problem; genomic sequencing;

机译：遗传学;分子生物物理学;（l，d）基序搜索问题;基因组测序;

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专利

1. Motif Difficulty (MD): A Predictive Measure of Problem Difficulty for Evolutionary Algorithms Using Network Motifs [J] . Jing Liu, Hussein A. Abbass, David G. Green, Evolutionary computation . 2012,第3期

机译：母题难度（MD）：使用网络母题的进化算法问题难度的预测度量
2. Subtle motifs: defining the limits of motif finding algorithms. [J] . Keich U, Pevzner PA Bioinformatics . 2002,第10期

机译：细微的图案：定义图案查找算法的限制。
3. An Algorithm for Motif Discovery with Iteration on Lengths of Motifs [J] . Fan Y., Wu W., Yang J., Computational Biology and Bioinformatics, IEEE/ACM Transactions on . 2015,第1期

机译：基于主题长度迭代的主题发现算法
4. A Consensus Algorithm for Simple Motifs Finding [C] . El Falah T., Ghnimi M., Elloumi M. International workshop on database and expert systems applications . 2014

机译：简单主题查找的共识算法
5. Algorithms to detect motifs and predict post-translational modification sites. [D] . Johnson, Steven. 2015

机译：用于检测主题并预测翻译后修饰位点的算法。
6. Insulin degludec/insulin aspart once daily in Type 2 diabetes: a comparison of simple or stepwise titration algorithms (BOOST®: SIMPLE USE) [O] . S. W. Park, W. M. W. Bebakar, P. G. Hernandez, -1

机译：在2型糖尿病中地格曲胰岛素/门冬胰岛素每天一次：简单或逐步滴定算法的比较（BOOST®：简单使用）
7. Figure 4: (A) One conserved sequence, which occurs 79 times in 46,264 binding site peaks from the ChIP-seq data-set. The mutation profile of this conserved sequence is illustrated, where ’_ ’ indicates this base is unchanged; DEL indicates this base is lost; INS X indicates a new base X is inserted in front of this base. (B) Several repeated elements patterns are listed. (C) In the first column, the top five DNA motifs, mined by meme-chip tools (Machanick Bailey, 2011) are illustrated. The resemblant conserved sequences, found by the CFSP algorithm are listed in the second column. In the third column, the position-specific scoring matrices, which are transformed from mutational information are listed. The similarity between meme motif and resemblant conserved sequence with PSSM format was calculated via a stamp motif comparison tool (Mahony Benos, 2007). The E-values for the similarity of those pairs is displayed in the fourth column. (D) One motif is selected in each group clustered by gkmsvm descriptors, and the corresponding motif found by the CFSP algorithm is listed below. (E) There are additional datasets (File No: ENCFF100GRL, ENCFF616IRT, ENCFF870CER, Target: SREBF1) collected from https://www.encodeproject.org. The top two motifs are selected in each file using meme tools, and the corresponding motifs found by our algorithm are listed below. [O] . -1

机译：图4：（a）一种保守序列，其发生在芯片-SEQ数据集中的46,264个结合位点峰值中的79倍。说明了这种保守序列的突变分布，其中'_'表示该碱度不变; del表示此基础丢失; INS X表示新的基础X插入此基础前面。（b）列出了几种重复的元素模式。（c）在第一栏中，示出了由MEME芯片工具（Machanick＆Bailey，2011）开采的前五个DNA主题。由CFSP算法发现的相应保守序列列于第二列中。在第三列中，列出了从突变信息转换的特定位置的评分矩阵。 MEME主题与PSSM格式的相似性与PSSM格式之间的相似性通过邮票图章比较工具（Mahony＆Benos，2007）计算。这些对相似性的电子值显示在第四列中。（d）在由GKMSVM描述符聚集的每个组中选择了一个图案，下面列出了CFSP算法的相应主题。（e）从https://www.encodeproject.org收集的，有附加数据集（文件no：cernff100grl，cenf616irl，conf8.20cer，target：srebf1）。使用MEME工具在每个文件中选择前两个图案，并且我们的算法发现的相应主题如下所示。
8. La Randomisation Permet la Construction d'Algorithmes Simples en O(N LOG* n) pourdes Problemes Lineaires Difficiles (Randomization Yields Simple O(n LOG* n) Algorithms for Difficult omega(N) Problems) [R] . Devillers, O. 1991

机译：La Randomisation permet la Construction d'algorithmes simples en O（N LOG * n）pourdes problemes Lineaires Difficiles（Randomization Yields simple O（n LOG * n）算法用于困难的欧米茄（N）问题）

A Simple Algorithm for (l, d) Motif Search1

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