MOTIF DISCOVERY IN HETEROGENEOUS SEQUENCE DATA

机译：异构序列数据中的MOTIF发现

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This paper introduces the first integrated algorithm designed to discover novel motifs in heterogeneous sequence data, which is comprised of coregulated genes from a single genome together with the orthologs of these genes from other genomes. Results axe presented for regulons in yeasts, worms, and mammals.

机译：本文介绍了第一个集成算法，该算法旨在发现异质序列数据中的新基序，该算法由来自单个基因组的核心调控基因以及来自其他基因组的这些基因的直系同源物组成。显示了酵母，蠕虫和哺乳动物中调节子的结果轴。

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《Pacific Symposium on Biocomputing 2004; Jan 6-10, 2004; Hawaii, USA》|2004年|p.348-359|共12页
会议地点 Honolulu HI(US);Honolulu HI(US)
作者
A. PRAKASH; M. BLANCHETTE; S; SINHA; M. TOMPA;
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Department of Computer Science and Engineering University of Washington Seattle. WA 98195-2350 U.S.A.;

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原文格式 PDF
正文语种 eng
中图分类 Q8;
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专利

1. Motif discovery with data mining in 3D protein structure databases: Discovery, validation and prediction of the u-shape zinc binding (HUF-ZINC) motif [J] . Maurer-Stroh S., Gao H., Han H., Journal of Bioinformatics and Computational Biology . 2013,第1期

机译：通过3D蛋白质结构数据库中的数据挖掘进行母题发现：u形锌结合（ HUF-ZINC）主题的发现，验证和预测
2. SMARTIV: combined sequence and structure de-novo motif discovery for in-vivo RNA binding data [J] . Maya Polishchuk, Inbal Paz, Zohar Yakhini, Nucleic acids research . 2018,第W1期

机译：SMARTIV：体内RNA结合数据的组合序列和结构创新基序发现
3. MotifLab: a tools and data integration workbench for motif discovery and regulatory sequence analysis [J] . Kjetil Klepper, Finn Drabl?s BMC Bioinformatics . 2013,第1期

机译：MotifLab：用于基序发现和调控序列分析的工具和数据集成工作台
4. MOTIF DISCOVERY IN HETEROGENEOUS SEQUENCE DATA [C] . A. PRAKASH, M. BLANCHETTE, S SINHA, Pacific symposium on biocomputing . 2003

机译：异构序列数据中的主题发现
5. Novel algorithms for motif discovery in bio-sequence datasets. [D] . Balla, Sudha. 2007

机译：用于生物序列数据集中的基序发现的新算法。
6. SMARTIV: combined sequence and structure de-novo motif discovery for in-vivo RNA binding data [O] . Maya Polishchuk, Inbal Paz, Zohar Yakhini, 2018

机译：SMARTIV：体内RNA结合数据的组合序列和结构创新基序发现
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。

MOTIF DISCOVERY IN HETEROGENEOUS SEQUENCE DATA

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