The Analysis of Signal Elements in Promoter Sequences.

启动子序列中的信号元件还没有得到很好的表征。最初，Mario-Ramrez博士收集了人类基因TSS周围约4700个序列的数据库，后来将数据库的大小增加了约2倍。然后，我们开发了基于（最大片段）得分统计的测试，以找到相对于TSS（转录起始位点）出现本地化的核苷酸单词（通常长度为8）。A-GLAM使用这些词作为扩展的“种子”，以开发表征共调节基因系统的PSSM（位置特异性评分矩阵）。其中大约80个词出现在两三个集群中。通过用微阵列数据和基因本体信息验证我们的结果，我们能够表明，同一个8个字母的单词可能具有两种不同的生物学功能，这取决于它相对于TSS的位置。虽然功能的位置依赖性是一种已知的现象，但我们的研究表明它在人类基因组中广泛存在。此外，通过金标准数据集和严格的统计测试，Spouge和Kim博士表明马尔可夫模型和位置信息显着提高了转录因子结合位点（TFBS）的预测（尽管尚未达到实际精度）。此外，他们表明，在现存的TFBS计划中使用的马尔可夫模型在理论上和实践上都不如他们提出的理论上正确的马尔可夫模型。我们的公开可用的程序A-GLAM实现位置信息和理论上健全的马尔可夫模型，找到TFBS图案。Tatiana奥尔洛娃（2009年6 - 7月志愿者）和Narayan Perumal（2009年7月访问者）合作使用AGLAM研究对免疫应答重要的TOL样受体的可能TFBS。Spouge博士和Acevedo-Luna女士目前正在将统计方法扩展到JASPAR数据库中已知的TFBS基序，根据其位置偏好对基序进行分类，并通过其位置偏好发现假定顺式调控模块中TFBS的组合。Kim和Spouge博士还开发了一种用于调用ChIP-seq数据中的峰的模型，以从实验数据中识别PFBS。

The signal elements in promoter sequences are not well characterized. Initially, Dr. Mario-Ramrez collected a database of about 4700 sequences around the TSS of human genes, later increasing the size of the database by about a factor of 2. We then developed tests based on (maximal segment) score statistics to find nucleotide words (generally of length 8) that appear localized relative to TSSs (transcription start sites). A-GLAM used these words as "seeds" for expansion to develop PSSMs (position-specific scoring matrices) characterizing systems of co-regulated genes. About 80 of these words occurred in two or three clusters. By validating our results with microarray data and gene ontology information, we were able to show that the same 8-letter word could have two different biological functions, depending on its position with respect to the TSS. Although positional dependency of function is a known phenomenon, our study showed that it is widespread in the human genome. In addition, with gold standard datasets and rigorous statistical tests, Drs. Spouge and Kim showed that Markov models and positional information improve transcription factor binding site (TFBS) prediction significantly (although not yet to practical accuracies). Moreover, they showed that the Markov models used in extant TFBS programs is inferior, both theoretically and practically, to the theoretically correct Markov model they proposed. Our publicly available program A-GLAM implements positional information and the theoretically sound Markov models to find TFBS motifs. Tatiana Orlova (Volunteer Jun-Jul 2009) and Narayan Perumal (Visitor Jul 2009) collaborated in using AGLAM to investigate possible TFBSs for the TOL-like receptors important to the immune response. Dr. Spouge and Ms. Acevedo-Luna are presently extending the statistical methods to the known TFBS motifs in the JASPAR database, to categorize motifs according to their positional preference, and to discover combinations of the TFBSs in putative cis-regulatory modules by their positional preferences. Drs. Kim and Spouge have also developed a model for calling peaks in ChIP-seq data, to identify PFBSs from experimental data.