Computational analysis of regulatory DNA sequences controlling gene transcription

所有细胞和生物体都拥有一套基于DNA的信息，这些信息描述了可以构建的蛋白质，以及制造在生命的生化过程中发挥重要作用的RNA分子的信息。 DNA序列中存在的信息是静态的-细胞具有相同的DNA，而不考虑细胞存在的地点，时间或条件。 为了对环境做出反应，细胞选择性地打开或关闭（或向上或向下）基因，允许产生适合所遇到条件的RNA和蛋白质。 例如，在葡萄酒发酵过程中，酵母从糖中产生酒精，开启了促进这一过程的基因。 该研究计划涉及计算机在基因调控分析中的跨学科应用，特别强调信息从DNA到RNA的转换（称为转录的过程）。 研究计划包括三个主要活动领域。首先，开发软件以允许识别在特定条件下打开的基因附近出现的特定DNA序列。 找到这些“开关”是破译细胞如何对变化的条件作出反应的第一步。这种方法建立在模式识别的人工智能方法之上。在第二步中，我们解决了DNA的非随机特性，使分析复杂化。在许多生物体中，与只在需要时打开的基因相比，“总是打开”的基因具有不同的A，C，G和T混合物。 由于这种混合差异，搜索模式变得复杂。解决这个看似微不足道的问题可以大大改善对生命科学中昂贵的实验室生成数据的解释。 第三个目标的重点是将开关DNA序列连接到“打开开关”的蛋白质上。 我们将计算分析已发表论文的文本，以预测哪些蛋白质控制这一过程。 该研究计划影响了所有生命科学的研究，从农业到生物工程，从微生物学到生物化学。

All cells and organisms possess a DNA-based set of information that describes the proteins that can be constructed, as well as information for making RNA molecules that perform important roles in the biochemical processes of life. The information present in the DNA sequences is static -cells have the same DNA present without regard to the place, time or conditions in which the cell exists. To respond to the environment, cells selectively turn on or off (or up or down) genes, allowing production of RNA and proteins suitable for the conditions encountered. For instance, yeast producing alcohol from sugar in wine fermentation turn on genes that facilitate this process. The research proposal addresses the interdisciplinary application of computers to the analysis of the regulation of genes, with a specific emphasis on the transition of information from DNA into RNA (the process called transcription). The research program includes three majn areas of activity. First, software is developed to allow for the identification of specific DNA sequences that appear near genes that are turned on in a specific condition. Finding these "on/off switches" is a first step in deciphering how cells respond to changing conditions. This approach builds on artificial intelligence methods for pattern recognition. In the second step, we address non-random properties of DNA that complicate the analysis. In many organisms, genes that are "always on" have different mixtures of A,C,G and T, compared to genes that only turn on when needed. Searching for patterns is complicated by this mixture difference. Solving this seemingly minor problem could greatly improve the interpretation of expensive laboratory-generated data from across the life sciences. Linking of the on/off switch DNA sequences to the proteins that "flip the switch" is the focus of the third aim. We will computationally analyze text from published papers to predict which proteins control the process. The research program impacts research across all of life sciences, from agriculture to bioengineering, and from microbiology to biochemistry.