Algorithms and Methods for small RNA studies

下一代测序的出现提供了从短序列到全基因组的数十亿种DNA元素。事实上，天文数据已经并将从整个基因组中产生，从基因组中表达的基因对实验、环境、地理空间和/或疾病相关条件的影响。快速和系统的功能分类，以及对这些序列的生物学作用的识别，构成了生物信息学和计算生物学的一个主要问题。我的发现研究涉及从组学数据预测小RNA的功能分类和进化模式的方法的设计。这些预测方法依赖于两个主要特点：比较组学方法的强大功能和将组学数据与几个相关的非组学数据相结合的综合方法。有了文献中深厚的基因组知识和海量异质产生的数据，我计划表现这些知识，自动推理得出结论，学习机器以适应新的特征，如祖先序列和隐藏模式，并识别新的小RNA模式。这笔发现赠款将允许使用人工智能、机器学习、图形模型、比较基因组学和组学研究的综合方法的创新方法。最终目标包括提供生物信息学工具，以高精度预测和分类这些小RNA类的功能和调节机制。我的发现计划将集中在三个主要目标上：1)设计准确的方法来识别不同类别的sRNA；2)开发从RNAseq数据中探索隐藏模式和特征的方法来改进分类；3)设计方法来利用比较基因组学和sRNA类的进化起源来改进分类。这项提案将提供新的备受期待的工具，包括在为主要组学平台构建的模块中。这一发现计划的结果将使我们更好地了解sRNA的生物学功能，这些功能影响到60%以上的蛋白质转录基因；提高我们对途径调控机制和分子标记开发的理解，帮助植物育种者选择对生物和非生物胁迫具有更强耐受性的更好的作物；开发sRNA癌症生物标记物；开发涉及sRNA调控基因的几种疾病的不同治疗方法，如心脏疾病、炎症性疾病和自身免疫性疾病。由于其巨大的经济价值，sRNA仍将是本十年药用农业产业的主要战略目标。这项发现建议将提供生物信息学、人工智能、分子生物学方面的高素质人才培训，以及在高影响力开放获取期刊上发表的几篇论文。

The emergence of Next Generation Sequencing provides billions of DNA elements from short sequence to full genomes. In fact, astronomic data have been and will be produced from whole genome, expressed genes from genomes regarding to their implication to either experimental, environmental, geospatial, and/or disease related conditions. The rapid and systematic functional classification, as well as the identification of the biological roles of such sequences, constitutes a major issue in bioinformatics and computational biology. My discovery research concerns the design of methods to predict from omics data the functional classification as well as evolutionary patterns of small RNAs. Such prediction methods rely on two main characteristics: the powerfulness of comparative omics methods and integrative approaches combining omics data to several related non-omics data. With the deep genomic knowledge present in the literature and the massive heterogeneous produced data, I plan to represent the knowledge, to automate the reasoning for drawing conclusions, to learn machines to adapt at new characteristics such as ancestral sequences and hidden patterns, and to recognize novel small RNA patterns. This discovery grant will permit innovative methods exploiting artificial intelligence, machine learning, graphical models, comparative genomics and integrative approaches for omics studies. The ultimate goal consisting of providing bioinformatics tools that predict and classify with high accuracy the function and regulatory mechanisms of these small RNA classes. My discovery program will focus on three main objectives: 1) design accurate methods for the identification of different classes of sRNAs; 2) develop method exploring hidden patterns and features from RNAseq data to improve the classification; 3) design methods exploiting comparative genomics and evolutionary origin of sRNA classes to improve classification. This proposal will provide new highly expected tools to be included in module constructed for main omics platforms. The outcome of this discovery program will allow a better understanding of sRNA biological functions that affect more than 60% of protein transcribed genes; improve our understanding of pathway regulation mechanisms and molecular marker development that help plant breeder to select better crops with greater tolerance to biotic and abiotic stresses; develop sRNA cancer biomarker; develop different therapy for several diseases that involved genes regulated by sRNAs such as Cardiac, inflammatory and autoimmune diseases. SRNAs will remain a main strategic target for pharmaceutical agricultural industries for this decade due to its huge economical values. This discovery proposal will provide training of high-qualified personals in bioinformatics, artificial intelligence, molecular biology as well as several papers in high impact open access journals.