Pattern Identification in Sequence Activity Data

序列活动数据中的模式识别

• 批准号: 8939733
• 负责人: Vipul Periwal
• 金额: $ 9.49万
• 依托单位: NATIONAL INSTITUTE OF DIABETES AND DIGESTIVE AND KIDNEY DISEASES
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/8939733
• 关键词: Amino Acid Sequence; Bayesian Analysis; Biological Assay; Calculi; Data; Data Collection; Data Set; Data Storage and Retrieval; Development; Digit structure; Face; Genome; Graph; Growth; Handwriting; Image; Methods; Modeling; Pattern; Peptide Sequence Determination; Problem Solving; Reporter; Science; Sequence Alignment; System; Techniques; Time; Transcription Initiation Site; combinatorial; commensal microbes; computing resources; genome wide association study; heuristics; insight; protein structure; quantum; theories

Finding patterns in data is one of the most challenging open questions in infor- mation science. The number of possible relationships scales combinatorially with the size of the dataset, overwhelming the exponential increase in avail- ability of computational resources. Physical insights have been instrumental in developing efficient computational heuristics. Using quantum field theory methods and rethinking three centuries of Bayesian inference, we formulated the problem in terms of finding landscapes of patterns and solved this problem exactly. The generality of our calculus is illustrated by applying it to handwritten digit images and to finding structural features in proteins from sequence alignments without any presumptions about model priors suited to specific datasets. We are applying this calculus to several problems besides protein structure: (1) Transcription start site identification at a genome scale from CAGE-seq data; (2) Commensal bacteria interactions; (3) Identifying dynamical systems from time-course data; (4) Calculating biophysical quantitative sequence activity models directly from massively parallel reporter assays without optimization; (5) Deriving the graph of interactions directly from expression data; (6) Direct computation of SNP interactions from genome-wide association studies.

在数据中找到模式是信息科学中最具挑战性的开放问题之一。可能的关系数量与数据集的大小结合起来，使计算资源的可用性呈指数增长。物理见解对开发有效的计算启发式方法起着重要作用。使用量子场理论方法并重新思考三个世纪的贝叶斯推论，我们在寻找模式的景观方面提出了问题，并准确地解决了这个问题。 通过将其应用于手写数字图像和从序列比对中找到蛋白质中的结构特征，可以说明我们的演算的一般性，而没有任何适用于适合特定数据集的模型先验的型号。除了蛋白质结构以外，我们还将该积分应用于几个问题：（1）从Cage-seq数据中以基因组量表进行转录起始位点识别； （2）共生细菌相互作用； （3）从时间课数据中识别动态系统； （4）在没有优化的情况下直接从大规模平行的记者分析中计算生物物理定量序列活性模型； （5）直接从表达数据得出相互作用的图； （6）从全基因组关联研究中直接计算SNP相互作用。