Managing and Modeling Time in Genomics Data

点击翻译按钮获取中文摘要

Understanding the mechanisms associated with observed biological phenotypes is a common goal of many genomic studies. However, it is often the case that measuring molecular entities at a single time-point is insufficient to capture the complexity of many biological systems; truly systematic measurement needs to consider dynamic changes across time and space. With decreasing cost and sample requirements, longitudinal genomic data are accumulating quickly. Yet tools that help researchers to model and understand temporal changes in genomics studies are largely missing. The long-term objective of the proposed research program is to develop tools and algorithms to handle temporal changes covering a wide range of time scales or frequencies. Specifically, we categorize the tools and algorithms to be developed into three “layers”: (a) a Data layer; (b) a Modeling layer; and (c) an Interpretation layer. The Data layer focuses on data quality and outlier management; the Modeling layer focuses on modeling time and analyzing genomics data in scalable ways; and the Interpretation layer helps researchers interpret their data and the outcomes of their analyses by linking users to knowledge embedded in online publications and discussions.Regarding the Data layer, “Garbage in, garbage out.” If the data at hand are not of high quality, it is unlikely that subsequent modeling and interpretation will be fruitful. We propose to develop methods for assessing the quality of, and removing random or systematic noise from, data generated from newer genomics technologies and platforms, including next-generation sequencing. A key idea here is that the space-time relationships between the data points can be exploited to assess and enhance data quality. It is also important to identify samples that are outliers. The time series nature of the data can be exploited to derive quality models, from which deviant samples can be flagged for further examination. We also plan to identify possible reasons that explain why certain samples are identified as outlying. Regarding the Modeling layer, time series often need to be augmented with latent variables for modeling and interpretation reasons. Moreover, the observations are typically obtained from a mixture of distributions, which are often unknown a priori. We propose to develop non-parametric Bayesian models to learn the latent structure of complex time series. One key challenge is the large number of features for modeling multi-omic data. Thus, we will focus on scalable or parallelizable schemes. Genomics data may be based on heterogeneous sub-populations of cells. Thus, for more effective modeling, it is important to develop methods to deconvolve the underlying composition to better capture the time-varying dynamics. Effective knowledge discovery from genomics data requires significant involvement of domain experts in interpreting the data and models. The Interpretation layer focuses on tools for linking users to knowledge embedded in online publications. For example, relationships involving genes/peptides identified using whole genome technologies are valuable to researchers in interpreting their results. Basic Google-style searches are not sufficient to replace more sophisticated natural language processing to extract relations from text, including temporal expressions, and temporal or sequential relationships among entities (e.g., genes). As social networking has gained widespread use in the past decade, research blogging has also been growing in the genomics and medical research communities. We propose to develop methods for summarizing online discussions among researchers. One novel idea is to explore how to produce abstractive summaries in response to user-given queries.