This document summarizes my research activities since the defense of my PhD in Decem- ber 2003. This work has been carried initially as a postdoctoral fellow at Stanford University with Mark Musen’s Stanford Medical Informatics group (now BMIR1), and then as an associate professor at University of Rennes 1, first with the UPRES-EA 3888 (which became UMR 936 INSERM – University of Rennes 1 in 2009) from 2005 to 2012, and then with the Dyliss team at IRISA since 2013. First, I will present the context in which my research takes place. We will see that the traditional approaches for analyzing life science data do not scale up and cannot handle their increasing quantity, complexity and connectivity. It has become necessary to develop automatic tools not only for performing the analyses, but also for helping the experts do it. Yet, processing the raw data is so difficult to automate that these tools usually hinge on annotations and metadata as machine-processable proxies that describe the data and the relations between them. Second, I will identify the main challenges. While generating these metadata is a challenge of its own that I will not tackle here, it is only the first step. Even if metadata tend to be more compact than the original data, each piece of data is typically associated with many metadata, so the problem of data quantity remains. These metadata have to be reused and combined, even if they have been generated by different people, in different places, in different contexts, so we also have a problem of integration. Eventually, the analyses require some reasoning on these metadata. Most of these analyses were not possible before the data deluge, so we are inventing and improving them now. This also means that we have to design new reasoning methods for answering life science questions using the opportunities created by the data deluge while not drowning in it. Arguably, biology has become an information science. Third, I will summarize the contributions presented in the document. Some of the reasoning methods that we develop rely on life science background knowledge. Ontologies are the formal representations of the symbolic part of this knowledge. The Semantic Web is a more general effort that provides an unified framework of technologies and associated tools for representing, sharing, combining metadata and pairing them with ontologies. I developed knowledge- based reasoning methods for life science data.