Many scientific applications are I/O-intensive, which makes optimization and scaling difficult, especially on parallel architectures. The I/O requirements of computational biology applications are different from other scientific applications; many are embarrassingly parallel and require repeated read-only access to a large global database. In the first part of this paper we examine the scalability of an embarrassingly parallel computational biology application: psLayout, which played a crucial role in the mapping of the human genome. Input file location is a important factor affecting the scalability and performance of this application. We designed a library for automatic data replication to address this issue. In the second part of this paper we describe our user-level library for location-transparent storage.