Comparative biology: beyond sequence analysis

Comparative analysis is a fundamental tool in biology. Conservation among species greatly assists the detection and characterization of functional elements, whereas inter-species differences are probably the best indicators of biological adaptation. Traditionally, comparative approaches were applied to the analysis of genomic sequences. With the growing availability of functional genomic data, comparative paradigms are now being extended also to the study of other functional attributes, most notably the gene expression. Here we review recent works applying comparative analysis to large-scale gene expression datasets and discuss the central principles and challenges of such approaches.

Introduction

One of the surprising discoveries of modern biology is the strong conservation of protein sequences, as well as cellular mechanisms, across evolution. Some of our metabolic genes, for example, display strong sequence and functional similarities to their bacterial counterparts. Moreover, central features of core cellular processes such as cell cycle progression, DNA replication or transcription are conserved from yeast to human. Indeed, this extensive conservation had motivated the use of model organisms as means for studying conserved processes that are more difficult to assay in complex systems. Sequence similarity, in particular, has emerged as a key tool in predicting functional properties. In fact, most annotations of newly sequenced genomes, including gene prediction, gene function or regulatory elements, are based on similarity with other sequences whose functions have been described (e.g. [1, 2, 3]).

Whereas most comparative studies focus on conserved properties as means for characterizing functional elements, inter-species differences are also of interest. These differences are arguably the best indicators of evolutionary history and provide much information about species-specific adaptations. Identifying such differences is thus central to our search for what makes us human and how biological diversity is generated.

Technological advances over the past decade have led to the accumulation of genome-scale data describing not only gene sequence but also functional properties including gene expression, protein–protein interactions, and the binding of transcription factors to DNA. Such data are now available for multiple organisms, and their complexity presents a new set of challenges to comparative analysis. For example, unlike sequence information, most functional properties are condition-dependent, a property that needs to be accounted for during inter-species comparisons. Furthermore, sequence analysis is typically gene-specific and compares specific sets of homologs. Functional properties, on the contrary, often reflect the integrated function of multiple genes, calling for novel methods that allow network-centered rather than gene-centered comparisons. Finally, functional genomic data generated with current technologies suffer from high levels of noise and therefore need to be filtered in order to obtain valid conclusions.

In this review we focus on recent studies that employ comparative methods to analyze gene expression data. We describe the different approaches employed in such an analysis and highlight the remaining challenges.