Computational methods for the identification of differential and coordinated gene expression.

With the first complete 'draft' of the human genome sequence expected for Spring 2000, the three basic challenges for today's bioinformatics are more than ever: (i) finding the genes; (ii) locating their coding regions; and (iii) predicting their functions. However, our capacity for interpreting vertebrate genomic and transcript (cDNA) sequences using experimental or computational means very much lags behind our raw sequencing power. If the performances of current programs in identifying internal coding exons are good, the precise 5'-->3' delineation of transcription units (and promoters) still requires additional experiments. Similarly, functional predictions made with reference to previously characterized homologues are leaving >50% of human genes unannotated or classified in uninformative categories ('kinase', 'ATP-binding', etc.). In the context of functional genomics, large-scale gene expression studies using massive cDNA tag sequencing, two-dimensional gel proteome analysis or microarray technologies are the only approaches providing genome-scale experimental information at a pace consistent with the progress of sequencing. Given the difficulty and cost of characterizing genes one by one, academic and industrial researchers are increasingly relying on those methods to prioritize their studies and choose their targets. The study of expression patterns can also provide some insight into the function, reveal regulatory pathways, indicate side effects of drugs or serve as a diagnostic tool. In this article, I review the theoretical and computational approaches used to: (i) identify genes differentially expressed (across cell types, developmental stages, pathological conditions, etc.); (ii) identify genes expressed in a coordinated manner across a set of conditions; and (iii) delineate clusters of genes sharing coherent expression features, eventually defining global biological pathways.