Computational methods for theidentification of differential and coordinated gene expression

Abstract

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.

Microarray profiling of skeletal muscle tissues from equally obese, non-diabetic insulin-sensitive and insulin-resistant Pima Indians

“…A traditional way to test for statistical significance in the face of such multiple testing is to apply the Bonferroni correction or a related step-up or step-down procedure [21], which would result in very small alpha levels. For example, an experiment-wise alpha level of 0.05 with 10,000 genes would require a p-value of 5×10 -6 ; this might result in absurdly low power for realistic sample sizes and would disregard many biologically significant changes [22]. Thus, we estimated the number of genes with true expression differences between the IS and IR groups by empirical testing using a different method (Q-RT-PCR).…”

Transcriptional profiles of unirradiated or UV-irradiated human cells expressing either the cancer-prone XPB/CS allele or the noncancer-prone XPB/TTD allele

“…Both values were used for comparing the different transcription profile of XP/CS and TTD before and after UVC exposure. However, to minimize the number of false positives (Claverie, 1999), probe sets with no statistical change between the two conditions (no change call) in one of the duplicates, and those with opposite change calls (increased and decreased) in the duplicates were eliminated. The remaining probe sets were considered differentially expressed only when rated different (increase or decrease) in two independent hybridizations and their signal log ratios were used as a quantitative estimate of the gene expression change.…”

A molecular profile of the mouse gastric parietal cell with and without exposure to Helicobacter pylori

“…GeneChips produce a false-positive rate of 1-2% (14,15). The most common way to filter false-positives from GENECHIP comparisons is to impose a threshold requirement for Ն2-fold change in the level of a transcript under the experimental conditions studied (16,17).…”