Evaluating between-pathway models with expression data.

Between-pathway models (BPMs) are network motifs consisting of pairs of putative redundant pathways. In this article, we show how adding another source of high-throughput data--microarray gene expression data from knockout experiments--allows us to identify a compensatory functional relationship between genes from the two BPM pathways. We evaluate the quality of the BPMs from four different studies, and we describe how our methods might be extended to refine pathways.

High throughput interaction data reveals degree conservation of hub proteins.

Research in model organisms relies on unspoken assumptions about the conservation of protein-protein interactions across species, yet several analyses suggest such conservation is limited. Fortunately, for many purposes the crucial issue is not global conservation of interactions, but preferential conservation of functionally important ones. An observed bias towards essentiality in highly-connected proteins implies the functional importance of such "hubs". We therefore define the notion of degree-conservation and demonstrate that hubs are preferentially degree-conserved. We show that a protein is more likely to be a hub if it has a high-degree ortholog, and that once a protein becomes a hub, it tends to remain so. We also identify a positive correlation between the average degree of a protein and the conservation of its interaction partners, and we find that the conservation of individual hub interactions is surprisingly high. Our work has important implications for prediction of protein function, computational inference of PPIs, and interpretation of data from model organisms.

Chemosensitivity prediction by transcriptional profiling.

In an effort to develop a genomics-based approach to the prediction of drug response, we have developed an algorithm for classification of cell line chemosensitivity based on gene expression profiles alone. Using oligonucleotide microarrays, the expression levels of 6,817 genes were measured in a panel of 60 human cancer cell lines (the NCI-60) for which the chemosensitivity profiles of thousands of chemical compounds have been determined. We sought to determine whether the gene expression signatures of untreated cells were sufficient for the prediction of chemosensitivity. Gene expression-based classifiers of sensitivity or resistance for 232 compounds were generated and then evaluated on independent sets of data. The classifiers were designed to be independent of the cells' tissue of origin. The accuracy of chemosensitivity prediction was considerably better than would be expected by chance. Eighty-eight of 232 expression-based classifiers performed accurately (with P < 0.05) on an independent test set, whereas only 12 of the 232 would be expected to do so by chance. These results suggest that at least for a subset of compounds genomic approaches to chemosensitivity prediction are feasible.

Transcriptional profiling in cancer: the path to clinical pharmacogenomics.

Cancer is a major source of morbidity and mortality, second only to heart disease as the leading cause of death in most developed countries. Our ability to treat the disease depends heavily on our understanding its aetiology. Different types of cancer often respond best to different courses of treatment, yet our understanding of cancer classification remains imperfect. Novel expression-monitoring technology provides unique opportunities to learn more about cancer at the molecular level, to improve classification methods, and to progress towards personalised cancer treatment. This review describes the techniques that make such advances possible, summarises recent work in the area, and discusses future developments needed to realise the potential of a thorough molecular classification of cancer.

Evaluation of normalization procedures for oligonucleotide array data based on spiked cRNA controls.

BACKGROUND: Affymetrix oligonucleotide arrays simultaneously measure the abundances of thousands of mRNAs in biological samples. Comparability of array results is necessary for the creation of large-scale gene expression databases. The standard strategy for normalizing oligonucleotide array readouts has practical drawbacks. We describe alternative normalization procedures for oligonucleotide arrays based on a common pool of known biotin-labeled cRNAs spiked into each hybridization. RESULTS: We first explore the conditions for validity of the 'constant mean assumption', the key assumption underlying current normalization methods. We introduce 'frequency normalization', a 'spike-in'-based normalization method which estimates array sensitivity, reduces background noise and allows comparison between array designs. This approach does not rely on the constant mean assumption and so can be effective in conditions where standard procedures fail. We also define 'scaled frequency', a hybrid normalization method relying on both spiked transcripts and the constant mean assumption while maintaining all other advantages of frequency normalization. We compare these two procedures to a standard global normalization method using experimental data. We also use simulated data to estimate accuracy and investigate the effects of noise. We find that scaled frequency is as reproducible and accurate as global normalization while offering several practical advantages. CONCLUSIONS: Scaled frequency quantitation is a convenient, reproducible technique that performs as well as global normalization on serial experiments with the same array design, while offering several additional features. Specifically, the scaled-frequency method enables the comparison of expression measurements across different array designs, yields estimates of absolute message abundance in cRNA and determines the sensitivity of individual arrays.

Molecular classification of cancer: class discovery and class prediction by gene expression monitoring.

Although cancer classification has improved over the past 30 years, there has been no general approach for identifying new cancer classes (class discovery) or for assigning tumors to known classes (class prediction). Here, a generic approach to cancer classification based on gene expression monitoring by DNA microarrays is described and applied to human acute leukemias as a test case. A class discovery procedure automatically discovered the distinction between acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL) without previous knowledge of these classes. An automatically derived class predictor was able to determine the class of new leukemia cases. The results demonstrate the feasibility of cancer classification based solely on gene expression monitoring and suggest a general strategy for discovering and predicting cancer classes for other types of cancer, independent of previous biological knowledge.

Radiation hybrid map of the mouse genome.

Radiation hybrid (RH) maps are a useful tool for genome analysis, providing a direct method for localizing genes and anchoring physical maps and genomic sequence along chromosomes. The construction of a comprehensive RH map for the human genome has resulted in gene maps reflecting the location of more than 30,000 human genes. Here we report the first comprehensive RH map of the mouse genome. The map contains 2,486 loci screened against an RH panel of 93 cell lines. Most loci (93%) are simple sequence length polymorphisms (SSLPs) taken from the mouse genetic map, thereby providing direct integration between these two key maps. We performed RH mapping by a new and efficient approach in which we replaced traditional gel- or hybridization-based assays by a homogeneous 5'-nuclease assays involving a single common probe for all genetic markers. The map provides essentially complete connectivity and coverage across the genome, and good resolution for ordering loci, with 1 centiRay (cR) corresponding to an average of approximately 100 kb. The RH map, together with an accompanying World-Wide Web server, makes it possible for any investigator to rapidly localize sequences in the mouse genome. Together with the previously constructed genetic map and a YAC-based physical map reported in a companion paper, the fundamental maps required for mouse genomics are now available.

A YAC-based physical map of the mouse genome.

A physical map of the mouse genome is an essential tool for both positional cloning and genomic sequencing in this key model system for biomedical research. Indeed, the construction of a mouse physical map with markers spaced at an average interval of 300 kb is one of the stated goals of the Human Genome Project. Here we report the results of a project at the Whitehead Institute/MIT Center for Genome Research to construct such a physical map of the mouse. We built the map by screening sequenced-tagged sites (STSs) against a large-insert yeast artificial chromosome (YAC) library and then integrating the STS-content information with a dense genetic map. The integrated map shows the location of 9,787 loci, providing landmarks with an average spacing of approximately 300 kb and affording YAC coverage of approximately 92% of the mouse genome. We also report the results of a project at the MRC UK Mouse Genome Centre targeted at chromosome X. The project produced a YAC-based map containing 619 loci (with 121 loci in common with the Whitehead map and 498 additional loci), providing especially dense coverage of this sex chromosome. The YAC-based physical map directly facilitates positional cloning of mouse mutations by providing ready access to most of the genome. More generally, use of this map in addition to a newly constructed radiation hybrid (RH) map provides a comprehensive framework for mouse genomic studies.

A physical map of 30,000 human genes.

A map of 30,181 human gene-based markers was assembled and integrated with the current genetic map by radiation hybrid mapping. The new gene map contains nearly twice as many genes as the previous release, includes most genes that encode proteins of known function, and is twofold to threefold more accurate than the previous version. A redesigned, more informative and functional World Wide Web site (www.ncbi.nlm.nih.gov/genemap) provides the mapping information and associated data and annotations. This resource constitutes an important infrastructure and tool for the study of complex genetic traits, the positional cloning of disease genes, the cross-referencing of mammalian genomes, and validated human transcribed sequences for large-scale studies of gene expression.

An STS-based map of the human genome.

A physical map has been constructed of the human genome containing 15,086 sequence-tagged sites (STSs), with an average spacing of 199 kilobases. The project involved assembly of a radiation hybrid map of the human genome containing 6193 loci and incorporated a genetic linkage map of the human genome containing 5264 loci. This information was combined with the results of STS-content screening of 10,850 loci against a yeast artificial chromosome library to produce an integrated map, anchored by the radiation hybrid and genetic maps. The map provides radiation hybrid coverage of 99 percent and physical coverage of 94 percent of the human genome. The map also represents an early step in an international project to generate a transcript map of the human genome, with more than 3235 expressed sequences localized. The STSs in the map provide a scaffold for initiating large-scale sequencing of the human genome.