GONOME: measuring correlations between GO terms and genomic positions

Background: Current methods to find significantly under- and over-represented gene ontology (GO) terms in a set of genes consider the genes as equally probable balls in a bag, as may be appropriate for transcripts in micro-array data. However, due to the varying length of genes and intergenic regions, that approach is inappropriate for deciding if any GO terms are correlated with a set of genomic positions. Results: We present an algorithm - GONOME - that can determine which GO terms are significantly associated with a set of genomic positions given a genome annotated with (at least) the starts and ends of genes. We show that certain GO terms may appear to be significantly associated with a set of randomly chosen positions in the human genome if gene lengths are not considered, and that these same terms have been reported as significantly over-represented in a number of recent papers. This apparent over-representation disappears when gene lengths are considered, as GONOME does. For example, we show that, when gene length is taken into account, the term development is not significantly enriched in genes associated with human CpG islands, in contradiction to a previous report. We further demonstrate the efficacy of GONOME by showing that occurrences of the proteosome-associated control element (PACE) upstream activating sequence in the S. cerevisiae genome associate significantly to appropriate GO terms. An extension of this approach yields a whole-genome motif discovery algorithm that allows identification of many other promoter sequences linked to different types of genes, including a large group of previously unknown motifs significantly associated with the terms 'translation' and 'translational elongation'. Conclusion: GONOME is an algorithm that correctly extracts over-represented GO terms from a set of genomic positions. By explicitly considering gene size, GONOME avoids a systematic bias toward GO terms linked to large genes. Inappropriate use of existing algorithms that do not take gene size into account has led to erroneous or suspect conclusions. Reciprocally GONOME may be used to identify new features in genomes that are significantly associated with particular categories of genes.

A Research Agenda for Assessing the Potential Contribution of Genomic Medicine to Tobacco Control

This paper identifies research priorities in evaluating the ways in which "genomic medicine"-the use of genetic information to prevent and treat disease-may reduce tobacco-related harm by: (1) assisting more smokers to quit; (2) preventing non-smokers from beginning to smoke tobacco; and (3) reducing the harm caused by tobacco smoking. The method proposed to achieve the first aim is pharmacogenetics", the use of genetic information to optimise the selection of smoking-cessation programmes by screening smokers for polymorphisms that predict responses to different methods of smoking cessation. This method competes with the development of more effective forms of smoking cessation that involve vaccinating smokers against the effects of nicotine and using new pharmaceuticals (such as cannabinoid antagonists and nicotine agonists). The second and third aims are more speculative. They include: screening the population for genetic susceptibility to nicotine dependence and intervening (eg, by vaccinating children and adolescents against the effects of nicotine) to prevent smoking uptake, and screening the population for genetic susceptibility to tobacco-related diseases. A framework is described for future research on these policy options. This includes: epidemiological modelling and economic evaluation to specify the conditions under which these strategies are cost-effective; and social psychological research into the effect of providing genetic information on smokers' preparedness to quit, and the general views of the public on tobacco smoking.

The prediction of disease risk in genomic medicine

In recent years, the phrase 'genomic medicine' has increasingly been used to describe a new development in medicine that holds great promise for human health. This new approach to health care uses the knowledge of an individual's genetic make-up to identify those that are at a higher risk of developing certain diseases and to intervene at an earlier stage to prevent these diseases. Identifying genes that are involved in disease aetiology will provide researchers with tools to develop better treatments and cures. A major role within this field is attributed to 'predictive genomic medicine', which proposes screening healthy individuals to identify those who carry alleles that increase their susceptibility to common diseases, such as cancers and heart disease. Physicians could then intervene even before the disease manifests and advise individuals with a higher genetic risk to change their behaviour - for instance, to exercise or to eat a healthier diet - or offer drugs or other medical treatment to reduce their chances of developing these diseases. These promises have fallen on fertile ground among politicians, health-care providers and the general public, particularly in light of the increasing costs of health care in developed societies. Various countries have established databases on the DNA and health information of whole populations as a first step towards genomic medicine. Biomedical research has also identified a large number of genes that could be used to predict someone's risk of developing a certain disorder. But it would be premature to assume that genomic medicine will soon become reality, as many problems remain to be solved. Our knowledge about most disease genes and their roles is far from sufficient to make reliable predictions about a patient’s risk of actually developing a disease. In addition, genomic medicine will create new political, social, ethical and economic challenges that will have to be addressed in the near future.

Unlocking hidden genomic sequence

Despite the success of conventional Sanger sequencing, significant regions of many genomes still present major obstacles to sequencing. Here we propose a novel approach with the potential to alleviate a wide range of sequencing difficulties. The technique involves extracting target DNA sequence from variants generated by introduction of random mutations. The introduction of mutations does not destroy original sequence information, but distributes it amongst multiple variants. Some of these variants lack problematic features of the target and are more amenable to conventional sequencing. The technique has been successfully demonstrated with mutation levels up to an average 18% base substitution and has been used to read previously intractable poly(A), AT-rich and GC-rich motifs.