Speeding disease gene discovery by sequence based candidate prioritization

BACKGROUND: Regions of interest identified through genetic linkage studies regularly exceed 30 centimorgans in size and can contain hundreds of genes. Traditionally this number is reduced by matching functional annotation to knowledge of the disease or phenotype in question. However, here we show that disease genes share patterns of sequence-based features that can provide a good basis for automatic prioritization of candidates by machine learning. RESULTS: We examined a variety of sequence-based features and found that for many of them there are significant differences between the sets of genes known to be involved in human hereditary disease and those not known to be involved in disease. We have created an automatic classifier called PROSPECTR based on those features using the alternating decision tree algorithm which ranks genes in the order of likelihood of involvement in disease. On average, PROSPECTR enriches lists for disease genes two-fold 77% of the time, five-fold 37% of the time and twenty-fold 11% of the time. CONCLUSION: PROSPECTR is a simple and effective way to identify genes involved in Mendelian and oligogenic disorders. It performs markedly better than the single existing sequence-based classifier on novel data. PROSPECTR could save investigators looking at large regions of interest time and effort by prioritizing positional candidate genes for mutation detection and case-control association studies

On Finitude: Life and Death Under Neoliberalism

Essay in contemporary Indian photography, for Fotofest 201

Gynaecological Cancers Risk

The International Agency for Research on Cancer suggests that the burden of women’s cancers including breast, ovarian, and womb will rise by 50% over the next 20 years. It is essential for us to improve early diagnosis and prevention of these cancers in our health systems. The last decade has seen significant strides in our ability to understand and predict a woman’s risk of these cancers and offer personalized medicine approaches for risk management. There have been improvements in identifying individuals at increased risk, as well as implementing and evaluating strategies for screening and prevention. In this special collection, we bring together 16 articles from leading scientists and researchers. These capture some of the important advances observed in estimating cancer risk, providing genetic testing, offering risk management to those at increased risk, as well as screening and prevention of breast, ovarian, and womb cancers in women. This makes an important contribution to the rapidly advancing knowledge base across the area of personalized medicine and precision prevention of ovarian, endometrial, and breast cancers