Summary: A database of curated genomic variants with clinically supported drug therapies and other oncological annotations is described. The accompanying web portal provides a search engine with two modes: one that allows users to query gene, cancer type, variant type or position for druggable mutations, and another to search for and to visualize, on three-dimensional protein structures, putative druggable sites that cluster with known druggable mutations. Availability and implementation: http://dinglab.wustl.edu/depo.

The problem of the effect of accent on the performance of Automatic Speech Recognition (ASR) systems is well known. In this paper, we study the effect of accent variability on the performance of the Indian English ASR task. We evaluate the test vocabularies on HMMs trained on (a) Accent specific training data (b) Accent pooled training data which combines all the accent specific training data (c) Accent pooled training data of reduced size matching the size of the accent specific training data. We demonstrate that the accent pooled training set performs the best on phonetically rich isolated word recognition task. But the accent specific HMMs perform better than the reduced accent pooled HMMs, indicating a possible approach of using a first stage accent identification to choose the correct accent trained HMMs for further recognition.