Pan-cancer analysis of whole genomes

Cancer is driven by genetic change, and the advent of massively parallel sequencing has enabled systematic documentation of this variation at the whole-genome scale(1-3). Here we report the integrative analysis of 2,658 whole-cancer genomes and their matching normal tissues across 38 tumour types from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). We describe the generation of the PCAWG resource, facilitated by international data sharing using compute clouds. On average, cancer genomes contained 4-5 driver mutations when combining coding and non-coding genomic elements; however, in around 5% of cases no drivers were identified, suggesting that cancer driver discovery is not yet complete. Chromothripsis, in which many clustered structural variants arise in a single catastrophic event, is frequently an early event in tumour evolution; in acral melanoma, for example, these events precede most somatic point mutations and affect several cancer-associated genes simultaneously. Cancers with abnormal telomere maintenance often originate from tissues with low replicative activity and show several mechanisms of preventing telomere attrition to critical levels. Common and rare germline variants affect patterns of somatic mutation, including point mutations, structural variants and somatic retrotransposition. A collection of papers from the PCAWG Consortium describes non-coding mutations that drive cancer beyond those in the TERT promoter(4); identifies new signatures of mutational processes that cause base substitutions, small insertions and deletions and structural variation(5,6); analyses timings and patterns of tumour evolution(7); describes the diverse transcriptional consequences of somatic mutation on splicing, expression levels, fusion genes and promoter activity(8,9); and evaluates a range of more-specialized features of cancer genomes(8,10-18).Peer reviewe

Learning to listen to your ego-(motion): Metric motion estimation from auditory signals

This paper is about robot ego-motion estimation relying solely on acoustic sensing. By equipping a robot with microphones, we investigate the possibility of employing the noise generated by the motors and actuators of the vehicle to estimate its motion. Audio-based odometry is not affected by the scene’s appearance, lighting conditions, and structure. This makes sound a compelling auxiliary source of information for ego-motion modelling in environments where more traditional methods, such as those based on visual or laser odometry, are particularly challenged. By leveraging multi-task learning and deep architectures, we provide a regression framework able to estimate the linear and the angular velocity at which the robot has been travelling. Our experimental evaluation conducted on approximately two hours of data collected with an unmanned outdoor field robot demonstrated an absolute error lower than 0:07 m/s and 0:02 rad/ss for the linear and angular velocity, respectively. When compared to a baseline approach, making use of single-task learning scheme, our system shows an improvement of up to 26% in the ego-motion estimation