Purpose: Although fusion genes serve as an effective target in hematologic malignancies, recurrent gene fusion events are relatively rare in solid tumors. To detect recurrent novel fusion transcripts we performed whole transcriptome sequencing primary breast cancer samples.Experimental Design: Whole-transcriptome sequencing of 120 fresh-frozen primary breast cancer samples and five adjacent normal breast tissues using the Illumina HiSeq2000 platform was performed. Three different fusion-detecting tools (deFuse, Chimerascan, and TopHat) were used, and the results were compared.Results: These tools detected 3831, 6630 and 516 fusion transcripts (FTs) overall.We primarily focused on the results obtained using the deFuse software. More FTs were identified from HER2 subtype breast cancer samples than from the luminal or triple-negative subtypes (p < 0.05). Seventy fusion candidates were selected for validation, and 32 (45.7%) were confirmed by RT-PCR and Sanger sequencing. Of the validated fusions, six were recurrent (found in 2 or more samples), three were in-frame (PRDX1-AKR1A1, TACSTD2-OMA1 and C2CD2-TFF1) and three were off-frame (CEACAM7-CEACAM6, CYP4X1-CYP4Z2P, and EEF1DP3-FRY).Notably, the novel read-through fusion, EEF1DP3-FRY, was identified and validated in 6.7% (8/120) of the breast cancer samples. This off-frame fusion results in early truncation of the FRY gene, which plays a key role in the structural integrity ii during mitosis. Three previously reported fusions, PPP1R1B-STARD3, MFGE8-HAPL, and ETV6-NTRK3, were detected in 8.3%, 3.3% and 0.8% of the 120 samples, respectively, by both deFuse and Chimerascan. The recently reported MAGI3-AKT3 fusion was not detected in our analysis. Conclusion

Statistical methods for genomewide association studies (GWAS) continue to improve. However, the increasing volume and variety of genetic and genomic data make computational speed and ease of data manipulation mandatory in future software. In our view, a collaborative effort of statistical geneticists is required to develop open source software targeted to genetic epidemiology. Our attempt to meet this need is called the OPENMENDELproject (https://openmendel.github.io). It aims to (1) enable interactive and reproducible analyses with informative intermediate results, (2) scale to big data analytics, (3) embrace parallel and distributed computing, (4) adapt to rapid hardware evolution, (5) allow cloud computing, (6) allow integration of varied genetic data types, and (7) foster easy communication between clinicians, geneticists, statisticians, and computer scientists. This article reviews and makes recommendations to the genetic epidemiology community in the context of the OPENMENDEL project.

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Experiments in particle physics produce enormous quantities of data that must be analyzed and interpreted by teams of physicists. This analysis is often exploratory, where scientists are unable to enumerate the possible types of signal prior to performing the experiment. Thus, tools for summarizing, clustering, visualizing and classifying high-dimensional data are essential. In this work, we show that meaningful physical content can be revealed by transforming the raw data into a learned high-level representation using deep neural networks, with measurements taken at the Daya Bay Neutrino Experiment as a case study. We further show how convolutional deep neural networks can provide an effective classification filter with greater than 97% accuracy across different classes of physics events, significantly better than other machine learning approaches.

RNA-seq; whole transcriptome sequencing; SQSTM1; sequestosome 1; siRNA; small interfering RNA; UVRAG; UV radiationresistance associated.The EWSR1 (EWS RNA-binding protein 1/Ewing Sarcoma Break Point Region 1) gene encodes a RNA/DNA binding protein that is ubiquitously expressed and involved in various cellular processes. EWSR1 deficiency leads to impairment of development and accelerated senescence but the mechanism is not known. Herein, we found that EWSR1 modulates the Uvrag (UV radiation resistance associated) gene at the post-transcription level. Interestingly, EWSR1 deficiency led to the activation of the DROSHA-mediated microprocessor complex and increased the level of Mir125a and Mir351, which directly target Uvrag. Moreover, the Mir125a-and Mir351-mediated reduction of Uvrag was associated with the inhibition of autophagy that was confirmed in ewsr1 knockout (KO) MEFs and ewsr1 KO mice. Taken together, our data indicate that EWSR1 is involved in the post-transcriptional regulation of Uvrag via a miRNA-dependent pathway, resulting in the deregulation of autophagy inhibition. The mechanism of Uvrag and autophagy regulation by EWSR1 provides new insights into the role of EWSR1 deficiency-related cellular dysfunction.