TREEOME: A framework for epigenetic and transcriptomic data integration to explore regulatory interactions controlling transcription

Motivation: Predictive modelling of gene expression is a powerful framework for the in silico exploration of transcriptional regulatory interactions through the integration of high-throughput -omics data. A major limitation of previous approaches is their inability to handle conditional and synergistic interactions that emerge when collectively analysing genes subject to different regulatory mechanisms. This limitation reduces overall predictive power and thus the reliability of downstream biological inference. Results: We introduce an analytical modelling framework (TREEOME: tree of models of expression) that integrates epigenetic and transcriptomic data by separating genes into putative regulatory classes. Current predictive modelling approaches have found both DNA methylation and histone modification epigenetic data to provide little or no improvement in accuracy of prediction of transcript abundance despite, for example, distinct anti-correlation between mRNA levels and promoter-localised DNA methylation. To improve on this, in TREEOME we evaluate four possible methods of formulating gene-level DNA methylation metrics, which provide a foundation for identifying gene-level methylation events and subsequent differential analysis, whereas most previous techniques operate at the level of individual CpG dinucleotides. We demonstrate TREEOME by integrating gene-level DNA methylation (bisulfite-seq) and histone modification (ChIP-seq) data to accurately predict genome-wide mRNA transcript abundance (RNA-seq) for H1-hESC and GM12878 cell lines. Availability: TREEOME is implemented using open-source software and made available as a pre-configured bootable reference environment. All scripts and data presented in this study are available online at http://sourceforge.net/projects/budden2015treeome/.Comment: 14 pages, 6 figure

Droplet group production in an AC electro-flow-focusing microdevice

We report the production of droplet groups with a controlled number of drops in a microfluidic electro-flow focusing device under the action of an AC electric field. This regime appears for moderate voltages (500-700 V peak-to-peak) and signal frequencies between 25 and 100 Hz, much smaller than the droplet production rate ( ≈500 Hz). For this experimental conditions the production frequency of a droplet package is twice the signal frequency. Since the continuous phase flow in the microchannel is a Hagen-Poiseuille flow, the smaller droplets of a group move faster than the bigger ones leading to droplet clustering downstream.Ministerio de Economía y Competitividad DPI2013-46485-C3-1-RMinisterio de Economía y Competitividad FIS2014-54539- PJunta de Andalucía P11-FQM-791

Development and Validation of the Gene Expression Predictor of High-grade Serous Ovarian Carcinoma Molecular SubTYPE (PrOTYPE).

PURPOSE: Gene expression-based molecular subtypes of high-grade serous tubo-ovarian cancer (HGSOC), demonstrated across multiple studies, may provide improved stratification for molecularly targeted trials. However, evaluation of clinical utility has been hindered by nonstandardized methods, which are not applicable in a clinical setting. We sought to generate a clinical grade minimal gene set assay for classification of individual tumor specimens into HGSOC subtypes and confirm previously reported subtype-associated features. EXPERIMENTAL DESIGN: Adopting two independent approaches, we derived and internally validated algorithms for subtype prediction using published gene expression data from 1,650 tumors. We applied resulting models to NanoString data on 3,829 HGSOCs from the Ovarian Tumor Tissue Analysis consortium. We further developed, confirmed, and validated a reduced, minimal gene set predictor, with methods suitable for a single-patient setting. RESULTS: Gene expression data were used to derive the predictor of high-grade serous ovarian carcinoma molecular subtype (PrOTYPE) assay. We established a de facto standard as a consensus of two parallel approaches. PrOTYPE subtypes are significantly associated with age, stage, residual disease, tumor-infiltrating lymphocytes, and outcome. The locked-down clinical grade PrOTYPE test includes a model with 55 genes that predicted gene expression subtype with >95% accuracy that was maintained in all analytic and biological validations. CONCLUSIONS: We validated the PrOTYPE assay following the Institute of Medicine guidelines for the development of omics-based tests. This fully defined and locked-down clinical grade assay will enable trial design with molecular subtype stratification and allow for objective assessment of the predictive value of HGSOC molecular subtypes in precision medicine applications.See related commentary by McMullen et al., p. 5271.Core funding for this project was provided by the National Institutes of Health (R01-CA172404, PI: S.J. Ramus; and R01-CA168758, PIs: J.A. Doherty and M.A.Rossing), the Canadian Institutes for Health Research (Proof-of-Principle I program, PIs: D.G.Huntsman and M.S. Anglesio), the United States Department of Defense Ovarian Cancer Research Program (OC110433, PI: D.D. Bowtell). A. Talhouk is funded through a Michael Smith Foundation for Health Research Scholar Award. M.S. Anglesio is funded through a Michael Smith Foundation for Health Research Scholar Award and the Janet D. Cottrelle Foundation Scholars program managed by the BC Cancer Foundation. J. George was partially supported by the NIH/National Cancer Institute award number P30CA034196. C. Wang was a Career Enhancement Awardee of the Mayo Clinic SPORE in Ovarian Cancer (P50 CA136393). D.G. Huntsman receives support from the Dr. Chew Wei Memorial Professorship in Gynecologic Oncology, and the Canada Research Chairs program (Research Chair in Molecular and Genomic Pathology). M. Widschwendter receives funding from the European Union’s Horizon 2020 European Research Council Programme, H2020 BRCA-ERC under Grant Agreement No. 742432 as well as the charity, The Eve Appeal (https://eveappeal.org.uk/), and support of the National Institute for Health Research (NIHR) and the University College London Hospitals (UCLH) Biomedical Research Centre. G.E. Konecny is supported by the Miriam and Sheldon Adelson Medical Research Foundation. B.Y. Karlan is funded by the American Cancer Society Early Detection Professorship (SIOP-06-258-01-COUN) and the National Center for Advancing Translational Sciences (NCATS), Grant UL1TR000124. H.R. Harris is 20 supported by the NIH/National Cancer Institute award number K22 CA193860. OVCARE (including the VAN study) receives support through the BC Cancer Foundation and The VGH+UBC Hospital Foundation (authors AT, BG, DGH, and MSA). The AOV study is supported by the Canadian Institutes of Health Research (MOP86727). The Gynaecological Oncology Biobank at Westmead, a member of the Australasian Biospecimen Network-Oncology group, was funded by the National Health and Medical Research Council Enabling Grants ID 310670 & ID 628903 and the Cancer Institute NSW Grants ID 12/RIG/1-17 & 15/RIG/1-16. The Australian Ovarian Cancer Study Group was supported by the U.S. Army Medical Research and Materiel Command under DAMD17-01-1-0729, The Cancer Council Victoria, Queensland Cancer Fund, The Cancer Council New South Wales, The Cancer Council South Australia, The Cancer Council Tasmania and The Cancer Foundation of Western Australia (Multi-State Applications 191, 211 and 182) and the National Health and Medical Research Council of Australia (NHMRC; ID199600; ID400413 and ID400281). BriTROC-1 was funded by Ovarian Cancer Action (to IAM and JDB, grant number 006) and supported by Cancer Research UK (grant numbers A15973, A15601, A18072, A17197, A19274 and A19694) and the National Institute for Health Research Cambridge and Imperial Biomedical Research Centres. Samples from the Mayo Clinic were collected and provided with support of P50 CA136393 (E.L.G., G.L.K, S.H.K, M.E.S.)

RoboCup simulation leagues : enabling replicable and robust investigation of complex robotic systems

Physically realistic simulated environments are powerful platforms for enabling measurable, replicable, and statistically robust investigation of complex robotic systems. Such environments are epitomized by the RoboCup (RC) simulation leagues, which have been successfully utilized to conduct massively parallel experiments on a variety of topics, including optimization of bipedal locomotion, self-localization from noisy perception data, and planning complex multiagent strategies without direct agent-to-agent communication. Many of these systems are later transferred to physical robots, making the simulation leagues invaluable beyond the scope of simulated soccer matches