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Deep Phenotyping - Harnessing Data Richness for Unsupervised High-Content Analysis
Huang Dong, Wang Yi, Maciej Hermanowicz, Ke Yiping, Maja Choma, Lee Kee Khoon, Frederic Bard

Recognising the key challenges, we develop an end-to-end computational framework for HCA dubbed “Deep Phenotyping” that perform unsupervised analysis to leverage on the data richness for the discovery of unknown sub-phenotypes with minimal labeling cost.

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The Case for CASE: Computer-Assisted Structure Elucidation
ACD/Labs

Modern CASE systems such as Structure Elucidator Suite provide the necessary capability accurately elucidate a novel chemical structure for complex molecules based on readily available NMR data sets. This allows organizations to avoid expensive, labor-intensive, and time-consuming synthetic efforts.

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A Unified Software Platform for Laboratory Informatics
Graham A. McGibbon, Hans de Bie, David Hardy, Ryan Sasaki, Patrick Wheeler, Carol Preisig

Reported here are capabilities in automated workflows involving analytical data with chemical structures. Specifically described is automated homogenization of data from a set of instruments, including NMR structure verification, as one solution.

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Addressing False Positive Variants Arising from Pseudogenes
Risha Govind1,2, Sam Wilkinson1,3, Nicola Whiffin1,2, Shibu John1,2, Rachel J. Buchan1,2, Elizabeth Edwards1,2, Deborah J. Morris-Rosendahl1,3, James S. Ware1,2, P.J. Barton1,2, Stuart A. Cook1,2

Clinical genetic testing has been transformed in recent years by the introduction of Next-Generation Sequencing (NGS).

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PredRet: Prediction of Retention Time by Direct Mapping between Multiple Chromatographic Systems
Jan Stanstrup, Steffen Neumann, Urška Vrhovšek

Retention time (RT) information is under-utilized in LC-MS based metabolomics and sharing of RTs between systems is not currently possible. PredRet is a new system that allows highly accurate mapping and prediction of RTs between LC systems.

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Predicting Regioselectivityand Labilityof Cytochrome P450 Metabolism using Quantum Mechanical Simulations
Tyzack, Nicholas Foster, Peter Hunt, Matthew Segall

Predicting Regioselectivity and Lability of Cytochrome P450 Metabolism using Quantum Mechanical Simulations

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The Power Decoder simulator for the evaluation of pooled shRNA screen performance
Jesse Stombaugh, Abel Licon, Žaklina Strezoska, Joshua Stahl, Sarah Bael Anderson, Michael Banos, Anja van Brabant Smith, Amanda Birmingham, Annaleen Vermeulen

Power Decoder (written in R and Python) simulates shRNA pooled screening experiments in silico to allow for the estimation of a screen’s statistical power. Populations of shRNAs were engineered in such a way that the magnitude of depletion and enrichment was known, then using the negative binomial distribution, an in silico model was developed to successfully resemble data from an actual laboratory experiment.

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Knockdown of long noncoding RNAs in breast cancer
1 Jennii Luu, 2 Jesper Maag, 1 Yanny Handoko, 3 Richard Redvers, 3,4 Robin L. Anderson, 5 Maren M. Gross , 2 Marcel E. Dinger, and 1,3 Kaylene J. Simpson 1 Victorian Centre for Functional Genomics, Peter MacCallum Cancer Centre; 2 Genome Informatics, The Kinghorn Cancer Centre, The Garvan Institute of Medical Research; 3 Metastasis Research Laboratory, Peter MacCallum Cancer Centre, 4 Sir Peter MacCallum Department of Oncology, University of Melbourne;

RNAi global collaboration study using Lincode siRNA in a primary screen of tumor and nontumor breast cell lines. Hundreds of lncRNAs are found to affect viability and cell morphology of breast cancer. Presented at Keystone Symposia on Long Noncoding RNAs: From Evolution to Function, Mar 15 - Mar 20, 2015.

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Specificity of highly potent miRNA inhibitors
Barbara Robertson, Andrew Dalby, Yuriy Fedorov, Jon Karpilow, Anastasia Khvorova1, Devin Leake, Annaleen Vermeulen

miRNA inhibitors are invaluable tools for elucidating the roles of miRNAs. However, potent inhibitors may also affect other miRNAs. To understand the potential cross-reactivity of miRNA inhibitors, various miRNA inhibitor designs were systematically tested. We demonstrate that mismatches both within and outside the seed region of the miRNA interfere with inhibition. Our findings indicate that features important for natural miRNA target recognition are also important for inhibitor specificity.

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Alternative miRNA design for therapeutic RNAi applications
Anja van Brabant Smith, Barb Robertson, Annaleen Vermeulen, Christina Yamada, Angela Reynolds, Anastasia Khvorova, Devin Leake

For in vivo applications, the design of miRNA inhibitors and miRNA mimics must be optimized for stability and potency. However, stabilized miRNA mimic molecules can lose functionality compared to standard miRNA mimic molecules due, in part, to the activity of the stabilized passenger strand acting as a miRNA inhibitor. We discuss how mismatches affect the activity of the stabilized miRNA mimics, perhaps by generating a passenger strand that is less functional as an inhibitor molecule.

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Scientific News
Gene Expression Controls Revealed
Researchers have modelled every atom in a key part of the process for switching on genes, revealing a whole new area for potential drug targets.
Making Genetic Data Easier to Search
Scripps team streamlines biomedical research by making genetic data easier to search.
Monovar Drills Down Into Cancer Genome
Rice, MD Anderson develop program to ID mutations in single cancer cells.
It’s Now Easier To Go With The Flow
Rice University tool simplifies comparison of flow cytometry data for laboratories.
Making Precision Medicine a Reality
Researchers are one step closer to understanding the genetic and biological basis of diseases like cancer, diabetes, Alzheimer’s and rheumatoid arthritis – and identifying new drug targets and therapies.
New Database for Sharing MS Clinical Trial Data
A new database containing nearly 2500 patient records from the placebo arms of nine multiple sclerosis (MS) clinical trials is now available for research by qualified investigators.
‘Precision Prevention’ for Colorectal Cancer
New risk prediction model — not yet ready for clinical use — incorporates genetic, lifestyle and environmental risk factors.
Characterizing Cancerous Genomic Variations
Tested on large tumor genomics database, REVEALER method allows researchers to connect genomics to cell function.
Uncovering Hidden Genomic Alterations that Drive Cancers
Tested on large tumor genomics database, REVEALER method allows researchers to connect genomics to cell function.
Spotting DNA Repair Genes Gone Awry
Ludwig researchers develop a two-pronged approach for identifying genes responsible for fixing DNA damage that can trigger cancer when compromised.
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