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Platform Agnostic Data Processing Routine for Targeted and Untargeted Metabolite Identification in Drug Discovery
Richard Lee,1 Vitaly Lashin,2 Andrey Paramonov,2 Alexandr Sakharov,2 Alexey Aminov2

A discussion of a new informatics solution that addresses common challenges in metabolite identification and characterization--from LC/MS data processing and metabolites prediction to reporting and databasing of assembled biotransformation knowledge.

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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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Building a digital pathology ecosystem for education and research
Yves Sucaet, Silke Smeets, Stijn Piessens, Sabrina D'Haese, Chris Groven, Wim Waelput, Peter In't Veld

We wanted to build a core digital pathology infrastructure to support different use cases. Various images platforms needed to be accessible through a single access point, and support different user profiles. We wanted a scalable solution that would allow interaction between equipment from different research groups.
We built a centralized infrastructure that integrates a variety of imaging platforms, and now have an interconnected network of heterogeneous and scalable information silos.

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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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Scientific News
Protein Nanocages Could Improve Drug Design and Delivery
HHMI scientists have designed and built 10 large protein icosahedra that are similar to viral capsids that carry viral DNA.
Connectome Map More Than Doubles Human Cortex’s Known Regions
Researchers at NIH have developed software that automatically detects the “fingerprint” of each of these areas in an individual’s brain scans.
Virus Inspired Cell Cargo Ships
Virus-inspired container design may lead to cell cargo ships following construction of ten large, two-component, icosahedral protein complexes.
Three-Drug Combinations Counter Antibiotic Resistance
Research shows that combinations of three different antibiotics can treat resistant bacteria, even if they are ineffective independently.
Supercomputing and Drug Discovery
New biotech company uses supercomputer simulations to speed up drug discovery and biotech molecule development.
Identifying Cancer Drug Targets Using 3D-Modelling
Researchers are now able to model genetic mutations manipulation of proteins that can potentially drive cancer.
Human Evolution Driven by Viruses
Study finds surprising percentage of protein adaptions in humans have been driven by viruses.
Earliest Sign of Alzheimer's Development Discovered
Researchers have identified the first physiological signs of late-onset Alzheimer's disease, underlining the importance of computational power in neurology.
Computational Method Offers Significant Boost in Finding New Cancer Targets
MIT and ARIAD Pharmaceuticals team have identified evidence of significant cancer mutations.
Drug Response Predicted by Cancer Cell Lines
Large-scale study could increase success rate of developing personalised cancer treatments.
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