|Picking the best CRISPR-Cas9 targets for functional gene knockout: a machine learning algorithm based on both specificity and functionality|
Shawn McClelland, Emily M. Anderson, Žaklina Strezoska, Elena Maksimova, Annaleen Vermeulen, Steve Lenger, Tyler Reed, and Anja van Brabant Smith Dharmacon, now part of GE Healthcare, 2650 Crescent Drive, Suite #100, Lafayette, CO 80026, US
The CRISPR-Cas9 system has the potential to significantly advance basic and applied research.
|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
|Scaffold design, function and over-expression of lentiviral-based microRNAs|
Angela Schoolmeesters, Melissa L. Kelley, Annaleen Vermeulen, Anja Smith, *Mayya Shveygert, *Xin Zhou, *Robert Blelloch Dharmacon, now part of GE Healthcare, 2650 Crescent Drive, Suite #100, Lafayette, CO 80026, USA
Here we describe the strategy for scaffold design, the importance of an optimal promoter, and demonstrate gene target down-regulation from the over-expression of lentiviral microRNA mimics.
|Homology-directed repair with Dharmacon™ Edit-R™ CRISPR-Cas9 and single-stranded DNA oligos|
John A. Schiel, Eldon T. Chou, Maren Mayer, Emily M. Anderson , and Anja van Brabant Smith | Dharmacon, now part of GE Healthcare, 2650 Crescent Drive, Suite #100, Lafayette, CO 80026, US
Here we demonstrate how to perform lipid based transfections for homology directed repair using DharmaFECT Duo, CRISPR-Cas9 reagents and, synthetic DNA donor oligos.
|Minimizing Carry-over for High Throughput Analysis|
Christian Berchtold1, Reto Bolliger2, Guenter Boehm2, Götz Schlotterbeck1
Minimal carry-over is a prerequisite for high throughput analysis. However, minimized carry-over and cycle time are competing and a careful optimization is mandatory. In this study the influence of wash conditions on carry-over of various compounds was investigated. A strategy to minimize carry-over was developed. The influences of different wash tasks were investigated. Finally the contribution of different system components such as injector valve or column was studied.
|A Comparison of ITEX Dynamic Headspace–GC/MS to other Enrichment Techniques for Analysis of Flavoring Compounds|
Douglas Doster1; Roger Pearson1; Sean Eppel1; Ken Rice1; Tom Flug2; Brian Peat2; Guenter Boehm2
Enrichment techniques are commonly used for the analysis of flavoring compounds in different matrices with GC/MS. Analysis of flavoring compounds is done by purge & trap, SPME or headspace, depending on requirements for sensitivity. The In-Tube Ex¬traction (ITEX) Dynamic Headspace uses a micro trap filled with an adsorbent material to efficiently extract the compounds. Here we evaluate if the ITEX can be used to effectively analyze for these compounds and reduce the analyst’s time involved.
|Automated in-gel digestion on a commercial autosampler directly coupled to nanoLC-MS/MS|
Achermann François, Bolliger Reto, Buchs Natasha, Doiron Nicholas, Lagache Braga Sophie, Heller Manfred, Boehm Guenter
SDS-PAGE separates protein samples from LC-MS incompatible contaminations, and is frequently used to fractionate proteins of entire proteomes. One disadvantage is that gel lanes have to be cut into many slices, followed by in-gel digestion of proteins and extraction of peptides. The number of these gel slices goes into the hundreds, rendering this process very repetitive and prone to mistakes and errors during sample handling. Automation reduces such risks and improves reproducibility.
|Automated sample preparation workflows for quantitative proteomics applications|
Oliver Popp1, Lucas Luethy2, Tamara Kanashova1, HaAn Nguyen1, Julia Kikuchi1, Guenter Boehm2, Thomas Blenkers3, Andreas Bruchmann3, Gunnar Dittmar1
Mass spectrometry based proteomics requires large scale identification of peptides, and depends upon efficient sample preparation. Recently, we presented two automated protein-digestion setups, in-solution and in-gel digestion. We extended these techniques by implementing dimethyl labelling (DML). Furthermore, we established an automated phospho-peptide (PP) enrichment procedure in a 96-well formate, generating phospho-proteomic data in very short time.
|Tools for studying and using small RNAs: from pathways to functions to therapies|
Kenneth Chang and Gregory J. Hannon
This poster provides an overview of the tools that have been developed to understand the functions of small RNAs and, conversely, the use of small RNAs as tools. Tools that are based on small RNAs have been exploited to investigate gene function in cultured cells and in living animals. Small RNA biogenesis, discovery and functional roles are explored in detail. Screening approaches to functional genomics, in vivo methods and potential therapeutic applications are discussed.
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