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Next-generation Sequencing03:00

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An ideal Y-Y transformer, grounded through neutral impedances, displays per-unit sequence networks akin to those of a single-phase ideal transformer when subjected to balanced positive- or negative-sequence currents. These currents do not produce neutral currents, and their associated voltage drops.
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A problem-solving strategy is a plan of action used to find a solution. Different strategies have distinct action plans. Trial and error involves trying different solutions until one works. For instance, to fix a broken printer, you might check ink levels, ensure the paper tray isn't jammed, and verify the printer's connection to your laptop. This method can be time-consuming but is commonly used. Thomas Edison, for example, used trial and error to find a suitable filament for the light...
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Drug discovery is a multifaceted process involving extensive screening, testing, and optimization of lead compounds to identify potential new drugs for therapeutic use. It combines several approaches, including screening large numbers of natural products, chemical modification of known active molecules, identification of new drug targets, and rational design based on biological mechanisms and drug-receptor structure. These approaches are carried out in both academic research laboratories and...
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An analytical methodology can be divided into four sequential steps: technique, method, procedure, and protocol. A technique is a scientific principle that rationalizes a specific phenomenon through chemical measurements. Adapting a technique for analyzing a sample of interest is termed a method. The procedure outlines the directions for performing the analysis via an analytical method. The protocol is the detailed guidelines on the procedure, which should be strictly followed to obtain the...
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Pooled shRNA Library Screening to Identify Factors that Modulate a Drug Resistance Phenotype
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Target discovery screens using pooled shRNA libraries and next-generation sequencing: A model workflow and analytical

Christiane Schaefer1, Nikhil Mallela2, Jochen Seggewiß3

  • 1Pediatric Hematology and Oncology, University Hospital Münster, Münster, Germany.

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|February 1, 2018
PubMed
Summary
This summary is machine-generated.

RNA interference screening using pooled shRNA libraries and next-generation sequencing (NGS) aids Ewing sarcoma target discovery. A novel analysis package, ProFED, simplifies data analysis for reproducible results.

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Area of Science:

  • Oncology
  • Genetics
  • Biotechnology

Background:

  • RNA interference (RNAi) screening is crucial for identifying novel therapeutic targets.
  • Developing high-throughput screening assays is resource-intensive and challenging.
  • Ewing sarcoma presents a significant challenge in therapeutic target discovery.

Purpose of the Study:

  • To describe a target discovery screen using pooled short hairpin RNA (shRNA) libraries and next-generation sequencing (NGS) in an Ewing sarcoma cell line model.
  • To provide a complete workflow, including methods, results, and pitfalls, for RNAi screening and data analysis.
  • To introduce ProFED, an open-source analysis package for shRNA and count-based datasets.

Main Methods:

  • Pooled shRNA libraries and NGS deconvolution were employed for target discovery.
  • A cell line model of Ewing sarcoma (A673) was used for specificity screening.
  • Lentiviral shRNA delivery, bioinformatics analysis, and the ProFED package were utilized.

Main Results:

  • Successful RNAi screening is feasible with a resource-saving screen depth (100-fold average shRNA representation).
  • Reproducible target hits can be generated despite dataset heterogeneity.
  • The ProFED package facilitates descriptive data analysis and hit calling via profile filtering.

Conclusions:

  • This study demonstrates a complete and feasible workflow for RNAi-based target discovery in Ewing sarcoma.
  • The findings suggest that specialized screening units are not essential for successful studies.
  • ProFED offers a versatile, open-source solution for analyzing shRNA and count-based datasets, complementing existing algorithms.