An integrated analytic pipeline for identifying and predicting genetic interactions based on perturbation data from high content double RNAi screening

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, we describe an integrated data analysis pipeline for identifying and predicting genetic interactions based on cellular responses to perturbations of single-And multiple-Agents. This pipeline was developed in the context of genome wide single-RNAi screens and smaller scale double-RNAi screens using Drosophila KC-167 cell lines, with the aim to reconstruct the molecular pathways regulating changes in cell shape. The TACC (Texas Advanced Computing Center) under XSEDE framework allocated 100,000 service unites (SUs) from its Stampede system to facilitate image quantification and signaling pathway modeling using fluorescence images of Drosophila cells, and recently a kinome-wide single RNAi screening has been reported [1].

Original languageEnglish (US)
Title of host publicationProceedings of the XSEDE 2014 Conference
Subtitle of host publicationEngaging Communities
PublisherAssociation for Computing Machinery
ISBN (Print)9781450328937
DOIs
StatePublished - 2014
Event2014 Annual Conference on Extreme Science and Engineering Discovery Environment, XSEDE 2014 - Atlanta, GA, United States
Duration: Jul 13 2014Jul 18 2014

Publication series

NameACM International Conference Proceeding Series

Other

Other2014 Annual Conference on Extreme Science and Engineering Discovery Environment, XSEDE 2014
Country/TerritoryUnited States
CityAtlanta, GA
Period7/13/147/18/14

Keywords

  • Cell morphogenesis
  • High content screening
  • Perturbation response
  • Reversible jump markov chain monte carlo
  • RNAi
  • Texas advanced computing center

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition
  • Software

Fingerprint

Dive into the research topics of 'An integrated analytic pipeline for identifying and predicting genetic interactions based on perturbation data from high content double RNAi screening'. Together they form a unique fingerprint.

Cite this