Abstract
BackgroundThe analysis of high-throughput screening data sets is an expanding field in bioinformatics. High-throughput screens by RNAi generate large primary data sets which need to be analyzed and annotated to identify relevant phenotypic hits. Large-scale RNAi screens are frequently used to identify novel factors that influence a broad range of cellular processes, including signaling pathway activity, cell proliferation, and host cell infection. Here, we present a web-based application utility for the end-to-end analysis of large cell-based screening experiments by cellHTS2.ResultsThe software guides the user through the configuration steps that are required for the analysis of single or multi-channel experiments. The web-application provides options for various standardization and normalization methods, annotation of data sets and a comprehensive HTML report of the screening data analysis, including a ranked hit list. Sessions can be saved and restored for later re-analysis. The web frontend for the cellHTS2 R/Bioconductor package interacts with it through an R-server implementation that enables highly parallel analysis of screening data sets. web cellHTS2 further provides a file import and configuration module for common file formats.ConclusionsThe implemented web-application facilitates the analysis of high-throughput data sets and provides a user-friendly interface. web cellHTS2 is accessible online at http://web-cellHTS2.dkfz.de. A standalone version as a virtual appliance and source code for platforms supporting Java 1.5.0 can be downloaded from the web cellHTS2 page. web cellHTS2 is freely distributed under GPL.
Highlights
The analysis of high-throughput screening data sets is an expanding field in bioinformatics
Whole genome sequences and methods for gene silencing by RNA interference (RNAi) have enabled loss-of-function analysis in ex vivo and in vivo, opening new avenues for functional analysis that were previously unfeasible [1,2]
Large-scale RNAi screens can exceed more than 100,000 data points per screening experiment and specialized statistical approaches have been developed for their analysis [710]
Summary
The analysis of high-throughput screening data sets is an expanding field in bioinformatics. Highthroughput screens by RNAi generate large primary data sets which need to be analyzed and annotated to identify relevant phenotypic hits. Large-scale RNAi screens are frequently used to identify novel factors that influence a broad range of cellular processes, including signaling pathway activity, cell proliferation, and host cell infection. High-throughput cell-based screens have become an important experimental tool for the analysis of many cellular processes. Different experimental methods to assess phenotypic changes are being used, from single-channel homogenous readouts to multi-channel cytometry and imaging, producing large data sets that need to be analyzed to extract phenotypically relevant information. High-throughput screens are mostly performed using 96- to 384-well plates and produce large data sets that need to be normalized, summarized and ranked to generate a list of significant phenotypic modifiers. Quality control assessments of assays and screening data are performed to provide benchmarks for the overall performance, such as experiment-wide performance of controls, reproducibility between replicate experiments, as well as other statistical quality control measures [1013]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
