Center for Infectious Disease and Vector Research

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Analysis of Drug-like Small Molecules and High-Throughput Screens with R/Bioconducto-Bioinformatics Workshop

Tyler Backman & Thomas Girke

Monday, December 16, 2013
9 a.m.Noon


Genomics Building , Auditorium
Parking Information


Description: This workshop introduces various R packages useful for analyzing drug-like compound and screening data sets. This includes the packages ChemmineR, ChemmineOBfmcsR, eiR and bioassayR. Efficient R functions will be introduced for handling/analyzing SDF/MOL files, interfacing with PubChem, structural similarity searching, clustering of compound libraries with a wide spectrum of algorithms and utilities for managing complex compound data sets. In addition, visualization functions for compound clusters and chemical structures will be introduced. The last part will cover the analysis of large publicly available high-throughput screening data sets like PubChem BioAssay. Basic knowledge of the R software, as introduced in the "Introduction to R" tutorial, will be expected in this workshop.  Maximum number of participants: 40  [ Schedule and Teaching Material ]   

How to sign up:
send chosen workshop date, your name, email address, title of your position, lab affiliation (PI) and department information to thomas.girke@ucr.edu.

Registration fee*: 
no charge for participants from registered labs, $23 for UCR members, $75 for participants from external academic institutions, and $102 for participants from commercial organizations and academic institutions outside of the US.

Laptop requirements:
Participants will work during the course from their own laptops (Win, Mac or Linux). The laptops should have R, the required libraries and exercise data sets pre-installed. The installation instructions for the R software are provided on the installation page of the manual. A functional wireless connection is recommended, but not essential.  

Additional Information:

Open to:

General Public


Reservations Required


Institute for Integrative Genome Biology

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