/HiTRACE

High Throughput Robust Analysis of Capillary Electrophoresis

Primary LanguageMATLABOtherNOASSERTION

HiTRACE (High Throughput Robust Analysis of Capillary Electrophoresis)

HiTRACE is a collection of MATLAB scripts to automate the key tasks in large-scale nucleic acid CE analysis, including the profile alignment that has heretofore been a rate-limiting step in the highest throughput experiments. It has been intensively used for quantitating data for RNA and DNA based on the mutate-and-map methodology, chromatin footprinting, and other high-throughput structure mapping techniques.

An online user-friendly GUI is available at the HiTRACE Web.

Installation

HiTRACE requires MATLAB version >= R2011a. The MATLAB installation must include Image Processing and Signal Processing toolboxes.

For Mac OS X users with version >= 10.10, you may need this patch to settle a Java exception.

To install HiTRACE, simply:

  • Download the zip or tar file of the repository and unpack; or
git clone https://github.com/ribokit/HiTRACE.git
  • In MATLAB, go to "Set Path". Then "Add with Subfolders" of the target path/to/HiTRACE/Scripts/.

Usage

Documentation (MATLAB tutorial) is available at https://ribokit.github.io/HiTRACE/.

License

Copyright © of HiTRACE Source Code is described in LICENSE.md.

Reference

Lee, S., Kim, H., Tian, S., Lee, T., Yoon, S., and Das, R. (2015)
Automated band annotation for RNA structure probing experiments with numerous capillary electrophoresis profiles
Bioinformatics 31 (17): 2808 - 2815.

Kladwang, W., Mann, T.H., Becka, A., Tian, S., Kim, H., Yoon, S., and Das, R. (2014)
Standardization of RNA chemical mapping experiments
Biochemistry 53 (19): 3063 - 3065.

Kim, H., Cordero, P., Das, R., and Yoon, S. (2013)
HiTRACE-Web: an online tool for robust analysis of high-throughput capillary electrophoresis
Nucleic Acid Research 41 (W1): W492 - W498.

Yoon, S., Kim, J., Hum, J., Kim, H., Park, S., Kladwang, W., and Das, R. (2011)
HiTRACE: High-throughput robust analysis for capillary electrophoresis
Bioinformatics 27 (13): 1798 - 1805.


Developed by Das lab (Leland Stanford Junior University) and Yoon lab (Seoul National University) and colleagues.

README by t47, April 2016.