TRIC is an alignment software for targeted proteomics (SRM or SWATH-MS) data. TRIC uses a graph-based alignment strategy based on non-linear retention time correction to integrate information from all available runs. The input consists of a set of csv files derived from a targeted proteomics experiment generated by OpenSWATH (using either mProphet or pyProphet) or generated by Peakview.
There are two basic running modes available. The first one uses a reference-based alignment where a single run is chosen as a reference and all other runs are aligned to it. This is a useful choice for a small number of runs that are chromatographically similar. The second mode generates a guidance tree based on chromatographic similarity of the input runs and uses this tree to align the targeted proteomics runs (the nodes in the tree are runs and the edges are pairwise alignments). Generally this mode is better for a large number of runs or for chromatographically dissimilar samples.
Availability and Tutorial
TRIC requires Python 2.7 and can be installed through pip. On Microsoft Windows you will first have to install Python (the easiest way to do this is to download Anaconda). You can then install TRIC by typing
pip install numpy pip install msproteomicstools
using the command line. This will install TRIC.py which you can then execute. You can also download the TRIC release directly from PyPI. To obtain the latest development version, please download the code from GitHub. If you are using Microsoft Windows and Anaconda, it is possible that BioPython does not properly install and you may have to install it through Anaconda:
conda install biopython
After installing TRIC, please familiarize yourself with the TRIC Tutorial. You can also find further advice on installing and running TRIC in Röst et al, 'Automated SWATH Data Analysis Using Targeted Extraction of Ion Chromatograms'. All command line parameters and their effects are explained in the tutorial and the associated tutorial paper (Röst et al). Currently, the recommended parameters for TRIC are
feature_alignment.py --in file1_input.csv file2_input.csv file3_input.csv --out aligned.csv --method LocalMST --realign_method lowess_cython --max_rt_diff 60 --mst:useRTCorrection True --mst:Stdev_multiplier 3.0 --target_fdr 0.01 --max_fdr_quality 0.05
Please read our data page.
Source Code and BugsThe source code and bugtracker is hosted on github
You can contact the authors on github