Hyper-Quant Files

HyperQuant
A Pipeline for integrating identification and quantitation results for Hyperplexing experiment
© Suruchi Aggarwal, Dr. Amit Kumar Yadav
Drug Discovery Research Center (DDRC), Translational Health Science and Technology Institute (THSTI), Faridabad, Haryana-121001, India

1. Requirement:
HyperQuant requires Active perl 5.8 or later on any operating system. If Perl is not installed, please visit http://www.activestate.com/activeperl/downloads for download and installation.
HyperQuant requires the cpan modules -Scalar::Util and Statistics::Basic to be installed. It can be installed from the perl package manager or from CPAN. 
It also requires folders of Identification results from MaxQuant or a txt file and folder containing quantification results from QuantWizIQ. For more details see documentation.

2. Usage:
1) Command 
This pipeline runs in two steps and requires parameter files as the input in both steps.
Example: 
> perl HyperQuant_mapper.pl paramsMapper.txt
> perl HyperQuant_combiner.pl paramsCombiner.txt

2) Parameters for mapping
For HyperQuant_mapper.pl to combine identification and quantitation of hyperplexing search, it requires a parameter file as command line input. The parameter file contains the parameter names and their values in a key=value format (without spaces) as shown below-
3) Parameters for combiner
a parameter file containing information about the result folder, decoy tag, quantitation method, quantitation basis, fasta and output name.

3. Output (tab-separated)
 Output file from HyperQuant_mapper.pl is suffixed as result_PSM_match. This file contains the Protein group, peptide sequence, spectrum title, unique peptide information, Label of the peptide, and PSM ratios and areas as given in quantitation file.
 The output file from HyperQuant_combiner.pl contains the protein ratios as combined from a single file or from replicates in an experiment. If user has multiple replicates in the experiment, it is advised for the user to run mapper on all replicates first and then use combiner. 
 This is so because if the replicates are not combined at the same time, the protein ratio may divert from its central tendency as then one will be calculating median of a median that increases randomness in the data obtained.

4. Citation:
Aggarwal, S., & Yadav, A. K. (2016). Dissecting the iTRAQ Data Analysis. In Methods in Molecular Biology (Vol. 1362, pp. 277–291). doi:10.1007/978-1-4939-3106-4_18
Aggarwal, S., Talukdar, N.C., and Yadav, A.K. (2019). Advances in Higher Order Multiplexing Techniques in Proteomics. J Proteome Res 18(6), 2360-2369. doi: 10.1021/acs.jproteome.9b00228.
5. License:
Suruchi Aggarwal, Amit Kumar Yadav
THSTI
6. Contact:
For feedback, queries and comments, please contact suruchi@thsti.res.in or amit.yadav@thsti.res.in