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Size-Exclusion Chromatography Algorithmic Toolkit
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README.md

SECAT: Size-Exclusion Chromatography Algorithmic Toolkit

SECAT is an algorithm for the network-centric data analysis of SEC-SWATH-MS data. The tool is implemented as a multi-step command line application.

Dependencies

SECAT depends on several Python packages (listed in setup.py) and the viper R/Bioconductor package, accessed via rpy2. SECAT has been tested on Linux (CentOS 7) and macOS (10.14) operating systems and might run on other versions too.

Please install viper from Bioconductor prior to SECAT.

Installation

We strongly advice to install SECAT in a Python virtualenv. SECAT is compatible with Python 3.7 and higher and installation should require a few minutes with a correctly set-up Python environment.

Install the development version of SECAT from GitHub:

pip install git+https://github.com/grosenberger/secat.git@master

Install the stable version of SECAT from the Python Package Index (PyPI):

pip install secat

Docker

SECAT is also available from Dockerhub:

docker pull grosenberger/secat:latest # "latest" can be replaced by the version number, e.g. "1.0.4"

Print the installed Python and R package versions:

docker run --name secat --rm -v $PWD:/data -i -t grosenberger/secat:latest Rscript -e 'installed.packages()[,"Version"]'
docker run --name secat --rm -v $PWD:/data -i -t grosenberger/secat:latest pip list

Run SECAT:

docker run --name secat --rm -v $PWD:/data -i -t grosenberger/secat:latest secat --help

Running SECAT

SECAT requires 1-4h running time with a SEC-SWATH-MS data set of two conditions and three replicates each, covering about 5,000 proteins and 80,000 peptides on a typical desktop computer with 4 CPU cores and 16GB RAM.

The exemplary input data (HeLa-CC.tgz and Common.tgz are required) can be found on Zenodo: DOI

The data set includes the expected output as SQLite-files. Note: Since the PyProphet semi-supervised learning step is initialized by a randomized seed, the output might vary slightly from run-to-run with numeric deviations. To completely reproduce the results, the pretrained PyProphet classifier can be applied to as described in the secat learn step. The Zenodo repository contains all parameters and instructions to reproduce the SECAT analysis results of the other data sets.

SECAT consists of the following steps:

1. Data preprocessing

The primary input for SECAT are quantitative, proteotypic/unique peptide-level profiles, e.g. acquired by SEC-SWATH-MS. The input can be supplied either as matrix (protein, peptide and run-wise peptide intensities columns) or as transposed long list. Protein identifiers need to be provided in UniProtKB/Swiss-Prot format. The column names can be freely specified (secat preprocess --columns; see help for a complete description).

The second required input file represents the experimental design and molecular weight calibration of the experiment. The primary column covers the run identifiers (matching the quantitative profiles above), with additional columns for SEC fraction identifiers (integer value), SEC molecular weight (float value), a group condition identifier (freetext value) and a replicate identifier (freetext value). The column names can be freely specified (secat preprocess --columns; see help for a complete description).

The third required file covers UniProtKB/Swiss-Prot meta data in XML format, matching the proteome, and can be obtained from UniProt.

Optionally, reference PPI networks can be specified to support semi-supervised learning and to restrict the peptide query space. SECAT can accept three files: A positive reference network and a negative reference network for the learning steps and a separate reference network to restrict the query space. SECAT natively supports HUPO-PSI MITAB (2.5-2.7), STRING-DB, BioPlex and PrePPI formats and provides filtering options to optionally exclude lower confidence PPIs. The inverted CORUM reference PPI network was generated by using the inverted set of PPI (i.e. all possible PPI that are not covered by CORUM) and removing all PPI in this set covered by STRING, IID, PrePPI or BioPlex.

The Zenodo archives linked above contain example files and parameter sets for all described analyses and can be used to test the algorithm and reproduce the results.

First, the input quantitative proteomics matrix and parameters are preprocessed to a single file:

secat preprocess
--out=hela_string.secat \ # Output filename
--sec=input/hela_sec_mw.csv \ # SEC annotation file
--net=common/9606.protein.links.v11.0.txt.gz \ # Reference PPI network
--posnet=common/corum_targets.txt.gz \ # Reference positive interaction network for learning
--negnet=common/corum_decoys.txt.gz \ # Reference negative interaction network for learning
--uniprot=common/uniprot_9606_20190402.xml.gz \ # Uniprot reference XML file
--min_interaction_confidence=0 # Minimum interaction confidence
input/pep*.tsv \ # Input data files

2. Signal processing

Next, the signal processing is conducted in a parallelized fashion:

secat score --in=hela_string.secat --threads=8

3. PPI detection

The statistical confidence of the PPI is evaluated by machine learning:

secat learn --in=hela_string.secat --threads=5

4. PPI quantification

Quantitative features are generated for all PPIs and proteins:

secat quantify --in=hela_string.secat --control_condition=inter

5. Export of results

CSV tables can be exported for import in downstream tools, e.g. Cytoscape:

secat export --in=hela_string.secat

6. Plotting of chromatograms

PDF reports can be generated for the top (or selected) results:

secat plot --in=hela_string.secat

7. Report of statistics

Statistics reports can be generated for the top (or selected) results:

secat statistics --in=hela_string.secat

Further options and default parameters

All options and the default parameters can be displayed by:

secat --help
secat preprocess --help
secat score --help
secat learn --help
secat quantify --help
secat export --help
secat plot --help
secat statistics --help
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