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pipasic - peptide intensity-weighted proteome abundance similarity correction
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Abstract
--------
Metaproteomic analysis allows studying the interplay of organisms or functional
groups and has become increasingly popular also for diagnostic purposes.
However, difficulties arise due to the high sequence similarity between related
organisms. Further, the state of conservation of proteins between species can
be correlated with their expression level which can lead to significant bias in
results and interpretation. These challenges are similar but not identical to
the challenges arising in the analysis of metagenomic samples and require
specific solutions.
We developed pipasic (peptide intensity-weighted proteome abundance similarity
correction) as a tool which corrects identification and spectral counting based
quantification results using peptide similarity estimation and expression level
weighting within a non-negative lasso framework. pipasic has distinct advantages
over approaches only regarding unique peptides or aggregating results to the
lowest common ancestor, as demonstrated on examples of viral diagnostics and an
acid mine drainage dataset.
Requirements
------------
pipasic was developed with Python 2.7.2, the following python libraries are
required:
- Biopython 1.60
- NumPy 1.7.1
- matplotlib 1.2.1 (optional for graphical output)
pipasic might be able to work with different software versions, but we tested
it using the given configuration.
Spectrum identification can be done with Inspect or Tide. We used the following
versions:
- InsPecT version 20100804
- Tide as part of Crux 1.36
Installation
------------
pipasic is a Python tool and does not require any installation (except the
requirements above). Download the source code or check out the repository and
run pipasic from the command line by calling
> python pipasic.py
See below for more details.
Usage
-----
Usage: pipasic.py SPECTRA DB [module options] [input and configuration options]
Overall pipasic calling tool, including:
- weighted (always) and unweighted (optional) similarity estimation
- correction, using matrix from similarity estimation
- peptide Identification by InsPecT/Tide
SPECTRA: Comma-separated string of spectrum files (mgf) - without file-extension!
DB: Comma-separated string of reference proteomes (fasta-files) - without file-extension!
if -S or -I: decoy database must exist as db_name+"_decoy.fasta"
Options:
-h, --help show this help message and exit
-U, --Unweighted calculate unweighted similarities for all given
proteomes
-I, --Identify identify given spectra with all given proteomes
-T, --Tide use Tide instead of InsPecT
-V Visualize results using matplotlib
-o OUTFILE, --outfile=OUTFILE
Output filename for results. Also serves as trunk for
other result files (graphics, data). [default:
results.txt]
-s SPEC_DIR, --spec_dir=SPEC_DIR
Directory of SPECTRA (mgf) files. Search in current
directory, if not given. [default: none]
-d DB_DIR, --db_dir=DB_DIR
Directory of proteinDBs. Search for DB files current
directory, if not given. [default: none]
-m MODS, --mods=MODS A string containing all modifications in question,
modification choice by filename if not given.
[default: none]
-i INSP_DIR, --inspect_dir=INSP_DIR
Inspect directory. [default: none]
-f FDR, --fdr_cutoff=FDR
False discovery rate cut-off for identification lists.
[default: 0.05]
-l LABELS, --labels=LABELS
Comma-separated string of short names for organisms in
the reference proteomes. If not given, the file name
is used. [default: none]
-N N, --N_spectra=N Number of spectra in original dataset, comma-separated
list if multiple datasets. [default: none]
-c COUNTS, --C_spectra=COUNTS
File containing numbers of spectra found by
identification (Numpy Array dump). [default: none]
-q, --quiet don't print status messages to stdout
Example run
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Download the example dataset from Sourceforge!
https://sourceforge.net/projects/pipasic/files/example.tar.gz/download
Extract the archive into your pipasic installation and follow the instructions
in example/README.
License
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pipasic is Open Source! Please find detailed licensing information in the
LICENSE file.