Compute first two isotopologues intensity from peptide sequence.
Project description
Seq-to-first-iso
Compute first two isotopologues intensity from peptide sequence
Seq-to-first-iso computes isotopologues M0 and M1 of peptides with a 99.99 % 12C enrichment for quantification by SLIM-labeling.
It simulates auxotrophies by differentiating labelled and unlabelled amino acids.
The documentation can be found here.
Try the demo with Binder:
Installation
With pip
$ pip install seq-to-first-iso
With conda
$ conda install seq-to-first-iso -c bioconda
Developer mode
Install conda
Clone repo:
$ git clone https://github.com/pierrepo/seq-to-first-iso
$ cd seq-to-first-iso
Create conda environment:
$ conda env create -f environment.yml
Remark: for a fully reproducible environment, you could also use:
$ conda env create -f environment.lock.yml
Activate conda environment:
$ conda activate seq-to-first-iso
Install local package:
$ pip install -e .
Usage
The script takes a file with one sequence of amino acids per line and returns a tsv of the file with columns:
sequence | mass | formula | formula_X | M0_NC | M1_NC | M0_12C | M1_12C |
---|
Once installed, the script can be called with:
$ seq-to-first-iso filename [-o output_name] [-n amino_acids...]
Optional arguments are in square brackets
This will create filename_stfi.tsv if filename is a correct file
0.3.0 : The input file can have annotations separated by a tabulation before the sequences
0.4.0 : Support for X!Tandem Post-Translational Modifications added
Options
-
-h, --help
:
Provide a help page -
-v, --version
:
Provide the version -
-o, --output
:
Change the name of the output file -
-n, --non-labelled-aa
:
Take 1 or more amino acid separated by a comma
Examples
- You can provide a list of amino acids which will keep default isotopic abundance:
Supposing peptides.txt :
YAQEISR
VGFPVLSVKEHK
LAMVIIKEFVDDLK
The command
$ seq-to-first-iso peptides.txt -n V,W
will create peptides_stfi.tsv :
sequence | mass | formula | formula_X | M0_NC | M1_NC | M0_12C | M1_12C |
---|---|---|---|---|---|---|---|
YAQEISR | 865.42938099921 | C37H59O13N11 | C37H59O13N11 | 0.6206414140575179 | 0.280870823368276 | 0.9206561231798033 | 0.05161907174495234 |
VGFPVLSVKEHK | 1338.7659712609 | C63H102O16N16 | C48H102O16N16X15 | 0.4550358985377136 | 0.34506032928190855 | 0.7589558393662944 | 0.18515489894512063 |
LAMVIIKEFVDDLK | 1632.91606619252 | C76H128O21N16S1 | C66H128O21N16S1X10 | 0.36994021481230627 | 0.3373188347614264 | 0.7475090558698947 | 0.15292723586285323 |
Where, in 12C enrichment conditions, the isotopologue intensity M0_12C and M1_12C are computed with unlabelled Valine and Tryptophan (V and W have default isotopic abundance)
- You can change the name of the output file:
$ seq-to-first-iso peptides.txt -o sequence
will create a file named sequence.tsv
Credits
-
Binder
- Jupyter et al., "Binder 2.0 - Reproducible, Interactive, Sharable Environments for Science at Scale." Proceedings of the 17th Python in Science Conference. 2018. 10.25080/Majora-4af1f417-011
-
Bioconda:
- Grüning, Björn, Ryan Dale, Andreas Sjödin, Brad A. Chapman, Jillian Rowe, Christopher H. Tomkins-Tinch, Renan Valieris, the Bioconda Team, and Johannes Köster. 2018. “Bioconda: Sustainable and Comprehensive Software Distribution for the Life Sciences”. Nature Methods, 2018 doi:10.1038/s41592-018-0046-7.
-
MIDAs:
- Alves G, Ogurtsov AY, Yu YK (2014) Molecular Isotopic Distribution Analysis (MIDAs) with adjustable mass accuracy. J Am Soc Mass Spectrom, 25: 57-70. DOI: 10.1007/s13361-013-0733-7
-
Pyteomics:
-
Goloborodko, A.A.; Levitsky, L.I.; Ivanov, M.V.; and Gorshkov, M.V. (2013) “Pyteomics - a Python Framework for Exploratory Data Analysis and Rapid Software Prototyping in Proteomics”, Journal of The American Society for Mass Spectrometry, 24(2), 301–304. DOI: 10.1007/s13361-012-0516-6
-
Levitsky, L.I.; Klein, J.; Ivanov, M.V.; and Gorshkov, M.V. (2018) “Pyteomics 4.0: five years of development of a Python proteomics framework”, Journal of Proteome Research. DOI: 10.1021/acs.jproteome.8b00717
-
-
SLIM-labeling:
- Léger T, Garcia C, Collomb L, Camadro JM. A Simple Light Isotope Metabolic Labeling (SLIM-labeling) Strategy: A Powerful Tool to Address the Dynamics of Proteome Variations In Vivo. Mol Cell Proteomics. 2017;16(11):2017–2031. doi:10.1074/mcp.M117.066936
Changelog
Dev
1.1.0 (2019-12-19)
- Add export_to_knime() function
1.0.0 (2019-12-05)
- Simplify interface
- Take .tsv as input file
- Take peptide sequence and charge as input data (from input file)
- Check input file and input dataframe
- Comply with PEP 8 and PEP 257
- Update API and CLI demo notebooks
0.5.1 (2019-07-10)
Fixed
- seq_to_xcomp() can now correctly take a pyteomics.mass.Composition object as the second parameter
0.5.0 (2019-07-03)
Added
- Flag for version number in CLI
- Jupyter notebook with Binder environment for demonstrations
- Documentation on Read the Docs
- Conda availability
Changed
- Breaking change : changed seq_to_midas() to seq_to_xcomp()
- Breaking change : changed seq_to_tsv() to seq_to_df()
0.4.3 (2019-06-26)
Fixed
- Fix requirements not being installed with
pip install
0.4.2 (2019-06-25)
Fixed
- Fix setup.cfg's installation requirements
0.4.1 (2019-06-24)
- Extend numpydoc style to all functions in seq_to_first_iso.py
0.4.0 (2019-06-21)
Changed
- Add support for Xtandem Parsing
- Breaking change: sequence_parser() now returns a dict with "annotations", "raw_sequences", "sequences", "modifications" and "ignored_lines"
- Add get_mods_composition() that returns a composition from a list of Unimod PTMs
- Remove the appended "_stfi" if -o flag is provided
0.3.0 (2019-04-18)
Changed
- Add support for files with annotations before the sequences
- Breaking change: sequence_parser() now returns (annotations, sequences, ignored_lines)
- seq_to_tsv() now accepts (sequences, unlabelled_aa, annotations=None)
Fixed
- Output files now have "_stfi" appended to differentiate from .tsv input files with the same name
0.2.1 (2019-04-17)
- Format CHANGELOG
0.2.0 (2019-04-17)
- Add bumpversion
Changed
- seq_to_tsv() no longer writes a file, instead it returns a dataframe
0.1.0 (2019-04-08)
- First release
BSD 3-Clause License
Copyright (c) 2019, Lilian Yang-crosson All rights reserved. Copyright (c) 2019, Pierre Poulain All rights reserved.
Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
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Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
-
Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
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Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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