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