Compute first two isotopologues intensity from peptide sequence.

# 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

$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)

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)

• 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)

Changed

• seq_to_tsv() no longer writes a file, instead it returns a dataframe

0.1.0 (2019-04-08)

• First release

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