reading mgf data and visualization

[Elena E]

Hello,

I am quite new to Python, and I was wondering if anyone has experience with reading mgf data in Python and visualizing peaks. 

 

I would greatly appreciate it if you could share a working sample code.

Many thanks,

Elena

[Lev Levitsky]

Hi!

I just updated this example in the docs, it should cover all the basics. Feel free to ask questions here or on Github.

Best regards,

Lev

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Lev Levitsky
Institute for Energy Problems of Chemical Physics RAS
Laboratory of Physical and Chemical Methods for Structure Analysis
Leninsky pr. 38, bld. 2 119334 Moscow Russia
tel: +7 499 1378257 fax: +7 499 1378257, +7 499 1378258

[Elena E]

Hi Lev,

Many thanks for this much appreciated!

Best wishes,

Eleni

[Elena E]

Dear Lev, 

Many thanks for the easy to follow example! It was extremely helpful! I was able to follow and produce the graphics!

I have a follow up question. The data I am playing around is a publically available Covid dataset and I was wondering how 1) I could find Covid positive/negative and 2) how does one include the peaks information to fit, say a regression model.

Many thanks once again!

Best wishes,

Elena

[Lev Levitsky]

Hi Elena,

I'm glad the examples helped! I wish I could continue being helpful, but these follow-up questions are harder for me to answer. They also definitely go beyond the scope of Pyteomics and my experience.

I'm not sure what you mean by "find Covid positive/negative" but I imagine that a public dataset should include some kind of annotation of positive and negative samples. Otherwise, I have no idea how this dataset can support the publication.

The recently adopted standard for annotation of public proteomics datasets is SDRF-Proteomics. If your dataset is annotated in this format, you can use sdrf-pipelines or just Pandas to parse the annotations, if you need to do it programmatically. Otherwise, I would just look at Supporting Information or the dataset itself for some kind of annotation. If it's in a consistent tabular format, Pandas can be used to read it in Python.

I can't say much about your second question, too, but a few notes:

• m/z and intensity values can be extracted using "m/z array" and "intensity array" keys from the dictionaries produced by Pyteomics parsers;
• these values are stored as NumPy arrays;
• if you need to track intensities of specific ion types across multiple spectra, then I suggest you look at spectrum_utils. Pyteomics optionally uses it for spectrum annotation (see example 4) but if you use it directly, you can access peak annotations through spectrum_utils spectrum objects;
• for a variety of regression models in Python, look at scikit-learn.

I hope it helps somewhat.

Best regards,

Lev

[Elena E]

Dear Lev,

Thank you very much for this! This helps a lot! 

Have a lovely day!

Best,

Elena