calisp 3.0.4

Isotope analysis of proteomics data

## Calisp.py, version 3.0.3

Calisp.py (Calgary approach to isotopes in proteomics) is a program that estimates isotopic composition (e.g. 13C/12C, delta13C, 15N/14N etc) of peptides from proteomics mass spectrometry data. Input data consist of mzML files and files with peptide spectrum matches.

Calisp was originally developed in Java. This newer, python version is much more concise, it consists of only six .py files, ~1,000 lines of code, compared to about fifty files and ~10,000 lines of code for the Java program. In addition, the Java program depends on another program, mcl, whereas calisp.py is purely python, which is easy to install, see below. The conciseness of the code makes the python version more transparent, easier to maintain and easier to further develop. I consider calisp.py is the successor of the Java version. One of the reasons to shift to python is the possibility to more effectively develop machine learning approaches to filter out noisy spectra. Future work will explore that possibility.

Benchmarking of Calisp.py has been completed. It works well, benchmarking procedures and outcomes are shared in the “benchmarking” folder. Parsing of .mzid files still needs to be implemented.

Calisp.py depends on numpy, scipy, pandas, tqdm, [pymzml](https://pymzml.readthedocs.io/en/latest/intro.html), pyarrow. These will be installed automatically by the pip command below. Calisp outputs the data as a Pandas DataFrame saved in (binary) [feather](https://arrow.apache.org/docs/python/feather.html) format. From there, the user can for example explore and visualize the results in a [Jupyter notebook](https://jupyter.org/). For that, a tutorial wil be provided. In the meantime, you can check out the two notebooks I created for benchmarking calisp.py, which are in the benchmarking folder.

Compared to previous versions of calisp, the workflow has been simplified. Calisp.py does not filter out any spectra, or adds up spectra to reduce noise - like the Java version does. It simply estimates the ratio for the target isotopes (e.g. 13C/12C) for every spectrum it can find. It estimates this ratio based on neutron abundance and using fast fourier transforms. The former applies to stable isotope probing experiments. The latter applies to natural abundances, or to isotope probing experiments with very little added label (e.g. using substrates wiht only 1% additional 13C). The motivation for omitting filtering is that collecting all spectra, including bad ones, will enable training of machine learning classifiers. Also, because it was shown that the median provides better estimates for species in microbial communities than the mean, adding up spectra to improve precision has lost its purpose. There is more power (and sensitivity) in numbers.

Because no data are filtered out and no spectra get added up, calisp.py, analyzes at least ten times as many spectra compared to the Java version. That means calisp.py is about ten times slower, it takes about 5-10 min per .mzML file on a Desktop computer. The user can perform filtering of the data using the Jupyter notebook as desired. For natural abundance data, it works well to only use those spectra that have a FFT fitting error (“error_fft”) of less than 0.001, as previously shown for the java program.

Installation:

>python -m virtualenv calisp

>source calisp/bin/activate

>pip install calisp

Usage:

>source calisp/bin/activate

>calisp.py –spectrum_file [path to .mzML file or folder with .mzML files] –peptide_file [path to .mzML file or folder

with .mzML files] –output_file [folder where calisp.py will save results files] –threads [# of threads used, default 4] –isotope [15N, 3H etc, default 13C], –bin_delimiter [character that separates the bin ID from the remainder of protein IDs, default ‘_’], –mass_accuracy [accuracy of peak m/z identifications, default 10 ppm] –compute_clumps [use only if you want to compute clumpiness]

Column names of the Pandas DataFrame created by calisp.py:

In the saved dataframe, each row contains one spectrum, defined by the following columns:

‘experiment’, ‘ms_run’, ‘bins’, ‘proteins’, ‘peptide’, ‘peptide_mass’, ‘C’, ‘N’, ‘O’, ‘H’, ‘S’, ‘psm_id’,’psm_mz’, ‘psm_charge’, ‘psm_neutrons’, ‘psm_rank’, ‘psm_precursor_id’, ‘psm_precursor_mz’, ‘spectrum_charge’, ‘spectrum_precursor_id’, ‘spectrum_total_intensity’, ‘spectrum_peak_count’, ‘spectrum_median_peak_spacing’, ‘spectrum_mass_irregularity’, ‘ratio_na’, ‘ratio_fft’,’error_fft’, ‘error_clumpy’ ‘flag_peptide_contains_sulfur’, ‘flag_peptide_has_modifications’, ‘flag_peptide_assigned_to_multiple_bins’, ‘flag_peptide_assigned_to_multiple_proteins’, ‘flag_peptide_mass_and_elements_undefined’, ‘flag_psm_has_low_confidence’,’flag_psm_is_ambiguous’, ‘flag_spectrum_is_contaminated’, ‘flag_spectrum_is_wobbly’, ‘flag_peak_at_minus_one_pos’ ‘i0’, …, ‘i19’, ‘m0’, …, ‘m19’, ‘c1’ … ‘c6’

The final 46 columns contain the normalized peak intensities (‘i0’ …) and m/z of the spectrum’s peaks (‘m0’ …), as well as the inferred clumpiness of the isotopes (‘c1’ …).

Please cite:

Kleiner M, Dong X, Hinzke T, Wippler J, Thorson E, Mayer B, Strous M (2018) A metaproteomics method to determine carbon sources and assimilation pathways of species in microbial communities. Proceedings of the National Academy of Sciences 115 (24), E5576-E5584. doi: [https://doi.org/10.1073/pnas.1722325115 ](https://doi.org/10.1073/pnas.1722325115 )

Kleiner M, Kouris A, Jensen M, Liu Y, McCalder J, Strous M (2021) Ultra-sensitive Protein-SIP to quantify activity and substrate uptake in microbiomes with stable isotopes. bioRxiv. doi: [https://doi.org/10.1101/2021.03.29.437612](https://doi.org/10.1101/2021.03.29.437612)

M Kösters, J Leufken, S Schulze, K Sugimoto, J Klein, R P Zahedi, M Hippler, S A Leidel, C Fufezan; pymzML v2.0: introducing a highly compressed and seekable gzip format, Bioinformatics, doi: [https://doi.org/10.1093/bioinformatics/bty046](https://doi.org/10.1093/bioinformatics/bty046)