Calisp (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 Galaxy tool uses the python reimplementation https://github.com/kinestetika/Calisp. Note that, in contrast to the Java version the python reimplementation does not use mcl . Compared to Java versions of calisp, the workflow has been simplified. Calisp does not filter out any isotopic patterns, or adds up isotopic patterns to reduce noise - like the Java version does. It simply estimates the ratio for the target isotopes (e.g. 13C/12C) for every isotopic pattern it can subsample. 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 with <1% additional 13C). The motivation for omitting filtering is that keeping all subsampled isotopic patterns, 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 isotopic patterns to improve precision has lost its purpose. There is more power (and sensitivity) in numbers.
Because no data are filtered out and no isotopic patterns get added up, calisp analyzes at least ten times as many isotopic patterns 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. 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. Note that this threshold is less stringent then thew one used by the java program.
Calisp needs two inputs: a spectra file in mzML format and tabular peptipe file (PSM). The PSM file contains a column "Spectrum File" that links the peptides to the original spectra files. The mzML files are identified by the run id information stored in the mzML files or the file name. In order to make the association via the file name work in Galaxy one can either
Each row contains one isotopic pattern, defined by the following columns:
| Header name | Content |
|---|---|
| experiment | filename of the peptide spectrum match (psm) file |
| ms_run | filename of the .mzml file |
| bins | bin/mag ids, separated by commas. Calisp expects the protein ids in the psm file to consist of two parts, separated by a delimiter (_ by default). The first part is the bin/mag id, the second part the protein id |
| proteins | the ids of the proteins associated with the pattern (without the bin id) |
| peptide | the aminoacid sequence of the peptide |
| peptide_mass | the mass of the peptide |
| C | # of carbon atoms in the peptide |
| N | # of nitrogen atoms in the peptide |
| O | # of oxygen atoms in the peptide |
| H | # of hydrogen atoms in the peptide |
| S | # of sulfur atoms in the peptide |
| psm_id | psm id |
| psm_mz | psm m over z |
| psm_charge | psm charge |
| psm_neutrons | number of neutrons inferred from custom 'neutron' modifications |
| psm_rank | rank of the psm |
| psm_precursor_id | id of the ms1 spectrum that was the source of the psm |
| psm_precursor_mz | mass over charge of the precursor of the psm |
| pattern_charge | charge of the pattern |
| pattern_precursor_id | id of the ms1 spectrum that was the source of the pattern |
| pattern_total_intensity | total intensity of the pattern |
| pattern_peak_count | # of peaks in the pattern |
| pattern_median_peak_spacing | medium mass difference between a pattern's peaks |
| spectrum_mass_irregularity | a measure for the standard deviation in the mass difference between a pattern's peaks |
| ratio_na | the estimated isotope ratio inferred from neutron abundance (sip experiments) |
| ratio_fft | the estimated isotope ratio inferred by the fft method (natural isotope abundances) |
| error_fft | the remaining error after fitting the pattern with fft |
| error_clumpy | the remaining error after fitting the pattern with the clumpy carbon method |
| flag_peptide_contains_sulfur | true if peptide contains sulfur |
| flag_peptide_has_modifications | true if peptide has no modifications |
| flag_peptide_assigned_to_multiple_bins | true if peptide is associated with multiple proteins from different bins/mags |
| flag_peptide_assigned_to_multiple_proteins | true if peptide is associated with multiple proteins |
| flag_peptide_mass_and_elements_undefined | true if peptide has unknown mass and elemental composition |
| flag_psm_has_low_confidence | true if psm was flagged as having low confidence (peptide identity uncertain) |
| flag_psm_is_ambiguous | true if psm could not be assigned with certainty |
| flag_pattern_is_contaminated | true if multiple patterns have one or more shared peaks |
| flag_pattern_is_wobbly | true if pattern_median_peak_spacing exceeds a treshold |
| flag_peak_at_minus_one_pos | true if a peak was detected immediately before the monoisotopic peak, could indicate overlap with another pattern |
| i0 - i19 | the intensities of the first 20 peaks of the pattern |
| m0 - m19 | the masses of the first 20 peaks of the pattern |
| c1 - c6 | contributions of clumps of 1-6 carbon to ratio_na. These are the outcomes of the clumpy carbon model. These results are only meaningful if the biomass was labeled to saturation. |