bafpipe 0.3.3

Automated deconvolution of Bruker mass spectra datasets using UniDec

BafPipe

Automated deconvolution of mass spectra datasets using UniDec.

Cite Marty et al. Anal. Chem. 2015. DOI: 10.1021/acs.analchem.5b00140

Automates the UniDec deconvolution algorithms for large Bruker mass spectra datasets and quantifies relative peak intensity. Converts Bruker .baf to numpy arrays using baf2sql and then automates unidec to deconvolute protein mass spectra.

pip install BafPipe

The software uses an input excel file containing deconvolution parameters, species identities/masses and data plotting parameters.

Setting up the input file

Sheet 1 of input file contains the directory folder of your mass spectra and the unidec configuration parameters.

You can also add masses to detect and a corresponding colour. These are done by stipulating Species + name and Color + name in the Parameter column, followed by the desired mass or colour in the Input column.

Example input directory

Parameter	Input	Comments
Directory	D:\mass spec\protein labelling
Species Protein	48127
Color Protein	orange
Start Scan	490
End Scan	540
Tolerance (Da)	10	Peak matching tolerance
Config masslb	15000	Deconvolution window low mass
Config massub	50000	Deconvolution window high mass
Config massbins	1	Mass bins for deconvolution - sample mass every
Config peakwindow	10
Config minmz	700	m/z lower bounds (defaults to 0)
Config maxmz		m/z upper bounds (defaults to 10e12)
Config startz	1
Config endz	100
Config numz	100
Config numit	60	number of iterations of deconvolution algorithm

Each file within the directory can be linked to custom variables defined in a second sheet of the input directory. This comes in handy if wanting to filter data or perform analyses/comparison on subsets of your experiment.

Any column names can be defined aside from 'Name' in column 0. Name corresponds to your filename (can be partial match).

Make sure var_ids=True load_input_file(var_ids = True)

Example variables table

Name	var1	var2	var3
Nexp 1	1	1	1
Nexp 2	2	13	3
Nexp 3	1	25	3
Nexp 4	3	25	1
Nexp 5	2	13	5

Running the code

path = r"C:\users\input_file.xlsx

eng = Meta2()

eng.load_input_file(path, unzip=False, clearhdf5=True, var_ids=True) # load input file and run deconvolution

eng.on_unidec()

spectra = eng.eng.data.spectra

msp.plot_spectra_separate(spectra,
attr = "massdat",
xlabel = "Mass [Da]", 
export=True,
c='black',
lw=0.7,
window=[None, None],
show_peaks=True,
legend=True,
directory=eng.directory,
fmt='png') # fmt = 'svg' for vector format