can you provide support for MetaMorpheus output

[trishorts]

Peptide identifications in MetaMorpheus have in-line PTM information, where the PTM is positioned on the previous amino acid. Examples follow.

The format for each PTM is exactly the same. PTM Category; PTM name; PTM motif.

I guess you could determine everything that you need. Happy to provide more info if you need it.

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Full Sequence

TDNAGDQHGGGGGGGGGAGAAGGGGGGENYDDPHK
ALANVNIGSLIC[Common Fixed:Carbamidomethyl on C]NVGAGGPAPAAGAAPAGGPAPSTAAAPAEEK
IGTYTGPLQHGIVYSGGSSDTIC[Common Fixed:Carbamidomethyl on C]DLLGAK
KYILGNPLTPGVTQGPQIDKEQYDK
LNDHFPLVVWQTGSGTQTNMNVNEVISNR
HIDC[Common Fixed:Carbamidomethyl on C]AHVYQNENEVGVAIQEK
LGALSGAAALGFASYGAHGAQFPDAYGK
IGPALSC[Common Fixed:Carbamidomethyl on C]GNTVVVKPAEQTPLTALHVASLIK
TLTGTVIDSGDGVTHVIPVAEGYVIGSC[Common Fixed:Carbamidomethyl on C]IK
ELEQVC[Common Fixed:Carbamidomethyl on C]NPIISGLYQGAGGPGPGGFGAQGPK
VVGFHVLGPN[Common Artifact:Deamidation on N]AGEVTQGFAAALK
SGDAAIVDMVPGKPMC[Common Fixed:Carbamidomethyl on C]VESFSDYPPLGR
KYTLPPGVDPTQVSSSLSPEGTLTVEAPMPK
STSFR[Common Biological:Dimethylation on R]GGMGSGGLATGIAGGLAGM[Common Variable:Oxidation on M]GGIQNEK
EAGFPPGVVNIVPGYGPTAGAAISSHMDIDK
DQAVENILVSPVVVASSLGLVSLGGK
KDLYAN[Common Artifact:Deamidation on N]TVLSGGTTM[Common Variable:Oxidation on M]YPGIADR

[EngelsI]

Hi, You can use this piece of code to convert the output. You can either put it in a '.txt' file or a '.csv' file format. Please change the path, file_name and file_type. If it doesn't work, let me know :)

[EngelsI]

`import pandas as pd

path = 'PATH_TO_FILE_LOCATION'
file_name = 'NAME_OF_THE_FILE'
file_type = '.txt'

data = pd.read_csv(path+'/'+file_name+file_type, sep=",", header=None)
data.columns = ["peptides"]
array = []
for pep in data['peptides'].values:
if '[' not in pep:
array.append([pep,''])
else:
temp = pep.split('[')
pep = ''
new_temp = []
for el in temp:
if ']' in el:
p = el.split(']')
new_temp += [p[0]+']',p[-1]]
pep+=p[-1]
else:
pep+=el
new_temp.append(el)
count = 0
ptm = []
for el in new_temp:
if ']' not in el:
count += len(el)
else:
el = el.split(':')[-1]
el = el.split(' on')[0]
ptm.append(str(count))
if el=='Deamidation':
el = 'Deamidated'#has to match unimod ID
ptm.append(el)
array.append([pep,'|'.join(ptm)])
df = pd.DataFrame(array,columns=['seq','modifications'])
df.to_csv(path+'/'+file_name+'_pep_rec.csv')`