Lineage Barcode Extract

If you’re looking to identify lineage defining mutations for your lineage of choice, look no further! You can extract this information using Freyja’s existing barcoding and a bit of python.

  1. Run a freyja update and extract barcodes to the directory of your choosing (will take a few minutes):

freyja update --outdir /my/local/directory/

  1. Load the barcodes using pandas and extract barcodes for your lineages of interest

import pandas as pd

def sortFun(x):
    # sort based on nuc position, ignoring nuc identities
    return int(x[1:(len(x)-1)])
# replace "/my/local/directory/" with your chosen directory
df = pd.read_csv('/my/local/directory/usher_barcodes.csv', index_col=0)

# specify your lineages of interest here
lins = ['B.1.617.2','BA.1','BA.2','B.1.1.7','P.1']
df = df.loc[lins]
# keep only columns with at least one mutation across the lins
keepcols = df.columns[df.sum(axis=0)>0]
df = df.loc[:, keepcols]

Optional: 3. Load in gene coordinate information to translate nucleotide mutations into possible AA mutations. An example file for doing this is available here. Then append AA mutations to the corresponding nucleotide mutations.

# build dataframe with gene data
import json
f1 = open('/my/local/directory/SARS-CoV-2.json',)
dat = json.load(f1)
f1.close()
df_genes = pd.DataFrame(columns=["gene","start","end"], dtype=object)

for d in dat["genes"]:
    new_row = {'gene':d,'start':dat["genes"][d]['coordinates']['from'],
               'end':dat["genes"][d]['coordinates']['to']}
    df_genes = df_genes.append(new_row,ignore_index=True)
df_genes = df_genes.sort_values('start').set_index('gene',drop=False)

# function for converting to gene-wise coordinate numbering
def getGeneNum(pos,df0):
    j=0
    while j<df0.shape[0]:
        if pos<=df0.iloc[j].loc['end'] and pos>=df0.iloc[j].loc['start']:
            return df0.iloc[j].loc['gene'], (pos - df0.iloc[j].loc['start'])//3+1
        else:
            j+=1
    return '',''

# add AA mutation info into our dataframe
cols = list(df.columns)
cols.sort(key=lambda x: sortFun(x))
df = df[cols]
for i, mut in enumerate(cols):
    pos = sortFun(mut)
    g,pa = getGeneNum(pos,df_genes)
    if len(g)==0:
        refAA=''
        mutAA=''
    else:
        posInTriple = (pos - df_genes.loc[g,'start']) % 3
        triple = ref0[pos-posInTriple-1:pos-posInTriple+2]
        refAA = triple.translate()
        tripleMut = triple
        tripleMut[posInTriple] = mut[-1]
        mutAA = tripleMut.translate()
        cols[i] = cols[i] +'('+ g+':'+str(refAA)+str(pa)+str(mutAA)+')'

df.columns = cols

  1. Export to csv format for later viewing.

df.to_csv('mutations_lineage_subset.csv')