proteovis

1. pycorn: extract data (zip) from ÄKTA/UNICORN and create chromatograms plotly.
2. pypage: Annotate SDS-PAGE images, automatically input marker size and add lane information.
3. combine these two together to visualize the fractions and lanes connected in AKTA.

install

!git clone [email protected]:Tsuchihashi-ryo/proteovis.git

%cd proteovis
!pip install .

Demo

import proteovis as pv

data = pv.pycorn.load_uni_zip("/content/PyCORN/samples/sample.zip")

df = pv.pycorn.utils.get_series_from_data(data,["UV 1_280","UV 2_254","Cond","pH","Conc B","Run Log",'Fractions'],)

fig = pv.graph.unicorn_ploty_graph(df)
fig.show() 

frac_df = pv.pycorn.utils.get_fraction_rectangle(df)

frac_df = pv.pycorn.utils.pooling_fraction(frac_df,["1.B.2","1.B.3","1.B.4","1.B.5"])
frac_df = pv.pycorn.utils.pooling_fraction(frac_df,["1.B.10","1.B.11"],name="pool2")


cbb_list = [
            "marker",
            "input",
            "1.A.4",
            "pool",
            "1.B.6",
            "1.B.7",
            "pool2",
            "1.B.12",
            "1.C.1",
            "1.C.3",
            "1.C.5",
            "1.C.8",
            "1.C.10",
            "1.D.4",
            ]
cbb_frac_df = frac_df[frac_df["Fraction_Start"].isin(cbb_list)]



palette = sns.color_palette("rainbow", len(frac_df))

fig2,use_color_palette = pv.graph.annotate_fraction(fig,frac_df,palette=palette,annotations=cbb_list)
fig2.show() 

use_color_palette["input"] = (0.9,0.9,0.9)

cbb = pv.pypage.PageImage("/content/PyCORN/samples/cbb.jpg",lane_width=50)
cbb.annotate_lanes(cbb_list)
cbb.check_image()

fig = cbb.annotated_imshow(use_color_palette,rectangle=True)
fig.show()

marker = cbb.get_lane(name="marker",start=0)
marker = pv.pypage.Marker(marker)
marker.check()

marker.annotate([198,"","",98,62,"",49,38,28,"","",17,14])
pv.pypage.write_marker(fig,marker)

Contributors

• pyahmed
• Tsuchihashi-ryo
• wackywendell