Getting Started

In [1]:

# %pip install pymsaviz

# %pip install pymsaviz

1. Simple Visualization¶

• Create MsaViz class instance from MSA file
• Plot figure by plotfig method

In [2]:

from pymsaviz import MsaViz, get_msa_testdata

msa_file = get_msa_testdata("HIGD2A.fa")
mv = MsaViz(msa_file)
fig = mv.plotfig()  # If save figure, use savefig method [e.g. mv.savefig("result.png")]

from pymsaviz import MsaViz, get_msa_testdata msa_file = get_msa_testdata("HIGD2A.fa") mv = MsaViz(msa_file) fig = mv.plotfig() # If save figure, use savefig method [e.g. mv.savefig("result.png")]

• Wrap style MSA visualization (wrap_length=60)
• Change color scheme (color_scheme='Flower')

In [3]:

from pymsaviz import MsaViz, get_msa_testdata

msa_file = get_msa_testdata("HIGD2A.fa")
mv = MsaViz(msa_file, wrap_length=60, color_scheme="Flower", show_count=True)
fig = mv.plotfig()

from pymsaviz import MsaViz, get_msa_testdata msa_file = get_msa_testdata("HIGD2A.fa") mv = MsaViz(msa_file, wrap_length=60, color_scheme="Flower", show_count=True) fig = mv.plotfig()

• Show grid (show_grid=True)
• Show consensus sequence & identity bar (show_consensus=True)

In [4]:

from pymsaviz import MsaViz, get_msa_testdata

msa_file = get_msa_testdata("MRGPRG.fa")
mv = MsaViz(msa_file, wrap_length=60, show_grid=True, show_consensus=True)
fig = mv.plotfig()

from pymsaviz import MsaViz, get_msa_testdata msa_file = get_msa_testdata("MRGPRG.fa") mv = MsaViz(msa_file, wrap_length=60, show_grid=True, show_consensus=True) fig = mv.plotfig()

• Limit MSA visualization start-end range (start=100, end=160)
• No display MSA characters (show_seq_char=False)

In [5]:

from pymsaviz import MsaViz, get_msa_testdata

msa_file = get_msa_testdata("MRGPRG.fa")
mv = MsaViz(msa_file, start=100, end=160, show_seq_char=False, show_consensus=True, consensus_color="tomato")
fig = mv.plotfig()

from pymsaviz import MsaViz, get_msa_testdata msa_file = get_msa_testdata("MRGPRG.fa") mv = MsaViz(msa_file, start=100, end=160, show_seq_char=False, show_consensus=True, consensus_color="tomato") fig = mv.plotfig()

2. Customized Visualization¶

• Set user-defined custom color scheme by set_custom_color_scheme method

In [6]:

from pymsaviz import MsaViz, get_msa_testdata

msa_file = get_msa_testdata("MRGPRG.fa")
mv = MsaViz(msa_file, start=100, end=160)
mv.set_custom_color_scheme({"A": "red", "P": "skyblue", "C": "lime", "H": "orange", "L": "pink"})
fig = mv.plotfig()

from pymsaviz import MsaViz, get_msa_testdata msa_file = get_msa_testdata("MRGPRG.fa") mv = MsaViz(msa_file, start=100, end=160) mv.set_custom_color_scheme({"A": "red", "P": "skyblue", "C": "lime", "H": "orange", "L": "pink"}) fig = mv.plotfig()

• Set user-defined custom color handle function by set_custom_color_func method

In [7]:

from pymsaviz import MsaViz, get_msa_testdata
from Bio.Align import MultipleSeqAlignment

msa_file = get_msa_testdata("MRGPRG.fa")
mv = MsaViz(msa_file, start=100, end=160, show_grid=True)

def custom_color_func(
    row_pos: int, col_pos: int, seq_char: str, msa: MultipleSeqAlignment
):
    # Note: Both `row_pos` and `col_pos` are 0-index starts
    if col_pos < 115 and seq_char != "-":
        return "salmon"
    if 115 <= col_pos < 130:
        if seq_char in ("A", "V", "P"):
            return "skyblue"
        else:
            return "lightyellow"
    if 130 <= col_pos < 145:
        if 1 <= row_pos <= 4:
            return "lime"
        else:
            return "white"
    # Use default color setting in other column (145 <= col_pos < 160)
    return None

mv.set_custom_color_func(custom_color_func)
fig = mv.plotfig()

from pymsaviz import MsaViz, get_msa_testdata from Bio.Align import MultipleSeqAlignment msa_file = get_msa_testdata("MRGPRG.fa") mv = MsaViz(msa_file, start=100, end=160, show_grid=True) def custom_color_func( row_pos: int, col_pos: int, seq_char: str, msa: MultipleSeqAlignment ): # Note: Both `row_pos` and `col_pos` are 0-index starts if col_pos < 115 and seq_char != "-": return "salmon" if 115 <= col_pos < 130: if seq_char in ("A", "V", "P"): return "skyblue" else: return "lightyellow" if 130 <= col_pos < 145: if 1 <= row_pos <= 4: return "lime" else: return "white" # Use default color setting in other column (145 <= col_pos < 160) return None mv.set_custom_color_func(custom_color_func) fig = mv.plotfig()

• Set user-defined highlight position by set_highlight_pos method

In [8]:

from pymsaviz import MsaViz, get_msa_testdata

msa_file = get_msa_testdata("MRGPRG.fa")
mv = MsaViz(msa_file, wrap_length=60)
mv.set_highlight_pos([10, 20, 30, (40, 120), (150, 200), 220, 270])
fig = mv.plotfig()

from pymsaviz import MsaViz, get_msa_testdata msa_file = get_msa_testdata("MRGPRG.fa") mv = MsaViz(msa_file, wrap_length=60) mv.set_highlight_pos([10, 20, 30, (40, 120), (150, 200), 220, 270]) fig = mv.plotfig()

• Set highlight position by consensus identity threshold using set_highlight_pos_by_identity_thr method

In [9]:

from pymsaviz import MsaViz, get_msa_testdata

msa_file = get_msa_testdata("MRGPRG.fa")
mv = MsaViz(msa_file, start=100, end=160, show_consensus=True)
# Only highlight 0 - 80 [%] consensus identity positions
mv.set_highlight_pos_by_ident_thr(min_thr=0, max_thr=80)
fig = mv.plotfig()

from pymsaviz import MsaViz, get_msa_testdata msa_file = get_msa_testdata("MRGPRG.fa") mv = MsaViz(msa_file, start=100, end=160, show_consensus=True) # Only highlight 0 - 80 [%] consensus identity positions mv.set_highlight_pos_by_ident_thr(min_thr=0, max_thr=80) fig = mv.plotfig()

• Add markers by add_markers method
• Add text annotation by add_text_annotation method

In [10]:

from pymsaviz import MsaViz, get_msa_testdata

msa_file = get_msa_testdata("MRGPRG.fa")
mv = MsaViz(msa_file, start=1, end=180, wrap_length=60, show_consensus=True)

# Extract MSA positions less than 50% consensus identity
pos_ident_less_than_50 = []
ident_list = mv._get_consensus_identity_list()
for pos, ident in enumerate(ident_list, 1):
    if ident <= 50:
        pos_ident_less_than_50.append(pos)

# Add markers
mv.add_markers([1])
mv.add_markers([10, 20], color="orange", marker="o")
mv.add_markers([30, (40, 50), 55], color="green", marker="+")
mv.add_markers(pos_ident_less_than_50, marker="x", color="blue")
# Add text annotations
mv.add_text_annotation((76, 102), "Gap Region", text_color="red", range_color="red")
mv.add_text_annotation((112, 123), "Gap Region", text_color="green", range_color="green")

fig = mv.plotfig()

from pymsaviz import MsaViz, get_msa_testdata msa_file = get_msa_testdata("MRGPRG.fa") mv = MsaViz(msa_file, start=1, end=180, wrap_length=60, show_consensus=True) # Extract MSA positions less than 50% consensus identity pos_ident_less_than_50 = [] ident_list = mv._get_consensus_identity_list() for pos, ident in enumerate(ident_list, 1): if ident <= 50: pos_ident_less_than_50.append(pos) # Add markers mv.add_markers([1]) mv.add_markers([10, 20], color="orange", marker="o") mv.add_markers([30, (40, 50), 55], color="green", marker="+") mv.add_markers(pos_ident_less_than_50, marker="x", color="blue") # Add text annotations mv.add_text_annotation((76, 102), "Gap Region", text_color="red", range_color="red") mv.add_text_annotation((112, 123), "Gap Region", text_color="green", range_color="green") fig = mv.plotfig()

• Marker list (https://matplotlib.org/stable/api/markers_api.html)

In [11]:

from pymsaviz import MsaViz, get_msa_testdata

msa_file = get_msa_testdata("MRGPRG.fa")
mv = MsaViz(msa_file, end=60, show_grid=True)

# Add various type markers
markers = [".", ",", "o", "v", "^", "<", ">", "1", "2", "3", "4", "8", "s", "p", "*", "h", "H", "+", "x", "D", "d", "|", "_"]
colors = ["red", "blue", "green", "chocolate", "magenta"]
for idx, marker in enumerate(markers, 1):
    color = colors[idx % len(colors)]
    mv.add_markers([idx * 2], marker=marker, color=color, size=8)
fig = mv.plotfig()

from pymsaviz import MsaViz, get_msa_testdata msa_file = get_msa_testdata("MRGPRG.fa") mv = MsaViz(msa_file, end=60, show_grid=True) # Add various type markers markers = [".", ",", "o", "v", "^", "<", ">", "1", "2", "3", "4", "8", "s", "p", "*", "h", "H", "+", "x", "D", "d", "|", "_"] colors = ["red", "blue", "green", "chocolate", "magenta"] for idx, marker in enumerate(markers, 1): color = colors[idx % len(colors)] mv.add_markers([idx * 2], marker=marker, color=color, size=8) fig = mv.plotfig()

Change detail plot parameters by set_plot_params method

• ticks_interval=5 (Default: 10)
• x_unit_size=0.20 (Default: 0.14)
• grid_color="black" (Default: "lightgrey")
• identity_color="tomato" (Default: "#A3A5FF")

In [12]:

from pymsaviz import MsaViz, get_msa_testdata

msa_file = get_msa_testdata("MRGPRG.fa")
mv = MsaViz(msa_file, start=100 ,end=140, show_grid=True, show_count=True, color_scheme="Identity")
mv.set_plot_params(ticks_interval=5, x_unit_size=0.20, grid_color="black", identity_color="tomato")
fig = mv.plotfig()

from pymsaviz import MsaViz, get_msa_testdata msa_file = get_msa_testdata("MRGPRG.fa") mv = MsaViz(msa_file, start=100 ,end=140, show_grid=True, show_count=True, color_scheme="Identity") mv.set_plot_params(ticks_interval=5, x_unit_size=0.20, grid_color="black", identity_color="tomato") fig = mv.plotfig()

Color block MSA display

In [13]:

from pymsaviz import MsaViz, get_msa_testdata

msa_file = get_msa_testdata("MRGPRG.fa")
mv = MsaViz(msa_file, color_scheme="Clustal", show_seq_char=False, show_count=True, sort=True, show_consensus=True, consensus_color="grey")
mv.set_plot_params(ticks_interval=50, x_unit_size=0.04, show_consensus_char=False)
fig = mv.plotfig()

from pymsaviz import MsaViz, get_msa_testdata msa_file = get_msa_testdata("MRGPRG.fa") mv = MsaViz(msa_file, color_scheme="Clustal", show_seq_char=False, show_count=True, sort=True, show_consensus=True, consensus_color="grey") mv.set_plot_params(ticks_interval=50, x_unit_size=0.04, show_consensus_char=False) fig = mv.plotfig()