ont-pybasecall-client-lib 7.13.6

Dependencies

ont-pybasecall-client-lib requires numpy in order to run. In order to use included helper functions for reading data from pod5 files it is also necessary to manually install pod5:

pip install pod5

Documentation and help

Information on the methods available may be viewed through Python’s help command::

>>> from pybasecall_client_lib import pyclient
>>> help(pyclient)
>>> from pybasecall_client_lib import client_lib
>>> help(client_lib)

Starting a basecall server

There must be a Dorado basecall server running in order to communicate with it. On most Oxford Nanopore devices a basecall server is always running on port 5555. On other devices, or if you want to run a separate basecall server, you must start one yourself:

from pybasecall_client_lib import helper_functions

# A basecall server requires:
#  * A location to put log files (on your PC)
#  * A port to run on
server_args = ["--log_path", "/home/myuser/basecall_server_logs",
               "--port", 5556]
# The second argument is the directory where the
# dorado_basecall_server executable is found. Update this as
# appropriate.
helper_functions.run_server(server_args, "/home/myuser/ont-dorado/bin")

See the the DOCUMENTATION.md file in the ont-dorado-server archive for more information on server arguments.

Basecall and align using PyBasecallClient

from pybasecall_client_lib.pyclient import PyBasecallClient

client = PyBasecallClient(
    "127.0.0.1:5555",
    "dna_r10.4.1_e8.2_400bps_5khz_fast",
    align_ref = "/path/to/align_ref.fasta",
    bed_file = "/path/to/bed_file.bed"
)
client.connect()

It is also possible to start the PyBasecallClient using a dorado model set, e.g:

from pybasecall_client_lib.pyclient import PyBasecallClient

client = PyBasecallClient(
    "127.0.0.1:5555",
    "dna_r10.4.1_e8.2_400bps_hac@v4.3.0|dna_r10.4.1_e8.2_400bps_hac@v4.3.0_5mC_5hmC@v1|",
)
client.connect()

Note that the helper_functions module requires that pod5 is installed.:

from pybasecall_client_lib.helper_functions import basecall_with_pybasecall_client

# Using the client generated in the previous example
called_reads = basecall_with_pybasecall_client(
    caller,
    "/path/to/input_folder"
)

for read in called_reads:
    read_id = read['metadata']['read_id']
    alignment_genome = read['metadata']['alignment_genome']
    sequence = read['datasets']['sequence']
    print(f"{read_id} sequence length is {len(sequence)}"
          f"alignment_genome is {alignment_genome}")

Basecall and get states, moves and modbases using BasecallClient

In order to retrieve the movement dataset, the move_enabled option must be set to True. NOTE: You shouldn’t turn on move_enabled if you don’t need it, because it generates a LOT of extra output data so it can hurt performance.

options = {'priority': PyBasecallClient.high_priority,
          'client_name': "test_client",
          'move_enabled': True }

client = PyBasecallClient(port_path, 'dna_r10.4.1_e8.2_400bps_5khz_modbases_5hmc_5mc_cg_hac')
result = client.set_params(options)
result = client.connect()

called_reads = basecall_with_pybasecall_client(client, input_path)

for read in called_reads:
    base_mod_context = read['metadata']['base_mod_context']
    base_mod_alphabet = read['metadata']['base_mod_alphabet']

    sequence = read['datasets']['sequence']
    movement = read['datasets']['movement']
    base_mod_probs = read['datasets']['base_mod_probs']

    print(f"{read_id} sequence length is {len(sequence)}, "
          f"base_mod_context is {base_mod_context}, base_mod_alphabet is {base_mod_alphabet}, "
          f"movement size is {movement.shape}, base_mod_probs size is {base_mod_probs.shape}")