NanoPypes

NanoPypes is a python package for managing and analysing ONT sequence data using distributed computing environments.

Quick-Start

Install

pip.:

pip install nanopypes

Parallel basecalling with ONT’s Albacore- pure python

Create an instance of Albacore.:

from nanopypes.albacore import *

config = "path/to/yaml/config"
albacore = Albacore(config)

Create an instance of Cluster to connect to your compute resources.:

cluster = Cluster(config)

Execute the basecall function.:

from nanopypes.nanopypes import basecall

basecall(albacore, cluster)

Parallel basecalling with ONT’s Albacore- command line

Single command- pass a config and/or individual parameters.:

parallel_basecaller path/to/yaml/config

For a full list of parameters.:

parallel_basecaller --help

Building the yaml config file.

Create a .yml file with the following parameters.:

basecall_config:
    input_path: path/to/raw/minion/data
    save_path: path/to/basecall/output
    flowcell: FLO-MIN106 # Flow cell used in sequencing run
    kit: SQK-LSK109 # Kit used in sequencing run
    barcoding: False
    output_format: fast5
    worker_threads: 1 # Albacore worker_threads- best to keep at 1
    recursive: False # all data will be non-recursive when Albacore is called
    reads_per_fastq: 1000
    cluster:
        job_time: 06:00  # May need optimizing based on number of reads
        mem: 40000 # Should be scaled with ncpus, workers and cores
        ncpus: 20 #1 worker per cpu
        project: /path/to/project/on/cluster
        queue: long
        workers: 20
        cores: 20
        memory: 40 GB
        scale_value: 100  # 100 scale_value / 20 cpus = 5 jobs
        cluster_type: LSF

Contents:

Indices and tables