evanroyrees

#!/usr/bin/env python
"""
# Alluvial plot generation of taxonomy information

## Setup Env

First create env to run script

```bash
conda create -n plotly -c plotly -c conda-forge plotly python-kaleido pandas tqdm -y

evanroyrees

#!/usr/bin/env bash
#SBATCH -p partition
#SBATCH -t 48:00:00
#SBATCH --nodes=1
#SBATCH --ntasks-per-node=16


# NOTE: To create the conda environment for autometa you can supply the Makefile command:
# make create_environment

evanroyrees

#!/usr/bin/env bash
#SBATCH -p partition
#SBATCH -t 48:00:00
#SBATCH --nodes=1
#SBATCH --ntasks-per-node=16

## First create environment to run Autometa, (and optionally GTDB-Tk and CheckM)
# git clone git@github.com:KwanLab/Autometa
# cd Autometa
# make create_environment

evanroyrees

#!/usr/bin/env python

import argparse
import os

import pandas as pd

def do_stuff(df: pd.DataFrame) -> pd.DataFrame:
    return df

evanroyrees

Autometa pipeline walkthrough using Nextflow

NOTE: These instructions are for working off of the KwanLab/dev branch

Overview

1. Install Autometa environment and commands
2. Configure nextflow so Autometa commands can be run through your scheduler
3. Configure run parameters (Set metagenome filepath and output directories)
4. Run autometa pipeline using nextflow

evanroyrees

#!/usr/bin/env python

import argparse
import os
import glob

from Bio import SeqIO

import pandas as pd

evanroyrees

import argparse
import logging as logger
import multiprocessing as mp
logger.basicConfig(
    format='%(asctime)s : %(name)s : %(levelname)s : %(message)s',
    datefmt='%m/%d/%Y %I:%M:%S %p',
    level=logger.DEBUG)

# This is pulled directly from autometa/common/coverage.py
parser = argparse.ArgumentParser(description='Construct contig coverage table given an input assembly and reads.')

evanroyrees

#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
merge nodes from autometa LCA output table before running `add_contig_taxonomy.py`
"""


import logging
import os