Thanh Lee thanhleviet

1. Nanopore basecalling with Dorado

dorado: https://cdn.oxfordnanoportal.com/software/analysis/dorado-0.4.3-linux-x64.tar.gz

Dataset:

https://dropoff.nbi.ac.uk/pickup?claimID=HejmBN7XJVbMRv74&claimPasscode=mxpYSGxyhKCmJG5z&emailAddr=1017

Claim ID: HejmBN7XJVbMRv74

	#!/usr/bin/env python3
	"""
	Git Analysis Report Generator
	This script generates an HTML report analysing commits in a Git repository.
	Thanh Le Viet - TGHI
	"""
	import subprocess
	from datetime import datetime
	import os
	from html import escape

	#!/usr/bin/env python
	import argparse
	import sys
	import os
	import pandas as pd
	from biom import load_table, Table

	def merge_biom_files_with_pandas(biom_files):
	dfs = [] # List to store DataFrames

	--[[

	SLURM job submit filter for QOS

	Some code and ideas pulled from https://github.com/edf-hpc/slurm-llnl-misc-plugins/blob/master/job_submit.lua

	--]]

	--########################################################################--
	--

	import os
	import csv
	import argparse

	def scan_paired_end_files(dir_path, pattern_list):
	# Initialize a list to store the sample IDs and file paths
	samples = []

	# Loop through all files in the directory
	for file_name in os.listdir(dir_path):

	library(patchwork)
	library(ggplot2)
	library(tidyverse)
	data(mtcars)
	data(iris)

	mtcars.tf <- mtcars %>%
	rownames_to_column("name") %>%
	pivot_longer(names_to = "metrics", values_to = "values", mpg:carb)

	#!/usr/bin/env python
	import requests
	import time
	import pathlib
	from joblib import Parallel, delayed
	from pqdm.processes import pqdm

	def url_template(id):
	url0 = "https://pubmlst.org/bigsdb?db=pubmlst_campylobacter_isolates&page=plugin&name=Contigs&format=text&isolate_id="
	url1 = "&match=1&pc_untagged=0&min_length=&header=1"

	library(dplyr)
	library(ggtree)

	tree <- read.tree("Benchmarking_tree_07Dec21.nwk")

	tips <- tree$tip.label %>%
	tibble(tip = .) %>%
	mutate(sample = gsub("_[a-zA-Z\\_]+","",tip))

	#!/usr/bin/env nextflow
	nextflow.enable.dsl=2
	ch_pilon = Channel.fromPath(params.sample_sheet)
	.splitCsv(header: true)
	.map {row -> tuple(row.sample_id,[row.sr1,row.sr2],row.contigs)}

	ch_pilon.view()