I was trying to make a plot showing how many genes with functional annotations are present across my pipeline and connect the dots to show the connection between different boxplot and geom jitter. But then I got stuck where the lines wouldn't connect to the dots because the position_jitter was somehow different. The original code was: tt %>% left_join(phylo %>% select(!NCBI_TaxID), by=c("Genome"="ID")) %>% filter(Order %in% c("Bacillales","Enterobacterales","Flavobacteriales")) %>% ggplot(aes(x=tool,y=n,col=Order,group=Genome))+

Before running :warning: Disclaimer :warning: If you are here and you didn't get the link from me. Please go to the official Anvio tutorial here and here. Meren, his team and Mike Lee do a fantastic job on their website and it will be much better. Installing softwares This page for anvio And we'll need a side set of script from Mike Lee: conda create -y -n bit -c conda-forge -c bioconda -c defaults -c astrobiomike bit

This is always a difficult topic for me. How to handle proper statistics in my large data set analysis. Here's a collection of papers and discussion on the best way to approach it. This page is a work in progress of notes copy paste Found two stat exchanges discussion on how to handle p value corrections with genomic datasets: Link Benjamini-Yekutieli method was designed to handle the situation with correlated test results better. It can provide an FDR that is less conservative than the BH value. Link

library(tidyverse) library(KEGGREST) Scrapping The scrapper use a list of kegg of interest, then go through the list to get the associated BRITE annotations. It create an arbitrary A to F/G categories that can be used down the line for annotation analysis. ko.list<-c("K26400","K26400","K26441", "K26441") The main function below. ko.scrap<-function(ko.list,a,b,m){ ko.tmp<-list()

First note in HackMD. Let see if this space is useful or just yet another forgetable thing that will accumulate digital dust. This note is designed to set up a Google environement to perform metagenomic co Assembly on mouse stool sample. Each individual mouse was sample multiple point in time allowing to boos the microbial signal. Google VM environment preparation sudo apt-get update sudo apt-get install bzip2 libxml2-dev sudo apt-get install git wget tar unzip sudo ln -s /usr/bin/python3 /usr/bin/python

Trying to exploit all the Bakta associated annotations. There are all those GO annotations that could be used to profile bacteria overview. The challenge is that GO anntotations follow a tree system and are not really organized in harmonious categories. Inspired by the GO ribbons I decided to use the same categories to try to profile bacteria. GOHLMeta<-tibble(GO=c("0003674", "0003824", "0030234", "0038023",

Digestion of this page and this page Considering the Desqe2 table you are doing you analysis and a metadata file that looks like: ## DataFrame with 8 rows and 3 columns ## names donor condition ## <factor> <factor> <factor> ## SRR1039508 SRR1039508 N61311 Untreated ## SRR1039509 SRR1039509 N61311 Dexamethasone ## SRR1039512 SRR1039512 N052611 Untreated

A while back Google remove the option to use their email adresses on low security settings. Which I was using with one of my dummy emails as a way to send me emails when a remote job is done. I thought about looking for an alternative and wondered how difficult it would be to transfer this messaging task to a Slack Bot. Turns out it's not that difficult: Install the Slack Developer Kit for Python: a. Open a terminal window and run the command: pip install slack-sdk. Get the bot token: