diff --git a/Monday/practicals_day1_PHINDAccess.Rmd b/Monday/practicals_day1_PHINDAccess.Rmd
index 79ec94180228492bf1757f10aa36da2c6495dc54..ddb27493e47fc92b7d1cde0be68d35659f6e9281 100644
--- a/Monday/practicals_day1_PHINDAccess.Rmd
+++ b/Monday/practicals_day1_PHINDAccess.Rmd
@@ -19,10 +19,9 @@ knitr::opts_chunk$set(echo = TRUE)
 # Set the random generator seed so we can reproduce exactly results without any stochasticity
 set.seed(1234)
 
-# Load packages that will be useful for the analysis
+# Load packages that will be useful for this practical
 
 library(ggplot2)
-# library(AnnotationDbi)
 library(org.Hs.eg.db)
 library(KEGGREST)
 library(biomaRt)
@@ -33,13 +32,15 @@ library(png)
 # Cosmetic choice: set light theme for ggplot2 plots
 theme_set(theme_light())
 
+# Set significance level for the statistical tests (e.g. False Discovery Rate)
+alpha <- 0.05
 ```
 
 # I. Functional annotation
 
 **Goals**
 
--  Identify and understand the steps for annotating your own datasets.
+-  Identify and understand the steps of a functional analysis
 
 - Understand the output of provided R code lines. There are often several ways to write an R code obtaining the same result. This practical offers one possible solution! 
 
@@ -57,10 +58,10 @@ Then, we can annotate our list of genes that we have obtained,   for example, fr
 For this exercise, the input dataset is from an experiment conducted in  Myc tb, with 3 biological conditions (FR, CR and HR) and 3 replicates per condition.
 
 ```{r, message=FALSE, warning=FALSE}
-# Import  data`
+# Import  data
 
 ## Define YOUR OWN working directory:
-mywd = "/Users/npietros/Documents/Course-PHINDAccess-2022/Hands on Day 1 - Annotation/"
+mywd = "/Users/npietros/Documents/Course-PHINDAccess-2022/Hands on Day 1-Annotation//"
 setwd(mywd)
 
 ## Read the matrix of gene counts as obtained from a typical sequencing experiment.
@@ -287,9 +288,6 @@ write.table(unique_go,
 
 The aim of this exercise is to learn how to look for KEGG annotations for Human and use them to annotate genes present in our dataset before performing any enrichment analysis.
 
-
-
-
 This same procedure can be used to annotate any other organisms present in the Kegg database.
 
 ### Get Kegg pathways for Human
@@ -457,6 +455,9 @@ names(Kegg.GeneIDs) <- names(Kegg.GeneSymbols) <- names(Kegg.Genes) <- KeggPathN
 # head(Kegg.GeneSymbols)
 # head(Kegg.Genes)
 
+# save this list for further use
+save(Kegg.GeneIDs, file = "output/Kegg.GeneIDs.RData")
+
 
 # Now, let's create a table that we can write into a file
 
@@ -596,4 +597,4 @@ In the reproducible research framework, an important step is to save all the ver
 
 ```{r sessionInfo, results='asis'}
 sessionInfo()
-```
+```
\ No newline at end of file