Merge branch 'main' of github.com:mdehollander/biosb-metagenomics int…

…o main

docs/functional-annotation/viromes.md

@@ -1,14 +1,14 @@
 # Functional annotation - Viromics
 
-We will now identify viral sequences from metagenomic data. You will be provided with a set of contigs assembled from infant fecal metagenomic samples. Our goals are to:
+We will now identify viral sequences from metagenomic data. You will be provided with a set of contigs assembled from infant fecal metagenomic samples (``/data/precomputed/virome/contigs.fa``). Our goals are to:
 
 1. Determine the **viral origin** of the contigs (and assign their taxonomy).
 2. Estimate the **completeness** of viral genomes.
 3. Predict the potential **bacterial host**.
 4. Functionally **annotate** each identified virus.
 
 
-By the end of this exercise, your objective will be to identify the bacteriophage that satisfies all of the following criteria:  
+By the end of this exercise, our objective will be to identify the bacteriophage that satisfies all of the following criteria:  
 > - Belongs to the ***Caudoviricetes*** class  
 > - Is predicted to have a **complete genome**  
 > - Is predicted to infect the ***Clostridium*** genus  
@@ -28,20 +28,20 @@ All these tools and the necessary databases have been preinstalled and are ready
 
 First, we will run **geNomad** to identify viral sequences in our dataset. We will utilize the ``--enable-score-calibration`` option to compute false discovery rates (FDR). For this practical exercise, no specific FDR thresholds will be applied. Further, we will add the ``--cleanup`` flag to remove intermediate files and the ``--disable-find-proviruses`` option to avoid geNomad performing an initial prunning to remove potential contaminant host regions from proviral sequences (we will do this in the next step with CheckV). 
 
-**Note:** The following command takes about 10 minutes. You can also continue with the results in ``/data/precomputed/virome/genomad_results/``.
+**Note:** The following command takes about 2 minutes. You can also continue with the results in ``/data/precomputed/virome/genomad_results/``.
 ```bash
 genomad end-to-end \
     --enable-score-calibration \
     --disable-find-proviruses \
     contigs.fa \
     geNomad_results \
     --cleanup \
-    /data/databases/geNomad_db/
+    /data/databases/genomad/genomad_db/
 ```
 
-You can find a description of the geNomad output files [here](https://github.com/jiarong/VirSorter2#detailed-description-on-output-files).
+You can find a description of the geNomad output files [here](https://portal.nersc.gov/genomad/quickstart.html#understanding-the-outputs).
 
-**Quiz**: Look into ``geNomad_results/viral_contigs_summary/viral_contigs_virus_summary.tsv``. 
+Look into ``geNomad_results/contigs_summary/contigs_virus_summary.tsv``. 
 
 ??? done "1. How many of the 100 initial contigs are identified as viral?"
     55 contigs.
@@ -53,7 +53,7 @@ You can find a description of the geNomad output files [here](https://github.com
     *Caudoviricetes*.
 
 ??? done "4. Do we identify any single-stranded DNA virus (ssDNA)?"
-    Yes, there are 12 ssDNA (belonging to Monodnaviria realm).     
+    Yes, there are 13 ssDNA (belonging to *Monodnaviria* realm).     
 
 **Note:** A useful resource where you can find all the information about the viral taxonomy is [here](https://ictv.global/taxonomy).  
 
@@ -65,18 +65,18 @@ While geNomad performs well at identifying proviruses from metagenomic data, **C
 ```bash
 checkv \
     end_to_end \
-    geNomad_results/viral_contigs_summary/viral_contigs_virus.fna \
+    geNomad_results/contigs_summary/contigs_virus.fna \
     CheckV_results \
-    -d /data/databases/checkv-db-v1.5
+    -d /data/databases/checkv-db-v1.4
 ```
 
-The CheckV output is described [here](https://bitbucket.org/berkeleylab/checkv/src/master/). Look into ``CheckV_results/quality_summary.tsv``.
+The CheckV output is described [here](https://bitbucket.org/berkeleylab/checkv/src/master/#markdown-header-output-files). Look into ``CheckV_results/quality_summary.tsv``.
 
 ??? done "1. How many proviruses do you find and how many viruses?"
     9 proviruses, 46 viruses.
 
 ??? done "2. How many low, medium, and high quality viruses do we detect? How many complete viruses? How many of them have direct terminal repeats?"
-    15 viruses with low-quality, 16 with medium-quality and 9 with high-quality.
+    11 viruses with low-quality, 20 with medium-quality and 9 with high-quality.
     15 viruses are complete. From them, 10 have direct terminal repeats (DTRs).
 
 **Note:** Depending on the project's goals, it may be advisable to select only viruses predicted to be of at least medium quality. However, for this exercise, we will proceed with all available sequences.
@@ -90,13 +90,13 @@ The CheckV output is described [here](https://bitbucket.org/berkeleylab/checkv/s
 
 We will use **iPHoP** for bacterial host assignment of the viruses. Although iPHoP provides both genus- and species-level host predictions, we will focus solely on genus-level assignments. This is because iPHoP offers high-confidence predictions at the genus level (with an estimated false discovery rate of less than 10%), while the confidence decreases at the species level.
 
-**Note:** The following command takes about 1 hour. Therefore, you should continue with the results in ``/data/precomputed/virome/iphop_results/``.
+**Note:** The following command can take around 1 hour. Therefore, you should continue with the results in ``/data/precomputed/virome/iphop_results/``.
 ```bash
 mkdir iphop_results
 
 iphop predict \
     --fa_file CheckV_results/combined.fna \
-    --db_dir /data/databases/Sept_2021_pub/ \
+    --db_dir /data/databases/Aug_2023_pub_rw/ \
     --num_threads 8 \
     --out_dir iphop_results
 ```
@@ -108,7 +108,7 @@ The iPHoP output is described [here](https://bitbucket.org/MAVERICLab/vcontact2/
 Look into ``iphop_results/Host_prediction_to_genus_m90.csv``. By default, all virus-host pairs for which the confidence score is higher than the selected cutoff (default = 90) are included. For this exercise, consider only the top hit for each virus (prediction with highest confidence score):
 
 ??? done "1. Is there any virus predicted to infect the **_Clostridium_** genus?"
-    Yes, 2 viruses are predicted to infect **_Clostridium_**.
+    Yes, 2 viruses are predicted to infect **_Clostridium_** (CS_426 and CS_2284).
 
 ??? done "2. Considering the genome completeness estimated in the previous step, which of these **_Clostridium_** phages is predicted to have a complete genome?"
     Both phages have a complete genome (with DTRs).
@@ -118,13 +118,13 @@ Look into ``iphop_results/Host_prediction_to_genus_m90.csv``. By default, all vi
 
 Multiple tools can be used for the functional annotation of viral genomes. Here, we will use **geNomad** (annotate module) to retrieve the annotations for each of the proteins in our predicted viral genomes.
 
-**Note:** The following command takes about 10 minutes. You can also continue with the results in ``/data/precomputed/virome/genomad_annotation_results/``.
+**Note:** The following command takes about 2 minutes. You can also continue with the results in ``/data/precomputed/virome/genomad_annotation_results/``.
 ```bash
 genomad annotate \
     CheckV_results/combined.fna \
     geNomad_annotation_results \
     --cleanup \
-    /data/databases/geNomad_db/
+    /data/databases/genomad/genomad_db/
 ```
 
 The detailed explanation of this step can be found [here](https://portal.nersc.gov/genomad/pipeline.html#annotate). Check now the ``geNomad_annotation_results/combined_annotate/combined_genes.tsv`` file:
@@ -133,7 +133,7 @@ The detailed explanation of this step can be found [here](https://portal.nersc.g
     No, all the viruses use the standard genetic code (translation table 11).
 
 ??? done "2. Are any of the identified viruses encoding proteins that enable integration into the bacterial genome (integrases)?"
-    Yes, at least 7 viruses encode integrases (GPD_74496, GPD_79092, ELGV_14024, CS_100, CS_659, CS_1726 and CS_2284)  
+    Yes, at least 6 viruses encode integrases (GPD_79092, ELGV_14024, CS_100, CS_659, CS_1726 and CS_2284)  
 
 **Note:** Genetic code 11 (translation table 11) is the standard code used for Bacteria, Archaea, prokaryotic viruses and chloroplast proteins.