changes

efdf6932 · Chloe Marie Loiseau · 7f7a0877 · efdf6932 · efdf6932 · efdf6932
Commit efdf6932 authored 7 years ago by Chloe Marie Loiseau
--- a/notebooks/CapeTown_Genomics_Tutorial_partI.ipynb
+++ b/notebooks/CapeTown_Genomics_Tutorial_partI.ipynb
@@ -17,11 +17,13 @@
   "source": [
    "## 0. Getting started\n",
    "### How to start the jupyter notebook\n",
-    "1. Access the cloud: ssh student01@86.119.40.206\n",
+    "1. Access the cloud: ssh studentXX@86.119.40.206\n",
    "2. Your password is: stphcourse2018\n",
-    "3. cp -r ../Workshop_SA.git\n",
-    "4. singularity exec ../container.img jupyter notebook --no-browser --ip='*' --port=YourPortNumber eg.30000\n",
-    "5. Type in the browser: http://86.119.40.206:YourPortNumber/?token=c0669c145a630ea14b6ec3b29b870811844fefe12c375feb\n"
+    "3. copy this folder to your home directory: cp -r /home/Workshop_SA/ .\n",
+    "4. In hour home, type: singularity exec /home/container.img jupyter notebook --no-browser --ip='*' --port=YourPortNumber eg.30000\n",
+    "\n",
+    "\n",
+    "If you wish to access the git from your webbrowser, the URL is: https://git.scicore.unibas.ch/TBRU/Workshop_SA"
   ]
  },
  {
@@ -39,7 +41,7 @@
    "- It is your bioinformatics 'lab book'.\n",
    "\n",
    "### Useful tips to use in the jupyter notebook\n",
-    "- Run the command in the 'code cell': Shift + Return\n",
+    "- Run the command in the 'code cell': Shift + Enter\n",
    "- You can change the cell type from Code to Markdown to include explanatory text in your notebook\n",
    "- Use the \"tab\" key to autocomplement commands\n",
    "- https://www.dataquest.io/blog/jupyter-notebook-tips-tricks-shortcuts/\n",
@@ -53,19 +55,47 @@
    "### Magics\n",
    "Taken from: https://blog.dominodatalab.com/lesser-known-ways-of-using-notebooks/\n",
    "\n",
-    "You can start notebooks with different kernels (e.g., R, Julia) — not just Python. What you might not know is that even within a notebook, you can run different types of code in different cells. With \"magics\", it is possible to use different languages \n",
+    "You can start notebooks with different kernels (e.g., R, Shell) — not just Python. What you might not know is that even within a notebook, you can run different types of code in different cells. With \"magics\", it is possible to use different languages \n",
    "By running % lsmagic in a cell you get a list of all the available magics. You can use % to start a single-line expression to run with the magics command. Or you can use a double %% to run a multi-line expression.\n",
    "\n",
    "Some of my favorites are:\n",
    "\n",
-    "!: to run a shell command.\n",
-    "% bash to run cell with bash in a subprocess.\n",
+    "    ! to run a shell command.\n",
+    "\n",
+    "    % bash to run cell with bash in a subprocess.\n",
    "\n",
    "### Using shell commands\n",
    "\n",
    "Any command that works at the command-line can be used in IPython by prefixing it with the ! character. For example, the ls, pwd, and echo commands can be run as follows:\n"
   ]
  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/scicore/home/gagneux/loiseau/Workshop_SA/notebooks\n",
+      "The files in my working directory are:\n",
+      "adapters\t\t\t\t  Drug_resistance_mutations_MTBC.txt\n",
+      "annotation\t\t\t\t  images\n",
+      "CapeTown_Genomics_Tutorial_partIII.ipynb  Locus_to_exclude_Mtb.txt\n",
+      "CapeTown_Genomics_Tutorial_partII.ipynb   reference_genome\n",
+      "CapeTown_Genomics_Tutorial_partI.ipynb\t  slurm_scripts\n"
+     ]
+    }
+   ],
+   "source": [
+    "! pwd\n",
+    "! echo 'The files in my working directory are:'\n",
+    "! ls"
+   ]
+  },
  {
   "cell_type": "markdown",
   "metadata": {},
@@ -75,19 +105,19 @@
    " - Perform essential steps of a Illumina whole-genome sequencing analysis pipeline of MTBC genomes.\n",
    "\n",
    "## Content of this tutorial:\n",
-    "- Finding genetic variants from raw sequencing data:\n",
-    "    - Looking into a fastq file: reads, Phred Quality scores\n",
-    "    - Raw read processing and quality assessment\n",
+    "- **Finding genetic variants from raw sequencing data**:\n",
+    "    - Looking into a fastq file: quality assessment of the reads\n",
+    "    - Raw read processing: trimming of illumina adapters and low quality bases \n",
    "    - Mapping processed reads to a reference genome (creation of a BAM file)\n",
    "    - BAM post-processing \n",
    "    - BAM quality assesment\n",
    "    - Variant identification (creation of a VCF file)\n",
    "    - Variant Annotation\n",
-    "<img src=\"images/Pipeline1.png\" width=\"500\">\n",
+    "<img src=\"images/Pipeline1.png\" width=\"600\">\n",
    "- You want to find genetic variants (SNPs, insertion, deletions) in these sequences.\n",
    "- To do so, you need to perform the following bioinformatics steps:\n",
    "\n",
-    "<img src=\"images/Pipeline2.png\" width=\"500\">\n",
+    "<img src=\"images/Pipeline2.png\" width=\"600\">\n",
    "\n"
   ]
  },
@@ -114,7 +144,7 @@
    "    Forward read: ~/Workshop_SA/data_Eldholm/ERR760779_1.fastq.gz\n",
    "    Reverse read: ~/Workshop_SA/data_Eldholm/ERR760779_2.fastq.gz\n",
    "    \n",
-    "The fastq files are compressed (.gz) to save space. Let's have a look at the first read of the file.\n",
+    "The fastq files are compressed (.gz) to save space. Let's have a look at the first read of the file using zcat.\n",
    "For this, read the first 4 lines of the file:"
   ]
  },
@@ -222,7 +252,11 @@
   "metadata": {},
   "source": [
    "Go back to the terminal and run from the command line type:\n",
-    "    - sbatch ~/Workshop_SA/notebooks/slurm_scripts/launch_fastqc.slurm"
+    "    - sbatch ~/Workshop_SA/notebooks/slurm_scripts/launch_fastqc.slurm\n",
+    "    \n",
+    "For this you will have to open a new terminal window and reconnect:\n",
+    "    - ssh studentXX@86.119.40.206\n",
+    "    - password: stphcourse2018"
   ]
  },
  {
@@ -244,11 +278,11 @@
    "    - an html which you can visualise using firefox for example\n",
    "    - a compressed folder (.zip). You can see the content of this folder by using the command 'unzip'. \n",
    "    \n",
-    "You can visualise the html file using firefox. \n",
+    "To visualise the html file open a new terminal on MobaXterm and type:\n",
+    "    - scp studentXX@86.119.40.206:/home/studentXX/ERR760779_1_fastqc.html Desktop\n",
    "\n",
-    "From the terminal, type:\n",
-    "    \n",
-    "    firefox ERR760779_1_fastqc.html"
+    "\n",
+    "The html file is now on your local computer, on your desktop. Double click on it."
   ]
  },
  {
@@ -342,10 +376,10 @@
   "metadata": {},
   "source": [
    "### Exercise: \n",
-    "- How many reads were dropped by Trimmomatic ? \n",
-    "- Why are complete reads dropped ? \n",
-    "- What is the percentage of reads we will find in the files ERR760779_**1P**.trimmed.fastq.gz and ERR760779_**2P**.trimmed.fastq.gz ?\n",
-    "- What is the percentage of reads we will find in the files ERR760779_**1U**.trimmed.fastq.gz and ERR760779_**2U**.trimmed.fastq.gz ?"
+    "- How many reads were dropped by Trimmomatic ? ................\n",
+    "- Why are complete reads dropped ? ................\n",
+    "- What is the percentage of reads we will find in the files ERR760779_**1P**.trimmed.fastq.gz and ERR760779_**2P**.trimmed.fastq.gz ? ................\n",
+    "- What is the percentage of reads we will find in the files ERR760779_**1U**.trimmed.fastq.gz and ERR760779_**2U**.trimmed.fastq.gz ? ................"
   ]
  },
  {
@@ -389,9 +423,18 @@
   "cell_type": "markdown",
   "metadata": {},
   "source": [
-    "As before, to visualise the html file produce, open it with firefox:\n",
-    "    - firefox ERR760779_1P.html"
+    "As before, to visualise the html file produced:\n",
+    "    - scp studentXX@86.119.40.206:/home/studentXX/ERR760779_1P.trimmed.html Desktop"
   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
  }
 ],
 "metadata": {

 %% Cell type:markdown id: tags:

 # 14-15.05.2018 - Cape Town Genomics Workshop part I

 %% Cell type:markdown id: tags:

 ## 0. Getting started
 ### How to start the jupyter notebook
-1. Access the cloud: ssh student01@86.119.40.206
+1. Access the cloud: ssh studentXX@86.119.40.206
 2. Your password is: stphcourse2018
-3. cp -r ../Workshop_SA.git
-4. singularity exec ../container.img jupyter notebook --no-browser --ip='*' --port=YourPortNumber eg.30000
-5. Type in the browser: http://86.119.40.206:YourPortNumber/?token=c0669c145a630ea14b6ec3b29b870811844fefe12c375feb
+3. copy this folder to your home directory: cp -r /home/Workshop_SA/ .
+4. In hour home, type: singularity exec /home/container.img jupyter notebook --no-browser --ip='*' --port=YourPortNumber eg.30000
+
+
+If you wish to access the git from your webbrowser, the URL is: https://git.scicore.unibas.ch/TBRU/Workshop_SA

 %% Cell type:markdown id: tags:



 ### Why use the jupyter notebook ?
 - **Reproducibility**: it allows you to record your computational analysis, your figures, and comments together.
 - It is your bioinformatics 'lab book'.

 ### Useful tips to use in the jupyter notebook
- Run the command in the 'code cell': Shift + Return
+- Run the command in the 'code cell': Shift + Enter
 - You can change the cell type from Code to Markdown to include explanatory text in your notebook
 - Use the "tab" key to autocomplement commands
 - https://www.dataquest.io/blog/jupyter-notebook-tips-tricks-shortcuts/


 %% Cell type:markdown id: tags:

 ### Magics
 Taken from: https://blog.dominodatalab.com/lesser-known-ways-of-using-notebooks/

-You can start notebooks with different kernels (e.g., R, Julia) — not just Python. What you might not know is that even within a notebook, you can run different types of code in different cells. With "magics", it is possible to use different languages
+You can start notebooks with different kernels (e.g., R, Shell) — not just Python. What you might not know is that even within a notebook, you can run different types of code in different cells. With "magics", it is possible to use different languages
 By running % lsmagic in a cell you get a list of all the available magics. You can use % to start a single-line expression to run with the magics command. Or you can use a double %% to run a multi-line expression.

 Some of my favorites are:

-!: to run a shell command.
-% bash to run cell with bash in a subprocess.
+    ! to run a shell command.
+
+    % bash to run cell with bash in a subprocess.

 ### Using shell commands

 Any command that works at the command-line can be used in IPython by prefixing it with the ! character. For example, the ls, pwd, and echo commands can be run as follows:

+%% Cell type:code id: tags:
+
+``` python
+! pwd
+! echo 'The files in my working directory are:'
+! ls
+```
+
+%% Output
+
+    /scicore/home/gagneux/loiseau/Workshop_SA/notebooks
+    The files in my working directory are:
+    adapters				  Drug_resistance_mutations_MTBC.txt
+    annotation				  images
+    CapeTown_Genomics_Tutorial_partIII.ipynb  Locus_to_exclude_Mtb.txt
+    CapeTown_Genomics_Tutorial_partII.ipynb   reference_genome
+    CapeTown_Genomics_Tutorial_partI.ipynb	  slurm_scripts
+
 %% Cell type:markdown id: tags:

 ## What you will learn in these tutorials:
 - Run programs via the command line.
 - Perform essential steps of a Illumina whole-genome sequencing analysis pipeline of MTBC genomes.

 ## Content of this tutorial:
- Finding genetic variants from raw sequencing data:
-    - Looking into a fastq file: reads, Phred Quality scores
-    - Raw read processing and quality assessment
+- **Finding genetic variants from raw sequencing data**:
+    - Looking into a fastq file: quality assessment of the reads
+    - Raw read processing: trimming of illumina adapters and low quality bases
    - Mapping processed reads to a reference genome (creation of a BAM file)
    - BAM post-processing
    - BAM quality assesment
    - Variant identification (creation of a VCF file)
    - Variant Annotation
-<img src="images/Pipeline1.png" width="500">
+<img src="images/Pipeline1.png" width="600">
 - You want to find genetic variants (SNPs, insertion, deletions) in these sequences.
 - To do so, you need to perform the following bioinformatics steps:

-<img src="images/Pipeline2.png" width="500">
+<img src="images/Pipeline2.png" width="600">


 %% Cell type:markdown id: tags:

 ## 1. Raw data quality control
 <img src="images/fastqc.png" width="500">

 %% Cell type:markdown id: tags:

 ### 1.1 FastQ file

 %% Cell type:markdown id: tags:

 #### The fastq file we will be working on is found here:
    Forward read: ~/Workshop_SA/data_Eldholm/ERR760779_1.fastq.gz
    Reverse read: ~/Workshop_SA/data_Eldholm/ERR760779_2.fastq.gz

-The fastq files are compressed (.gz) to save space. Let's have a look at the first read of the file.
+The fastq files are compressed (.gz) to save space. Let's have a look at the first read of the file using zcat.
 For this, read the first 4 lines of the file:

 %% Cell type:code id: tags:

 ``` python
 ! zcat ~/Workshop_SA/data_Eldholm/ERR760779_1.fastq.gz | head -n 4
 ```

 %% Cell type:markdown id: tags:

 Let's do the same thing for the reverse read:

 %% Cell type:code id: tags:

 ``` python
 ! zcat ~/Workshop_SA/data_Eldholm/ERR760779_2.fastq.gz | head -n 4
 ```

 %% Cell type:markdown id: tags:

 ### Exercise:
 Get the file size, number of reads, total number of bases, and coverage per fastq file (length of *M. tuberculosis*  genome: 4'411'532 bp)

 - file size         : ..................
 - number of reads   : ............
 - lenght of reads   : ............
 - estimated coverage: .........
 - Phred score encoding : .......

 An estimation of the coverage can be calculated using the number of reads (N), the length of the *M. tuberculosis* genome (G) and the read length (L)

 Clue:

 <img src="images/phred_score_encoding.png" width="700">

 You can also use this piece of code to detect the encoding of the phred quality scores in your fastq file:

 %% Cell type:code id: tags:

 ``` python
 %%!
 zcat ~/Workshop_SA/data_Eldholm/ERR760779_1.fastq.gz| head -n 10000 |\
  awk '{if(NR%4==0) printf("%s",$0);}' |  od -A n -t u1 | \
  awk 'BEGIN{min=100;max=0;} \
      {for(i=1;i<=NF;i++) \
          {if($i>max) max=$i; \
               if($i<min) min=$i;}}END \
          {if(max<=74 && min<59) \
                     print "Phred+33"; \
           else
           if(max>73 && min>=64) \
                     print "Phred+64"; \
           else \
           if(min>=59 && min<64 && max>73) \
                     print "Solexa+64"; else print "Unknown score encoding!";}'
 ```

 %% Cell type:markdown id: tags:

 ### 1.2 FastQC: quality control of the raw data

 You can access the documentation of fastQC here: https://www.bioinformatics.babraham.ac.uk/projects/fastqc/


 Calling FastQC with the -h flag we get an indication of the different arguments we can use:

 %% Cell type:code id: tags:

 ``` python
 ! fastqc -h
 ```

 %% Cell type:markdown id: tags:

 Go back to the terminal and run from the command line type:
    - sbatch ~/Workshop_SA/notebooks/slurm_scripts/launch_fastqc.slurm

+For this you will have to open a new terminal window and reconnect:
+    - ssh studentXX@86.119.40.206
+    - password: stphcourse2018
+
 %% Cell type:code id: tags:

 ``` python
 ! cat ~/Workshop_SA/notebooks/slurm_scripts/launch_fastqc.slurm
 ```

 %% Cell type:markdown id: tags:

 The command produces two file:
    - an html which you can visualise using firefox for example
    - a compressed folder (.zip). You can see the content of this folder by using the command 'unzip'.

-You can visualise the html file using firefox.
+To visualise the html file open a new terminal on MobaXterm and type:
+    - scp studentXX@86.119.40.206:/home/studentXX/ERR760779_1_fastqc.html Desktop

-From the terminal, type:

-    firefox ERR760779_1_fastqc.html
+The html file is now on your local computer, on your desktop. Double click on it.

 %% Cell type:markdown id: tags:

 In the fastqc report you can see that:
    - the quality of the sequences decreases towards the end of the reads
    - there are 'over-represented sequences'. These are the Illumina adapters.

 To improve the quality of the raw sequences, we will use the software Trimmomatic.
 Trimmomatic is used to remove low quality bases and remove illumina adapters.

 %% Cell type:markdown id: tags:

 ## 2. Raw read processing

 ### 2.1 Removal of illumina adapters and low quality bases

 <img src="images/trimmomatic.png" width="350">
 Trimmomatic manual: http://www.usadellab.org/cms/uploads/supplementary/Trimmomatic/TrimmomaticManual_V0.32.pdf
 #### What do we remove adapter sequences  from our reads ?
 Removal of adapter sequences in a process called **read trimming**, or **clipping**, is one of the first steps in analyzing NGS data.
 Adapters have to be ligated to every single DNA molecule during library preparation. For Illumina short read sequencing, the corresponding protocols involve (in most cases) a DNA fragmentation step, followed by the ligation of certain oligonucleotides to the 5’ and 3’ ends. These 5' and 3' adapter sequences have important functions in Illumina sequencing, since they hold barcoding sequences, forward/reverse primers (for paired-end sequencing) and the important binding sequences for immobilizing the fragments to the flowcell and allowing bridge-amplification.
 <img src="images/Illumina_adapters.png" width="350">

 Adapter contamination will lead to **NGS alignment errors** and an **increased number of unaligned reads**, since the adapter sequences are synthetic and do not occur in the genomic sequence.

 Source : https://www.ecseq.com/support/ngs/trimming-adapter-sequences-is-it-necessary


 %% Cell type:markdown id: tags:

 From the command-line type:
    - sbatch ~/Workshop_SA/notebooks/slurm_scripts/launch_trimmomatic.slurm

 %% Cell type:code id: tags:

 ``` python
 ! cat ~/Workshop_SA/notebooks/slurm_scripts/launch_trimmomatic.slurm
 ```

 %% Cell type:markdown id: tags:

 For running trimmomatic in paired-end mode:
 2 input files:
    -  forward reads: ERR760779_1.fastq.gz
    -  reverse reads : ERR760779_2.fastq.gz
 4 output files:
    - forward paired reads:   ERR760779_**1P**.trimmed.fastq.gz
    - forward unpaired reads: ERR760779_ **1U**.trimmed.fastq.gz
    - reverse paired reads:   ERR760779_**2P**.trimmed.fastq.gz
    - reverse unpaired reads: ERR760779_**2U**.trimmed.fastq.gz

 When Trimmomatic performs trimming of adapters and low quality bases, the length of the reads will drop.
 If the read length drops below a certain threshold (minlen) then the read will be removed from the fastq, resulting in *unpaired* reads. These *unpaired* reads will go into the output files **1U** or **2U**.

 There are many possible *options* that come with Trimmomatic (cf. manual). Here are three we will use today:
 - ILLUMINACLIP: Cut adapter and other illumina-specific sequences from the read.
 - SLIDINGWINDOW: Performs a sliding window trimming approach. It starts scanning at the 5‟ end and clips the read once the average quality within the window falls below a threshold.
 - MINLEN: Drop the read if it is below a specified length

 %% Cell type:code id: tags:

 ``` python
 ! cat trimmomatic.e
 ```

 %% Cell type:markdown id: tags:

 ### Exercise:
- How many reads were dropped by Trimmomatic ?
- Why are complete reads dropped ?
- What is the percentage of reads we will find in the files ERR760779_**1P**.trimmed.fastq.gz and ERR760779_**2P**.trimmed.fastq.gz ?
- What is the percentage of reads we will find in the files ERR760779_**1U**.trimmed.fastq.gz and ERR760779_**2U**.trimmed.fastq.gz ?
+- How many reads were dropped by Trimmomatic ? ................
+- Why are complete reads dropped ? ................
+- What is the percentage of reads we will find in the files ERR760779_**1P**.trimmed.fastq.gz and ERR760779_**2P**.trimmed.fastq.gz ? ................
+- What is the percentage of reads we will find in the files ERR760779_**1U**.trimmed.fastq.gz and ERR760779_**2U**.trimmed.fastq.gz ? ................

 %% Cell type:markdown id: tags:

 ### 2.2 Quality Check of the processed reads

 We can run FastQC on the trimmed data to see how the quality of the reads has improved:

 In the command line, type;
    - sbatch ~/Workshop_SA/notebooks/slurm_scripts/launch_fastqc_on_trimmed_data.slurm

 %% Cell type:code id: tags:

 ``` python
 ! cat ~/Workshop_SA/notebooks/slurm_scripts/launch_fastqc_on_trimmed_data.slurm
 ```

 %% Output

    #!/bin/bash
    
    #SBATCH --job-name=fastqc_trim
    #SBATCH --cpus-per-task=1
    #SBATCH --mem-per-cpu=4G
    
    singularity exec /home/container.img fastqc -t 1 -q ERR760779_1P.trimmed.fastq.gz -o .

 %% Cell type:markdown id: tags:

-As before, to visualise the html file produce, open it with firefox:
-    - firefox ERR760779_1P.html
+As before, to visualise the html file produced:
+    - scp studentXX@86.119.40.206:/home/studentXX/ERR760779_1P.trimmed.html Desktop
+
+%% Cell type:code id: tags:
+
+``` python
+```

--- a/notebooks/Locus_to_exclude_Mtb.txt
+++ b/notebooks/Locus_to_exclude_Mtb.txt
+Chrom	ChromStart	ChromEnd	locus tag	Comment	
+NC_000962	23182	23269	IG18_Rv0018c-Rv0019c	
+NC_000962	33582	33794	Rv0031	remnant of A transposase
+NC_000962	80194	80623	IG71_Rv0071-Rv0072	
+NC_000962	103710	104663	Rv0094c	50bp_duplicated	
+NC_000962	104663	104805	IG_Rv0094c-Rv0095c	
+NC_000962	104805	105215	Rv0095c	50bp_duplicated	
+NC_000962	105215	105324	IG_Rv0095c-Rv0096	
+NC_000962	105324	106715	Rv0096	PPE family protein
+NC_000962	131382	132872	Rv0109	PE-PGRS family protein
+NC_000962	149533	150996	Rv0124	PE-PGRS family protein
+NC_000962	154130	154231	IG127_Rv0126-Rv0127	
+NC_000962	177543	179309	Rv0151c	PE family protein	
+NC_000962	179309	179319	IG_Rv0151c-Rv0152c	
+NC_000962	179319	180896	Rv0152c	PE family protein	
+NC_000962	187433	188839	Rv0159c	PE family protein	
+NC_000962	188839	188931	IG_Rv0159c-Rv0160c	
+NC_000962	188931	190439	Rv0160c	PE family protein	
+NC_000962	307877	309547	Rv0256c	PPE family protein	
+NC_000962	309547	309699	IG_Rv0256c-Rv0257	
+NC_000962	309699	310073	Rv0257	50bp_duplicated
+NC_000962	332708	333136	Rv0277c	50bp_duplicated	
+NC_000962	333136	333437	IG_Rv0277c-Rv0278c	
+NC_000962	333437	336310	Rv0278c	PE-PGRS family protein	
+NC_000962	336310	336560	IG_Rv0278c-Rv0279c	
+NC_000962	336560	339073	Rv0279c	PE-PGRS family protein	
+NC_000962	339073	339364	IG_Rv0279c-Rv0280	
+NC_000962	339364	340974	Rv0280	PPE family protein
+NC_000962	349624	349932	Rv0285	PE family protein
+NC_000962	349935	351476	Rv0286	PPE family protein
+NC_000962	361334	363109	Rv0297	PE-PGRS family protein
+NC_000962	366150	372764	Rv0304c	PPE family protein	
+NC_000962	372764	372820	IG_Rv0304c-Rv0305c	
+NC_000962	372820	375711	Rv0305c	PPE family protein	
+NC_000962	399535	400050	Rv0335c	PE family protein	
+NC_000962	400050	400192	IG_Rv0335c-Rv0336	
+NC_000962	400192	401703	Rv0336	50bp_duplicated
+NC_000962	423639	424019	Rv0353	50bp_duplicated
+NC_000962	424019	424269	IG_Rv0353-Rv0354c	
+NC_000962	424269	424694	Rv0354c	PPE family protein	
+NC_000962	424694	424777	IG_Rv0354c-Rv0355c	
+NC_000962	424777	434679	Rv0355c	PPE family protein	
+NC_000962	466672	467406	Rv0387c	PPE family protein	
+NC_000962	467406	467459	IG_Rv0387c-Rv0388c	
+NC_000962	467459	468001	Rv0388c	PPE family protein	
+NC_000962	472781	474106	Rv0393	50bp_duplicated
+NC_000962	475816	476184	Rv0397	50bp_duplicated
+NC_000962	530751	532214	Rv0442c	PPE family protein	
+NC_000962	543174	544730	Rv0453	PPE family protein
+NC_000962	576787	577338	Rv0487	50bp_duplicated
+NC_000962	579349	580581	Rv0490	50bp_duplicated
+NC_000962	606551	608062	Rv0515	50bp_duplicated
+NC_000962	616832	616845	IG533_Rv0525-Rv0526	
+NC_000962	622793	624577	Rv0532	PE-PGRS family protein
+NC_000962	630040	631686	Rv0538	50bp_duplicated
+NC_000962	642812	642888	IG559_Rv0551c-Rv0552	
+NC_000962	671996	675916	Rv0578c	PE-PGRS family protein	
+NC_000962	701406	702014	Rv0605	repeat region
+NC_000962	706930	706947	IG622_Rv0612-Rv0613c	
+NC_000962	831776	832303	Rv0740	50bp_duplicated
+NC_000962	832303	832534	IG_Rv0740-Rv0741	
+NC_000962	832534	832848	Rv0741	transposase
+NC_000962	832848	832981	IG_Rv0741-Rv0742	
+NC_000962	832981	833508	Rv0742	PE-PGRS family protein
+NC_000962	835701	838052	Rv0746	PE-PGRS family protein
+NC_000962	838052	838451	IG_Rv0746-Rv0747	
+NC_000962	838451	840856	Rv0747	PE-PGRS family protein
+NC_000962	842033	842278	Rv0750	50bp_duplicated
+NC_000962	846159	847913	Rv0754	PE-PGRS family protein
+NC_000962	847913	850527	IG_Rv0754-Rv0755A	
+NC_000962	848103	850040	Rv0755c	PPE family protein	
+NC_000962	850342	850527	Rv0755A	transposase	
+NC_000962	863159	863255	IG784_Rv0769-Rv0770	
+NC_000962	889072	889398	Rv0795	transposase IS6110
+NC_000962	889347	890333	Rv0796	transposase IS6110
+NC_000962	889347	889398	IG_Rv0795-Rv0796	
+NC_000962	890333	890388	IG_Rv0796-Rv0797	
+NC_000962	890388	891482	Rv0797	50bp_duplicated
+NC_000962	908181	908483	Rv0814c	50bp_duplicated	
+NC_000962	916477	917646	Rv0823c	50bp_duplicated	
+NC_000962	921575	921865	Rv0829	50bp_duplicated
+NC_000962	924951	925364	Rv0832	PE-PGRS family protein
+NC_000962	925361	927610	Rv0833	PE-PGRS family protein
+NC_000962	927610	927837	IG_Rv0833-Rv0834c	
+NC_000962	927837	930485	Rv0834c	PE-PGRS family protein	
+NC_000962	947312	947644	Rv0850	transposase
+NC_000962	960152	960341	IG877_Rv0861c-Rv0862c	
+NC_000962	964312	965535	Rv0867c	50bp_duplicated	
+NC_000962	968424	970244	Rv0872c	PE-PGRS family protein	
+NC_000962	976872	978203	Rv0878c	PPE family protein	
+NC_000962	1020058	1021329	Rv0915c	PPE family protein	
+NC_000962	1021329	1021344	IG_Rv0915c-Rv0916c	
+NC_000962	1021344	1021643	Rv0916c	PE family protein	
+NC_000962	1025497	1026816	Rv0920c	transposase	
+NC_000962	1026816	1027104	IG_Rv0920c-Rv0921	
+NC_000962	1027104	1027685	Rv0921	resolvase
+NC_000962	1027685	1029337	Rv0922	transposase
+NC_000962	1090373	1093144	Rv0977	PE-PGRS family protein
+NC_000962	1093144	1093361	IG_Rv0977-Rv0978c	
+NC_000962	1093361	1094356	Rv0978c	PE-PGRS family protein	
+NC_000962	1095078	1096451	Rv0980c	PE-PGRS family protein	
+NC_000962	1158918	1159307	Rv1034c	transposase	
+NC_000962	1159307	1159375	IG_Rv1034c-Rv1035c	
+NC_000962	1159375	1160061	Rv1035c	transposase	
+NC_000962	1160061	1160095	IG_Rv1035c-Rv1036c	
+NC_000962	1160095	1160433	Rv1036c	truncated IS1560 transposase	
+NC_000962	1160433	1160544	IG_Rv1036c-Rv1037c	
+NC_000962	1160544	1160828	Rv1037c	50bp_duplicated	
+NC_000962	1160828	1160855	IG_Rv1037c-Rv1038c	
+NC_000962	1160855	1161151	Rv1038c	50bp_duplicated	
+NC_000962	1161151	1161297	IG_Rv1038c-Rv1039c	
+NC_000962	1161297	1162472	Rv1039c	PPE family protein	
+NC_000962	1162472	1162549	IG_Rv1039c-Rv1040c	
+NC_000962	1162549	1163376	Rv1040c	PE family protein	
+NC_000962	1163376	1164572	IG_Rv1040c-Rv1041c	
+NC_000962	1164572	1165435	Rv1041c	IS like-2 transposase	
+NC_000962	1165092	1165499	Rv1042c	IS like-2 transposase	
+NC_000962	1169423	1170670	Rv1047	transposase
+NC_000962	1188421	1190424	Rv1067c	PE-PGRS family protein	
+NC_000962	1190424	1190757	IG_Rv1067c-Rv1068c	
+NC_000962	1190757	1192148	Rv1068c	PE-PGRS family protein	
+NC_000962	1211560	1213863	Rv1087	PE-PGRS family protein
+NC_000962	1213863	1214513	IG_Rv1087-Rv1088	
+NC_000962	1214513	1214947	Rv1088	PE family protein
+NC_000962	1214769	1215131	Rv1089	PE family protein
+NC_000962	1216469	1219030	Rv1091	PE-PGRS family protein
+NC_000962	1251617	1252972	Rv1128c	repeat_region	
+NC_000962	1262272	1264128	Rv1135c	PPE family protein	
+NC_000962	1276300	1277748	Rv1148c	50bp_duplicated	
+NC_000962	1277748	1277893	IG_Rv1148c-Rv1149	
+NC_000962	1277893	1278300	Rv1149	transposase
+NC_000962	1278269	1278820	Rv1150	Possible fragment of transposase 
+NC_000962	1298764	1299804	Rv1168c	PPE family protein	
+NC_000962	1299804	1299822	IG_Rv1168c-Rv1169c	
+NC_000962	1299822	1300124	Rv1169c	PE family protein	
+NC_000962	1301755	1302681	Rv1172c	PE family protein	
+NC_000962	1306002	1306201	IG1195_Rv1174c-Rv1175c	
+NC_000962	1339003	1339302	Rv1195	PE family protein
+NC_000962	1339302	1339349	IG_Rv1195-Rv1196	
+NC_000962	1339349	1340524	Rv1196	PPE family protein
+NC_000962	1340524	1340659	IG_Rv1196-Rv1197	
+NC_000962	1340659	1340955	Rv1197	50bp_duplicated
+NC_000962	1340955	1341006	IG_Rv1197-Rv1198	
+NC_000962	1341006	1341290	Rv1198	50bp_duplicated
+NC_000962	1341290	1341358	IG_Rv1198-Rv1199c	
+NC_000962	1341358	1342605	Rv1199c	transposase	
+NC_000962	1357293	1357625	Rv1214c	PE family protein	
+NC_000962	1384989	1386677	Rv1243c	PE-PGRS family protein	
+NC_000962	1441348	1442718	Rv1288	50bp_duplicated
+NC_000962	1450697	1451779	Rv1295	50bp_duplicated
+NC_000962	1468171	1469505	Rv1313c	transposase	
+NC_000962	1479199	1480824	Rv1318c	50bp_duplicated	
+NC_000962	1480824	1480894	IG_Rv1318c-Rv1319c	
+NC_000962	1480894	1482501	Rv1319c	50bp_duplicated	
+NC_000962	1488154	1489965	Rv1325c	PE-PGRS family protein	
+NC_000962	1532443	1533633	Rv1361c	PPE family protein	
+NC_000962	1541994	1542980	Rv1369c	transposase	
+NC_000962	1542929	1543255	Rv1370c	transposase	
+NC_000962	1561464	1561772	Rv1386	PE family protein
+NC_000962	1561769	1563388	Rv1387	PPE family protein
+NC_000962	1572127	1573857	Rv1396c	PE-PGRS family protein	
+NC_000962	1606386	1607972	Rv1430	PE family protein
+NC_000962	1618209	1619684	Rv1441c	PE-PGRS family protein	
+NC_000962	1630638	1634627	Rv1450c	PE-PGRS family protein	
+NC_000962	1636004	1638229	Rv1452c	PE-PGRS family protein	
+NC_000962	1643319	1644260	Rv1458c	50bp_duplicated	
+NC_000962	1655609	1656721	Rv1468c	PE-PGRS family protein	
+NC_000962	1678942	1679172	Rv1489A	50bp_duplicated	
+NC_000962	1684005	1686257	Rv1493	50bp_duplicated
+NC_000962	1751297	1753333	Rv1548c	PPE family protein	
+NC_000962	1761744	1762937	Rv1557	repeat_region
+NC_000962	1762937	1762947	IG_Rv1557-Rv1558	
+NC_000962	1762947	1763393	Rv1558	repeat_region
+NC_000962	1779194	1779298	Rv1572c	repeat_region	
+NC_000962	1779298	1779314	IG_Rv1572c-Rv1573	
+NC_000962	1779314	1779724	Rv1573	phiRV1 phage protein
+NC_000962	1779724	1779930	IG_Rv1573-Rv1574	
+NC_000962	1779930	1780241	Rv1574	repeat_region
+NC_000962	1779930	1780241	Rv1574	phiRV1 phage related protein
+NC_000962	1780199	1780699	Rv1575	repeat_region
+NC_000962	1780199	1780699	Rv1575	phiRV1 phage protein
+NC_000962	1780643	1782064	Rv1576c	phiRV1 phage protein	
+NC_000962	1782064	1782072	IG_Rv1576c-Rv1577c	
+NC_000962	1782072	1782584	Rv1577c	phiRv1 phage protein	
+NC_000962	1782584	1782758	IG_Rv1577c-Rv1578c	
+NC_000962	1782758	1783228	Rv1578c	phiRv1 phage protein	
+NC_000962	1783228	1783309	IG_Rv1578c-Rv1579c	
+NC_000962	1783309	1783623	Rv1579c	phiRv1 phage protein	
+NC_000962	1783620	1783892	Rv1580c	phiRv1 phage protein	
+NC_000962	1783892	1783906	IG_Rv1580c-Rv1581c	
+NC_000962	1783906	1784301	Rv1581c	phiRv1 phage protein	
+NC_000962	1784301	1784497	IG_Rv1581c-Rv1582c	
+NC_000962	1784497	1785912	Rv1582c	phiRv1 phage protein	
+NC_000962	1785912	1786310	Rv1583c	phiRv1 phage protein	
+NC_000962	1786307	1786528	Rv1584c	phiRv1 phage protein	
+NC_000962	1786528	1786584	IG_Rv1584c-Rv1585c	
+NC_000962	1786584	1787099	Rv1585c	phiRv1 phage protein	
+NC_000962	1787096	1788505	Rv1586c	phiRv1 integrase	
+NC_000962	1788162	1789163	Rv1587c	REP13E12 repeat-containing protein	
+NC_000962	1789163	1789168	IG_Rv1587c-Rv1588c	
+NC_000962	1789168	1789836	Rv1588c	REP13E12 repeat-containing protein	
+NC_000962	1855764	1856696	Rv1646	PE family protein
+NC_000962	1862347	1865382	Rv1651c	PE-PGRS family protein	
+NC_000962	1907321	1907593	IG1711_Rv1682-Rv1683	
+NC_000962	1927211	1928575	Rv1702c	repeat_region	
+NC_000962	1931497	1932654	Rv1705c	PPE family protein	
+NC_000962	1932654	1932694	IG_Rv1705c-Rv1706c	
+NC_000962	1932694	1933878	Rv1706c	PPE family protein	
+NC_000962	1981614	1984775	Rv1753c	PPE family protein	
+NC_000962	1987745	1988731	Rv1756c	putative transposase	
+NC_000962	1988680	1989006	Rv1757c	putative transposase	
+NC_000962	1989006	1989042	IG_Rv1757c-Rv1758	
+NC_000962	1989042	1989566	Rv1758	putative transposase
+NC_000962	1989566	1989833	IG_Rv1758-Rv1759c	
+NC_000962	1989833	1992577	Rv1759c	PE-PGRS family protein	
+NC_000962	1996152	1996478	Rv1763	putative transposase
+NC_000962	1996427	1997413	Rv1764	putative transposase
+NC_000962	1997413	1997418	IG_Rv1764-Rv1765c	
+NC_000962	1997418	1998515	Rv1765c	50bp_duplicated	
+NC_000962	1998515	1999142	IG_Rv1765c-Rv1765A	
+NC_000962	1999142	1999357	Rv1765A	transposase	
+NC_000962	2000614	2002470	Rv1768	PE-PGRS family protein
+NC_000962	2025301	2026398	Rv1787	PPE family protein
+NC_000962	2026398	2026477	IG_Rv1787-Rv1788	
+NC_000962	2026477	2026776	Rv1788	PE family protein
+NC_000962	2026776	2026790	IG_Rv1788-Rv1789	
+NC_000962	2026790	2027971	Rv1789	PPE family protein
+NC_000962	2027971	2028425	IG_Rv1789-Rv1790	
+NC_000962	2028425	2029477	Rv1790	PPE family protein
+NC_000962	2029477	2029904	IG_Rv1790-Rv1791	
+NC_000962	2029904	2030203	Rv1791	PE family protein
+NC_000962	2030694	2030978	Rv1793	50bp_duplicated
+NC_000962	2039453	2041420	Rv1800	PPE family protein
+NC_000962	2041420	2042001	IG_Rv1800-Rv1801	
+NC_000962	2042001	2043272	Rv1801	PPE family protein
+NC_000962	2043272	2043384	IG_Rv1801-Rv1802	
+NC_000962	2043384	2044775	Rv1802	PPE family protein
+NC_000962	2044775	2044923	IG_Rv1802-Rv1803c	
+NC_000962	2044923	2046842	Rv1803c	PE-PGRS family protein	
+NC_000962	2048072	2048371	Rv1806	PE family protein
+NC_000962	2048371	2048398	IG_Rv1806-Rv1807	
+NC_000962	2048398	2049597	Rv1807	PPE family protein
+NC_000962	2049597	2049921	IG_Rv1807-Rv1808	
+NC_000962	2049921	2051150	Rv1808	PPE family protein
+NC_000962	2051150	2051282	IG_Rv1808-Rv1809	
+NC_000962	2051282	2052688	Rv1809	PPE family protein
+NC_000962	2061178	2062674	Rv1818c	PE-PGRS family protein	
+NC_000962	2073943	2074437	Rv1829	50bp_duplicated
+NC_000962	2087971	2089518	Rv1840c	PE-PGRS family protein	
+NC_000962	2156706	2157299	Rv1910c	50bp_duplicated	
+NC_000962	2157299	2157382	IG_Rv1910c-Rv1911c	
+NC_000962	2157382	2157987	Rv1911c	50bp_duplicated	
+NC_000962	2162932	2167311	Rv1917c	PPE family protein	
+NC_000962	2167311	2167649	IG_Rv1917c-Rv1918c	
+NC_000962	2167649	2170612	Rv1918c	PPE family protein	
+NC_000962	2195989	2197353	Rv1945	repeat_region
+NC_000962	2226244	2227920	Rv1983	PE-PGRS family protein
+NC_000962	2260665	2261144	Rv2013	transposase
+NC_000962	2261098	2261688	Rv2014	transposase
+NC_000962	2261688	2261816	IG_Rv2014-Rv2015c	
+NC_000962	2261816	2263072	Rv2015c	50bp_duplicated	
+NC_000962	2294531	2306986	Rv2048c	50bp_duplicated	
+NC_000962	2338709	2340874	Rv2082	50bp_duplicated
+NC_000962	2343027	2343332	Rv2085	repeat_region
+NC_000962	2347373	2348554	Rv2090	50bp_duplicated
+NC_000962 	2356729	2358206	Rv2098c	PE-PGRS family protein
+NC_000962	2365465	2365791	Rv2105	transposase
+NC_000962	2365740	2366726	Rv2106	transposase
+NC_000962	2366726	2367359	IG_Rv2106-Rv2107	
+NC_000962	2367359	2367655	Rv2107	PE family protein
+NC_000962	2367655	2367711	IG_Rv2107-Rv2108	
+NC_000962	2367711	2368442	Rv2108	PPE family protein
+NC_000962	2370905	2372569	Rv2112c	50bp_duplicated	
+NC_000962	2381071	2382492	Rv2123	PPE family protein
+NC_000962	2387202	2387972	Rv2126c	PE-PGRS family protein	
+NC_000962	2423240	2424838	Rv2162c	PE-PGRS family protein	
+NC_000962	2430159	2431145	Rv2167c	transposase	
+NC_000962	2431094	2431420	Rv2168c	transposase	
+NC_000962	2439282	2439947	Rv2177c	transposase	
+NC_000962	2459678	2461327	Rv2196	50bp_duplicated
+NC_000962	2530836	2531897	Rv2258c	50bp_duplicated	
+NC_000962	2549124	2550029	Rv2277c	50bp_duplicated	
+NC_000962	2550029	2550065	IG_Rv2277c-Rv2278	
+NC_000962	2550065	2550391	Rv2278	transposase
+NC_000962	2550340	2551326	Rv2279	transposase
+NC_000962	2600731	2601879	Rv2328	PE family protein
+NC_000962	2617667	2618908	Rv2340c	PE-PGRS family protein	
+NC_000962	2625888	2626172	Rv2346c	50bp_duplicated	
+NC_000962	2626172	2626223	IG_Rv2346c-Rv2347c	
+NC_000962	2626223	2626519	Rv2347c	50bp_duplicated	
+NC_000962	2632923	2634098	Rv2352c	PPE family protein	
+NC_000962	2634098	2634528	IG_Rv2352c-Rv2353c	
+NC_000962	2634528	2635592	Rv2353c	PPE family protein	
+NC_000962	2635592	2635628	IG_Rv2353c-Rv2354	
+NC_000962	2635628	2635954	Rv2354	transposase
+NC_000962	2635903	2636889	Rv2355	transposase
+NC_000962	2636889	2637688	IG_Rv2355-Rv2356c	
+NC_000962	2637688	2639535	Rv2356c	PPE family protein	
+NC_000962	2651753	2651938	Rv2371	PE-PGRS family protein
+NC_000962	2692799	2693884	Rv2396	PE-PGRS family protein
+NC_000962	2706017	2706736	Rv2408	PE family protein
+NC_000962	2720776	2721777	Rv2424c	transposase	
+NC_000962	2727336	2727920	Rv2430c	PPE family protein	
+NC_000962	2727920	2727967	IG_Rv2430c-Rv2431c	
+NC_000962	2727967	2728266	Rv2431c	PE family protein	
+NC_000962	2762531	2763175	Rv2460c	repeat_region	
+NC_000962	2763172	2763774	Rv2461c	repeat_region	
+NC_000962	2784657	2785643	Rv2479c	transposase	
+NC_000962	2785592	2785918	Rv2480c	transposase	
+NC_000962	2795301	2797385	Rv2487c	PE-PGRS family protein	
+NC_000962	2800846	2801145	Rv2489c	repeat_region	
+NC_000962	2801145	2801254	IG_Rv2489c-Rv2490c	
+NC_000962	2801254	2806236	Rv2490c	PE-PGRS family protein	
+NC_000962	2828556	2829803	Rv2512c	IS1081 transposase	
+NC_000962	2835785	2837263	Rv2519	PE family protein
+NC_000962	2866468	2867127	Rv2543	50bp_duplicated
+NC_000962	2867124	2867786	Rv2544	50bp_duplicated
+NC_000962	2921551	2923182	Rv2591	PE-PGRS family protein
+NC_000962	2935046	2936788	Rv2608	PPE family protein
+NC_000962	2943600	2944985	Rv2615c	PE-PGRS family protein	
+NC_000962	2960105	2962441	Rv2634c	PE-PGRS family protein	
+NC_000962	2972160	2972486	Rv2648	transposase IS6110
+NC_000962	2972435	2973421	Rv2649	transposase IS6110
+NC_000962	2973421	2973795	IG_Rv2649-Rv2650c	
+NC_000962	2973795	2975234	Rv2650c	phiRv2 prophage protein	
+NC_000962	2975234	2975242	IG_Rv2650c-Rv2651c	
+NC_000962	2975242	2975775	Rv2651c	phiRv2 prophage protease	
+NC_000962	2975775	2975928	IG_Rv2651c-Rv2652c	
+NC_000962	2975928	2976554	Rv2652c	phiRv2 prophage protein	
+NC_000962	2976554	2976586	IG_Rv2652c-Rv2653c	
+NC_000962	2976586	2976909	Rv2653c	phiRv2 prophage protein	
+NC_000962	2976909	2976989	IG_Rv2653c-Rv2654c	
+NC_000962	2976989	2977234	Rv2654c	phiRv2 prophage protein	
+NC_000962	2977231	2978658	Rv2655c	phiRv2 prophage protein	
+NC_000962	2978658	2978660	IG_Rv2655c-Rv2656c	
+NC_000962	2978660	2979052	Rv2656c	phiRv2 prophage protein	
+NC_000962	2979049	2979309	Rv2657c	phiRv2 prophage protein	
+NC_000962	2979691	2980818	Rv2659c	phiRv2 prophage integrase	
+NC_000962	2982699	2982980	Rv2665	50bp_duplicated
+NC_000962	2982980	2983071	IG_Rv2665-Rv2666	
+NC_000962	2983071	2983874	Rv2666	truncated IS1081 transposase
+NC_000962	2989291	2990592	Rv2673	50bp_duplicated
+NC_000962	2996105	2996737	Rv2680	50bp_duplicated
+NC_000962	3005845	3007062	Rv2689c	50bp_duplicated	
+NC_000962	3007062	3007236	IG_Rv2689c-Rv2690c	
+NC_000962	3007236	3009209	Rv2690c	repeat_region	
+NC_000962	3053914	3055491	Rv2741	PE-PGRS family protein
+NC_000962	3076894	3078078	Rv2768c	PPE family protein	
+NC_000962	3078078	3078158	IG_Rv2768c-Rv2769c	
+NC_000962	3078158	3078985	Rv2769c	PE family protein	
+NC_000962	3078985	3079309	IG_Rv2769c-Rv2770c	
+NC_000962	3079309	3080457	Rv2770c	PPE family protein	
+NC_000962	3082352	3082756	Rv2774c	50bp_duplicated	
+NC_000962	3100202	3101581	Rv2791c	transposase	
+NC_000962	3101581	3102162	Rv2792c	resolvase	
+NC_000962	3112867	3113271	Rv2805	50bp_duplicated
+NC_000962	3113658	3114812	Rv2807	50bp_duplicated
+NC_000962	3115741	3116142	Rv2810c	transposase	
+NC_000962	3116818	3118227	Rv2812	transposase
+NC_000962	3120566	3121552	Rv2814c	transposase	
+NC_000962	3121501	3121827	Rv2815c	transposase	
+NC_000962	3132892	3133539	Rv2825c	50bp_duplicated	
+NC_000962	3135788	3136333	Rv2828c	50bp_duplicated	
+NC_000962	3162268	3164115	Rv2853	PE-PGRS family protein
+NC_000962	3170720	3171646	Rv2859c	50bp_duplicated	
+NC_000962	3191644	3192201	Rv2882c	50bp_duplicated	
+NC_000962	3194166	3195548	Rv2885c	transposase	
+NC_000962	3195545	3196432	Rv2886c	resolvase	
+NC_000962	3200794	3202020	Rv2892c	PPE family protein	
+NC_000962	3245445	3251075	Rv2931	50bp_duplicated
+NC_000962	3251072	3255688	Rv2932	50bp_duplicated
+NC_000962	3288464	3289705	Rv2943	IS1533 transposase
+NC_000962	3289705	3290235	Rv2943A	transposase	
+NC_000962	3289790	3290506	Rv2944	IS1533 transposase
+NC_000962	3313283	3313672	Rv2961	transposase
+NC_000962	3318816	3318900	IG3012_Rv2965c-Rv2966c	
+NC_000962	3319468	3319662	IG3013_Rv2966c-Rv2967c	
+NC_000962	3332787	3333788	Rv2977c	50bp_duplicated	
+NC_000962	3333785	3335164	Rv2978c	transposase	
+NC_000962	3335164	3335748	Rv2979c	resolvase	
+NC_000962	3335748	3335960	IG_Rv2979c-Rv2980	
+NC_000962	3335960	3336505	Rv2980	50bp_duplicated
+NC_000962	3376939	3378243	Rv3018c	PPE family protein	
+NC_000962	3378243	3378329	IG_Rv3018c-Rv3018A	
+NC_000962	3378329	3378415	Rv3018A	PE family protein	
+NC_000962	3379376	3380452	Rv3021c	PPE family protein	
+NC_000962	3380440	3380682	Rv3022c	PPE family protein	
+NC_000962	3380679	3380993	Rv3022A	PE family protein	
+NC_000962	3380993	3381375	IG_Rv3022A-Rv3023c	
+NC_000962	3381375	3382622	Rv3023c	transposase	
+NC_000962	3465778	3467091	Rv3097c	PE-PGRS family protein	
+NC_000962	3481451	3482698	Rv3115	transposase
+NC_000962	3490476	3491651	Rv3125c	PPE family protein	
+NC_000962	3501334	3501732	Rv3135	PPE family protein
+NC_000962	3501732	3501794	IG_Rv3135-Rv3136	
+NC_000962	3501794	3502936	Rv3136	PPE family protein
+NC_000962	3510088	3511317	Rv3144c	PPE family protein	
+NC_000962	3527391	3529163	Rv3159c	PPE family protein	
+NC_000962	3551281	3551607	Rv3184	transposase
+NC_000962	3551556	3552542	Rv3185	transposase
+NC_000962	3552542	3552764	IG_Rv3185-Rv3186	
+NC_000962	3552764	3553090	Rv3186	transposase
+NC_000962	3553039	3554025	Rv3187	transposase
+NC_000962	3557311	3558345	Rv3191c	transposase	
+NC_000962	3663689	3664222	Rv3281	50bp_duplicated
+NC_000962	3710433	3710759	Rv3325	transposase
+NC_000962	3710708	3711694	Rv3326	transposase
+NC_000962	3711694	3711749	IG_Rv3326-Rv3327	
+NC_000962	3711749	3713461	Rv3327	transposase
+NC_000962	3729364	3736935	Rv3343c	PPE family protein	
+NC_000962	3736935	3736984	IG_Rv3343c-Rv3344c	
+NC_000962	3736984	3738438	Rv3344c	PE-PGRS family protein	
+NC_000962	3738158	3742774	Rv3345c	PE-PGRS family protein	
+NC_000962	3742774	3743198	IG_Rv3345c-Rv3346c	
+NC_000962	3743198	3743455	Rv3346c	50bp_duplicated	
+NC_000962	3743455	3743711	IG_Rv3346c-Rv3347c	
+NC_000962	3743711	3753184	Rv3347c	PPE family protein	
+NC_000962	3753184	3753765	IG_Rv3347c-Rv3348	
+NC_000962	3753765	3754256	Rv3348	transposase
+NC_000962	3754256	3754293	IG_Rv3348-Rv3349c	
+NC_000962	3754293	3755033	Rv3349c	transposase	
+NC_000962	3755033	3755952	IG_Rv3349c-Rv3350c	
+NC_000962	3755952	3767102	Rv3350c	PPE family protein	
+NC_000962	3769514	3769807	Rv3355c	50bp_duplicated	
+NC_000962	3778568	3780334	Rv3367	PE-PGRS family protein
+NC_000962	3795100	3796086	Rv3380c	transposase	
+NC_000962	3796035	3796361	Rv3381c	transposase	
+NC_000962	3800092	3800796	Rv3386	transposase
+NC_000962	3800786	3801463	Rv3387	transposase
+NC_000962	3801463	3801653	IG_Rv3387-Rv3388	
+NC_000962	3801653	3803848	Rv3388	PE-PGRS family protein
+NC_000962	3841714	3842076	Rv3424c	50bp_duplicated	
+NC_000962	3842076	3842239	IG_Rv3424c-Rv3425	
+NC_000962	3842239	3842769	Rv3425	PPE family protein
+NC_000962	3842769	3843036	IG_Rv3425-Rv3426	
+NC_000962	3843036	3843734	Rv3426	PPE family protein
+NC_000962	3843734	3843885	IG_Rv3426-Rv3427c	
+NC_000962	3843885	3844640	Rv3427c	transposase	
+NC_000962	3844640	3844738	IG_Rv3427c-Rv3428c	
+NC_000962	3844738	3845970	Rv3428c	transposase	
+NC_000962	3845970	3847165	IG_Rv3428c-Rv3429	
+NC_000962	3847165	3847701	Rv3429	PPE family protein
+NC_000962	3847642	3848805	Rv3430c	transposase	
+NC_000962	3848805	3849294	IG_Rv3430c-Rv3431c	
+NC_000962	3849294	3850139	Rv3431c	repeat_region	
+NC_000962	3883525	3884193	Rv3466	repeat_region
+NC_000962	3883964	3884917	Rv3467	repeat_region
+NC_000962	3890830	3891156	Rv3474	transposase IS6110
+NC_000962	3891105	3892091	Rv3475	transposase IS6110
+NC_000962	3894093	3894389	Rv3477	PE family protein
+NC_000962	3894389	3894426	IG_Rv3477-Rv3478	
+NC_000962	3894426	3895607	Rv3478	PE family protein
+NC_000962	3926569	3930714	Rv3507	PE-PGRS family protein
+NC_000962	3930714	3931005	IG_Rv3507-Rv3508	
+NC_000962	3931005	3936710	Rv3508	PE-PGRS family protein
+NC_000962	3939617	3941761	Rv3511	PE-PGRS family protein
+NC_000962	3941724	3944963	Rv3512	PE-PGRS family protein
+NC_000962	3944963	3945092	IG_Rv3512-Rv3513c	
+NC_000962	3945092	3945748	Rv3513c	50bp_duplicated	
+NC_000962	3945748	3945794	IG_Rv3513c-Rv3514	
+NC_000962	3945794	3950263	Rv3514	PE-PGRS family protein
+NC_000962	3950263	3950824	IG_Rv3514-Rv3515c	
+NC_000962	3950824	3952470	Rv3515c	50bp_duplicated	
+NC_000962	3969343	3970563	Rv3532	PPE family protein
+NC_000962	3970563	3970705	IG_Rv3532-Rv3533c	
+NC_000962	3970705	3972453	Rv3533c	PPE family protein	
+NC_000962	3978059	3979498	Rv3539	PPE family protein
+NC_000962	3997980	3999638	Rv3558	PPE family protein
+NC_000962	4031404	4033158	Rv3590c	PE-PGRS family protein	
+NC_000962	4036731	4038050	Rv3595c	PE-PGRS family protein	
+NC_000962	4052950	4053603	Rv3611	50bp_duplicated
+NC_000962	4059984	4060268	Rv3619c	50bp_duplicated	
+NC_000962	4060268	4060295	IG_Rv3619c-Rv3620c	
+NC_000962	4060295	4060591	Rv3620c	50bp_duplicated	
+NC_000962	4060591	4060648	IG_Rv3620c-Rv3621c	
+NC_000962	4060648	4061889	Rv3621c	PPE family protein	
+NC_000962	4061889	4061899	IG_Rv3621c-Rv3622c	
+NC_000962	4061899	4062198	Rv3622c	PE family protein	
+NC_000962	4075752	4076099	Rv3636	transposase
+NC_000962	4076099	4076484	IG_Rv3636-Rv3637	
+NC_000962	4076484	4076984	Rv3637	transposase
+NC_000962	4076984	4077730	Rv3638	transposase
+NC_000962	4077730	4077884	IG_Rv3638-Rv3639c	
+NC_000962	4077884	4078450	Rv3639c	50bp_duplicated	
+NC_000962	4078450	4078520	IG_Rv3639c-Rv3640c	
+NC_000962	4078520	4079749	Rv3640c	transposase	
+NC_000962	4091233	4091517	Rv3650	PE family protein
+NC_000962 	4093632	4093946	Rv3652	PE-PGRS family protein	
+NC_000962	4093940	4094527	Rv3653PE-PGRS family protein	
+NC_000962	4119795	4120955	Rv3680	50bp_duplicated
+NC_000962	4153740	4155674	Rv3710	50bp_duplicated
+NC_000962	4189285	4190232	Rv3738c	PPE family protein	
+NC_000962	4190232	4190284	IG_Rv3738c-Rv3739c	
+NC_000962	4190284	4190517	Rv3739c	PPE family protein	
+NC_000962	4196171	4196506	Rv3746c	PE family protein	
+NC_000962	4252993	4254327	Rv3798	transposase
+NC_000962	4276571	4278085	Rv3812	PE-PGRS family protein
+NC_000962	4299812	4301566	Rv3826	50bp_duplicated
+NC_000962	4301563	4302789	Rv3827c	transposase	
+NC_000962	4302786	4303397	Rv3828c	resolvase	
+NC_000962	4318775	4319266	Rv3844	transposase
+NC_000962	4351075	4352181	Rv3873	PPE family protein
+NC_000962	4353010	4355010	Rv3876	50bp_duplicated
+NC_000962	4374484	4375683	Rv3892c	PPE family protein	
+NC_000962	4375683	4375762	IG_Rv3892c-Rv3893c	
+NC_000962	4375762	4375995	Rv3893c	PE family protein	
--- a/notebooks/slurm_scripts/launch_GATK.slurm
+++ b/notebooks/slurm_scripts/launch_GATK.slurm
+#!/bin/bash 
+
+#SBATCH --job-name=GATK
+#SBATCH --cpus-per-task=1
+#SBATCH --mem-per-cpu=4G
+#SBATCH --time=6:00:00
+#SBATCH --output=GATK.o
+#SBATCH --error=GATK.e
+
+singularity exec container.img gatk-launch -T RealignerTargetCreator -nt 1 -R ~/Workshop_SA/notebooks/reference_genome/MTB_ancestor_reference.fasta -o ERR760779.intervals -I ERR760779.dedup.bam
+
+
+singularity exec container.img gatk-launch --disable_bam_indexing -T IndelRealigner R ~/Workshop_SA/notebooks/reference_genome/MTB_ancestor_reference.fasta -targetIntervals ERR760779.intervals -I ERR760779.dedup.bam -o ERR760779.dedup.realigned.bam
--- a/notebooks/slurm_scripts/launch_index2.slurm
+++ b/notebooks/slurm_scripts/launch_index2.slurm
+#!/bin/bash 
+
+#SBATCH --job-name=index
+#SBATCH --cpus-per-task=1
+#SBATCH --mem-per-cpu=2G
+
+singularity exec /home/container.img samtools index ERR760779.dedup.realigned.bam