Miseq-covid.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "signed-silver",
   "metadata": {},
   "source": [
    "# Analyze SARS-COV2 reads from Miseq (illumina) to get SARS-COV strain for samples"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "stupid-forest",
   "metadata": {},
   "source": [
    "## Import and load stuff for python to work"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "personalized-adoption",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The rmagic extension is already loaded. To reload it, use:\n",
      "  %reload_ext rmagic\n"
     ]
    }
   ],
   "source": [
    "%load_ext rmagic\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "synthetic-mistake",
   "metadata": {},
   "source": [
    "## Be sure that your dependencies are installed :\n",
    "- mamba \n",
    "- seqkit\n",
    "- csvtk\n",
    "- bbmap suite\n",
    "- bwa\n",
    "- samtools/sambamba\n",
    "- bamaddrg\n",
    "- freebayes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "immune-coverage",
   "metadata": {},
   "source": [
    "## Conda environment with dependencies"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "interpreted-apache",
   "metadata": {},
   "source": [
    "..TODO yaml file to upload to github repository "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "stupid-timer",
   "metadata": {},
   "source": [
    "## Have a look to the raw reads"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "sixth-antigua",
   "metadata": {},
   "source": [
    "Create a samples.txt file with samples name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "contrary-funds",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%bash\n",
    "cd PRJNA673096\n",
    "ls -1 > ../samples.txt\n",
    "cd ../"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "complimentary-seminar",
   "metadata": {},
   "source": [
    "Create a cvs file with seq numbers and min/max length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "hispanic-progressive",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%bash\n",
    "touch PRJNA673096.raw\n",
    "for sample in `cat samples.txt`\n",
    "do\n",
    "seqkit stats PRJNA673096/${sample}/${sample}_1* | awk '!/file/{print $1,$4,$6,$8}' >> PRJNA673096.raw\n",
    "done\n",
    "cat  PRJNA673096.raw |sed -e \"s/,//g\" | sed -e \"s/_1.fastq.gz//g\" | sed -e \"s/ /,/g\" > PRJNA673096.csv\n",
    "cat PRJNA673096.csv | csvtk add-header -n sample,number_reads,min_length,max_length > PRJNA673096_raw.csv\n",
    "rm PRJNA673096.csv PRJNA673096.raw"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "speaking-revision",
   "metadata": {},
   "source": [
    "Have a look of the fastq.gz raw files (number of reads and max_legnth)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "fifteen-employee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'number_reads'}>,\n",
       "        <AxesSubplot:title={'center':'max_length'}>]], dtype=object)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df1 = pd.read_csv(\"PRJNA673096_raw.csv\")\n",
    "df1.hist(column=['number_reads','max_length'],figsize=(15,5))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "instant-breeding",
   "metadata": {},
   "source": [
    "## QC analysis\n",
    "\n",
    "Will qc everything below 20 en phread and all reads with less than 100 pb with bbmap"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "earlier-jesus",
   "metadata": {},
   "source": [
    "bbduk will take more than 1 core (not run in jupyter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "conventional-nomination",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%bash \n",
    "ADAPTORS='~/working_directory/Useful_Files/adapters.fa'\n",
    "for sample in `cat samples.txt`\n",
    "do\n",
    "bbduk.sh in=PRJNA673096/${sample}/${sample}_1.fastq.gz in2=PRJNA673096/${sample}/${sample}_2.fastq.gz out=01_QC/${sample}_Q1.fastq.gz out2=01_QC/${sample}_Q2.fastq.gz qtrim=r trimq=20 ref=$ADAPTORS maq=20 minlen=100\n",
    "done"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "central-confusion",
   "metadata": {},
   "source": [
    "Check read lost with seqkit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "agreed-johnson",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%bash\n",
    "cd 01_QC\n",
    "seqkit stats *_Q1.fastq.gz |  awk '!/file/{print $1,$4,$6,$8}' | sed -e \"s/,//g\" | \n",
    "sed -e \"s/_1.fastq.gz//g\"  | sed -e \"s/ /,/g\" | csvtk add-header -n sample,number_reads,min_length,max_length > ../PRJNA673096_QC.csv"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "blocked-slave",
   "metadata": {},
   "source": [
    "## Mapping "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "super-judgment",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%bash\n",
    "cd ../"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "lasting-exhibition",
   "metadata": {},
   "source": [
    "Steps in the loop:\n",
    "\n",
    "    1. Mapping with bbmap (with some statistics and logs) -> constats.txt and basecov.txt\n",
    "    2. Sorting and creating a bam file\n",
    "    3. Mark PCR duplicates that result from exact duplication of a template during amplification (sambamba creates a index file too) \n",
    "    \n",
    "** sambamba freeze in serine and not in alanine (do not run in serine nor valine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "south-church",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%bash\n",
    "bwa index wuhan.fasta\n",
    "for sample in `cat samples.txt`\n",
    "do\n",
    "bwa mem wuhan.fasta 01_QC/${sample}_Q1.fastq.gz 01_QC/${sample}_Q2.fastq.gz  > 02_map/${sample}.sam 2> 02_map/${sample}.align.log\n",
    "sambamba view -t 20 -S 02_map/${sample}.sam -f bam  | sambamba sort -o > 02_map/sorted_${sample}.bam \n",
    "\n",
    "\n",
    "\n",
    "sambamba markdup -t 30 $ 02_map/sorted_${sample}.bam 02_map/dup_${sample}_dup.bam\n",
    "done"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "informal-microwave",
   "metadata": {},
   "source": [
    "Move all qualite log txt to a folder (there are a lot of them). Also for sam."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eleven-prediction",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%bash\n",
    "mkdir 02_map/logs\n",
    "mv 02_map/*txt 02_map/logs\n",
    "mkdir 02_map/sam\n",
    "mv 02_map/*sam 02_map/sam"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "historic-satellite",
   "metadata": {},
   "source": [
    "Name your files for joint calling with bamaddrg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "suitable-order",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%bash\n",
    "mkdir 02_map/final_bam\n",
    "for sample in `cat samples.txt`\n",
    "do\n",
    "bamaddrg -b 02_map/${sample}_dup.bam -s ${sample} > 02_map/final_bam/${sample}_final.bam\n",
    "# and re-index\n",
    "samtools index 02_map/final_bam/${sample}_final.bam\n",
    "done"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "hairy-cabin",
   "metadata": {},
   "source": [
    "## Creating a vcf from bam files with freebayes (joint calling)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "precise-honolulu",
   "metadata": {},
   "source": [
    "making a bam variable storing all bam files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "eight-effects",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%bash\n",
    "cd 02_map/final_bam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "architectural-aging",
   "metadata": {},
   "outputs": [],
   "source": [
    "BAMs=`ls *bam | python -c 'import sys; print(\" \".join([x.strip() for x in sys.stdin.readlines()]))'`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "conceptual-sharp",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/WORKING_DIRECTORY/lvelo/covid19/MiSeq_Pipeline'"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%bash\n",
    "freebayes -f ../../wuhan.fasta --gvcf --ploidy 1 -C 10 -L bam.list | bgzip >../../PRJNA673096.vcf.gz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "chicken-rochester",
   "metadata": {},
   "outputs": [],
   "source": [
    "pwd\n"
   ]
  }
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