BP_Group18.ipynb

{
 "metadata": {
  "name": "",
  "signature": "sha256:bc9bb0b9f030ae63ffda86b937791251a85955c3785326628d638e361e623477"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "raw",
     "metadata": {},
     "source": [
      "%load_ext rmagic\n",
      "%R library(dplyr)\n",
      "\n",
      "import os\n",
      "#os.chdir(\"../\")\n",
      "prelim_groups=open(\"phage_by_group.txt\")\n",
      "\n",
      "next_step=[]\n",
      "\n",
      "for line in prelim_groups.readlines():\n",
      "    line=line.rstrip(\"\\n\")\n",
      "    vec=line.split(\"\\t\")\n",
      "    next_step.append(vec[0])\n",
      "\n",
      "#os.mkdir(\"big_pipeline\")\n",
      "os.chdir(\"big_pipeline\")"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%load_ext rmagic\n",
      "%R library(dplyr); library(ggplot2)\n",
      "\n",
      "\n",
      "group_number=\"18\"\n",
      "\n",
      "group=\"group\"+group_number\n",
      "\n",
      "import os\n",
      "#os.chdir(\"../\")\n",
      "prelim_groups=open(\"phage_by_group.txt\")\n",
      "phage_grp={}\n",
      "for line in prelim_groups.readlines():\n",
      "    line=line.rstrip(\"\\n\")\n",
      "    vec=line.split(\"\\t\")\n",
      "    phage_grp[vec[0]]=vec[1]\n",
      "groups=list(phage_grp.values())\n",
      "unique=[]\n",
      "for value in groups:\n",
      "    if value not in unique:\n",
      "        unique.append(value)\n",
      "grouplist=[]\n",
      "for value in phage_grp.keys():\n",
      "    if phage_grp.get(value)=='CNSv2.0:'+group_number:\n",
      "        grouplist.append(value)\n",
      "print(grouplist)\n",
      "os.chdir(\"./\"+group)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "\n",
        "Attaching package: \u2018dplyr\u2019\n",
        "\n",
        "The following object is masked from \u2018package:stats\u2019:\n",
        "\n",
        "    filter\n",
        "\n",
        "The following objects are masked from \u2018package:base\u2019:\n",
        "\n",
        "    intersect, setdiff, setequal, union\n",
        "\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "['1.111.A.', '1.111.B.']\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "step1c1=open(\"step1.contig1.sh\",\"w\")\n",
      "step1c1.write(\"#!/bin/bash\\n#SBATCH -n 16\\n#SBATCH -N 1\\n#SBATCH -o s1c1.out\\n#SBATCH -e s1c1.err\\n#SBATCH -p sched_mit_chisholm\\n\\n\")\n",
      "step1c1.write(\"module add engaging/clc-assembly-cell\\nmodule add engaging/samtools/1.1\\nmodule add engaging/bedtools/2.22.1\\n\\n\")\n",
      "for line in grouplist:\n",
      "    name=line.rstrip(\"\\n\")\n",
      "    step1c1.write(\"\\n\\n#\"+name+\"\\nclc_assembler -o \"+name+\"step1.clc.fasta -m 500 -p fb ss 100 300 -q ../\"+ name+\"*.fastq\\n\")\n",
      "    step1c1.write(\"samtools faidx \"+name+\"step1.clc.fasta 'contig_1'>\"+name+\"step1.contig1.fasta\\n\\nclc_mapper -o \"+name+\"step1.contig1.cas -p fb ss 100 300 -q ../\"+name+\"*.fastq -d \"+name+\"step1.contig1.fasta -a local -r ignore\\n\")\n",
      "    step1c1.write(\"clc_cas_to_sam -a \"+name+\"step1.contig1.cas -o \"+name+\"step1.contig1.bam\\n\\nsamtools sort \"+name+\"step1.contig1.bam \"+name+\"sorted.step1.contig1\\nsamtools index \"+name+\"sorted.step1.contig1.bam\\n\\nbedtools genomecov -ibam \"+name+\"sorted.step1.contig1.bam -d > \"+name+\"step1.contig1.genomecoverage.txt\\n\\n\")\n",
      "step1c1.close()\n",
      "!open step1.contig1.sh -a textedit"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!scp step1.contig1.sh jbrown@eofe4.mit.edu:/nobackup1/jbrown/nahant_phage_reads/group18/"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "step1.contig1.sh                                0%    0     0.0KB/s   --:-- ETA\r",
        "step1.contig1.sh                              100% 1426     1.4KB/s   00:00    \r\n"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!scp jbrown@eofe4.mit.edu:/nobackup1/jbrown/nahant_phage_reads/group18/*genomecoverage* ./\n",
      "!scp jbrown@eofe4.mit.edu:/nobackup1/jbrown/nahant_phage_reads/group18/*step1.contig1.fasta ./"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1.111.A.step1.contig1.genomecoverage.txt        0%    0     0.0KB/s   --:-- ETA"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1.111.A.step1.contig1.genomecoverage.txt      100%  735KB 735.4KB/s   00:00    \r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1.111.B.step1.contig1.genomecoverage.txt        0%    0     0.0KB/s   --:-- ETA"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1.111.B.step1.contig1.genomecoverage.txt      100%  736KB 735.6KB/s   00:00    \r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1.111.A.step1.contig1.fasta                     0%    0     0.0KB/s   --:-- ETA"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1.111.A.step1.contig1.fasta                   100%   40KB  39.9KB/s   00:00    \r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1.111.B.step1.contig1.fasta                     0%    0     0.0KB/s   --:-- ETA"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1.111.B.step1.contig1.fasta                   100%   40KB  39.9KB/s   00:00    \r\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%R -i grouplist\n",
      "\n",
      "for (item in grouplist){\n",
      "    filename<-paste(item,\"step1.contig1.genomecoverage.txt\",sep=\"\")\n",
      "    coverage<-read.table(filename, sep=\"\\t\")\n",
      "    with(coverage, plot(V2, V3, type=\"l\", col=\"aquamarine4\", xlab=\"genome\", ylab=\"coverage\",main=item,cex=2))\n",
      "}"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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NZQeBBUsiQRXqpFihhd2N+65h+UxfuarCJzLSFoU4sRhhwxVmYl/M9nGU4vQQ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\n2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      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Gr9CYo6vw3EdOeNVdq0WTpnmvee6Bx5odepfmojrvjVdTY9nXW00XWB2GffLt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\nLzD7+VHa0f23Kov9jnuV0q/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L5SzQokkuwIOPi3RtMj9oWkhHvx/Loya+xtPgESHuXrj4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\nQMBSAv8HsAV4GNOWEpsAAAAASUVORK5CYII=\n"
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from pyfaidx import Fasta\n",
      "\n",
      "contiginfo=\"virname\\tcontig\\tcontig_len\\tvir_contig\\n\"\n",
      "for phage in grouplist:   #set up the name of the virus\n",
      "    fasta1=phage+\"step1.contig1.fasta\"\n",
      "    f=Fasta(fasta1)\n",
      "    for seq in f.keys():\n",
      "        seqlen=len(str(f[seq]))\n",
      "        contigname=seq\n",
      "        contiginfo+=(phage+\"\\t\"+contigname+\"\\t\"+str(seqlen)+\"\\t\"+phage+\":\"+contigname+\"\\n\")\n",
      "        "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%R -i contiginfo\n",
      "contig<-textConnection(contiginfo,open=\"r\",encoding=\"UTF-8\")\n",
      "contigtable<-read.table(contig, sep=\"\\t\", header=TRUE)\n",
      "#?textConnection\n",
      "\n",
      "with(contigtable, hist(contig_len))\n",
      "with(contigtable, boxplot(contig_len))\n",
      "with(contigtable, summary(contig_len))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. \n",
        "  40210   40210   40210   40210   40210   40210 \n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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7RAABBBBAgADMMYAAAggggEAAAgTgANDZJQIIIIAAAgRgjgEEEEAAAQQCECAA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\nmaeBQS9dqIkmPYZ05HPLli2lcePG8uy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      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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gQIBAuQSSOAI2DKlcnUprCBAgQKCxQBJHwIYhNd5RliBAgACBcgkkEcCGIZWrU2kNAQIE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\n"
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%R -i grouplist\n",
      "###Cos-dip phages\n",
      "\n",
      "\n",
      "next_step<-vector()\n",
      "\n",
      "cos_pattern<-vector()\n",
      "cos_count<-0\n",
      "\n",
      "cos_sd_factor=4\n",
      "\n",
      "for (item in grouplist){\n",
      "    filename<-paste(item,\"step1.contig1.genomecoverage.txt\",sep=\"\")\n",
      "    if(file.info(filename)$size>0) {\n",
      "        coverage<-read.table(filename, sep=\"\\t\")\n",
      "\n",
      "#Check for cos dip:\n",
      "       \n",
      "        length<-nrow(coverage)\n",
      "        cos_omit_ends_cov<-coverage[(200:(length-200)),]   #excluding larger ends of the contig in this case      \n",
      "        cos_below_ave<-cos_omit_ends_cov[cos_omit_ends_cov$V3<(mean(coverage$V3)-(sd(coverage$V3)*cos_sd_factor)),]\n",
      "        minlocation<-0    \n",
      "        if (nrow(cos_below_ave)>0){\n",
      "            \n",
      "            cos_below_ave$gaps<-c(diff(cos_below_ave$V2),1)\n",
      "            cos_below_ave_peaks<-cos_below_ave[cos_below_ave$gaps<5,]\n",
      "            size<-max(cos_below_ave_peaks$V2)-min(cos_below_ave_peaks$V2)\n",
      "            if (size < 1000 & size>50){\n",
      "            \n",
      "             \n",
      "                with(coverage, plot(V2, V3, type=\"n\",xlab=\"position\",ylab=\"coverage\", main=paste(item, size, sep=\" \")))\n",
      "                with(coverage, lines(V2,V3))\n",
      "                with(cos_below_ave, points(V2,V3, col=\"green\",pch=19))\n",
      "                cos_pattern<-c(cos_pattern,item)        #add significant minimum coverage phage contigs to list\n",
      "                cos_count=cos_count+1\n",
      "\n",
      "        } else {next_step<-c(next_step, item)}\n",
      "        } else {next_step<-c(next_step,item)}\n",
      "            }}\n",
      "\n",
      "after_cos<-next_step\n",
      "\n",
      "#mu-like phages\n",
      "\n",
      "grouplist<-next_step\n",
      "next_step<-vector()\n",
      "\n",
      "mu<-vector()\n",
      "mu_count<-0\n",
      "\n",
      "mu_factor=5\n",
      "\n",
      "for (item in grouplist){\n",
      "    filename<-paste(item,\"step1.contig1.genomecoverage.txt\",sep=\"\")\n",
      "    if(file.info(filename)$size>0) {\n",
      "         coverage<-read.table(filename, sep=\"\\t\")\n",
      "         length=nrow(coverage)\n",
      "         peaks<-coverage[coverage$V3>(mean(coverage$V3)+sd(coverage$V3)*mu_factor),]\n",
      "         mu_size<-max(peaks$V2)-min(peaks$V2) \n",
      "                #max_peaks<-max(peaks$V2)\n",
      "                #min_peaks<-min(peaks$V2)\n",
      "                #mu_size<-max_peaks-min_peaks\n",
      "                \n",
      "                \n",
      "               if (((min(peaks$V2)<500&max(peaks$V2)<500)|(min(peaks$V2)>(length-500)&max(peaks$V2)>length-500)) & mu_size<300 & (coverage$V2[coverage$V3==min(coverage$V3)]<500|coverage$V2[coverage$V3==min(coverage$V3)]>(length-500))){\n",
      "                           mu_count<-mu_count+1\n",
      "                           mu<-c(mu, item)\n",
      "                            with(coverage, plot(V2, V3, type=\"l\", lty=2, col=\"navy blue\", main=paste(item, \"Mu-like\", sep=\"\")))         \n",
      "                            }\n",
      "        else {next_step<-c(next_step, item)}\n",
      "        }}\n",
      "after_cos_mu<-next_step\n",
      "\n",
      "#internal maxima\n",
      "grouplist<-after_cos_mu\n",
      "#grouplist<-next_step\n",
      "next_step<-vector()\n",
      "\n",
      "stretch=6\n",
      "sd_factor=1\n",
      "\n",
      "tr_candidate<-vector()\n",
      "tr_start<-vector()\n",
      "tr_stop<-vector()\n",
      "\n",
      "\n",
      "#phages that have both an extremely high coverage peak and an extremely low coverage peak\n",
      "hl<-vector()\n",
      "hl_count<-0\n",
      "\n",
      "#sip=\"small internal peak\"\n",
      "sip<-vector()\n",
      "sip_start<-vector()\n",
      "sip_stop<-vector()\n",
      "sip_count<-0\n",
      "\n",
      "count<-0\n",
      "\n",
      "#create coverage plots for each genome\n",
      "for (item in grouplist){\n",
      "    filename<-paste(item,\"step1.contig1.genomecoverage.txt\",sep=\"\")\n",
      "    if(file.info(filename)$size>0) {\n",
      "    \n",
      "    coverage<-read.table(filename, sep=\"\\t\")\n",
      "    length<-nrow(coverage)\n",
      "    coverage$V3_stretch<-coverage$V3^stretch\n",
      "    \n",
      "    coverage1<-coverage[(500:(length-500)),] \n",
      "    above_ave<-coverage1[coverage1$V3_stretch>(mean(coverage$V3_stretch)+sd(coverage$V3_stretch)*sd_factor),]\n",
      "    if (nrow(above_ave)>0){\n",
      "    above_ave$gaps<-c(diff(above_ave$V2),1)\n",
      "    \n",
      "    aamin<-min(above_ave$V2)\n",
      "    aamax<-max(above_ave$V2)\n",
      "    aa_region_mean<-mean(coverage[aamin:aamax,]$V3)\n",
      "    all_mean<-mean(coverage$V3)\n",
      "        \n",
      "    size<-aamax-aamin\n",
      "\n",
      "    #print(paste(item, paste(aamin, aamax, sep=\" to \"),\"region coverage is\", aa_region, \"; total coverage is\", all_mean,sep=\" \"))\n",
      "    if(nrow(above_ave)>1 & (aa_region_mean>(all_mean+(sd(coverage$V3)*sd_factor))) & nrow(coverage)>15000){\n",
      "        if (size<1000){\n",
      "            above_ave$gaps<-c(diff(above_ave$V2),1)\n",
      "\n",
      "            size<-max(above_ave$V2)-min(above_ave$V2)\n",
      "            start<-min(above_ave$V2)\n",
      "            stop<-max(above_ave$V2)\n",
      "\n",
      "            length<-nrow(coverage)\n",
      "            min_cov<-min(coverage$V3)\n",
      "            mean_cov<-mean(coverage$V3)\n",
      "            sd_cov<-sd(coverage$V3)\n",
      "\n",
      "            #numbers needed for mu test:\n",
      "            difference<-diff(coverage$V3)\n",
      "            difference<-c(0,difference)\n",
      "            cov1<-cbind(coverage, difference)\n",
      "            length<-nrow(cov1)\n",
      "            mid<-length/2\n",
      "            cut_spot<-with(cov1[1:mid,], V2[which.max(difference)])\n",
      "            if (cut_spot>300){cut_spot<-0}\n",
      "            cut_spot2<-with(cov1[mid:length,], V2[which.min(difference)])\n",
      "            if (cut_spot2<length-300){cut_spot2<-length}\n",
      "\n",
      "            minlocation<-round(median(coverage$V2[coverage$V3==min_cov]))\n",
      "            if ((minlocation>(length-1000)|minlocation<1000) & length>3000){\n",
      "                    minlocation<-round(median(coverage$V2[coverage$V3[100:nrow(coverage)-100]==min(coverage$V3[100:nrow(coverage)-100])]))\n",
      "                    minlocation<-round(median(coverage$V2[which.min(coverage$V3[1000:(length-1000)])]))+1000 #id location of minimum coverage that is not within 1000 bp of either terminus\n",
      "                    min_cov=min(coverage$V3[1000:(length-1000)])\n",
      "                }\n",
      "\n",
      "            \n",
      "                if (min_cov<mean_cov-sd_cov*2 & minlocation>(start-200) & minlocation<(start+200)) { \n",
      "\n",
      "                    sip<-c(sip, item)\n",
      "                    with(coverage, plot(V2, V3, type=\"l\", lty=2, col=\"forest green\", main=paste(item, \"high low\", sep=\"\")))\n",
      "                    sip_count<-sip_count+1\n",
      "                } \n",
      "                else {\n",
      "                    sip<-c(sip, item)\n",
      "                    sip_start<-c(sip_start, min(above_ave$V2))\n",
      "                    sip_stop<-c(sip_stop, max(above_ave$V2))\n",
      "                    with(coverage, plot(V2, V3, type=\"l\", lty=2, col=\"orange\", main=paste(item, \"small internal coverage max\", sep=\"\")))\n",
      "\n",
      "                    sip_count=sip_count+1\n",
      "                }\n",
      "\n",
      "            } else {\n",
      "\n",
      "            \n",
      "            tr_candidate<-c(tr_candidate, item)\n",
      "            tr_start<-c(tr_start, min(above_ave$V2))\n",
      "            tr_stop<-c(tr_stop, max(above_ave$V2))\n",
      "            with(coverage, plot(V2, V3, type=\"l\", lty=2, col=\"black\", main=paste(item, \"long terminal repeats\", sep=\" \")))\n",
      "            with(coverage[aamin:aamax,], points(V2, V3, pch=19, cex=.5, col=\"green\"))\n",
      "\n",
      "            count=count+1\n",
      "\n",
      "            } \n",
      "    }  else {next_step<-c(next_step, item)}\n",
      "    }\n",
      "    }\n",
      "    }\n",
      "\n",
      "after_cos_mu_tr<-next_step\n",
      "\n",
      "#multiple peaks \n",
      "\n",
      "#grouplist<-after_cos_mu_tr\n",
      "grouplist<-next_step\n",
      "next_step<-vector()\n",
      "\n",
      "stretch=5\n",
      "mp_sd_factor=5\n",
      "\n",
      "mp<-vector() #mp stands for multiple peaks\n",
      "mp_count<-0\n",
      "\n",
      "\n",
      "for (item in grouplist){\n",
      "    filename<-paste(item,\"step1.contig1.genomecoverage.txt\",sep=\"\")\n",
      "    if(file.info(filename)$size>0) {\n",
      "        coverage<-read.table(filename, sep=\"\\t\")\n",
      "        length<-nrow(coverage)\n",
      "\n",
      "\n",
      "        #with(above_ave, plot(V2, gaps, col=\"maroon\", pch=19, xlim=c(0, max(coverage$V2)), main=paste(item, size,max(gaps), sep=\":\")))\n",
      "        coverage$V3_stretch<-coverage$V3^stretch        \n",
      "\n",
      "        mp_omit_ends_cov<-coverage[(200:(length-200)),]   #excluding larger ends of the contig in this case\n",
      "        mp_above_ave<-mp_omit_ends_cov[mp_omit_ends_cov$V3>(mean(coverage$V3)+sd(coverage$V3)*mp_sd_factor),]\n",
      "    if (nrow(mp_above_ave)>0){\n",
      "        \n",
      "        mp_above_ave$gaps<-c(diff(mp_above_ave$V2),1)\n",
      "        mp_above_ave_peaks<-mp_above_ave[mp_above_ave$gaps>2000,] \n",
      "        \n",
      "        if (nrow(mp_above_ave_peaks)>0){\n",
      "                with(coverage, plot(V2, V3, type=\"l\", lty=2, col=\"orange\", main=paste(item, nrow(mp_above_ave_peaks), sep=\" \")))\n",
      "                with(mp_above_ave_peaks, points(V2, V3, col=\"green\", pch=19))\n",
      "                abline(mean(coverage$V3),0, col=\"purple\")\n",
      "                abline(mean(coverage$V3)+sd(coverage$V3)*sd_factor,0, col=\"red\")\n",
      "                #with(coverage[coverage$V3>(mean(coverage$V3)+sd(coverage$V3)*sd_factor),], points(V2, V3, col=\"green\", cex=.2, pch=19))\n",
      "                mp<-c(mp, item)\n",
      "                mp_count<-mp_count+1\n",
      "   \n",
      "        \n",
      "        }else {next_step<-c(next_step, item)}}\n",
      "    else {\n",
      "            next_step<-c(next_step, item)}\n",
      "    }\n",
      "    }\n",
      "after_cos_mu_tr_mp<-next_step\n",
      "\n",
      "#headful phage test; iteration 1:\n",
      "\n",
      "splits=c(1200,1300,1400,1500,1600,1800,2000)\n",
      "mean_threshold=6\n",
      "\n",
      "#grouplist<-next_step\n",
      "grouplist<-after_cos_mu_tr_mp\n",
      "next_step<-vector()\n",
      "\n",
      "#splits=c(1400)\n",
      "splitspots=vector()\n",
      "coverage_shift=vector()\n",
      "everything_else=vector()\n",
      "\n",
      "\n",
      "headful=vector()\n",
      "headful_count=0\n",
      "\n",
      "for (item in grouplist){\n",
      "    filename<-paste(item,\"step1.contig1.genomecoverage.txt\",sep=\"\")\n",
      "    if(file.info(filename)$size>0) {\n",
      "    coverage<-read.table(filename, sep=\"\\t\")\n",
      "    if(nrow(coverage)>max(splits)){\n",
      "    #midpoint=0\n",
      "    length<-nrow(coverage)\n",
      "    \n",
      "    for (s in splits){\n",
      "        \n",
      "        coverage$bins<-as.numeric(cut(coverage$V2, round(length/s)))\n",
      "        bin_sum<-group_by(coverage, bins)%>%summarise(mean=mean(V3), stdev=sd(V3)) \n",
      "        bin_sum$dif<-c(abs(diff(bin_sum$mean)),0)\n",
      "        threshold<-mean(bin_sum$dif)*mean_threshold\n",
      "        if ((max(bin_sum$dif)>threshold) & (nrow(bin_sum[bin_sum$dif>threshold,])==1)){\n",
      "\n",
      "            with(coverage, plot(V2,V3,main=paste(item, \"binsize used was\" ,s, sep=\" \"), type=\"n\", ylab=\"Coverage\", xlab=\"Genome\"))\n",
      "            with(coverage, lines(V2,V3, lty=2, col=\"black\"))\n",
      "            with(bin_sum, points(bins*s, mean, pch=19, col=\"forest green\"))\n",
      "            #with(bin_sum, points((bins[bin_sum$dif==max(bin_sum$dif)])*binsize, mean[bin_sum$dif==max(bin_sum$dif)], col=\"red\", pch=19, cex=1.5))\n",
      "            with(bin_sum, points((bins[bin_sum$dif==max(bin_sum$dif)])*s, mean[bin_sum$dif==max(bin_sum$dif)], col=\"purple\", pch=8, cex=2))\n",
      "\n",
      "            headful<-c(headful, item)\n",
      "            headful_count<-headful_count+1\n",
      "            \n",
      "            #with(bin_sum, plot(bins*s, dif, main=paste(phage, 'binned by', s, \"bp\", sep=\" \"), xlab=\"genome\",ylab=\"difference in mean coverage\"))\n",
      "            #with(bin_sum, points((bins[bin_sum$dif==max(bin_sum$dif)])*binsize, dif[bin_sum$dif==max(bin_sum$dif)], col=\"red\", pch=19))     \n",
      "            #with(bin_sum, points(bins[bin_sum$dif>threshold]*binsize, dif[bin_sum$dif>threshold], col=\"red\", pch=8, cex=2))\n",
      "\n",
      "            break\n",
      "        } \n",
      "    }\n",
      "    if (item %in% headful==FALSE){\n",
      "        #with(coverage, plot(V2, V3, lty=2, col=\"dark grey\", main=paste(phage,\"no pattern yet identified\", sep=\" \")))\n",
      "        next_step<-c(next_step, item)\n",
      "    }\n",
      "    \n",
      "    }\n",
      "    } \n",
      "    }\n",
      "\n",
      "after_cos_mu_tr_mp_hf1<-next_step\n",
      "\n",
      "#evenness check~round 1\n",
      "grouplist<-next_step\n",
      "#grouplist<-after_cos_mu_tr_mp_hf1\n",
      "next_step<-vector()     \n",
      "\n",
      "even<-vector()\n",
      "even_count<-0\n",
      "\n",
      "upper_sd_factor<-3\n",
      "\n",
      "for (item in grouplist){\n",
      "    filename<-paste(item,\"step1.contig1.genomecoverage.txt\",sep=\"\")\n",
      "    if(file.info(filename)$size>0) {\n",
      "        coverage<-read.table(filename, sep=\"\\t\")\n",
      "        even_above_ave<-coverage[coverage$V3>(mean(coverage$V3)+sd(coverage$V3)*upper_sd_factor),]\n",
      "        \n",
      "        if (nrow(even_above_ave)<10)  {\n",
      "            with(coverage, plot(V2, V3, main=paste(item, \"even coverage\", sep=\" \"), type='l', col=\"slateblue3\"))\n",
      "            even<-c(even, item)\n",
      "            even_count<-even_count+1\n",
      "\n",
      "        } else {next_step<-c(next_step, item)}\n",
      "        }\n",
      "    }\n",
      "after_cos_mu_tr_mp_hf1_even<-next_step\n",
      "\n",
      "#headful phage test; iteration 2:\n",
      "\n",
      "\n",
      "splits=c(800,1000,1200,1300,1400,1500,1600,1800,2000)\n",
      "mean_threshold=5\n",
      "\n",
      "\n",
      "#grouplist<-next_step\n",
      "grouplist<-after_cos_mu_tr_mp_hf1_even\n",
      "next_step<-vector()\n",
      "\n",
      "#splits=c(1400)\n",
      "splitspots=vector()\n",
      "coverage_shift=vector()\n",
      "everything_else=vector()\n",
      "mean_threshold=4\n",
      "\n",
      "for (item in grouplist){\n",
      "    filename<-paste(item,\"step1.contig1.genomecoverage.txt\",sep=\"\")\n",
      "    if(file.info(filename)$size>0) {\n",
      "    coverage<-read.table(filename, sep=\"\\t\")\n",
      "    if(nrow(coverage)>max(splits)){\n",
      "    #midpoint=0\n",
      "    length<-nrow(coverage)\n",
      "    \n",
      "    for (s in splits){\n",
      "        \n",
      "        coverage$bins<-as.numeric(cut(coverage$V2, round(length/s)))\n",
      "        bin_sum<-group_by(coverage, bins)%>%summarise(mean=mean(V3), stdev=sd(V3)) \n",
      "        bin_sum$dif<-c(abs(diff(bin_sum$mean)),0)\n",
      "        threshold<-mean(bin_sum$dif)*mean_threshold\n",
      "        if ((max(bin_sum$dif)>threshold) & (nrow(bin_sum[bin_sum$dif>threshold,])==1)){\n",
      "\n",
      "            with(coverage, plot(V2,V3,main=paste(item, \"binsize used was\" ,s, sep=\" \"), type=\"n\", ylab=\"Coverage\", xlab=\"Genome\"))\n",
      "            with(coverage, lines(V2,V3, lty=2, col=\"black\"))\n",
      "            with(bin_sum, points(bins*s, mean, pch=19, col=\"forest green\"))\n",
      "            #with(bin_sum, points((bins[bin_sum$dif==max(bin_sum$dif)])*binsize, mean[bin_sum$dif==max(bin_sum$dif)], col=\"red\", pch=19, cex=1.5))\n",
      "            with(bin_sum, points((bins[bin_sum$dif==max(bin_sum$dif)])*s, mean[bin_sum$dif==max(bin_sum$dif)], col=\"purple\", pch=8, cex=2))\n",
      "\n",
      "            headful<-c(headful, item)\n",
      "            headful_count<-headful_count+1\n",
      "            \n",
      "            #with(bin_sum, plot(bins*s, dif, main=paste(phage, 'binned by', s, \"bp\", sep=\" \"), xlab=\"genome\",ylab=\"difference in mean coverage\"))\n",
      "            #with(bin_sum, points((bins[bin_sum$dif==max(bin_sum$dif)])*binsize, dif[bin_sum$dif==max(bin_sum$dif)], col=\"red\", pch=19))     \n",
      "            #with(bin_sum, points(bins[bin_sum$dif>threshold]*binsize, dif[bin_sum$dif>threshold], col=\"red\", pch=8, cex=2))\n",
      "\n",
      "            break\n",
      "        } \n",
      "    }\n",
      "    if (item %in% headful==FALSE){\n",
      "        #with(coverage, plot(V2, V3, lty=2, col=\"dark grey\", main=paste(phage,\"no pattern yet identified\", sep=\" \")))\n",
      "        next_step<-c(next_step, item)\n",
      "    }\n",
      "    \n",
      "    }\n",
      "    } \n",
      "    }\n",
      "\n",
      "after_cos_mu_tr_mp_hf1_even_hf2<-next_step\n",
      "\n",
      "\n",
      "#evenness check round 2\n",
      "\n",
      "grouplist<-next_step\n",
      "#grouplist<-after_cos_mu_tr_mp_hf1_even_hf2\n",
      "next_step<-vector()     \n",
      "\n",
      "upper_sd_factor<-5\n",
      "\n",
      "for (item in grouplist){\n",
      "    filename<-paste(item,\"step1.contig1.genomecoverage.txt\",sep=\"\")\n",
      "    if(file.info(filename)$size>0) {\n",
      "        coverage<-read.table(filename, sep=\"\\t\")\n",
      "        even_above_ave<-coverage[coverage$V3>(mean(coverage$V3)+sd(coverage$V3)*upper_sd_factor),]\n",
      "        \n",
      "        if (nrow(even_above_ave)<10)  {\n",
      "            with(coverage, plot(V2, V3, main=paste(item, \"even coverage\", sep=\" \"), type='l', col=\"slateblue3\"))\n",
      "            even<-c(even, item)\n",
      "            even_count<-even_count+1\n",
      "\n",
      "        } else {next_step<-c(next_step, item)}\n",
      "        }\n",
      "    }\n",
      "after_cos_mu_tr_mp_hf1_even_hf2_even2<-next_step\n",
      "\n",
      "\n",
      "#the rest\n",
      "npd<-vector()   #npd stands for \"no pattern determined\"\n",
      "npd_count<-0\n",
      "\n",
      "\n",
      "for (item in next_step){\n",
      "    filename<-paste(item,\"step1.contig1.genomecoverage.txt\",sep=\"\")\n",
      "    if(file.info(filename)$size>0) {\n",
      "        coverage<-read.table(filename, sep=\"\\t\")\n",
      "        with(coverage, plot(V2, V3, type=\"l\", col=\"dark gray\", lty=2))\n",
      "        npd<-c(npd, item)\n",
      "        npd_count<-npd_count+1\n",
      "        }}\n",
      "\n",
      "\n",
      "print(paste(\"there are\", mu_count, \"mu-like phages;\",cos_count, \"cos-like phages;\", sip_count, \"small internal peak phages;\",sep=\" \"))\n",
      "print(paste(\"there are\", count, \"long terminal repeat phages;\", mp_count,\"phages with multiple peaks;\",headful_count,\"headful phages;\", sep=\" \"))\n",
      "print(paste(\"and\", even_count, \"phages with even genome coverage; and\",npd_count,\"phages with no determined pattern\",sep=\" \"))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "[1] \"there are 0 mu-like phages; 2 cos-like phages; 0 small internal peak phages;\"\n",
        "[1] \"there are 0 long terminal repeat phages; 0 phages with multiple peaks; 0 headful phages;\"\n",
        "[1] \"and 0 phages with even genome coverage; and 0 phages with no determined pattern\"\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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vvCV1Ovjggxf5HXLIIYt+d/ux7bbbdgseSpgMmfI91qFkXsjkaU97WsH3917r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\nAABJRU5ErkJggg==\n"
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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E9UMf+lDCkpd542233TYtv/zyk/JAorNxbYBPfOIT9rGvewggZV5llVX6ij9I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\ngn7e+2USimM5c23Yrnf2WjbA2DDMpffjMC7xW9ZBqJAgWA+sAZO0Bx0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DhiDSm+EI5CTg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\n"
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%R\n",
      "sips<-cbind(sip, \"short_internal_peak\")\n",
      "mus<-cbind(mu, \"mu_pattern\")\n",
      "coses<-cbind(cos_pattern, \"cos_pattern\")\n",
      "ltrs<-cbind(tr_candidate, \"long_internal_peak\")\n",
      "mps<-cbind(mp, \"multiple_coverage_peaks\")\n",
      "headfuls<-cbind(headful, \"shifts_in_coverage\")\n",
      "evens<-cbind(even, \"even_coverage\")\n",
      "no_patterns<-cbind(npd, \"no_pattern\")\n",
      "\n",
      "groups<-list(sips,mus,coses,ltrs,mps,headfuls,evens, no_patterns)\n",
      "\n",
      "read_cov<-data.frame(phage=character(), cov_category=character())\n",
      "\n",
      "for (group in groups){\n",
      "    if (ncol(group)>1){\n",
      "    colnames(group)<-c(\"phage\", \"cov_category\")\n",
      "    read_cov<-rbind(read_cov, group)\n",
      "}}\n",
      "head(read_cov)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "     phage cov_category\n",
        "1 1.111.A.  cos_pattern\n",
        "2 1.111.B.  cos_pattern\n"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###*Looking at the relationship between coverage assignment and genome relatedness:*"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%R library(dplyr);library(ggplot2);library(magrittr); library(gplots); library(dendextend); library(RColorBrewer); library(colorspace)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "\n",
        "Attaching package: \u2018gplots\u2019\n",
        "\n",
        "The following object is masked from \u2018package:stats\u2019:\n",
        "\n",
        "    lowess\n",
        "\n",
        "\n",
        "Welcome to dendextend version 0.18.3\n",
        "\n",
        "Type ?dendextend to access the overall documentation and\n",
        "vignette('dendextend') for the package vignette.\n",
        "You can execute a demo of the package via: demo(dendextend)\n",
        "\n",
        "More information is available on the dendextend project web-site:\n",
        "https://github.com/talgalili/dendextend/\n",
        "\n",
        "Contact: <tal.galili@gmail.com>\n",
        "Suggestions and bug-reports can be submitted at: https://github.com/talgalili/dendextend/issues\n",
        "\n",
        "\t\t\tTo suppress the this message use:\n",
        "\t\t\tsuppressPackageStartupMessages(library(dendextend))\n",
        "\n",
        "\n",
        "Attaching package: \u2018dendextend\u2019\n",
        "\n",
        "The following object is masked from \u2018package:magrittr\u2019:\n",
        "\n",
        "    %>%\n",
        "\n",
        "The following object is masked from \u2018package:dplyr\u2019:\n",
        "\n",
        "    %>%\n",
        "\n",
        "The following object is masked from \u2018package:stats\u2019:\n",
        "\n",
        "    cutree\n",
        "\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "array(['colorspace', 'RColorBrewer', 'dendextend', 'gplots', 'magrittr',\n",
        "       'ggplot2', 'dplyr', 'tools', 'stats', 'graphics', 'grDevices',\n",
        "       'utils', 'datasets', 'methods', 'base'], \n",
        "      dtype='|S12')"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%R -i group_number\n",
      "\n",
      "palette(c(\"springgreen1\", \"violet\",\"hotpink4\",\"yellow3\",\"darkorange\",\"aquamarine4\",\"skyblue3\",\"red3\",\"slateblue\",\"darkorchid\",\"cyan3\",\"gray\",\"black\",\"brown\"))\n",
      "group_colors<-function(group_list) {\n",
      "    n<-nrow(group_list)\n",
      "    color_assignments<-cbind(group_list, rainbow_hcl(n, c = 50, l = 70, start = 0, end = 360*(n-1)/n, gamma = NULL, fixup = TRUE, alpha = 1))\n",
      "    #color_assignments<-cbind(group_list, sequential_hcl(n, h = 260, c. = c(80, 0), l = c(30, 90), power = 1.5, gamma = NULL, fixup = TRUE, alpha = 1))\n",
      "    #color_assignments<-cbind(group_list, colorlist[1:n])\n",
      "    colnames(color_assignments)<-c(\"category\",\"color\")\n",
      "    return(color_assignments)\n",
      "}\n",
      "\n",
      "\n",
      "#looking into the relatedness of group members \n",
      "\n",
      "blast<-read.table(\"/Users/jmb/Desktop/ViralFate/phage_v_phage_blast_info.txt\", sep=\"\\t\")\n",
      "phage_grps<-read.table(\"/Users/jmb/Desktop/ViralFate/PhageGenomeAssembly/phage_by_group.txt\")\n",
      "\n",
      "#group_number<-1\n",
      "i<-paste('CNSv2.0:',group_number, sep=\"\")\n",
      "\n",
      "#colors\n",
      "grps_list<-group_by(phage_grps, V2)%>%summarise(.)\n",
      "#terminase<-read.table(\"/Users/jmb/Desktop/ViralFate/ProtComparisons/terminase_categories.txt\", sep=\"\\t\", header=FALSE)\n",
      "terminase<-read.table(\"/Users/jmb/Desktop/ViralFate/Terminase/new_terminase_categories.txt\", sep=\"\\t\", header=FALSE)\n",
      "\n",
      "terminase_colors<-group_by(terminase, V3)%>%summarise()%>%group_colors()\n",
      "terminase$term_color<-terminase_colors$color[match(terminase$V3, terminase_colors$category)]\n",
      "colnames(terminase)[1]<-\"phage\"\n",
      "\n",
      "cov_colors<-group_by(read_cov, cov_category)%>%summarise(.)%>%group_colors()\n",
      "read_cov$cov_color<-cov_colors$color[match(read_cov$cov_category, cov_colors$category)]\n",
      "print(\"done\")\n",
      "\n",
      "#make data matrix of specific group to look at (group i)\n",
      "\n",
      "data<-filter(blast, t_grp==i, q_grp==i)\n",
      "#print(paste(i, nrow(data), sep=\":\"))\n",
      "shared<-group_by(data, q_phage, t_phage)%>%summarise(.,sim=1-n()/(median(t_num_orfs)))\n",
      "shared$sim[shared$q_phage==shared$t_phage]<-0\n",
      "\n",
      "new_mat<-group_by(data, t_phage)%>%summarise(.)%>%.[,1]\n",
      "phage<-new_mat$t_phage\n",
      "    \n",
      "for (member in phage){\n",
      "    add<-filter(shared, q_phage==member)[c(2,3)]\n",
      "    colnames(add)[2]<-member\n",
      "    new_mat<-merge(new_mat, add, by=\"t_phage\", all.x=TRUE)\n",
      "\n",
      "}\n",
      "\n",
      "new_mat[is.na(new_mat)]<-1\n",
      "new_mat[new_mat<0]<-0\n",
      "rownames(new_mat)<-new_mat$t_phage\n",
      "new_mat$t_phage<-NULL\n",
      "\n",
      "d<-new_mat %>% as.dist %>% hclust %>% as.dendrogram\n",
      "\n",
      "div<-cutree(d, k = NULL, h = 0.4)\n",
      "division<-data.frame(div)\n",
      "division$phage<-rownames(division)\n",
      "\n",
      "division_colors<-data.frame(div)%>%group_by(div)%>%summarise()%>%group_colors()\n",
      "\n",
      "k_val<-data.frame(div)%>%group_by(div)%>%summarise()%>%nrow\n",
      "\n",
      "division$div_col<-division_colors$color[match(division$div, division_colors$category)]\n",
      "\n",
      "#definining potential colors\n",
      "\n",
      "terminase_cols<-as.numeric(terminase$term_color[match(labels(d),terminase$phage)])\n",
      "read_cov_cols<-as.numeric(read_cov$cov_color[match(labels(d),read_cov$phage)])\n",
      "div_cols<-as.numeric(division$div_col[match(labels(d),division$phage)])\n",
      "\n",
      "#read_coverage_pattern_colors<-read_coverage_pattern_colors[order.dendrogram(dend)]\n",
      "\n",
      "#plot hclust with leaves colored by read coverage pattern, marked by terminase type and boxed by hclust threshold\n",
      "\n",
      "layout(matrix(c(1,1,2,3),2,2,byrow=TRUE), widths=c(10, 20), heights=c(15, 10))\n",
      "d %>% set(\"labels_colors\", read_cov_cols) %>% set(\"labels_colors\", read_cov_cols)%>%set(\"leaves_pch\",19)%>%set(\"leaves_col\",terminase_cols)%>%set(\"leaves_cex\",2)%>%plot(main=(paste(\"Group\", group_number))) \n",
      "if(k_val>1){\n",
      "    d %>% rect.dendrogram(k=k_val, border=\"gray\", lty=2, lwd=3, lower_rect=-0.15)\n",
      "    }\n",
      "par(mar=c(0,0,0,0))\n",
      "plot.new()\n",
      "legend(\"center\",legend = cov_colors$category, fill = cov_colors$color, title=\"Coverage Category\")\n",
      "plot.new()\n",
      "legend(\"center\",legend = terminase_colors$category, col = terminase_colors$color, pch=19, title=\"Terminase Category\")\n",
      "\n",
      "\n",
      "#d %>% set(\"labels_colors\", terminase_cols) %>% plot(main=\"terminase colors\")\n",
      "#legend(\"topleft\", legend = terminase_colors$category, fill = terminase_colors$color)\n",
      "\n",
      "#d %>% set(\"labels_colors\", div_cols) %>% plot(main=\"hclust division colors\")\n",
      "#legend(\"topleft\", legend = division_colors$category, fill = division_colors$color)\n",
      "\n",
      "all<-merge(read_cov, terminase, by=\"phage\", all.x=TRUE)%>%merge(.,division, by=\"phage\") %>% ggplot(., aes(x=as.character(div), fill=cov_category)) + geom_bar() + labs(x=\"hclust Group\", y=\"Abundance\", title=\"Summary\")\n",
      "\n",
      "plot(all)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "[1] \"done\"\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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ABCAAAQggwHwHIAABCEAAAlkggABnATpZQgACEIAABBBgvgMQgAAEIACBLBBAgLMAnSwh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\n8EO74IILbPvtt7eTTz7ZTjzxRJs7d27SdDV0LZeC8oJ08cU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      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Gm56zo4OoLl26mN2+GlzfffedfPPNN/Lhhx+Kjjx79uwpGsi6a/i2224zv++9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\nDgEEEEAgVQX+H84AgPZH2lOAAAAAAElFTkSuQmCC\n"
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 111
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "##*Cos Pattern Phages*"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%R\n",
      "\n",
      "cos_split<-vector()\n",
      "\n",
      "for (item in cos_pattern){\n",
      "    filename<-paste(item,\"step1.contig1.genomecoverage.txt\",sep=\"\")\n",
      "    \n",
      "    coverage<-read.table(filename, sep=\"\\t\")\n",
      "       \n",
      "    length<-nrow(coverage)\n",
      "    cos_omit_ends_cov<-coverage[(200:(length-200)),]   #excluding larger ends of the contig in this case      \n",
      "    cos_below_ave<-cos_omit_ends_cov[cos_omit_ends_cov$V3<(mean(coverage$V3)-(sd(coverage$V3)*cos_sd_factor)),]\n",
      "    \n",
      "    cos_below_ave$gaps<-c(diff(cos_below_ave$V2),1)\n",
      "    cos_below_ave_peaks<-cos_below_ave[cos_below_ave$gaps<5,]\n",
      "    size<-max(cos_below_ave_peaks$V2)-min(cos_below_ave_peaks$V2)\n",
      "    dip_midpoint<-round(mean(cos_below_ave_peaks$V2))\n",
      "    cos_split<-c(cos_split, dip_midpoint)\n",
      "    }"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%Rpull cos_split cos_pattern\n",
      "\n",
      "coscut=cos_split.tolist()\n",
      "\n",
      "for i in range(0,len(cos_pattern)):\n",
      "    phage=cos_pattern[i]\n",
      "    new_file=open(phage+\"step1.contig1.rearrange.fasta\",\"w\")\n",
      "    cut=coscut[i]\n",
      "    fastafile=phage+\"step1.contig1.fasta\"\n",
      "    f=open(fastafile)                                       #open fasta file\n",
      "    sequence=\"\"\n",
      "    for row in f:                           #define title and sequences\n",
      "        if row.startswith(\">\"):\n",
      "            title=row\n",
      "        else:\n",
      "            sequence+=row\n",
      "    sequence=sequence.replace(\"\\n\",\"\")\n",
      "    upstream=sequence[0:int(cut)-1]                    #id sequence before start of small subunit terminase\n",
      "    downstream=sequence[int(cut)-1:] \n",
      "    newseq=downstream+upstream\n",
      "    \n",
      "    new_file.write(\">\"+phage+\"step1.contig1.cos.rearrange\\n\"+newseq)\n",
      "    new_file.close()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 11
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###*Send rearranged contigs to server:*"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###*Finishing re-arranged/manipulated assemblies*\n",
      ">Re-recruit reads to rearranged phage contigs"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!scp *step1.contig1.rearrange.fasta jbrown@eofe4.mit.edu:/nobackup1/jbrown/nahant_phage_reads/group18/\n",
      "script=open(\"rearrange_recruitment.slurm\",\"w\")\n",
      "script.write(\"#!/bin/bash\\n#SBATCH -n 16\\n#SBATCH -N 1\\n#SBATCH -o rearrange.out\\n#SBATCH -e rearrange.err\\n#SBATCH -p sched_mit_chisholm\\n\\n\")\n",
      "script.write(\"module add engaging/clc-assembly-cell\\nmodule add engaging/samtools/1.1\\nmodule add engaging/bedtools/2.22.1\\n\\n\")\n",
      "\n",
      "for virus in grouplist:\n",
      "    target_seq=virus+\"step1.contig1.rearrange.fasta\"\n",
      "    reads=\"../\"+virus+\"*.fastq\"\n",
      "    recruitfile=virus+\"s1c1.rearrange.cas\"\n",
      "    outputfasta=virus+\"recruit2.fasta\"\n",
      "    directed=virus+\"rearrange.\"\n",
      "\n",
      "    script.write(\"clc_mapper -o \"+recruitfile+\" -p fb ss 100 300 -q \"+reads+\" -d \"+target_seq+\" -a local -r ignore \\n\")\n",
      "    script.write(\"clc_extract_consensus -z none -a \"+recruitfile+\" -o \"+outputfasta+\"\\n\")\n",
      "    script.write(\"clc_cas_to_sam -a \"+recruitfile+\" -o \"+directed+\"bam\\n\")\n",
      "    script.write(\"samtools sort \"+directed+\"bam \"+directed+\"sorted\\n\")\n",
      "    script.write(\"samtools index \"+directed+\"sorted.bam\\n\")\n",
      "    script.write(\"bedtools genomecov -ibam \"+directed+\"sorted.bam -d > \"+directed+\"genomecoverage.txt\\n\\n\")\n",
      "\n",
      "script.close()\n",
      "!open rearrange_recruitment.slurm -a textedit\n",
      "!scp rearrange_recruitment.slurm jbrown@eofe4.mit.edu:/nobackup1/jbrown/nahant_phage_reads/group18/"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1.111.A.step1.contig1.rearrange.fasta           0%    0     0.0KB/s   --:-- ETA\r",
        "1.111.A.step1.contig1.rearrange.fasta         100%   39KB  39.3KB/s   00:00    \r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1.111.B.step1.contig1.rearrange.fasta           0%    0     0.0KB/s   --:-- ETA\r",
        "1.111.B.step1.contig1.rearrange.fasta         100%   39KB  39.3KB/s   00:00    \r\n"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!scp jbrown@eofe4.mit.edu:/nobackup1/jbrown/nahant_phage_reads/group18/*.recruit2.fasta ./\n",
      "!scp jbrown@eofe4.mit.edu:/nobackup1/jbrown/nahant_phage_reads/group18/*rearrange.genomecoverage.txt ./"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1.111.A.recruit2.fasta                          0%    0     0.0KB/s   --:-- ETA"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1.111.A.recruit2.fasta                        100%   40KB  40.0KB/s   00:00    \r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1.111.B.recruit2.fasta                          0%    0     0.0KB/s   --:-- ETA"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1.111.B.recruit2.fasta                        100%   40KB  40.0KB/s   00:00    \r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1.111.A.rearrange.genomecoverage.txt            0%    0     0.0KB/s   --:-- ETA"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1.111.A.rearrange.genomecoverage.txt           89% 1616KB   1.6MB/s   00:00 ETA"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1.111.A.rearrange.genomecoverage.txt          100% 1796KB   1.8MB/s   00:01    \r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1.111.B.rearrange.genomecoverage.txt            0%    0     0.0KB/s   --:-- ETA"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1.111.B.rearrange.genomecoverage.txt           98% 1776KB   1.7MB/s   00:00 ETA"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1.111.B.rearrange.genomecoverage.txt          100% 1796KB   1.8MB/s   00:01    \r\n"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%R -i grouplist\n",
      "\n",
      "for (item in grouplist){\n",
      "    filename<-paste(item,\"rearrange.genomecoverage.txt\",sep=\"\")\n",
      "    coverage<-read.table(filename, sep=\"\\t\")\n",
      "    with(coverage, plot(V2, V3, type=\"l\", col=\"purple4\", cex=2, xlab=\"genome\", ylab=\"coverage\",main=paste(item,\"rearranged\", sep=\" \")))\n",
      "}"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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AM6JnrhutZo9fqvbuyhrbNpIB3XH2iEiKxVTmx3FL1LM3fB7TvZHehP1d0NK5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\nG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      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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CfeXq5c3Ou9dKqJMnQ9+I7Y7ffPArWe3K5SrERNXTfi4+6+VsGGGhG0yjoqpd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\nHWs3K8mgKHRPNl4utOUcA167dq1ZvHh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Ef+kd4oE7DrQ61Fhc82DooECRZ1ARL+zQoLp1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\n9vmj9ggggAAChgr8f30uz1uzHYDpAAAAAElFTkSuQmCC\n"
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###*Re-orientation to have terminase gene toward the start of the contig:*\n",
      ">not required for headful phages...re-oriented during the rearrangement process"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "out=open(\"blast_terminase_commands.sh\",\"w\")\n",
      "\n",
      "out.write(\"#!/bin/bash\\n\")\n",
      "out.write(\"#SBATCH -n 16\\n#SBATCH -N 1\\n#SBATCH -o terminase_blast.out\\n#SBATCH -e terminase_blast.err\\n#SBATCH -p sched_mit_chisholm\\n\\n\")\n",
      "\n",
      "out.write(\"module add engaging/ncbi-blast/2.2.30+\\n\\n\")\n",
      "\n",
      "for line in grouplist:\n",
      "    name=line.rstrip(\"\\n\")\n",
      "    inputfasta=name+\"recruit2.fasta\"\n",
      "    blastoutputname=name+\"vs.terminases.txt\"\n",
      "    blastcommand=\"blastx -db /nobackup1/jbrown/blast_db/NCBI_phage_terminases_20150414.fasta -query \"+inputfasta+\" -num_descriptions 1 -outfmt 6 -out \"+blastoutputname\n",
      "    out.write(blastcommand+\"\\n\")\n",
      "out.close()\n",
      "!open blast_terminase_commands.sh -a textedit\n",
      "!scp blast_terminase_commands.sh jbrown@eofe4.mit.edu:/nobackup1/jbrown/nahant_phage_reads/group18/"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "blast_terminase_commands.sh                     0%    0     0.0KB/s   --:-- ETA\r",
        "blast_terminase_commands.sh                   100%  504     0.5KB/s   00:00    \r\n"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!scp jbrown@eofe4.mit.edu:/nobackup1/jbrown/nahant_phage_reads/group18/*vs.terminases.txt ./"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1.111.A.vs.terminases.txt                       0%    0     0.0KB/s   --:-- ETA"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1.111.A.vs.terminases.txt                     100%   61KB  61.4KB/s   00:00    \r\n",
        "\r",
        "1.111.B.vs.terminases.txt                       0%    0     0.0KB/s   --:-- ETA"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1.111.B.vs.terminases.txt                     100%   60KB  60.4KB/s   00:00    \r\n"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from pyfaidx import Fasta\n",
      "from Bio.Seq import Seq\n",
      "\n",
      "for line in grouplist:\n",
      "    name=line.rstrip(\"\\n\")\n",
      "    \n",
      "    blastoutputname=name+\"vs.terminases.txt\"\n",
      "    blastout=open(blastoutputname)\n",
      "    blastout=blastout.read()\n",
      "    vec=blastout.split(\"\\t\")\n",
      "    if len(vec)>2:\n",
      "        start=int(vec[6])\n",
      "        inputfasta=name+\"recruit2.fasta\"\n",
      "        f=Fasta(inputfasta)\n",
      "        viromelen=len(f[0])\n",
      "        threshhold=len(f[0])/2\n",
      "        sequence=str(f[0])\n",
      "        seq=Seq(str(sequence))\n",
      "        outfasta=open(name+\"oc.fasta\",\"w\")\n",
      "        outfasta.write(\">\"+name+\"\\n\")\n",
      "\n",
      "        if start>threshhold:\n",
      "            print(name+\": must reverse sequence \")\n",
      "            out_seq=seq.reverse_complement()\n",
      "    \n",
      "        else:\n",
      "            print(name+\": sequence is in correct orientation\")\n",
      "            out_seq=seq\n",
      "        outfasta.write(str(out_seq))\n",
      "        outfasta.close()\n",
      "    else:\n",
      "        print(name+\": no blast match\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "1.111.A.: must reverse sequence \n",
        "1.111.B.: sequence is in correct orientation\n"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###*re-name contig files and contigs to be consistent*"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from pyfaidx import Fasta\n",
      "\n",
      "allfastqs=open(\"../allfastq.txt\")   #set up name dict of original names:\n",
      "name_dict={}            \n",
      "for lin in allfastqs.readlines():\n",
      "    phage=lin.split(\"_\")[0]\n",
      "    name=lin.split(\".f\")[0]\n",
      "    name_dict[phage]=name\n",
      "\n",
      "\n",
      "for element in grouplist:         #create final fasta file with original name as phage fasta name\n",
      "    fullname=name_dict[element]\n",
      "    filename=element+\"oc.fasta\"\n",
      "    f=Fasta(filename)\n",
      "    sequence=str(f[0])\n",
      "    output=open(element+\"final.fasta\",\"w\")\n",
      "    output.write(\">Vibriophage_\"+fullname+\"\\n\"+sequence)\n",
      "    output.close()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 24
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}