{ "metadata": { "name": "", "signature": "sha256:d006f748cf1a61db8f2a140e199ecd6cf57c8d6b56d13fd5b891fa576f5a36ac" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "#Analysis of RNA-Seq data that corresponds to oyster gill BS-Seq data\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##Galaxy\n", "\n", "Upload via FTP\n", "\n", "\"NGS_Raw_Data_17A1B3BA.png\"/ \n", "\n", "\"Galaxy_17A28D59.png\"/\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Number of Reads \n", "\"Galaxy_17AAFB84.png\"/" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Grooming - assuming 1.8 thus Sanger\n", "Doing this because Galaxy will not recognize" ] }, { "cell_type": "code", "collapsed": false, "input": [ "#Tophat complete, had to go back and quality trim individual files" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "markdown", "metadata": {}, "source": [ "\"Galaxy_17AAF717.png\"/" ] }, { "cell_type": "code", "collapsed": false, "input": [ "#checking accepted hits \n", "!samtools view -c /Volumes/web/cnidarian/BiGill_RNAseqGalaxy_Tophat.bam" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "24876898\r\n" ] } ], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "#only mapped reads\n", "!samtools view -c -F 4 /Volumes/web/cnidarian/BiGill_RNAseqGalaxy_Tophat.bam" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "24876898\r\n" ] } ], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [ "!samtools flagstat /Volumes/web/cnidarian/BiGill_RNAseqGalaxy_Tophat.bam" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "24876898 + 0 in total (QC-passed reads + QC-failed reads)\r\n", "0 + 0 duplicates\r\n", "24876898 + 0 mapped (100.00%:nan%)\r\n", "24876898 + 0 paired in sequencing\r\n", "12557606 + 0 read1\r\n", "12319292 + 0 read2\r\n", "108356 + 0 properly paired (0.44%:nan%)\r\n", "1412054 + 0 with itself and mate mapped\r\n", "23464844 + 0 singletons (94.32%:nan%)\r\n", "0 + 0 with mate mapped to a different chr\r\n", "0 + 0 with mate mapped to a different chr (mapQ>=5)\r\n" ] } ], "prompt_number": 4 }, { "cell_type": "markdown", "metadata": {}, "source": [ "---" ] }, { "cell_type": "code", "collapsed": false, "input": [ "#Cufflinks needs GTF\n", "!head /Volumes/web/cnidarian/TJGR_CDSgffread_filtered.gtf" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "# /usr/local2/cufflinks-1.0.3.Linux_x86_64/gffread oyster.v9.glean.final.rename.CDS.gff -o gffread_filtered.gtf\r\n", "##gff-version 3\r\n", "C16582\tGLEAN\tmRNA\t35\t385\t.\t-\t.\tID=CGI_10000001\r\n", "C16582\tGLEAN\tCDS\t35\t385\t.\t-\t0\tParent=CGI_10000001\r\n", "C17212\tGLEAN\tmRNA\t31\t363\t.\t+\t.\tID=CGI_10000002\r\n", "C17212\tGLEAN\tCDS\t31\t363\t.\t+\t0\tParent=CGI_10000002\r\n", "C17316\tGLEAN\tmRNA\t30\t257\t.\t+\t.\tID=CGI_10000003\r\n", "C17316\tGLEAN\tCDS\t30\t257\t.\t+\t0\tParent=CGI_10000003\r\n", "C17476\tGLEAN\tmRNA\t34\t257\t.\t-\t.\tID=CGI_10000004\r\n", "C17476\tGLEAN\tCDS\t34\t74\t.\t-\t2\tParent=CGI_10000004\r\n" ] } ], "prompt_number": 2 }, { "cell_type": "markdown", "metadata": {}, "source": [ "--- \n", "Running cufflinks 1 \n", "\n", "\"Galaxy_17AAF7AC.png\"/" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\"Galaxy_17ABC793.png\"/" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "--- \n", "\n", "\"Galaxy_17ABC847.png\"/" ] }, { "cell_type": "code", "collapsed": false, "input": [ "#hung for days; restart" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 7 }, { "cell_type": "markdown", "metadata": {}, "source": [ "\"Galaxy_17AFDD6C.png\"/" ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Cufflinks worked in Galaxy" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\"Galaxy_17B3FB13.png\"/" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "downloaded " ] }, { "cell_type": "code", "collapsed": false, "input": [ "!head /Volumes/web/cnidarian/BiGillCufflinks_assembled_transcripts1.gtf" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "C10091\tCufflinks\ttranscript\t2\t155\t1000\t.\t.\tgene_id \"CUFF.1\"; transcript_id \"CUFF.1.1\"; FPKM \"212.5240682926\"; frac \"1.000000\"; conf_lo \"10.704568\"; conf_hi \"24.291136\"; cov \"167.595205\";\r\n", "C10091\tCufflinks\texon\t2\t155\t1000\t.\t.\tgene_id \"CUFF.1\"; transcript_id \"CUFF.1.1\"; exon_number \"1\"; FPKM \"212.5240682926\"; frac \"1.000000\"; conf_lo \"10.704568\"; conf_hi \"24.291136\"; cov \"167.595205\";\r\n", "C10959\tCufflinks\ttranscript\t2\t162\t1000\t.\t.\tgene_id \"CUFF.2\"; transcript_id \"CUFF.2.1\"; FPKM \"49.8667629708\"; frac \"1.000000\"; conf_lo \"1.969068\"; conf_hi \"9.451525\"; cov \"39.324630\";\r\n", "C10959\tCufflinks\texon\t2\t162\t1000\t.\t.\tgene_id \"CUFF.2\"; transcript_id \"CUFF.2.1\"; exon_number \"1\"; FPKM \"49.8667629708\"; frac \"1.000000\"; conf_lo \"1.969068\"; conf_hi \"9.451525\"; cov \"39.324630\";\r\n", "C11045\tCufflinks\ttranscript\t1\t60\t1000\t.\t.\tgene_id \"CUFF.3\"; transcript_id \"CUFF.3.1\"; FPKM \"28466.3975885016\"; frac \"1.000000\"; conf_lo \"29.588525\"; conf_hi \"66.574181\"; cov \"22448.430340\";\r\n", "C11045\tCufflinks\texon\t1\t60\t1000\t.\t.\tgene_id \"CUFF.3\"; transcript_id \"CUFF.3.1\"; exon_number \"1\"; FPKM \"28466.3975885016\"; frac \"1.000000\"; conf_lo \"29.588525\"; conf_hi \"66.574181\"; cov \"22448.430340\";\r\n", "C11484\tCufflinks\ttranscript\t3\t116\t1000\t.\t.\tgene_id \"CUFF.4\"; transcript_id \"CUFF.4.1\"; FPKM \"245.8763696681\"; frac \"1.000000\"; conf_lo \"3.893227\"; conf_hi \"16.129083\"; cov \"193.896630\";\r\n", "C11484\tCufflinks\texon\t3\t116\t1000\t.\t.\tgene_id \"CUFF.4\"; transcript_id \"CUFF.4.1\"; exon_number \"1\"; FPKM \"245.8763696681\"; frac \"1.000000\"; conf_lo \"3.893227\"; conf_hi \"16.129083\"; cov \"193.896630\";\r\n", "C11558\tCufflinks\ttranscript\t1\t130\t1000\t.\t.\tgene_id \"CUFF.5\"; transcript_id \"CUFF.5.1\"; FPKM \"332.4008375524\"; frac \"1.000000\"; conf_lo \"10.242182\"; conf_hi \"24.386147\"; cov \"262.129306\";\r\n", "C11558\tCufflinks\texon\t1\t130\t1000\t.\t.\tgene_id \"CUFF.5\"; transcript_id \"CUFF.5.1\"; exon_number \"1\"; FPKM \"332.4008375524\"; frac \"1.000000\"; conf_lo \"10.242182\"; conf_hi \"24.386147\"; cov \"262.129306\";\r\n" ] } ], "prompt_number": 2 }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "\n", "This is a screenshot of file #28. interesting \n", "\n", "\"IGV_-_Session__http__eagle.fish.washington.edu_cnidarian_igv_session_073013.xml_17B4011F.png\"/" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The two other files that cufflinks produced\n", "\n", "\"Galaxy_17B40900.png\"/\n", "\n", "\n", " \n", "" ] }, { "cell_type": "code", "collapsed": false, "input": [ "!wc /Volumes/web/cnidarian/BiGillCufflinks_transcripts_exp1.tabular" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ " 42867 557271 4118574 /Volumes/web/cnidarian/BiGillCufflinks_transcripts_exp1.tabular\r\n" ] } ], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "!wc /Volumes/web/cnidarian/BiGillCufflinks_gene_exp1.tabular" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ " 37092 482196 3179694 /Volumes/web/cnidarian/BiGillCufflinks_gene_exp1.tabular\r\n" ] } ], "prompt_number": 5 }, { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Using Bedtools to get exon coverage" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "!coveragebed -hist -abam /Volumes/web/cnidarian/BiGill_RNAseqGalaxy_Tophat.bam -b /Volumes/web/trilobite/Crassostrea_gigas_v9_tracks/Cgigas_v9_exon.gff > /Volumes/web/cnidarian/BiGill_ThBAM_cov_exon_1.txt" ] }, { "cell_type": "code", "collapsed": false, "input": [ "!head /Volumes/web/cnidarian/BiGill_ThBAM_cov_exon_1.txt" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "C16582\tGLEAN\tCDS\t35\t385\t.\t-\t0\tParent=CGI_10000001;\t0\t49\t351\t0.1396011\r\n", "C16582\tGLEAN\tCDS\t35\t385\t.\t-\t0\tParent=CGI_10000001;\t1\t1\t351\t0.0028490\r\n", "C16582\tGLEAN\tCDS\t35\t385\t.\t-\t0\tParent=CGI_10000001;\t2\t2\t351\t0.0056980\r\n", "C16582\tGLEAN\tCDS\t35\t385\t.\t-\t0\tParent=CGI_10000001;\t3\t4\t351\t0.0113960\r\n", "C16582\tGLEAN\tCDS\t35\t385\t.\t-\t0\tParent=CGI_10000001;\t5\t2\t351\t0.0056980\r\n", "C16582\tGLEAN\tCDS\t35\t385\t.\t-\t0\tParent=CGI_10000001;\t6\t2\t351\t0.0056980\r\n", "C16582\tGLEAN\tCDS\t35\t385\t.\t-\t0\tParent=CGI_10000001;\t7\t2\t351\t0.0056980\r\n", "C16582\tGLEAN\tCDS\t35\t385\t.\t-\t0\tParent=CGI_10000001;\t8\t1\t351\t0.0028490\r\n", "C16582\tGLEAN\tCDS\t35\t385\t.\t-\t0\tParent=CGI_10000001;\t9\t2\t351\t0.0056980\r\n", "C16582\tGLEAN\tCDS\t35\t385\t.\t-\t0\tParent=CGI_10000001;\t10\t2\t351\t0.0056980\r\n" ] } ], "prompt_number": 9 }, { "cell_type": "code", "collapsed": false, "input": [ "from pandas import *\n", "\n", "# read data from data file into a pandas DataFrame \n", "ex = read_table(\"http://eagle.fish.washington.edu/cnidarian/BiGill_ThBAM_cov_exon_1.txt\", # name of the data file\n", " #sep=\",\", # what character separates each column?\n", " na_values=[\"\", \" \"]) # what values should be considered \"blank\" values?" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 13 }, { "cell_type": "code", "collapsed": false, "input": [ "print ex\n", "#need to figure out column titles @fu" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Int64Index: 2030332 entries, 0 to 2030331\n", "Data columns:\n", "C16582 2030332 non-null values\n", "GLEAN 2030332 non-null values\n", "CDS 2030332 non-null values\n", "35 2030332 non-null values\n", "385 2030332 non-null values\n", ". 2020281 non-null values\n", "- 2020281 non-null values\n", "0 2020281 non-null values\n", "Parent=CGI_10000001; 2020281 non-null values\n", "0.1 2020281 non-null values\n", "49 2020281 non-null values\n", "351 2020281 non-null values\n", "0.1396011 2020281 non-null values\n", "dtypes: float64(6), int64(1), object(6)" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n" ] } ], "prompt_number": 15 }, { "cell_type": "code", "collapsed": false, "input": [ "ex ['0.1396011'].hist(bins=50);\n", "#Axis limits are changed using the axis([xmin, xmax, ymin, ymax]) function.\n", "#plt.axis([0, 1, 0, 400000])" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "pyout", "prompt_number": 17, "text": [ "" ] }, { "output_type": "display_data", "png": 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RkZGREXE4HNLd3S07d+6UQ4cOiYhIZWWl7Nq1S0RELly4INnZ2TI+Pi4+n0+s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\nItKMRYOIiDRj0SAiIs3+P9id8Pu7oQbjAAAAAElFTkSuQmCC\n" } ], "prompt_number": 17 }, { "cell_type": "code", "collapsed": false, "input": [ "!coveragebed -abam /Volumes/web/cnidarian/BiGill_RNAseqGalaxy_Tophat.bam -b /Volumes/web/trilobite/Crassostrea_gigas_v9_tracks/Cgigas_v9_exon.gff > /Volumes/web/cnidarian/BiGill_ThBAM_cov_exon_2.txt" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 10 }, { "cell_type": "code", "collapsed": false, "input": [ "!head /Volumes/web/cnidarian/BiGill_ThBAM_cov_exon_2.txt" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "C16582\tGLEAN\tCDS\t35\t385\t.\t-\t0\tParent=CGI_10000001;\t410\t302\t351\t0.8603989\r\n", "C17212\tGLEAN\tCDS\t31\t363\t.\t+\t0\tParent=CGI_10000002;\t16\t314\t333\t0.9429429\r\n", "C17316\tGLEAN\tCDS\t30\t257\t.\t+\t0\tParent=CGI_10000003;\t15\t218\t228\t0.9561403\r\n", "C17476\tGLEAN\tCDS\t104\t257\t.\t-\t0\tParent=CGI_10000004;\t2\t73\t154\t0.4740260\r\n", "C17476\tGLEAN\tCDS\t34\t74\t.\t-\t2\tParent=CGI_10000004;\t0\t0\t41\t0.0000000\r\n", "C17998\tGLEAN\tCDS\t196\t387\t.\t-\t0\tParent=CGI_10000005;\t0\t0\t192\t0.0000000\r\n", "C18346\tGLEAN\tCDS\t174\t551\t.\t+\t0\tParent=CGI_10000009;\t3577\t378\t378\t1.0000000\r\n", "C18428\tGLEAN\tCDS\t286\t546\t.\t-\t0\tParent=CGI_10000010;\t3474\t261\t261\t1.0000000\r\n", "C18964\tGLEAN\tCDS\t203\t658\t.\t-\t0\tParent=CGI_10000011;\t22\t274\t456\t0.6008772\r\n", "C18980\tGLEAN\tCDS\t30\t674\t.\t+\t0\tParent=CGI_10000012;\t15\t446\t645\t0.6914729\r\n" ] } ], "prompt_number": 11 }, { "cell_type": "raw", "metadata": {}, "source": [ "DEFAULT: After each interval in B, coverageBed will report:\n", "\n", "The number of features in A that overlapped (by at least one base pair) the B interval.\n", "The number of bases in B that had non-zero coverage from features in A.\n", "The length of the entry in B.\n", "The fraction of bases in B that had non-zero coverage from features in A.\n", "Below are the number of features in A (N=...) overlapping B and fraction of bases in B with coverage.\n", "\n", "Chromosome ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n", "\n", "BED FILE B *************** *************** ****** **************\n", "\n", "BED File A ^^^^ ^^^^ ^^ ^^^^^^^^^ ^^^ ^^ ^^^^\n", " ^^^^^^^^ ^^^^^ ^^^^^ ^^\n", "\n", "Result [ N=3, 10/15 ] [ N=1, 2/15 ] [N=1,6/6] [N=6, 12/14 ]\n" ] }, { "cell_type": "code", "collapsed": false, "input": [ "#into SQLShare" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 12 }, { "cell_type": "markdown", "metadata": {}, "source": [ "```\n", "SELECT Column1 as seqid,\n", " Column4 as start,\n", " Column5 as [end],\n", " 'BiGillExonExp' as Feature,\n", " ((cast([Column10]as float)/(cast([Column12]as float)))) as feat_bp\n", " FROM [sr320@washington.edu].[BiGill_ThBAM_cov_exon_2.txt]\u200b\n", "```" ] }, { "cell_type": "code", "collapsed": false, "input": [ "!head /Volumes/web/cnidarian/BiGill_ThBam_exon_feature.csv" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "seqid,start,end,Feature,feat_bp\r", "\r\n", "C16582,35,385,BiGillExonExp,1.16809116809117\r", "\r\n", "C17212,31,363,BiGillExonExp,0.048048048048048\r", "\r\n", "C17316,30,257,BiGillExonExp,0.0657894736842105\r", "\r\n", "C17476,104,257,BiGillExonExp,0.012987012987013\r", "\r\n", "C17476,34,74,BiGillExonExp,0\r", "\r\n", "C17998,196,387,BiGillExonExp,0\r", "\r\n", "C18346,174,551,BiGillExonExp,9.46296296296296\r", "\r\n", "C18428,286,546,BiGillExonExp,13.3103448275862\r", "\r\n", "C18964,203,658,BiGillExonExp,0.0482456140350877\r", "\r\n" ] } ], "prompt_number": 25 }, { "cell_type": "code", "collapsed": false, "input": [ "!tr ',' \"\\t\" /Volumes/web/cnidarian/BiGill_ThBam_exon_feature.igv" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 26 }, { "cell_type": "code", "collapsed": false, "input": [ "!head /Volumes/web/cnidarian/BiGill_ThBam_exon_feature.igv" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "seqid\tstart\tend\tFeature\tfeat_bp\r", "\r\n", "C16582\t35\t385\tBiGillExonExp\t1.16809116809117\r", "\r\n", "C17212\t31\t363\tBiGillExonExp\t0.048048048048048\r", "\r\n", "C17316\t30\t257\tBiGillExonExp\t0.0657894736842105\r", "\r\n", "C17476\t104\t257\tBiGillExonExp\t0.012987012987013\r", "\r\n", "C17476\t34\t74\tBiGillExonExp\t0\r", "\r\n", "C17998\t196\t387\tBiGillExonExp\t0\r", "\r\n", "C18346\t174\t551\tBiGillExonExp\t9.46296296296296\r", "\r\n", "C18428\t286\t546\tBiGillExonExp\t13.3103448275862\r", "\r\n", "C18964\t203\t658\tBiGillExonExp\t0.0482456140350877\r", "\r\n" ] } ], "prompt_number": 27 }, { "cell_type": "code", "collapsed": false, "input": [ "!head /Volumes/web/cnidarian/BiGill_ThBam_exon_feature.sorted.igv" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "seqid\tstart\tend\tFeature\tfeat_bp\r\n", "C16582\t35\t385\tBiGillExonExp\t1.16809116809117\r\n", "C17212\t31\t363\tBiGillExonExp\t0.048048048048048\r\n", "C17316\t30\t257\tBiGillExonExp\t0.0657894736842105\r\n", "C17476\t34\t74\tBiGillExonExp\t0\r\n", "C17476\t104\t257\tBiGillExonExp\t0.012987012987013\r\n", "C17998\t196\t387\tBiGillExonExp\t0\r\n", "C18346\t174\t551\tBiGillExonExp\t9.46296296296296\r\n", "C18428\t286\t546\tBiGillExonExp\t13.3103448275862\r\n", "C18964\t203\t658\tBiGillExonExp\t0.0482456140350877\r\n" ] } ], "prompt_number": 32 }, { "cell_type": "markdown", "metadata": {}, "source": [ "```\n", "SELECT \n", " Gene,\n", " count(feat_bp) as count,\n", " stdev(feat_bp) as sd,\n", " sum(feat_bp) as sum,\n", " (stdev(feat_bp))/(sum(feat_bp)) as cv \n", " FROM [sr320@washington.edu].[BiGill_RNAseq_exon]\n", " Group by Gene\n", " Having sum(feat_bp) > 0\n", "```" ] }, { "cell_type": "code", "collapsed": false, "input": [ "!head /Volumes/web/cnidarian/BiGill_exonexp_gene.csv" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Gene,count,sd,sum,cv\r", "\r\n", "Parent=CGI_10028436;,8,0.0154124519245155,0.485755413498187,0.031728832034052\r", "\r\n", "Parent=CGI_10024278;,7,0.00429195136554519,0.0169956140350877,0.252532880346917\r", "\r\n", "Parent=CGI_10019070;,8,0.103813488307781,1.67397722515665,0.0620160697216576\r", "\r\n", "Parent=CGI_10021016;,4,0.032419484481148,0.42331787781172,0.0765842554269992\r", "\r\n", "Parent=CGI_10007821;,6,0.0676276042041267,1.11924329996958,0.0604226124971795\r", "\r\n", "Parent=CGI_10014879;,12,0.174623636138173,11.5634662794622,0.0151013227277984\r", "\r\n", "Parent=CGI_10024860;,4,0.0952480651199777,0.57039170639803,0.166987114384009\r", "\r\n", "Parent=CGI_10022407;,1,,0.0609318996415771,\r", "\r\n", "Parent=CGI_10017802;,1,,0.00432900432900433,\r", "\r\n" ] } ], "prompt_number": 35 }, { "cell_type": "code", "collapsed": false, "input": [ "!tr ',' \"\\t\" /Volumes/web/cnidarian/BiGill_exonexp_gene.txt" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 36 }, { "cell_type": "code", "collapsed": false, "input": [ "!head /Volumes/web/cnidarian/BiGill_exonexp_gene.txt" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Gene\tcount\tsd\tsum\tcv\r", "\r\n", "Parent=CGI_10028436;\t8\t0.0154124519245155\t0.485755413498187\t0.031728832034052\r", "\r\n", "Parent=CGI_10024278;\t7\t0.00429195136554519\t0.0169956140350877\t0.252532880346917\r", "\r\n", "Parent=CGI_10019070;\t8\t0.103813488307781\t1.67397722515665\t0.0620160697216576\r", "\r\n", "Parent=CGI_10021016;\t4\t0.032419484481148\t0.42331787781172\t0.0765842554269992\r", "\r\n", "Parent=CGI_10007821;\t6\t0.0676276042041267\t1.11924329996958\t0.0604226124971795\r", "\r\n", "Parent=CGI_10014879;\t12\t0.174623636138173\t11.5634662794622\t0.0151013227277984\r", "\r\n", "Parent=CGI_10024860;\t4\t0.0952480651199777\t0.57039170639803\t0.166987114384009\r", "\r\n", "Parent=CGI_10022407;\t1\t\t0.0609318996415771\t\r", "\r\n", "Parent=CGI_10017802;\t1\t\t0.00432900432900433\t\r", "\r\n" ] } ], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "sort: invalid number at field start: invalid count at start of `POS5'\r\n" ] } ], "prompt_number": 11 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "#need to make igv file\n", "#clean up Gene name and join\n", "#find tails of genes with uniform versus variable expression @fu" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 45 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Make Basic BiGill methylated CG (>50%) feature track " ] }, { "cell_type": "code", "collapsed": false, "input": [ "!head /Volumes/web/cnidarian/BiGill_mCG_feature.csv" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Column1,Column4,Column4,feature,Column6\r", "\r\n", "C1009,89,89,BiGill_cgM,0.833\r", "\r\n", "C10093,107,107,BiGill_cgM,0.875\r", "\r\n", "C10107,93,93,BiGill_cgM,0.667\r", "\r\n", "C10137,13,13,BiGill_cgM,0.91\r", "\r\n", "C10137,18,18,BiGill_cgM,0.947\r", "\r\n", "C10137,30,30,BiGill_cgM,0.906\r", "\r\n", "C10137,39,39,BiGill_cgM,0.885\r", "\r\n", "C10333,85,85,BiGill_cgM,0.7\r", "\r\n", "C10333,92,92,BiGill_cgM,0.857\r", "\r\n" ] } ], "prompt_number": 40 }, { "cell_type": "code", "collapsed": false, "input": [ "!tr ',' \"\\t\" /Volumes/web/cnidarian/BiGill_mCG_feature.igv" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 41 }, { "cell_type": "code", "collapsed": false, "input": [ "!samtools sort -h\n" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "sort: illegal option -- h\r\n", "Usage: samtools sort [-on] [-m ] \r\n" ] } ], "prompt_number": 44 }, { "cell_type": "markdown", "metadata": {}, "source": [ "\"IGV_-_Session__http__eagle.fish.washington.edu_cnidarian_igv_session_073013.xml_17AFF782.png\"/\n", "\n", "link \n", "`http://eagle.fish.washington.edu/cnidarian/igv_session_080513.xml`" ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "CLC Exon Specific Expression " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\"Illumina_17CE4FBE.png\"/" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\"RNA-Seq_Analysis_17CE61C7.png\"/" ] }, { "cell_type": "code", "collapsed": false, "input": [ "#complete, though oddly not many reads map.....\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "markdown", "metadata": {}, "source": [ "\"CLC_Genomics_Workbench_6.5_17CFABB2.png\"/" ] }, { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "CLC Gene Expression" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\"CLC_Genomics_Workbench_6.5_18060151.png\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\"CLC_Genomics_Workbench_6.5_180602CC.png\"/" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "\"CLC_Genomics_Workbench_6.5_1806021D.png\"/" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Full Report: \n", "" ] }, { "cell_type": "heading", "level": 5, "metadata": {}, "source": [ "RPKM per Gene" ] }, { "cell_type": "code", "collapsed": false, "input": [ "#snapshot" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "markdown", "metadata": {}, "source": [ "\"CLC_Genomics_Workbench_6.5_1806036F.png\"/" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "full table \n", "" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from pandas import *\n", "\n", "# read data from data file into a pandas DataFrame \n", "BiGillrna = read_table(\"http://eagle.fish.washington.edu/cnidarian/BiGill_RNAseq_gene.1\" # name of the data file\n", " #sep=\",\", # what character separates each column?\n", " #na_values=[\"\", \" \"]) # what values should be considered \"blank\" values?\n", " )" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 9 }, { "cell_type": "code", "collapsed": false, "input": [ "BiGillrna" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 21, "text": [ "\n", "Int64Index: 28027 entries, 0 to 28026\n", "Data columns:\n", "Feature ID 28027 non-null values\n", "Expression values 28027 non-null values\n", "Transcripts annotated 28027 non-null values\n", "Detected transcripts 28027 non-null values\n", "Exon length 28027 non-null values\n", "Unique gene reads 28027 non-null values\n", "Total gene reads 28027 non-null values\n", "Unique exon reads 28027 non-null values\n", "Total exon reads 28027 non-null values\n", "Ratio of unique to total (exon reads) 28027 non-null values\n", "Unique exon-exon reads 28027 non-null values\n", "Total exon-exon reads 28027 non-null values\n", "Unique intron-exon reads 28027 non-null values\n", "Total intron-exon reads 28027 non-null values\n", "Exons 28027 non-null values\n", "RPKM 28027 non-null values\n", "Median coverage 28027 non-null values\n", "Chromosome 28027 non-null values\n", "Chromosome region start 28027 non-null values\n", "Chromosome region end 28027 non-null values\n", "dtypes: float64(3), int64(14), object(3)" ] } ], "prompt_number": 21 }, { "cell_type": "code", "collapsed": false, "input": [ "BiGillrna['Total gene reads'].hist(bins=1000);\n", "#Axis limits are changed using the axis([xmin, xmax, ymin, ymax]) function.\n", "#plt.axis([0, 0, 0, 20000])\n", "#plt.xlabel('x', fontsize=20)\n", "#plt.ylabel('RPKM', fontsize= 20)\n", "#plt.title('Gill', fontsize= 20)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 20, "text": [ "" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "" ] } ], "prompt_number": 20 }, { "cell_type": "code", "collapsed": false, "input": [ "\"\"\"\n", "Simple demo of a scatter plot.\n", "\"\"\"\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "\n", "N = 50\n", "x = BiGillrna['Total gene reads']\n", "y = np.random.rand(N)\n", "area = np.pi * (15 * np.random.rand(N))**2 # 0 to 15 point radiuses\n", "\n", "plt.scatter(x, y, s=area, alpha=0.5)\n", "plt.show()" ], "language": "python", "metadata": {}, "outputs": [ { "ename": "ValueError", "evalue": "x and y must be the same size", "output_type": "pyerr", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0marea\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpi\u001b[0m \u001b[0;34m*\u001b[0m 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\u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/Users/sr320/anaconda/lib/python2.7/site-packages/matplotlib/pyplot.pyc\u001b[0m in \u001b[0;36mscatter\u001b[0;34m(x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, faceted, verts, hold, **kwargs)\u001b[0m\n\u001b[1;32m 2916\u001b[0m \u001b[0mvmin\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmax\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvmax\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0malpha\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2917\u001b[0m \u001b[0mlinewidths\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlinewidths\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfaceted\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfaceted\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverts\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mverts\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2918\u001b[0;31m **kwargs)\n\u001b[0m\u001b[1;32m 2919\u001b[0m \u001b[0mdraw_if_interactive\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2920\u001b[0m \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/Users/sr320/anaconda/lib/python2.7/site-packages/matplotlib/axes.pyc\u001b[0m in \u001b[0;36mscatter\u001b[0;34m(self, x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, faceted, verts, **kwargs)\u001b[0m\n\u001b[1;32m 6057\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6058\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 6059\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"x and y must be the same size\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 6060\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6061\u001b[0m \u001b[0ms\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# This doesn't have to match x, y in size.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mValueError\u001b[0m: x and y must be the same size" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "" ] } ], "prompt_number": 30 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [ { "ename": "SyntaxError", "evalue": "invalid syntax (, line 1)", "output_type": "pyerr", "traceback": [ "\u001b[0;36m File \u001b[0;32m\"\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m plot BiGillrna[Total gene reads]\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n" ] } ], "prompt_number": 29 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Identifying examples of MERV" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Step 1) Based on exon specific expression data (RPKM) from CLC, identified all genes where number of exons was greater than 4 and exon expression was > 0 for all exons. This resulted in 38 genes." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Step 2) Calculated Expression Coefficient of Variance (stdev/mean RPKM) and average RPKM for all genes." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Step 3) For comparison, simply took the two genes with highest avg expression that also had a contrasting CV (1.6 v 0.5) AND similar avg expression levels (199 v 197) AND same number of exons (5). " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\"Untitled_180A143B.png\"/" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "IGV session: " ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Changing Insert Size" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\"Screenshot_10_24_13_1_28_PM_1819BAC5.png\"/" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Upon changing insert size and inadvertably a couple more parameters, output changed" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "New (larger insert +) \n", "\n", "\"CLC_Genomics_Workbench_6.5_1819BBF6.png\"/" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Original \n", "\n", "\"CLC_Genomics_Workbench_6.5_1819BB68.png\"/" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\"RNA-Seq_Analysis_1819BEA7.png\"/" ] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }