{ "metadata": { "name": "", "signature": "sha256:73744090dd0dab2ff39b897e913164cc3b09c8357712fcb925e3028ba91ce16d" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Quality Check of BiGo Larvae Experiment" ] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "#directory on greenbird\n", "ls /Volumes/NGS\\ Drive/NGS\\ Raw\\ Data/Cg_larvae_BSseq " ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "FCD2CA8.tar\r\n", "filtered_BS_CgF_TTAGGC_L007_R1.fastq.gz\r\n", "filtered_BS_CgF_TTAGGC_L007_R2.fastq.gz\r\n", "filtered_BS_CgLarv_T1D3_TGACCA_L007_R1.fastq.gz\r\n", "filtered_BS_CgLarv_T1D3_TGACCA_L007_R2.fastq.gz\r\n", "filtered_BS_CgLarv_T1D5_ACAGTG_L007_R1.fastq.gz\r\n", "filtered_BS_CgLarv_T1D5_ACAGTG_L007_R2.fastq.gz\r\n", "filtered_BS_CgLarv_T3D5_CAGATC_L007_R1.fastq.gz\r\n", "filtered_BS_CgLarv_T3D5_CAGATC_L007_R2.fastq.gz\r\n", "filtered_BS_CgM1_ACTTGA_L007_R1.fastq.gz\r\n", "filtered_BS_CgM1_ACTTGA_L007_R2.fastq.gz\r\n", "filtered_BS_CgM3_GATCAG_L007_R1.fastq.gz\r\n", "filtered_BS_CgM3_GATCAG_L007_R2.fastq.gz\r\n", "filtered_Bs_CgLarve_T3D3_GCCAAT_L007_R1.fastq.gz\r\n", "filtered_Bs_CgLarve_T3D3_GCCAAT_L007_R2.fastq.gz\r\n" ] } ], "prompt_number": 23 }, { "cell_type": "code", "collapsed": false, "input": [ "cd Shared/Apps/bsmap-2.73/" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "/Users/Shared/Apps/bsmap-2.73\n" ] } ], "prompt_number": 32 }, { "cell_type": "code", "collapsed": false, "input": [ "! /Users/Shared/Apps/bsmap-2.73/bsmap" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "\r\n", "BSMAP v2.73\r\n", "Usage:\tbsmap [options]\r\n", " -a query a file, FASTA/FASTQ/BAM format\r\n", " -d reference sequences file, FASTA format\r\n", " -o output alignment file, BSP/SAM/BAM format, if omitted, the output will be written to STDOUT in SAM format.\r\n", "\r\n", " Options for alignment:\r\n", " -s seed size, default=16(WGBS mode), 12(RRBS mode). min=8, max=16.\r\n", " -v if this value is between 0 and 1, it's interpreted as the mismatch rate w.r.t to the read length.\r\n", " otherwise it's interpreted as the maximum number of mismatches allowed on a read, <=15.\r\n", " example: -v 5 (max #mismatches = 5), -v 0.1 (max #mismatches = read_length * 10%)\r\n", " default=0.08.\r\n", " -g gap size, BSMAP only allow 1 continuous gap (insert or deletion) with up to 3 nucleotides\r\n", " default=0\r\n", " -w maximum number of equal best hits to count, <=1000\r\n", " -B start from the Nth read or read pair, default: 1\r\n", " -E end at the Nth read or read pair, default: 4,294,967,295\r\n", " -I index interval, default=4\r\n", " -k set the cut-off ratio for over-represented kmers, default=5e-07\r\n", " example: -k 1e-6 means the top 0.0001% over-represented kmer will be skipped in alignment\r\n", " -p number of processors to use, default=8\r\n", " -D activating RRBS mapping mode and set restriction enzyme digestion sites. \r\n", " digestion position marked by '-', example: -D C-CGG for MspI digestion.\r\n", " default: none (whole genome shotgun bisulfite mapping mode)\r\n", " -S seed for random number generation used in selecting multiple hits\r\n", " other seed values generate pseudo random number based on read index number, to allow reproducible mapping results. \r\n", " default=0. (get seed from system clock, mapping results not resproducible.)\r\n", " -n [0,1] set mapping strand information. default: 0\r\n", " -n 0: only map to 2 forward strands, i.e. BSW(++) and BSC(-+), \r\n", " for PE sequencing, map read#1 to ++ and -+, read#2 to +- and --.\r\n", " -n 1: map SE or PE reads to all 4 strands, i.e. ++, +-, -+, -- \r\n", " -M set alignment information for the additional nucleotide transition. \r\n", " is in the form of two different nucleotides N1N2, \r\n", " indicating N1 in the reads could be mapped to N2 in the reference sequences.\r\n", " default: -M TC, corresponds to C=>U(T) transition in bisulfite conversion. \r\n", " example: -M GA could be used to detect A=>I(G) transition in RNA editing. \r\n", "\r\n", " Options for trimming:\r\n", " -q quality threshold in trimming, 0-40, default=0 (no trim)\r\n", " -z base quality, default=33 [Illumina is using 64, Sanger Institute is using 33]\r\n", " -f filter low-quality reads containing >n Ns, default=5\r\n", " -A 3-end adapter sequence, default: none (no trim)\r\n", " -L map the first N nucleotides of the read, default:144 (map the whole read).\r\n", "\r\n", " Options for reporting:\r\n", " -r [0,1] how to report repeat hits, 0=none(unique hit/pair only); 1=random one, default:1.\r\n", " -R print corresponding reference sequences in SAM output, default=off\r\n", " -u report unmapped reads, default=off\r\n", " -H do not print header information in SAM format output\r\n", "\r\n", " Options for pair-end alignment:\r\n", " -b query b file\r\n", " -m minimal insert size allowed, default=28\r\n", " -x maximal insert size allowed, default=500\r\n", "\r\n", " -h help\r\n", "\r\n" ] } ], "prompt_number": 34 }, { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "BSMAP and Methratio" ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Female" ] }, { "cell_type": "code", "collapsed": false, "input": [ "#F\n", "! /Users/Shared/Apps/bsmap-2.73/bsmap -a /Volumes/NGS\\ Drive/NGS\\ Raw\\ Data/Cg_larvae_BSseq/filtered_BS_CgF_TTAGGC_L007_R1.fastq.gz -b /Volumes/NGS\\ Drive/NGS\\ Raw\\ Data/Cg_larvae_BSseq/filtered_BS_CgF_TTAGGC_L007_R2.fastq.gz -d /Volumes/web/cnidarian/oyster.v9.fa -o /Volumes/web/cnidarian/BiGo_lar_F.sam -p 8" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "\r\n", "BSMAP v2.73\r\n", "Start at: Tue Oct 1 15:12:31 2013\r\n", "\r\n", "Input reference file: /Volumes/web/cnidarian/oyster.v9.fa" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " \t(format: FASTA)\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Load in 11969 db seqs, total size 558601156 bp. 19 secs passed\r\n", "total_kmers: 43046721\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Create seed table. 34 secs passed\r\n", "max number of mismatches: read_length * 8% \tmax gap size: 0\r\n", "kmer cut-off ratio:5e-07\r\n", "max multi-hits: 100\tmax Ns: 5\tseed size: 16\tindex interval: 4\r\n", "quality cutoff: 0\tbase quality char: '!'\r\n", "min fragment size:28\tmax fragemt size:500\r\n", "start from read #1\tend at read #4294967295\r\n", "additional alignment: T in reads => C in reference\r\n", "mapping strand (read_1): ++,-+\r\n", "mapping strand (read_2): +-,--\r\n", "Pair-end alignment(8 threads)\r\n", "Input read file #1: /Volumes/NGS Drive/NGS Raw Data/Cg_larvae_BSseq/filtered_BS_CgF_TTAGGC_L007_R1.fastq.gz" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " \t(format: gzipped FASTQ)\r\n", "Input read file #2: /Volumes/NGS Drive/NGS Raw Data/Cg_larvae_BSseq/filtered_BS_CgF_TTAGGC_L007_R2.fastq.gz" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " \t(format: gzipped FASTQ)\r\n", "Output file: /Volumes/web/cnidarian/BiGo_lar_F.sam\t (format: SAM)\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #4: \t323336 read pairs finished. 76 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #2: \t50000 read pairs finished. 101 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #0: \t150000 read pairs finished. 104 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #6: \t100000 read pairs finished. 104 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #1: \t200000 read pairs finished. 105 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #3: \t300000 read pairs finished. 106 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #5: \t250000 read pairs finished. 106 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Total number of aligned reads: \r\n", "pairs: 180566 (56%)\r\n", "single a: 48357 (15%)\r\n", "single b: 37390 (12%)\r\n", "Done.\r\n", "Finished at Tue Oct 1 15:14:18 2013\r\n", "Total time consumed: 107 secs\r\n" ] } ], "prompt_number": 41 }, { "cell_type": "code", "collapsed": false, "input": [ "#methratio on female \n", "!python /Users/Shared/Apps/bsmap-2.74/methratio.py -d /Volumes/web/cnidarian/oyster.v9.fa -u -z -g -o /Volumes/web/cnidarian/BiGo_lar_F_methratio_v9_A.txt -s /Users/Shared/Apps/bsmap-2.74/samtools /Volumes/web/cnidarian/BiGo_lar_F.sam" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "@ Wed Oct 2 12:26:36 2013: reading reference /Volumes/web/cnidarian/oyster.v9.fa ...\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "@ Wed Oct 2 12:27:19 2013: reading /Volumes/web/cnidarian/BiGo_lar_F.sam ...\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "[samopen] SAM header is present: 11969 sequences.\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "@ Wed Oct 2 12:27:41 2013: combining CpG methylation from both strands ...\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "@ Wed Oct 2 12:28:25 2013: writing /Volumes/web/cnidarian/BiGo_lar_F_methratio_v9_A.txt ...\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "@ Wed Oct 2 12:31:18 2013: done.\r\n", "total 351170 valid mappings, 2879485 covered cytosines, average coverage: 1.09 fold.\r\n" ] } ], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "from pandas import *\n", "\n", "# read data from data file into a pandas DataFrame \n", "F = read_table(\"http://eagle.fish.washington.edu/cnidarian/BiGo_lar_F_methratio_v9_A.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": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "!head /Volumes/web/cnidarian/BiGo_lar_F_methratio_v9_A.txt" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "chr\tpos\tstrand\tcontext\tratio\teff_CT_count\tC_count\tCT_count\trev_G_count\trev_GA_count\tCI_lower\tCI_upper\r\n", "C10211\t59\t+\tTGCTG\t0.000\t1.00\t0\t1\t0\t0\t0.000\t0.793\r\n", "C10211\t70\t+\tGACAA\t0.000\t1.00\t0\t1\t0\t0\t0.000\t0.793\r\n", "C10211\t75\t+\tGACGT\t0.000\t1.00\t0\t1\t1\t1\t0.000\t0.793\r\n", "C10211\t79\t+\tTTCAC\t0.000\t1.00\t0\t1\t0\t0\t0.000\t0.793\r\n", "C10211\t81\t+\tCACCT\t0.000\t1.00\t0\t1\t0\t0\t0.000\t0.793\r\n", "C10211\t82\t+\tACCTT\t0.000\t1.00\t0\t1\t0\t0\t0.000\t0.793\r\n", "C10211\t99\t+\tTTCCT\t0.000\t1.00\t0\t1\t0\t0\t0.000\t0.793\r\n", "C10211\t100\t+\tTCCTG\t0.000\t1.00\t0\t1\t0\t0\t0.000\t0.793\r\n", "C10211\t106\t+\tAACTG\t0.000\t1.00\t0\t1\t0\t0\t0.000\t0.793\r\n" ] } ], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [ "!wc /Volumes/web/cnidarian/BiGo_lar_F_methratio_v9_A.txt" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ " 2879486 34553830 167118205 /Volumes/web/cnidarian/BiGo_lar_F_methratio_v9_A.txt\r\n" ] } ], "prompt_number": 14 }, { "cell_type": "code", "collapsed": false, "input": [ "F" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 15, "text": [ "\n", "Int64Index: 2879485 entries, 0 to 2879484\n", "Data columns:\n", "chr 2879485 non-null values\n", "pos 2879485 non-null values\n", "strand 2879485 non-null values\n", "context 2879483 non-null values\n", "ratio 2871586 non-null values\n", "eff_CT_count 2879485 non-null values\n", "C_count 2879485 non-null values\n", "CT_count 2879485 non-null values\n", "rev_G_count 2879485 non-null values\n", "rev_GA_count 2879485 non-null values\n", "CI_lower 2871586 non-null values\n", "CI_upper 2871586 non-null values\n", "dtypes: float64(4), int64(5), object(3)" ] } ], "prompt_number": 15 }, { "cell_type": "code", "collapsed": false, "input": [ "F['CT_count'].hist(bins=100);\n", "#Axis limits are changed using the axis([xmin, xmax, ymin, ymax]) function.\n", "plt.axis([0, 120, 0, 1000])\n" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 13, "text": [ "[0, 120, 0, 1000]" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "" ] } ], "prompt_number": 13 }, { "cell_type": "heading", "level": 4, "metadata": {}, "source": [ "Lets do some datachecks in R" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "read in F \n", "`f<-read.table(\"http://eagle.fish.washington.edu/cnidarian/BiGo_lar_F_methratio_v9_A.txt\", header=T, sep=\"\\t` \n", "\n", "filter for `CG` and 5 fold \n", "`F_cg<-f[grepl(\".{2}CG.{1}\",f$context) & f$CT_count >= 5, ]` \n", "\n", "only 401 obs. \n", "\n", "\n", "\"Calculator_and_RStudio_18059556.png\"/\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Somthing is funny- on 3k 5 fold? and even then # do not add up" ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "TID3 Larvae" ] }, { "cell_type": "code", "collapsed": false, "input": [ "#TID3\n", "! /Users/Shared/Apps/bsmap-2.73/bsmap -a /Volumes/NGS\\ Drive/NGS\\ Raw\\ Data/Cg_larvae_BSseq/filtered_BS_CgLarv_T1D3_TGACCA_L007_R1.fastq.gz -b /Volumes/NGS\\ Drive/NGS\\ Raw\\ Data/Cg_larvae_BSseq/filtered_BS_CgLarv_T1D3_TGACCA_L007_R2.fastq.gz -d /Volumes/web/cnidarian/oyster.v9.fa -o /Volumes/web/cnidarian/BiGo_lar_T1D3.sam -p 8" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "\r\n", "BSMAP v2.73\r\n", "Start at: Tue Oct 1 13:33:06 2013\r\n", "\r\n", "Input reference file: /Volumes/web/cnidarian/oyster.v9.fa \t(format: FASTA)\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Load in 11969 db seqs, total size 558601156 bp. 8 secs passed\r\n", "total_kmers: 43046721\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Create seed table. 23 secs passed\r\n", "max number of mismatches: read_length * 8% \tmax gap size: 0\r\n", "kmer cut-off ratio:5e-07\r\n", "max multi-hits: 100\tmax Ns: 5\tseed size: 16\tindex interval: 4\r\n", "quality cutoff: 0\tbase quality char: '!'\r\n", "min fragment size:28\tmax fragemt size:500\r\n", "start from read #1\tend at read #4294967295\r\n", "additional alignment: T in reads => C in reference\r\n", "mapping strand (read_1): ++,-+\r\n", "mapping strand (read_2): +-,--\r\n", "Pair-end alignment(8 threads)\r\n", "Input read file #1: /Volumes/NGS Drive/NGS Raw Data/Cg_larvae_BSseq/filtered_BS_CgLarv_T1D3_TGACCA_L007_R1.fastq.gz" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " \t(format: gzipped FASTQ)\r\n", "Input read file #2: /Volumes/NGS Drive/NGS Raw Data/Cg_larvae_BSseq/filtered_BS_CgLarv_T1D3_TGACCA_L007_R2.fastq.gz" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " \t(format: gzipped FASTQ)\r\n", "Output file: /Volumes/web/cnidarian/BiGo_lar_T1D3.sam\t (format: SAM)\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #7: \t100000 read pairs finished. 120 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #3: \t150000 read pairs finished. 121 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", 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"output_type": "stream", "stream": "stdout", "text": [ "Thread #3: \t2550000 read pairs finished. 728 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #2: \t2600000 read pairs finished. 730 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #6: \t2650000 read pairs finished. 739 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #1: \t2700000 read pairs finished. 741 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #4: \t2750000 read pairs finished. 744 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #5: \t2800000 read pairs finished. 748 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #7: \t2850000 read pairs finished. 824 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #0: \t2900000 read pairs finished. 827 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #3: \t2950000 read pairs finished. 828 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #2: \t3000000 read pairs finished. 831 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #6: \t3050000 read pairs finished. 840 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #1: \t3100000 read pairs finished. 841 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #4: \t3150000 read pairs finished. 843 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #5: \t3200000 read pairs finished. 845 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #3: \t3343987 read pairs finished. 860 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #7: \t3250000 read pairs finished. 861 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #0: \t3300000 read pairs finished. 862 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Total number of aligned reads: \r\n", "pairs: 1639918 (49%)\r\n", "single a: 709577 (21%)\r\n", "single b: 541078 (16%)\r\n", "Done.\r\n", "Finished at Tue Oct 1 13:47:34 2013\r\n", "Total time consumed: 868 secs\r\n" ] } ], "prompt_number": 39 }, { "cell_type": "code", "collapsed": false, "input": [ "#TID3 methratio\n", "!python /Users/Shared/Apps/bsmap-2.74/methratio.py -d /Volumes/web/cnidarian/oyster.v9.fa -u -z -g -o /Volumes/web/cnidarian/BiGo_lar_TID3_methratio_v9_A.txt -s /Users/Shared/Apps/bsmap-2.74/samtools /Volumes/web/cnidarian/BiGo_lar_T1D3.sam" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "@ Wed Oct 2 12:37:26 2013: reading reference /Volumes/web/cnidarian/oyster.v9.fa ...\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "@ Wed Oct 2 12:37:59 2013: reading /Volumes/web/cnidarian/BiGo_lar_T1D3.sam ...\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "[samopen] SAM header is present: 11969 sequences.\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "@ Wed Oct 2 12:41:29 2013: combining CpG methylation from both strands ...\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "@ Wed Oct 2 12:42:09 2013: writing /Volumes/web/cnidarian/BiGo_lar_TID3_methratio_v9_A.txt ...\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "@ Wed Oct 2 12:51:24 2013: done.\r\n", "total 3716891 valid mappings, 27890024 covered cytosines, average coverage: 1.34 fold.\r\n" ] } ], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "from pandas import *\n", "\n", "# read data from data file into a pandas DataFrame \n", "TID3 = read_table(\"http://eagle.fish.washington.edu/cnidarian/BiGo_lar_TID3_methratio_v9_A.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": 17 }, { "cell_type": "code", "collapsed": false, "input": [ "TID3['CT_count'].hist(bins=100);\n", "#Axis limits are changed using the axis([xmin, xmax, ymin, ymax]) function.\n", "plt.axis([0, 200, 0, 1000])\n", "#not sure what this does\n", "#where ('CT_count') < 100\n" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 31, "text": [ "[0, 200, 0, 1000]" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "" ] } ], "prompt_number": 31 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "T1D5" ] }, { "cell_type": "code", "collapsed": false, "input": [ "#T1D5\n", "! /Users/Shared/Apps/bsmap-2.73/bsmap -a /Volumes/NGS\\ Drive/NGS\\ Raw\\ Data/Cg_larvae_BSseq/filtered_BS_CgLarv_T1D5_ACAGTG_L007_R1.fastq.gz -b /Volumes/NGS\\ Drive/NGS\\ Raw\\ Data/Cg_larvae_BSseq/filtered_BS_CgLarv_T1D5_ACAGTG_L007_R2.fastq.gz -d /Volumes/web/cnidarian/oyster.v9.fa -o /Volumes/web/cnidarian/BiGo_lar_T1D5.sam -p 8" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "\r\n", "BSMAP v2.73\r\n", "Start at: Tue Oct 1 13:47:35 2013\r\n", "\r\n", "Input reference file: /Volumes/web/cnidarian/oyster.v9.fa \t(format: FASTA)\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Load in 11969 db seqs, total size 558601156 bp. 8 secs passed\r\n", "total_kmers: 43046721\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Create seed table. 24 secs passed\r\n", "max number of mismatches: read_length * 8% \tmax gap size: 0\r\n", "kmer cut-off ratio:5e-07\r\n", "max multi-hits: 100\tmax Ns: 5\tseed size: 16\tindex interval: 4\r\n", "quality cutoff: 0\tbase quality char: '!'\r\n", "min fragment size:28\tmax fragemt size:500\r\n", "start from read #1\tend at read #4294967295\r\n", "additional alignment: T in reads => C in reference\r\n", "mapping strand (read_1): ++,-+\r\n", "mapping strand (read_2): +-,--\r\n", "Pair-end alignment(8 threads)\r\n", "Input read file #1: /Volumes/NGS Drive/NGS Raw Data/Cg_larvae_BSseq/filtered_BS_CgLarv_T1D5_ACAGTG_L007_R1.fastq.gz" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " \t(format: gzipped FASTQ)\r\n", "Input read file #2: /Volumes/NGS Drive/NGS Raw Data/Cg_larvae_BSseq/filtered_BS_CgLarv_T1D5_ACAGTG_L007_R2.fastq.gz" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " \t(format: gzipped FASTQ)\r\n", "Output file: 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"stream", "stream": "stdout", "text": [ "Thread #1: \t2400000 read pairs finished. 661 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #3: \t2709822 read pairs finished. 674 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #7: \t2450000 read pairs finished. 717 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #4: \t2500000 read pairs finished. 721 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #2: \t2550000 read pairs finished. 723 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #6: \t2600000 read pairs finished. 724 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #0: \t2650000 read pairs finished. 724 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #5: \t2700000 read pairs finished. 725 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Total number of aligned reads: \r\n", "pairs: 1142766 (42%)\r\n", "single a: 764422 (28%)\r\n", "single b: 567963 (21%)\r\n", "Done.\r\n", "Finished at Tue Oct 1 13:59:46 2013\r\n", "Total time consumed: 731 secs\r\n" ] } ], "prompt_number": 40 }, { "cell_type": "code", "collapsed": false, "input": [ "#TID5 methratio\n", "!python /Users/Shared/Apps/bsmap-2.74/methratio.py -d /Volumes/web/cnidarian/oyster.v9.fa -u -z -g -o /Volumes/web/cnidarian/BiGo_lar_TID5_methratio_v9_A.txt -s /Users/Shared/Apps/bsmap-2.74/samtools /Volumes/web/cnidarian/BiGo_lar_T1D5.sam" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "@ Wed Oct 2 12:54:31 2013: reading reference /Volumes/web/cnidarian/oyster.v9.fa ...\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "@ Wed Oct 2 12:55:04 2013: reading /Volumes/web/cnidarian/BiGo_lar_T1D5.sam ...\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "[samopen] SAM header is present: 11969 sequences.\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "@ Wed Oct 2 12:57:52 2013: combining CpG methylation from both strands ...\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "@ Wed Oct 2 12:58:33 2013: writing /Volumes/web/cnidarian/BiGo_lar_TID5_methratio_v9_A.txt ...\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "@ Wed Oct 2 13:07:53 2013: done.\r\n", "total 2964457 valid mappings, 24242913 covered cytosines, average coverage: 1.26 fold.\r\n" ] } ], "prompt_number": 5 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "T3D3" ] }, { "cell_type": "code", "collapsed": false, "input": [ "#T3D3\n", "! /Users/Shared/Apps/bsmap-2.73/bsmap -a /Volumes/NGS\\ Drive/NGS\\ Raw\\ Data/Cg_larvae_BSseq/filtered_Bs_CgLarve_T3D3_GCCAAT_L007_R1.fastq.gz -b /Volumes/NGS\\ Drive/NGS\\ Raw\\ Data/Cg_larvae_BSseq/filtered_Bs_CgLarve_T3D3_GCCAAT_L007_R2.fastq.gz -d /Volumes/web/cnidarian/oyster.v9.fa -o /Volumes/web/cnidarian/BiGo_lar_T3D3.sam -p 8" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "\r\n", "BSMAP v2.73\r\n", "Start at: Wed Oct 2 07:01:57 2013\r\n", "\r\n", "Input reference file: /Volumes/web/cnidarian/oyster.v9.fa" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " \t(format: FASTA)\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Load in 11969 db seqs, total size 558601156 bp. 15 secs passed\r\n", "total_kmers: 43046721\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Create seed table. 29 secs passed\r\n", "max number of mismatches: read_length * 8% \tmax gap size: 0\r\n", "kmer cut-off ratio:5e-07\r\n", "max multi-hits: 100\tmax Ns: 5\tseed size: 16\tindex interval: 4\r\n", "quality cutoff: 0\tbase quality char: '!'\r\n", "min fragment size:28\tmax fragemt size:500\r\n", "start from read #1\tend at read #4294967295\r\n", "additional alignment: T in reads => C in reference\r\n", "mapping strand (read_1): ++,-+\r\n", "mapping strand (read_2): +-,--\r\n", "Pair-end alignment(8 threads)\r\n", "Input read file #1: /Volumes/NGS Drive/NGS Raw Data/Cg_larvae_BSseq/filtered_Bs_CgLarve_T3D3_GCCAAT_L007_R1.fastq.gz" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " \t(format: gzipped FASTQ)\r\n", "Input read file #2: /Volumes/NGS Drive/NGS Raw Data/Cg_larvae_BSseq/filtered_Bs_CgLarve_T3D3_GCCAAT_L007_R2.fastq.gz" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " \t(format: gzipped FASTQ)\r\n", "Output file: /Volumes/web/cnidarian/BiGo_lar_T3D3.sam\t (format: SAM)\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #2: \t50000 read pairs finished. 122 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #3: \t150000 read pairs finished. 122 secs 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finished. 1380 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #3: \t5300000 read pairs finished. 1390 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #5: \t5350000 read pairs finished. 1392 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #7: \t5400000 read pairs finished. 1398 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #0: \t5450000 read pairs finished. 1399 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #1: \t5500000 read pairs finished. 1410 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #4: \t5550000 read pairs finished. 1420 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #6: \t5600000 read pairs finished. 1433 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #0: \t5837666 read pairs finished. 1449 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #2: \t5650000 read pairs finished. 1451 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #3: \t5700000 read pairs finished. 1453 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #5: \t5750000 read pairs finished. 1454 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #7: \t5800000 read pairs finished. 1455 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Total number of aligned reads: \r\n", "pairs: 3130107 (54%)\r\n", "single a: 1140569 (20%)\r\n", "single b: 880396 (15%)\r\n", "Done.\r\n", "Finished at Wed Oct 2 07:26:12 2013\r\n", "Total time consumed: 1455 secs\r\n" ] } ], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "#T3D3 methratio\n", "!python /Users/Shared/Apps/bsmap-2.74/methratio.py -d /Volumes/web/cnidarian/oyster.v9.fa -u -z -g -o /Volumes/web/cnidarian/BiGo_lar_T3D3_methratio_v9_A.txt -s /Users/Shared/Apps/bsmap-2.74/samtools /Volumes/web/cnidarian/BiGo_lar_T3D3.sam" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "@ Wed Oct 2 13:07:54 2013: reading reference /Volumes/web/cnidarian/oyster.v9.fa ...\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "@ Wed Oct 2 13:08:27 2013: reading /Volumes/web/cnidarian/BiGo_lar_T3D3.sam ...\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "[samopen] SAM header is present: 11969 sequences.\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "@ Wed Oct 2 13:15:01 2013: combining CpG methylation from both strands ...\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "@ Wed Oct 2 13:15:41 2013: writing /Volumes/web/cnidarian/BiGo_lar_T3D3_methratio_v9_A.txt ...\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "@ Wed Oct 2 13:29:25 2013: done.\r\n", "total 6941630 valid mappings, 45580028 covered cytosines, average coverage: 1.55 fold.\r\n" ] } ], "prompt_number": 6 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "T3D5" ] }, { "cell_type": "code", "collapsed": false, "input": [ "#T3D5\n", "! /Users/Shared/Apps/bsmap-2.73/bsmap -a /Volumes/NGS\\ Drive/NGS\\ Raw\\ Data/Cg_larvae_BSseq/filtered_BS_CgLarv_T3D5_CAGATC_L007_R1.fastq.gz -b /Volumes/NGS\\ Drive/NGS\\ Raw\\ Data/Cg_larvae_BSseq/filtered_BS_CgLarv_T3D5_CAGATC_L007_R2.fastq.gz -d /Volumes/web/cnidarian/oyster.v9.fa -o /Volumes/web/cnidarian/BiGo_lar_T3D5.sam -p 8" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "\r\n", "BSMAP v2.73\r\n", "Start at: Wed Oct 2 07:26:12 2013\r\n", "\r\n", "Input reference file: /Volumes/web/cnidarian/oyster.v9.fa \t(format: FASTA)\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Load in 11969 db seqs, total size 558601156 bp. 8 secs passed\r\n", "total_kmers: 43046721\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Create seed table. 24 secs passed\r\n", "max number of mismatches: read_length * 8% \tmax gap size: 0\r\n", "kmer cut-off ratio:5e-07\r\n", "max multi-hits: 100\tmax Ns: 5\tseed size: 16\tindex interval: 4\r\n", "quality cutoff: 0\tbase quality char: '!'\r\n", "min fragment size:28\tmax fragemt size:500\r\n", "start from read #1\tend at read #4294967295\r\n", "additional alignment: T in reads => C in reference\r\n", "mapping strand (read_1): ++,-+\r\n", "mapping strand (read_2): +-,--\r\n", "Pair-end alignment(8 threads)\r\n", "Input read file #1: /Volumes/NGS Drive/NGS Raw Data/Cg_larvae_BSseq/filtered_BS_CgLarv_T3D5_CAGATC_L007_R1.fastq.gz" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " \t(format: gzipped FASTQ)\r\n", "Input read file #2: /Volumes/NGS Drive/NGS Raw Data/Cg_larvae_BSseq/filtered_BS_CgLarv_T3D5_CAGATC_L007_R2.fastq.gz \t(format: gzipped FASTQ)\r\n", "Output file: /Volumes/web/cnidarian/BiGo_lar_T3D5.sam\t (format: SAM)\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #5: \t100000 read pairs finished. 120 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #0: \t150000 read pairs finished. 120 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #3: \t50000 read pairs finished. 121 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #6: \t200000 read pairs finished. 121 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #4: \t250000 read pairs finished. 122 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #7: \t300000 read pairs finished. 123 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #1: 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finished. 1116 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #4: \t4350000 read pairs finished. 1117 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #2: \t4400000 read pairs finished. 1117 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #0: \t4421486 read pairs finished. 1118 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Total number of aligned reads: \r\n", "pairs: 2414419 (55%)\r\n", "single a: 819661 (19%)\r\n", "single b: 637662 (14%)\r\n", "Done.\r\n", "Finished at Wed Oct 2 07:44:50 2013\r\n", "Total time consumed: 1118 secs\r\n" ] } ], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "#T3D5 methratio\n", "!python /Users/Shared/Apps/bsmap-2.74/methratio.py -d /Volumes/web/cnidarian/oyster.v9.fa -u -z -g -o /Volumes/web/cnidarian/BiGo_lar_T3D5_methratio_v9_A.txt -s /Users/Shared/Apps/bsmap-2.74/samtools /Volumes/web/cnidarian/BiGo_lar_T3D5.sam" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "@ Wed Oct 2 13:29:26 2013: reading reference /Volumes/web/cnidarian/oyster.v9.fa ...\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "@ Wed Oct 2 13:30:10 2013: reading /Volumes/web/cnidarian/BiGo_lar_T3D5.sam ...\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "[samopen] SAM header is present: 11969 sequences.\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "@ Wed Oct 2 13:35:11 2013: combining CpG methylation from both strands ...\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "@ Wed Oct 2 13:35:52 2013: writing /Volumes/web/cnidarian/BiGo_lar_T3D5_methratio_v9_A.txt ...\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "@ Wed Oct 2 13:47:24 2013: done.\r\n", "total 5275651 valid mappings, 37156094 covered cytosines, average coverage: 1.45 fold.\r\n" ] } ], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [ "!wc /Volumes/web/cnidarian/BiGo_lar_T3D5_methratio_v9_A.txt" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ " 37156095 445873112 2157638420 /Volumes/web/cnidarian/BiGo_lar_T3D5_methratio_v9_A.txt\r\n" ] } ], "prompt_number": 14 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "M1" ] }, { "cell_type": "code", "collapsed": false, "input": [ "#M1\n", "! /Users/Shared/Apps/bsmap-2.73/bsmap -a /Volumes/NGS\\ Drive/NGS\\ Raw\\ Data/Cg_larvae_BSseq/filtered_BS_CgM1_ACTTGA_L007_R1.fastq.gz -b /Volumes/NGS\\ Drive/NGS\\ Raw\\ Data/Cg_larvae_BSseq/filtered_BS_CgM1_ACTTGA_L007_R2.fastq.gz -d /Volumes/web/cnidarian/oyster.v9.fa -o /Volumes/web/cnidarian/BiGo_lar_M1.sam -p 8" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "\r\n", "BSMAP v2.73\r\n", "Start at: Wed Oct 2 07:44:50 2013\r\n", "\r\n", "Input reference file: /Volumes/web/cnidarian/oyster.v9.fa \t(format: FASTA)\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Load in 11969 db seqs, total size 558601156 bp. 8 secs passed\r\n", "total_kmers: 43046721\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Create seed table. 24 secs passed\r\n", "max number of mismatches: read_length * 8% \tmax gap size: 0\r\n", "kmer cut-off ratio:5e-07\r\n", "max multi-hits: 100\tmax Ns: 5\tseed size: 16\tindex interval: 4\r\n", "quality cutoff: 0\tbase quality char: '!'\r\n", "min fragment size:28\tmax fragemt size:500\r\n", "start from read #1\tend at read #4294967295\r\n", "additional alignment: T in reads => C in reference\r\n", "mapping strand (read_1): ++,-+\r\n", "mapping strand (read_2): +-,--\r\n", "Pair-end alignment(8 threads)\r\n", "Input read file #1: /Volumes/NGS Drive/NGS Raw Data/Cg_larvae_BSseq/filtered_BS_CgM1_ACTTGA_L007_R1.fastq.gz" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " \t(format: gzipped FASTQ)\r\n", "Input read file #2: /Volumes/NGS Drive/NGS Raw Data/Cg_larvae_BSseq/filtered_BS_CgM1_ACTTGA_L007_R2.fastq.gz \t(format: gzipped FASTQ)\r\n", "Output file: /Volumes/web/cnidarian/BiGo_lar_M1.sam\t (format: SAM)\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #0: \t100000 read pairs finished. 122 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #1: \t50000 read pairs finished. 122 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #7: \t200000 read pairs finished. 122 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #2: \t150000 read pairs finished. 123 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #6: \t250000 read pairs finished. 123 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread 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] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #6: \t3500000 read pairs finished. 927 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #7: \t3550000 read pairs finished. 929 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #2: \t3600000 read pairs finished. 935 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #1: \t3650000 read pairs finished. 1006 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #0: \t3700000 read pairs finished. 1015 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #3: \t3850000 read pairs finished. 1022 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #4: \t3800000 read pairs finished. 1023 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #5: \t3750000 read pairs finished. 1026 secs 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finished. 1126 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #6: \t4300000 read pairs finished. 1128 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #7: \t4350000 read pairs finished. 1128 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #2: \t4400000 read pairs finished. 1136 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #1: \t4450000 read pairs finished. 1202 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #0: \t4500000 read pairs finished. 1213 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #3: \t4550000 read pairs finished. 1220 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #4: \t4600000 read pairs finished. 1223 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #5: \t4650000 read pairs finished. 1226 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #6: \t4700000 read pairs finished. 1228 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #7: \t4750000 read pairs finished. 1229 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #2: \t4800000 read pairs finished. 1236 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #1: \t4850000 read pairs finished. 1302 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #0: \t4900000 read pairs finished. 1314 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #3: \t4950000 read pairs finished. 1320 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #4: \t5000000 read pairs finished. 1322 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #5: \t5050000 read pairs finished. 1326 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #6: \t5100000 read pairs finished. 1329 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #7: \t5150000 read pairs finished. 1330 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #2: \t5200000 read pairs finished. 1336 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #1: \t5250000 read pairs finished. 1393 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #0: \t5300000 read pairs finished. 1401 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #7: \t5543163 read pairs finished. 1403 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #3: \t5350000 read pairs finished. 1403 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #4: \t5400000 read pairs finished. 1404 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #5: \t5450000 read pairs finished. 1405 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #6: \t5500000 read pairs finished. 1405 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Total number of aligned reads: \r\n", "pairs: 2600662 (47%)\r\n", "single a: 1090658 (20%)\r\n", "single b: 894893 (16%)\r\n", "Done.\r\n", "Finished at Wed Oct 2 08:08:15 2013\r\n", "Total time consumed: 1405 secs\r\n" ] } ], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "#M1 methratio\n", "!python /Users/Shared/Apps/bsmap-2.74/methratio.py -d /Volumes/web/cnidarian/oyster.v9.fa -u -z -g -o /Volumes/web/cnidarian/BiGo_lar_M1_methratio_v9_A.txt -s /Users/Shared/Apps/bsmap-2.74/samtools /Volumes/web/cnidarian/BiGo_lar_M1.sam" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "@ Wed Oct 2 13:47:24 2013: reading reference /Volumes/web/cnidarian/oyster.v9.fa ...\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "@ Wed Oct 2 13:47:58 2013: reading /Volumes/web/cnidarian/BiGo_lar_M1.sam ...\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "[samopen] SAM header is present: 11969 sequences.\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "@ Wed Oct 2 13:53:31 2013: combining CpG methylation from both strands ...\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "@ Wed Oct 2 13:54:11 2013: writing /Volumes/web/cnidarian/BiGo_lar_M1_methratio_v9_A.txt ...\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "@ Wed Oct 2 14:07:48 2013: done.\r\n", "total 5799674 valid mappings, 37975071 covered cytosines, average coverage: 1.57 fold.\r\n" ] } ], "prompt_number": 8 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "M3" ] }, { "cell_type": "code", "collapsed": false, "input": [ "#M3\n", "! /Users/Shared/Apps/bsmap-2.73/bsmap -a /Volumes/NGS\\ Drive/NGS\\ Raw\\ Data/Cg_larvae_BSseq/filtered_BS_CgM3_GATCAG_L007_R1.fastq.gz -b /Volumes/NGS\\ Drive/NGS\\ Raw\\ Data/Cg_larvae_BSseq/filtered_BS_CgM3_GATCAG_L007_R2.fastq.gz -d /Volumes/web/cnidarian/oyster.v9.fa -o /Volumes/web/cnidarian/BiGo_lar_M3.sam -p 8" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "\r\n", "BSMAP v2.73\r\n", "Start at: Wed Oct 2 08:08:16 2013\r\n", "\r\n", "Input reference file: /Volumes/web/cnidarian/oyster.v9.fa \t(format: FASTA)\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Load in 11969 db seqs, total size 558601156 bp. 8 secs passed\r\n", "total_kmers: 43046721\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Create seed table. 24 secs passed\r\n", "max number of mismatches: read_length * 8% \tmax gap size: 0\r\n", "kmer cut-off ratio:5e-07\r\n", "max multi-hits: 100\tmax Ns: 5\tseed size: 16\tindex interval: 4\r\n", "quality cutoff: 0\tbase quality char: '!'\r\n", "min fragment size:28\tmax fragemt size:500\r\n", "start from read #1\tend at read #4294967295\r\n", "additional alignment: T in reads => C in reference\r\n", "mapping strand (read_1): ++,-+\r\n", "mapping strand (read_2): +-,--\r\n", "Pair-end alignment(8 threads)\r\n", "Input read file #1: /Volumes/NGS Drive/NGS Raw Data/Cg_larvae_BSseq/filtered_BS_CgM3_GATCAG_L007_R1.fastq.gz" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " \t(format: gzipped FASTQ)\r\n", "Input read file #2: /Volumes/NGS Drive/NGS Raw Data/Cg_larvae_BSseq/filtered_BS_CgM3_GATCAG_L007_R2.fastq.gz" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " \t(format: gzipped FASTQ)\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Output file: /Volumes/web/cnidarian/BiGo_lar_M3.sam\t 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passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #3: \t4800000 read pairs finished. 1235 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #1: \t4850000 read pairs finished. 1285 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #0: \t4900000 read pairs finished. 1299 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #2: \t4950000 read pairs finished. 1311 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #5: \t5000000 read pairs finished. 1314 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #4: \t5050000 read pairs finished. 1319 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #7: \t5100000 read pairs finished. 1321 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #6: \t5150000 read pairs finished. 1322 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #3: \t5200000 read pairs finished. 1337 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #6: \t5526741 read pairs finished. 1370 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #1: \t5250000 read pairs finished. 1375 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #0: \t5300000 read pairs finished. 1382 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #2: \t5350000 read pairs finished. 1386 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #5: \t5400000 read pairs finished. 1387 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #4: \t5450000 read pairs finished. 1388 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #7: \t5500000 read pairs finished. 1389 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Total number of aligned reads: \r\n", "pairs: 3032870 (55%)\r\n", "single a: 996127 (18%)\r\n", "single b: 789107 (14%)\r\n", "Done.\r\n", "Finished at Wed Oct 2 08:31:25 2013\r\n", "Total time consumed: 1389 secs\r\n" ] } ], "prompt_number": 4 }, { "cell_type": "raw", "metadata": {}, "source": [ "#M3 methratio\n", "!python /Users/Shared/Apps/bsmap-2.74/methratio.py -d /Volumes/web/cnidarian/oyster.v9.fa -u -z -g -o /Volumes/web/cnidarian/BiGo_lar_M3_methratio_v9_A.txt -s /Users/Shared/Apps/bsmap-2.74/samtools /Volumes/web/cnidarian/BiGo_lar_M3.sam" ] }, { "cell_type": "code", "collapsed": false, "input": [ "!wc /Volumes/web/cnidarian/BiGo_lar_M3_methratio_v9_A.txt" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ " 42369118 508429391 2460930058 /Volumes/web/cnidarian/BiGo_lar_M3_methratio_v9_A.txt\r\n" ] } ], "prompt_number": 33 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Mapping Summary" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "Female\n", "pairs: 180566 (56%)\n", "single a: 48357 (15%)\n", "single b: 37390 (12%)\n", " \n", "T1D3\n", "pairs: 1639918 (49%)\n", "single a: 709577 (21%)\n", "single b: 541078 (16%)\n", "\n", "T1D5\n", "pairs: 1142766 (42%)\n", "single a: 764422 (28%)\n", "single b: 567963 (21%)\n", "\n", "T3D3 \n", "pairs: 3130107 (54%)\n", "single a: 1140569 (20%)\n", "single b: 880396 (15%)\n", "\n", "T3D5\n", "pairs: 2414419 (55%)\n", "single a: 819661 (19%)\n", "single b: 637662 (14%)\n", "\n", "M1\n", "pairs: 2600662 (47%)\n", "single a: 1090658 (20%)\n", "single b: 894893 (16%)\n", "\n", "M3\n", "pairs: 3032870 (55%)\n", "single a: 996127 (18%)\n", "single b: 789107 (14%)\n", "\n", "\n", "All methratio coverage about 1.3x" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\"BiGo_larvae_180350D3.png\"/" ] }, { "cell_type": "markdown", "metadata": {}, "source": [] }, { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Uploading methratio files to SQLShare" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Parsing files for characterization" ] }, { "cell_type": "code", "collapsed": false, "input": [ "!python /Users/sr320/sqlshare-pythonclient/tools/singleupload.py -h" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Usage: singleupload.py [options] \r\n", "\r\n", "Options:\r\n", " -h, --help show this help message and exit\r\n", " -u USERNAME, --user=USERNAME\r\n", " SQLshare user name\r\n", " -p PASSWORD, --password=PASSWORD\r\n", " SQLshare password\r\n", " -d DATASETNAME, --datasetname=DATASETNAME\r\n", " Dataset name (defaults to filename if not supplied\r\n" ] } ], "prompt_number": 21 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "F" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "_Uploading Female data_ \n", "`python /Users/sr320/sqlshare-pythonclient/tools/singleupload.py -d BiGo_lar_F /Volumes/web/cnidarian/BiGo_lar_F_methratio_v9_A.txt`" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "Query\n", "python /Users/sr320/sqlshare-pythonclient/tools/fetchdata.py -s \"SELECT chr as seqname,'methratio' as source,'CpG' as feature, pos as start, pos + 1 as [end], ratio as score, strand, '.' as frame, '.' as attribute FROM [sr320@washington.edu].[BiGO_lar_M3] where context like '__CG_' and CT_Count > 5\" -f tsv -o /Volumes/web/cnidarian/BiGo_lar_M3_methratio_CG.txt" ] }, { "cell_type": "code", "collapsed": false, "input": [ "#2.8 million lines\n", "#!!!!!! 3447 loci 5x coverage\n", "# 401 CG" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 66 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "M3" ] }, { "cell_type": "code", "collapsed": false, "input": [ "#M3 methratio output has 42 million lines\n", "#!!!!!!!! 41 million lines have CT_count <5 ---- 97% \n", "#" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 35 }, { "cell_type": "code", "collapsed": false, "input": [ "#19 M - strand\n", "#23 m + strand" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "from pandas import *\n", "\n", "# read data from data file into a pandas DataFrame \n", "m3 = read_table(\"http://eagle.fish.washington.edu/cnidarian/BiGo_lar_M3_methratio_v9_A.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": 39 }, { "cell_type": "code", "collapsed": false, "input": [ "m3['CT_count'].hist(bins=5000);\n", "#Axis limits are changed using the axis([xmin, xmax, ymin, ymax]) function.\n", "plt.axis([0, 10, 0, 10000000])" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 75, "text": [ "[0, 10, 0, 10000000]" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "" ] } ], "prompt_number": 75 }, { "cell_type": "code", "collapsed": false, "input": [ "m3['CT_count'].hist(bins=5000);\n", "#Axis limits are changed using the axis([xmin, xmax, ymin, ymax]) function.\n", "plt.axis([0, 100, 0, 2000])" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 78, "text": [ "[0, 100, 0, 2000]" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "" ] } ], "prompt_number": 78 }, { "cell_type": "code", "collapsed": false, "input": [ "#3010 CG loci have >20 coverage" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "!head /Volumes/web/cnidarian/BiGo_lar_M3_methratio_v9_A.txt" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "chr\tpos\tstrand\tcontext\tratio\teff_CT_count\tC_count\tCT_count\trev_G_count\trev_GA_count\tCI_lower\tCI_upper\r\n", "C10005\t78\t-\tTAGGG\t0.000\t1.00\t0\t1\t0\t0\t0.000\t0.793\r\n", "C10005\t79\t-\tAGGGA\t0.000\t1.00\t0\t1\t0\t0\t0.000\t0.793\r\n", "C10005\t80\t-\tGGGAA\t0.000\t1.00\t0\t1\t0\t0\t0.000\t0.793\r\n", "C10005\t86\t-\tATGTG\t0.000\t1.00\t0\t1\t0\t0\t0.000\t0.793\r\n", "C10005\t88\t-\tGTGGG\t0.000\t1.00\t0\t1\t0\t0\t0.000\t0.793\r\n", "C10005\t89\t-\tTGGGT\t0.000\t1.00\t0\t1\t0\t0\t0.000\t0.793\r\n", "C10005\t90\t-\tGGGTT\t0.000\t1.00\t0\t1\t0\t0\t0.000\t0.793\r\n", "C10005\t94\t-\tTTGTT\t0.000\t1.00\t0\t1\t0\t0\t0.000\t0.793\r\n", "C10005\t100\t-\tAAGTC\t0.000\t1.00\t0\t1\t0\t0\t0.000\t0.793\r\n" ] } ], "prompt_number": 59 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "#Mac's gill sample\n", "#methratio output had 91 million lines\n", "#69 million lines have CT_count <5 --- 75%" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "#Claire's sperm data\n", "#methratio output had 126 million lines (after ratio NA removed) vs 127/22\n", "#22 million lines have CT_count <5 --- 17%" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "for context ... \n", "\"MBD-Seq_C._gigas_gill_manuscript_-_Google_Drive_18059D1D.png\"/\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "\"GonadMethylation_WorkingMS_-_Google_Drive_1805A072.png\"/" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "!python /Users/sr320/sqlshare-pythonclient/tools/singleupload.py -d BiGo_lar_M3 /Volumes/web/cnidarian/BiGo_lar_M3_methratio_v9_A.txt" ] }, { "cell_type": "code", "collapsed": false, "input": [ "#Creating GFF of CG with 5x\n", "!python /Users/sr320/sqlshare-pythonclient/tools/fetchdata.py -s \"SELECT chr as seqname,'methratio' as source,'CpG' as feature, pos as start, pos + 1 as [end], ratio as score, strand, '.' as frame, '.' as attribute FROM [sr320@washington.edu].[BiGO_lar_M3] where context like '__CG_' and CT_Count >= 5\" -f tsv -o /Volumes/web/cnidarian/BiGo_lar_M3_methratio_CG.txt" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 36 }, { "cell_type": "code", "collapsed": false, "input": [ "!head /Volumes/web/cnidarian/BiGo_lar_M3_methratio_CG.txt" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "seqname\tsource\tfeature\tstart\tend\tscore\tstrand\tframe\tattribute\r", "\r\n", "C10295\tmethratio\tCpG\t38\t39\t0.000\t+\t.\t.\r", "\r\n", "C10295\tmethratio\tCpG\t51\t52\t0.000\t+\t.\t.\r", "\r\n", "C10295\tmethratio\tCpG\t142\t143\t0.000\t+\t.\t.\r", "\r\n", "C10845\tmethratio\tCpG\t110\t111\t0.000\t+\t.\t.\r", "\r\n", "C10845\tmethratio\tCpG\t145\t146\t0.000\t+\t.\t.\r", "\r\n", "C11248\tmethratio\tCpG\t71\t72\t0.000\t+\t.\t.\r", "\r\n", "C11870\tmethratio\tCpG\t109\t110\t0.000\t+\t.\t.\r", "\r\n", "C12272\tmethratio\tCpG\t65\t66\t1.000\t+\t.\t.\r", "\r\n", "C14220\tmethratio\tCpG\t96\t97\t0.000\t+\t.\t.\r", "\r\n" ] } ], "prompt_number": 37 }, { "cell_type": "code", "collapsed": false, "input": [ "!wc /Volumes/web/cnidarian/BiGo_lar_M3_methratio_CG.txt" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ " 182754 1644786 9561122 /Volumes/web/cnidarian/BiGo_lar_M3_methratio_CG.txt\r\n" ] } ], "prompt_number": 38 }, { "cell_type": "markdown", "metadata": {}, "source": [ "\"SQLShare_-_View_Query_1805A472.png\"/" ] }, { "cell_type": "code", "collapsed": false, "input": [ "#scratch\n", "!python /Users/sr320/sqlshare-pythonclient/tools/fetchdata.py -s \"SELECT chr as seqname,'methratio' as source,'CpG' as feature, pos as start, pos + 1 as [end], ratio as score, strand, '.' as frame, '.' as attribute FROM [sr320@washington.edu].[BiGO_lar_M3] where context like '__CT_' and CT_Count >= 5\" -f tsv -o /Volumes/web/cnidarian/BiGo_lar_M3_methratio_CT.txt" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 53 }, { "cell_type": "code", "collapsed": false, "input": [ "!wc /Volumes/web/cnidarian/BiGo_lar_M3_methratio_CT.txt" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ " 131803 1186227 6885390 /Volumes/web/cnidarian/BiGo_lar_M3_methratio_CT.txt\r\n" ] } ], "prompt_number": 54 }, { "cell_type": "code", "collapsed": false, "input": [ "#confirming CNs add up \n", "#CG 182k\n", "#CC 72k\n", "#CA 240k\n", "#CT 131k\n", "\n", "# - 625k\n", "#^ that would have just been positive strand.\n", "# yes add up\n" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "from pandas import *\n", "\n", "# read data from data file into a pandas DataFrame \n", "m3CG = read_table(\"http://eagle.fish.washington.edu/cnidarian/BiGo_lar_M3_methratio_CG.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": 60 }, { "cell_type": "code", "collapsed": false, "input": [ "!head /Volumes/web/cnidarian/BiGo_lar_M3_methratio_CG.txt" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "seqname\tsource\tfeature\tstart\tend\tscore\tstrand\tframe\tattribute\r", "\r\n", "C10295\tmethratio\tCpG\t38\t39\t0.000\t+\t.\t.\r", "\r\n", "C10295\tmethratio\tCpG\t51\t52\t0.000\t+\t.\t.\r", "\r\n", "C10295\tmethratio\tCpG\t142\t143\t0.000\t+\t.\t.\r", "\r\n", "C10845\tmethratio\tCpG\t110\t111\t0.000\t+\t.\t.\r", "\r\n", "C10845\tmethratio\tCpG\t145\t146\t0.000\t+\t.\t.\r", "\r\n", "C11248\tmethratio\tCpG\t71\t72\t0.000\t+\t.\t.\r", "\r\n", "C11870\tmethratio\tCpG\t109\t110\t0.000\t+\t.\t.\r", "\r\n", "C12272\tmethratio\tCpG\t65\t66\t1.000\t+\t.\t.\r", "\r\n", "C14220\tmethratio\tCpG\t96\t97\t0.000\t+\t.\t.\r", "\r\n" ] } ], "prompt_number": 61 }, { "cell_type": "code", "collapsed": false, "input": [ "!wc /Volumes/web/cnidarian/BiGo_lar_M3_methratio_CG.txt" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ " 182754 1644786 9561122 /Volumes/web/cnidarian/BiGo_lar_M3_methratio_CG.txt\r\n" ] } ], "prompt_number": 62 }, { "cell_type": "code", "collapsed": false, "input": [ "m3CG['score'].hist(bins=500);\n", "#Axis limits are changed using the axis([xmin, xmax, ymin, ymax]) function.\n", "plt.axis([0, 1, 0, 10000])" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 85, "text": [ "[0, 1, 0, 10000]" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "" ] } ], "prompt_number": 85 }, { "cell_type": "code", "collapsed": false, "input": [ "#worried about score...\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 86 }, { "cell_type": "markdown", "metadata": {}, "source": [ "cleaning \n", "```sql\n", "SELECT [chr]\n", " , CAST([pos] as INTEGER) as [pos]\n", " , [strand]\n", " , [context]\n", " , CAST([ratio] as FLOAT) as [ratio]\n", " , CAST([eff_CT_count] as INTEGER) as [eff_CT_count]\n", " , CAST([C_count] as INTEGER) as [C_count]\n", " , CAST([CT_count] as INTEGER) as [CT_count]\n", " , CAST([rev_G_count] as INTEGER) as [rev_G_count]\n", " , CAST([CI_lower] as FLOAT) as [CI_lower]\n", " , CAST([CI_upper] as FLOAT) as [CI_upper]\n", "FROM [sr320@washington.edu].[BiGo_lar_M3]\n", "WHERE [ratio] <> 'NA'\n", " and\n", " context like '__CG_'\n", " and \n", " CT_count >= 5\n", "\u200b\n", "```" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from pandas import *\n", "\n", "# read data from data file into a pandas DataFrame \n", "m3c = read_csv(\"http://eagle.fish.washington.edu/cnidarian/clean_BiGo_lar_M3_cg5.csv\", # 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": 9 }, { "cell_type": "code", "collapsed": false, "input": [ "m3c['ratio'].hist(bins=4);\n", "#Axis limits are changed using the axis([xmin, xmax, ymin, ymax]) function.\n", "#plt.axis([0, 1, 0, 10000])" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 98, "text": [ "" ] }, { "metadata": {}, "output_type": "display_data", "png": 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fvx8mkwkXL15ERkYGxsbGsHHjRgCLVw4jIyPK9sFgEBaLBWazGcFg8Lr5pW2G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\n", "text": [ "" ] } ], "prompt_number": 98 }, { "cell_type": "markdown", "metadata": {}, "source": [ "\"Run_Query_1805ED9A.png\"/" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\"SQLShare_-_View_Query_1805EADF.png\"/" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\"SQLShare_-_View_Query_1805EB60.png\"/" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# 136751 CG loci have methylation ratio of 0 @ 5 coverage\n", "# 12,270 between 0-0.25\n", "# 7572 0.25-.5\n", "# 7323 0.5-0.75\n", "# 18559 >0.75\n", "# 46k have non 0 ratios " ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 92 }, { "cell_type": "code", "collapsed": false, "input": [ "m3c['ratio'].hist(bins=10);\n", "#Axis limits are changed using the axis([xmin, xmax, ymin, ymax]) function.\n", "plt.axis([0, 1, 0, 20000])\n", "plt.xlabel('Methylation(%)', fontsize=20)\n", "plt.ylabel('count', fontsize= 20)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 116, "text": [ "" ] }, { "metadata": {}, "output_type": "display_data", "png": 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rzfPT0tJEbm6uKC4uFiNHjjTP37lzpxg/frwQQojQ0FBhNBrNy4KDg8Xp06dbxQBAADUC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\nHeksrMnF5cuX8d1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"text": [ "" ] } ], "prompt_number": 116 }, { "cell_type": "code", "collapsed": false, "input": [ "m3c['ratio'].hist(bins=100);\n", "#Axis limits are changed using the axis([xmin, xmax, ymin, ymax]) function.\n", "#plt.axis([0, 1, 0, 20000])\n", "plt.xlabel('Methylation(%)', fontsize=20)\n", "plt.ylabel('count', fontsize= 20)\n", "plt.title('Sperm (M3)', fontsize= 20)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 10, "text": [ "" ] }, { "metadata": {}, "output_type": "display_data", "png": 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HT09PpKWl2X0/iIjIdnXe51PTXXfdhbi4OLz99tuOGJNT8D4fIqL6uZX7fGya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\n98NgMAhJksSUKVMs2iMjI4UkSeL111+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"text": [ "" ] } ], "prompt_number": 10 }, { "cell_type": "code", "collapsed": false, "input": [ "m3c['ratio'].hist(bins=10);\n", "#Axis limits are changed using the axis([xmin, xmax, ymin, ymax]) function.\n", "#plt.axis([0, 1, 0, 20000])\n", "plt.xlabel('Methylation(%)', fontsize=20)\n", "plt.ylabel('count', fontsize= 20)\n", "plt.title('Sperm (M3)', fontsize= 20)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 11, "text": [ "" ] }, { "metadata": {}, "output_type": "display_data", "png": 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Nz3UR82DAXBgwF9bB4kNERDbHYbcyHHYjIqocDrsREVGNwuJDJjimrcc8GDAXBsyFdbD4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\ndpuffvoJBw8eRHh4OCIiIoz6HB0dMWf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"text": [ "" ] } ], "prompt_number": 11 }, { "cell_type": "code", "collapsed": false, "input": [ "12270 + 7572 + 7323 + 18559" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 104, "text": [ "45724" ] } ], "prompt_number": 104 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "45724 + 136751" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 105, "text": [ "182475" ] } ], "prompt_number": 105 }, { "cell_type": "markdown", "metadata": {}, "source": [ "\"Run_Query_1805EC9E.png\"/" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\"Run_Query_1805ED3F.png\"/" ] }, { "cell_type": "code", "collapsed": false, "input": [ "#3010 CG have > 20x coverage" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "T3D3" ] }, { "cell_type": "code", "collapsed": false, "input": [ "!wc /Volumes/web/cnidarian/BiGo_lar_T3D3_methratio_v9_A.txt" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ " 45580029 546960327 2647413384 /Volumes/web/cnidarian/BiGo_lar_T3D3_methratio_v9_A.txt\r\n" ] } ], "prompt_number": 68 }, { "cell_type": "raw", "metadata": {}, "source": [ "!python /Users/sr320/sqlshare-pythonclient/tools/singleupload.py -d BiGo_lar_T3D3 /Volumes/web/cnidarian/BiGo_lar_T3D3_methratio_v9_A.txt" ] }, { "cell_type": "code", "collapsed": false, "input": [ "#45 Million lines\n", "\n" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "!python /Users/sr320/sqlshare-pythonclient/tools/fetchdata.py -s \"SELECT chr as seqname,'methratio' as source,'CpG' as feature, pos as start, pos + 1 as [end], ratio as score, strand, '.' as frame, '.' as attribute FROM [sr320@washington.edu].[BiGO_lar_T3D3] where context like '__CG_' and CT_Count >= 5\" -f tsv -o /Volumes/web/cnidarian/BiGo_lar_T3D3_methratio_CG.txt" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "!head /Volumes/web/cnidarian/BiGo_lar_T3D3_methratio_CG.txt" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "seqname\tsource\tfeature\tstart\tend\tscore\tstrand\tframe\tattribute\r", "\r\n", "C10295\tmethratio\tCpG\t38\t39\t0.000\t+\t.\t.\r", "\r\n", "C10295\tmethratio\tCpG\t51\t52\t0.000\t+\t.\t.\r", "\r\n", "C10295\tmethratio\tCpG\t142\t143\t0.000\t+\t.\t.\r", "\r\n", "C10845\tmethratio\tCpG\t110\t111\t0.000\t+\t.\t.\r", "\r\n", "C10845\tmethratio\tCpG\t145\t146\t0.000\t+\t.\t.\r", "\r\n", "C11870\tmethratio\tCpG\t109\t110\t0.000\t+\t.\t.\r", "\r\n", "C12052\tmethratio\tCpG\t112\t113\t0.000\t+\t.\t.\r", "\r\n", "C12570\tmethratio\tCpG\t118\t119\t0.000\t+\t.\t.\r", "\r\n", "C13546\tmethratio\tCpG\t84\t85\t0.000\t+\t.\t.\r", "\r\n" ] } ], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "!wc /Volumes/web/cnidarian/BiGo_lar_T3D3_methratio_CG.txt" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ " 185149 1666341 9685366 /Volumes/web/cnidarian/BiGo_lar_T3D3_methratio_CG.txt\r\n" ] } ], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "from pandas import *\n", "\n", "# read data from data file into a pandas DataFrame \n", "T3D3cg = read_table(\"http://eagle.fish.washington.edu/cnidarian/BiGo_lar_T3D3_methratio_CG.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": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "T3D3cg['score'].hist(bins=100);\n", "#Axis limits are changed using the axis([xmin, xmax, ymin, ymax]) function.\n", "#plt.axis([0, 1, 0, 20000])\n", "plt.xlabel('Methylation(%)', fontsize=20)\n", "plt.ylabel('count', fontsize= 20)\n", "plt.title('Larvae (T3D3)', fontsize= 20)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 6, "text": [ "" ] }, { "metadata": {}, "output_type": "display_data", "png": 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Cr9dDr9dDo9HgwIED8PDwwOjRo5GWloaamhro9XrodDqEhYXB09MTTk5OyMrKghACK1eu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\nHZbT6/UIDQ3F+++/D61Wi+eeew7Dhg3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"text": [ "" ] } ], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [ "T3D3cg['score'].hist(bins=10);\n", "#Axis limits are changed using the axis([xmin, xmax, ymin, ymax]) function.\n", "#plt.axis([0, 1, 0, 20000])\n", "plt.xlabel('Methylation(%)', fontsize=20)\n", "plt.ylabel('count', fontsize= 20)\n", "plt.title('Larvae (T3D3)', fontsize= 20)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 12, "text": [ "" ] }, { "metadata": {}, "output_type": "display_data", "png": 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qoVKpcOjQIbi5uSEqKgppaWmoqamBVquFRqNBSEgI3N3d4eDggKysLAghsGbNGowfPx4A\nEBUVJT0KJT09HeHh4QD0N+Jt3boVlZWVqKiowLZt2zBq1ChbHTYREQHWu9ptypQp0s1kKpVKrFy5\n0mi+t7e30Yu5/v3vfwsfHx/Rp08fkZmZKbVnZ2eLgIAA4ePjI55++mmp/eLFi+LBBx8Uvr6+YsiQ\nIUKr1UrzVq5cKXx9fYWvr69YvXp1o/FZMRV2b8eOHbYOwS4wDwbMhQFzYdCS703eZFpPJpO1uA+T\niKg9acn3Jh8sSibMfbjljY55MGAuDJgLy2DxISIiq2O3Wz12uxERNU9Lvjf5SoWr5OXl2ToE3Hbb\nbejatautwyAialU886knk8nQrZsvZDLb1eOamjLMnj0Tb765xGYxAPo+7WHDhtk0BnvAPBgwFwbM\nhQHPfCzkzz/3A3C1YQT/wcWL1nktMhGRLfHMp57+2W5nYOvi89RTxfjvf/9jwxiIiMzDS62JiKhN\nYfEhE7yPQY95MGAuDJgLy2DxISIiq+OYTz2O+RARNQ/HfIiIqE1h8SET7NPWYx4MmAsD5sIyWHyI\niMjqOOZTj2M+RETNwzEfIiJqU1h8yAT7tPWYBwPmwoC5sAwWHyIisjqO+dTjmA8RUfNwzIeIiNoU\nFh8ywT5tPebBgLkwYC4sg8WHiIisjmM+9TjmQ0TUPG1izGfmzJlwc3NDYGCg1Pbcc8+hX79+CAoK\nwsSJE1FVVSXNS0pKglqtRt++fbF161ap/eDBgwgMDIRarcbcuXOl9kuXLmHy5MlQq9UIDQ1FQUGB\nNC81NRV+fn7w8/PDRx991MpHSkREf8dqxWfGjBnIzMw0aouMjMTRo0fx888/w8/PD0lJSQCA3Nxc\nbNiwAbm5ucjMzMSsWbOk6pqQkICUlBRoNBpoNBppmykpKXB1dYVGo0FiYiLmz58PACgvL8crr7yC\nAwcO4MCBA1i0aBEqKyutddhtEvu09ZgHA+bCgLmwDIW1djR06FDk5+cbtUVEREg/DxkyBJ9++ikA\nICMjA1OnToVSqYSXlxd8fX2RlZWFXr16obq6GiEhIQCA6dOnY9OmTRg9ejQ2b96MRYsWAQCio6Mx\ne/ZsAMA333yDyMhIODk5SfvMzMzElClTGolyNoA+9T87AegPYFj99M76P1tz+jiATvqp+l/wYcOG\ncdpG0zk5OXYVjy2nc3Jy7CoeTttmuuHnv36XXxdhRVqtVgQEBDQ6b+zYseLjjz8WQggxe/ZssXbt\nWmlefHy8SE9PF9nZ2WLkyJFS++7du8XYsWOFEEIEBAQInU4nzfPx8RFnzpwRb775pli8eLHU/uqr\nr4o333zTZP8ABHBGAMKGn2XiqacSW5ZkIiIraUkJsYur3f7973/jpptuwkMPPWTrUIiIyApsXnxW\nr16NLVu24OOPP5baPDw8UFRUJE0XFxdDpVLBw8MDxcXFJu0N6xQWFgIAamtrUVVVBVdXV5NtFRUV\nSetQ464+xW7PmAcD5sKAubAMmxafzMxMLF26FBkZGejUqZPUHhUVhbS0NNTU1ECr1UKj0SAkJATu\n7u5wcHBAVlYWhBBYs2YNxo8fL62TmpoKAEhPT0d4eDgA/UUNW7duRWVlJSoqKrBt2zaMGjXK+gdL\nREQGluv9u7YpU6aIW2+9VSiVSqFSqURKSorw9fUVt912m+jfv7/o37+/SEhIkJb/97//LXx8fESf\nPn1EZmam1J6dnS0CAgKEj4+PePrpp6X2ixcvigcffFD4+vqKIUOGCK1WK81buXKl8PX1Fb6+vmL1\n6tWNxgeO+RARNUtLSghvMq3Hm0yJiJqnTdxkSm0H+7T1mAcD5sKAubAMFh8iIrI6drvVY7cbEVHz\nsNuNiIjaFBYfMsE+bT3mwYC5MGAuLIPFh4iIrI5jPvU45kNE1Dwc8yEiojaFxYdMsE9bj3kwYC4M\nmAvLYPEhIiKr45hPPY75EBE1D8d8iIioTWHxIRPs09ZjHgyYCwPmwjJYfIiIyOo45lOPYz5ERM3D\nMR8iImpTWHzIBPu09ZgHA+bCgLmwDBYfIiKyOo751OOYDxFR83DMh4iI2hQWHzLBPm095sGAuTBg\nLiyDxYeIiKzOasVn5syZcHNzQ2BgoNRWXl6OiIgI+Pn5ITIyEpWVldK8pKQkqNVq9O3bF1u3bpXa\nDx48iMDAQKjVasydO1dqv3TpEiZPngy1Wo3Q0FAUFBRI81JTU+Hn5wc/Pz989NFHrXykbd+wYcNs\nHYJdYB4MmAsD5sIyrFZ8ZsyYgczMTKO2JUuWICIiAseOHUN4eDiWLFkCAMjNzcWGDRuQm5uLzMxM\nzJo1SxrUSkhIQEpKCjQaDTQajbTNlJQUuLq6QqPRIDExEfPnzwegL3CvvPIKDhw4gAMHDmDRokVG\nRY6IiKzPasVn6NChcHZ2NmrbvHkzYmNjAQCxsbHYtGkTACAjIwNTp06FUqmEl5cXfH19kZWVhZKS\nElRXVyMkJAQAMH36dGmdq7cVHR2N7du3AwC++eYbREZGwsnJCU5OToiIiDApgmSMfdp6zIMBc2HA\nXFiGwpY7Ly0thZubGwDAzc0NpaWlAICTJ08iNDRUWk6lUkGn00GpVEKlUkntHh4e0Ol0AACdTgdP\nT08AgEKhgKOjI8rKynDy5EmjdRq21bjZAPrU/+wEoD+AYfXTO+v/bM3p4wA66afqf8EbTvE5bf3p\nnJwcu4rHltM5OTl2FQ+nbTPd8HN+fj5aTFiRVqsVAQEB0rSTk5PRfGdnZyGEELNnzxZr166V2uPj\n40V6errIzs4WI0eOlNp3794txo4dK4QQIiAgQOh0Ommej4+POHPmjHjzzTfF4sWLpfZXX31VvPnm\nmyaxARDAGQEIG36WiaeeSmxhlomIrKMlJcSmV7u5ubnh1KlTAICSkhL06NEDgP6MpqioSFquuLgY\nKpUKHh4eKC4uNmlvWKewsBAAUFtbi6qqKri6uppsq6ioyOhMiIiIrM+mxScqKgqpqakA9FekTZgw\nQWpPS0tDTU0NtFotNBoNQkJC4O7uDgcHB2RlZUEIgTVr1mD8+PEm20pPT0d4eDgAIDIyElu3bkVl\nZSUqKiqwbds2jBo1ygZH23ZcfYrdnjEPBsyFAXNhGVYb85k6dSp27dqFM2fOwNPTE6+88gqef/55\nxMTEICUlBV5eXvjkk08AAP7+/oiJiYG/vz8UCgWSk5PrH38DJCcnIy4uDhcuXMCYMWMwevRoAEB8\nfDymTZsGtVoNV1dXpKWlAQBcXFzw0ksvYfDgwQCABQsWwMnJyVqHTUREjeCz3erx2W5ERM3DZ7sR\nEVGbwuJDJtinrcc8GDAXBsyFZbD4EBGR1XHMpx7HfIiImodjPkRE1Kaw+JAJ9mnrMQ8GzIUBc2EZ\nLD5ERGR1HPOpxzEfIqLm4ZgPERG1KSw+ZIJ92nrMgwFzYcBcWAaLDxERWR3HfOpxzIeIqHk45kNE\nRG0Kiw+ZYJ+2HvNgwFwYMBeWweJDRERWxzGfehzzISJqHo75EBFRm2JW8SksLERVVdU1lzl79iwK\nCwstEhTZFvu09ZgHA+bCgLmwDLOKj5eXF5YvX37NZd5++214e3tbJCgiIrqxWbTbjcNHN4Zhw4bZ\nOgS7wDwYMBcGzIVlWKz4lJaWomvXrpbaHBER3cAUTc1ITU01upIhJycHH330kclyV65cQUFBAdas\nWYPAwMDWi5SsZufOnfzfHZiHqzEXBsyFZTRZfGbMmGE0vWnTJmzatKnJDXXp0gULFiywXGRERHTD\navI+n9WrV0s/z5w5E+PHj8f48eNNluvQoQNcXV1x5513wsnJ6bqCSEpKwtq1ayGXyxEYGIhVq1bh\n3LlzmDx5MgoKCuDl5YVPPvlE2n5SUhJWrlyJDh064O2330ZkZCQA4ODBg4iLi8PFixcxZswY6SKJ\nS5cuYfr06Th06BBcXV2xYcMG9OrVyzgRvM+HiKhZWnKfD4QZ7r33XrF69WpzFm02rVYrvL29xcWL\nF4UQQsTExIjVq1eL5557Trz++utCCCGWLFki5s+fL4QQ4ujRoyIoKEjU1NQIrVYrfHx8RF1dnRBC\niMGDB4usrCwhhBD33Xef+Prrr4UQQrz77rsiISFBCCFEWlqamDx5skkcAARwRgDChp9l4qmnElsl\nz0RElmZmCWmUWRcc7Ny5E7GxsddX3f6Gg4MDlEolzp8/j9raWpw/fx49e/bE5s2bpX3GxsZKXX4Z\nGRmYOnUqlEolvLy84Ovri6ysLJSUlKC6uhohISEAgOnTp0vrXL2t6OhobN++vVWO5UbB+xj0mAcD\n5sKAubCMJsd8rMXFxQXPPPMMbrvtNnTu3BmjRo1CREQESktL4ebmBgBwc3NDaWkpAODkyZMIDQ2V\n1lepVNDpdFAqlVCpVFK7h4cHdDodAECn08HT0xMAoFAo4OjoiPLycri4uPwlmtkA+tT/7ASgP4Bh\n9dM76/9szenjADrpp+p/wRsGNjlt/emcnBy7iseW0zk5OXYVD6dtM93wc35+PlrM3FOkHTt2iDFj\nxoju3bsLhUIh5HK50Ucmkwm5XN7sU6/jx4+Lfv36iTNnzojLly+LCRMmiDVr1ggnJyej5ZydnYUQ\nQsyePVusXbtWao+Pjxfp6ekiOztbjBw5UmrfvXu3GDt2rBBCiICAAKHT6aR5Pj4+oqyszGj7YLcb\nEVGzNKOEmDDrzOerr77C+PHjUVdXB09PT/j5+UGhMF1VP2jfPNnZ2bjzzjvh6qof6J84cSL27dsH\nd3d3nDp1Cu7u7igpKUGPHj0A6M9oioqKpPWLi4uhUqng4eGB4uJik/aGdQoLC9GzZ0/U1taiqqqq\nkbMeIiKyFrPGfBYuXAilUonMzEwUFBRgz5492Llzp8lnx44dzQ6gb9++2L9/Py5cuAAhBL799lv4\n+/tj3LhxSE1NBaC/52jChAkAgKioKKSlpaGmpgZarRYajQYhISFwd3eHg4MDsrKyIITAmjVrpKvz\noqKipG2lp6cjPDy82XG2J1efYrdnzIMBc2HAXFiGWWc+R44cweTJk6VLmi0pKCgI06dPR3BwMORy\nOQYOHIjHH38c1dXViImJQUpKinSpNQD4+/sjJiYG/v7+UCgUSE5Ols64kpOTERcXhwsXLmDMmDEY\nPXo0ACA+Ph7Tpk2DWq2Gq6sr0tLSLH4cRERkPrPe53PLLbcgNjYWy5Yts0ZMNsH7fIiImqfV3+cz\ncuRI7Nu377p2QERE9FdmFZ8lS5bgxIkTePXVV/nk6naAfdp6zIMBc2HAXFiGWWM+ixYtwu23344F\nCxZg1apV6N+/f5OP0lm5cqVFAyQiohuPWWM+crn5b16oq6trUUC2wjEfIqLmacmYj1lnPr///vt1\nbZyIiKgxZhUfLy+vVg6D7MlOvq8EAPNwNebCgLmwDIu+RpuIiMgcZo35FBYWmr3B2267rUUB2QrH\nfIiImqfVx3y8vLya3EnD0wWEEJDJZLhy5cp1BUJERO2HWcVn+vTpjbZXVlYiJycHhYWFGDZsmMnb\nQaltYp+2HvNgwFwYMBeWYVbxufqV2n915coVLF68GO+995708E4iIqJrMWvMxxyhoaHo3bs31q1b\nZ4nNWR3HfIiImqfVn+1mjjvvvBPbtm2z1OaIiOgGZrHiU1FRgT///NNSmyMb4rOr9JgHA+bCgLmw\nDIsUn23btmHDhg0ICAiwxOaIiOgGZ9YFB8OHD2/0Fdm1tbUoKipCQUEBZDIZXn75ZYsHSNbHK3n0\nmAcD5sKAubAMs4rPrl27mpzn7OyM0aNH49lnn8WIESMsFhgREd24zOp2q6ura/JTVlaGLVu2sPDc\nQNinrcc8GDAXBsyFZfDZbkREZHXXdZ9PdXU1Kisr4ejoCAcHh9aIy+p4nw8RUfNY5T6fy5cvIykp\nCT4+PnBycoKXlxecnZ3h6+uLpKQk1NbWXlcARETU/phVfGpqahAZGYkXXngBBQUFUKlUGDx4MFQq\nFbRaLV544QWEh4ejpqamteMlK2Cfth7zYMBcGDAXlmFW8fnPf/6DXbt2YezYscjLy0NBQQH279+P\ngoIC/Pbbb4iKisL333+PZcuWtXa8RER0AzCr+Kxbtw633347Pv/8c6jVaqN5vr6++PTTT3H77bdf\n93PdKitVyLZGAAAgAElEQVQrMWnSJPTr1w/+/v7IyspCeXk5IiIi4Ofnh8jISFRWVkrLJyUlQa1W\no2/fvti6davUfvDgQQQGBkKtVmPu3LlS+6VLlzB58mSo1WqEhoaioKDguuJsL3gfgx7zYMBcGDAX\nlmFW8Tl+/DjGjBmDDh06NDq/Q4cOuO+++3D8+PHrCmLu3LkYM2YM8vLycPjwYfTt2xdLlixBREQE\njh07hvDwcCxZsgQAkJubiw0bNiA3NxeZmZmYNWuWNOCVkJCAlJQUaDQaaDQaZGZmAgBSUlLg6uoK\njUaDxMREzJ8//7riJCIiyzCr+CiVyr99btv58+ehVCqbHUBVVRW+//57zJw5EwCgUCjg6OiIzZs3\nIzY2FgAQGxuLTZs2AQAyMjIwdepUKJVKeHl5wdfXF1lZWSgpKUF1dTVCQkIA6N9B1LDO1duKjo7G\n9u3bmx1ne8I+bT3mwYC5MGAuLMOsJxwEBQUhPT0dCxYsQI8ePUzmnzlzBunp6QgKCmp2AFqtFt27\nd8eMGTPw888/Y9CgQXjrrbdQWloKNzc3AICbmxtKS0sBACdPnkRoaKi0vkqlgk6ng1KphEqlkto9\nPDyg0+kAADqdDp6envoDri9u5eXlcHFx+Us0swH0qf/ZCUB/AMPqp3fW/9ma08cBdNJP1f+CN5zi\nc9r60zk5OXYVjy2nc3Jy7CoeTttmuuHn/Px8tJgww4YNG4RMJhO9evUSK1asECdOnBDnz58XJ06c\nECkpKcLb21vIZDKRlpZmzuaM/Pjjj0KhUIgDBw4IIYSYO3euePHFF4WTk5PRcs7OzkIIIWbPni3W\nrl0rtcfHx4v09HSRnZ0tRo4cKbXv3r1bjB07VgghREBAgNDpdNI8Hx8fUVZWZrR9AAI4IwBhw88y\n8dRTic3OIRGRLZhZQhpl1plPTEwMcnJysGTJEjz++ONGDxkV9eMt//jHPzB58uRmFz+VSiVdug0A\nkyZNQlJSEtzd3XHq1Cm4u7ujpKREOuPy8PBAUVGRtH5xcTFUKhU8PDxQXFxs0t6wTmFhIXr27Ina\n2lpUVVU1ctZDRETWYvZNpq+99hr27t2L+Ph49O/fH97e3ujfvz/i4+Oxd+9e6YKA5nJ3d4enpyeO\nHTsGAPj2229x++23Y9y4cdJruVNTUzFhwgQAQFRUFNLS0lBTUwOtVguNRoOQkBC4u7vDwcEBWVlZ\nEEJgzZo1GD9+vLROw7bS09MRHh5+XbG2F1efYrdnzIMBc2HAXFiGWWc+DcLCwhAWFmbxIN555x08\n/PDDqKmpgY+PD1atWoUrV64gJiYGKSkp8PLywieffAIA8Pf3R0xMDPz9/aFQKJCcnCydiSUnJyMu\nLg4XLlzAmDFjMHr0aABAfHw8pk2bBrVaDVdXV6SlpVn8GIiIyHxmPdtt48aNeO+997B27Vr07NnT\nZH5xcTGmT5+O2bNnY+LEia0SaGvjs92IiJqn1Z/t9uGHH6KioqLRwgPox22qqqrw4YcfXlcQRETU\nvphVfH755RcEBwdfc5nBgwfj8OHDFgmKbIt92nrMgwFzYcBcWIZZxae8vFy656Yprq6uOH36tEWC\nIiKiG5tZxafh0TTXcvz4cTg5OVkkKLKthhvL2jvmwYC5MGAuLMOs4nP33Xdj8+bNyMvLa3R+Xl4e\nMjIyMHToUIsGR0RENyazis8zzzyDy5cvY+jQoVi+fDmOHTuGc+fO4bfffsNbb72Fu+++G7W1tXj2\n2WdbO16yAvZp6zEPBsyFAXNhGWbd5xMSEoL33nsPs2bNQmJiIhITE6V7a4QQUCgUeP/9942euUZE\nRNQUs+7zaZCbm4v33nsP+/fvR2VlJZycnBAWFoaEhAT069evNeNsdbzPh4ioeVpyn0+znnDg7++P\nd95557p2RERE1MDsZ7tR+8E+bT3mwYC5MGAuLIPFh4iIrK5ZYz43Mo75EBE1T6s/242IiMiSWHzI\nBPu09ZgHA+bCgLmwDBYfIiKyOo751OOYDxFR83DMh4iI2hQWHzLBPm095sGAuTBgLiyDxYeIiKyO\nYz71OOZDRNQ8HPMhIqI2hcWHTLBPW495MGAuDJgLy2DxISIiq7OL4nPlyhUMGDAA48aNAwCUl5cj\nIiICfn5+iIyMRGVlpbRsUlIS1Go1+vbti61bt0rtBw8eRGBgINRqNebOnSu1X7p0CZMnT4ZarUZo\naCgKCgqsd2BtFN9Rr8c8GDAXBsyFZdhF8Vm+fDn8/f2lt6MuWbIEEREROHbsGMLDw7FkyRIA+pfZ\nbdiwAbm5ucjMzMSsWbOkwa6EhASkpKRAo9FAo9EgMzMTAJCSkgJXV1doNBokJiZi/vz5tjlIIiKS\n2Lz4FBcXY8uWLXj00UelQrJ582bExsYCAGJjY7Fp0yYAQEZGBqZOnQqlUgkvLy/4+voiKysLJSUl\nqK6uRkhICABg+vTp0jpXbys6Ohrbt2+39iG2OezT1mMeDJgLA+bCMpr1JtPWkJiYiKVLl+Ls2bNS\nW2lpKdzc3AAAbm5uKC0tBQCcPHkSoaGh0nIqlQo6nQ5KpRIqlUpq9/DwgE6nAwDodDp4enoCABQK\nBRwdHVFeXg4XF5dGopkNoE/9z04A+gMYVj+9s/7P1pw+DqCTfqr+F7zhFJ/T1p/Oycmxq3hsOZ2T\nk2NX8XDaNtMNP+fn56PFhA198cUXYtasWUIIIXbs2CHGjh0rhBDCycnJaDlnZ2chhBCzZ88Wa9eu\nldrj4+NFenq6yM7OFiNHjpTad+/eLW0rICBA6HQ6aZ6Pj48oKysziQWAAM4IQNjws0w89VSihbJL\nRNS6WlJCbHrm88MPP2Dz5s3YsmULLl68iLNnz2LatGlwc3PDqVOn4O7ujpKSEvTo0QOA/oymqKhI\nWr+4uBgqlQoeHh4oLi42aW9Yp7CwED179kRtbS2qqqqaOOshIiJrsemYz2uvvYaioiJotVqkpaVh\nxIgRWLNmDaKiopCamgoASE1NxYQJEwAAUVFRSEtLQ01NDbRaLTQaDUJCQuDu7g4HBwdkZWVBCIE1\na9Zg/Pjx0joN20pPT0d4eLhtDrYNufoUuz1jHgyYCwPmwjJsPuZztYar3Z5//nnExMQgJSUFXl5e\n+OSTTwAA/v7+iImJgb+/PxQKBZKTk6V1kpOTERcXhwsXLmDMmDEYPXo0ACA+Ph7Tpk2DWq2Gq6sr\n0tLSbHNwREQk4bPd6vHZbkREzcNnuxERUZvC4kMm2KetxzwYMBcGzIVlsPgQEZHVccynHsd8iIia\nh2M+RETUprD4kAn2aesxDwbMhQFzYRksPkREZHUc86nHMR8ioubhmA8REbUpLD5kgn3aesyDAXNh\nwFxYBosPERFZHcd86nHMh4ioeTjmQ0REbQqLD5lgn7Ye82DAXBgwF5bB4kNERFbHMZ96HPMhImoe\njvkQEVGbwuJDJtinrcc8GDAXBsyFZbD4EBGR1XHMpx7HfIiImodjPkRE1Kaw+JAJ9mnrMQ8GzIUB\nc2EZLD5ERGR1Ni8+RUVFGD58OG6//XYEBATg7bffBgCUl5cjIiICfn5+iIyMRGVlpbROUlIS1Go1\n+vbti61bt0rtBw8eRGBgINR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sFP379xedO3cWpaWlUvuqVasa3U6DBQsWCJlMJhwdHcWR\nI0eM5j300ENCJpOJTz75RGq7ePGiuOWWW8Stt94qamtrjZbfsWOHkMlk4pFHHjFq79Wrl/D29jZq\nq6qqavSBlMXFxaJnz56iX79+JvNkMpkYPnx4o8eh1WqFTCYTM2bMMGqPjIwUMplMvPbaa0btP/zw\ng1AoFMLV1VX8+eefUntDvpRKpfjuu++M1vnnP/8pZDKZeOONN4zaFy5cKGQymVi/fr1JXH/++afw\n9PQU3bt3F/PmzROTJk0SMplMTJo0SQghRHV1tfDy8hLTpk1r9Lia8uSTTwqZTCZ27NjRrPXIdnjm\nQ3bhsccew5UrV6Suk4KCAmzbtg0PP/wwOnXq1Og6P//8M3bv3o3o6GjExMQYzXN0dMTChQtx8eJF\nfPrpp82OZ86cObj99ttNYgSAH3/8UWrr2LEjZs6ciVOnTpm85bI5DyZ1cHBo9IGUHh4eiI6Oxq+/\n/trilxoWFxdj27Zt6NWrF/7xj38YzQsLC8PUqVNRXl6Ozz77zGTdKVOmmJxVPf744wCM8wFAunKx\nsVd0dO3aFd9++y2Cg4ORmpqK/fv3IyEhQXoQ5fPPP49Lly5h+fLlKCwsxLhx49ClSxc4ODggNjYW\n1dXVjR5bw760Wq05qSA70K6eak32KyQkBIGBgVi5ciVefPFFfPjhhxBCSF/4jdm3bx8AoLKyEgsX\nLjSZf/r0aQBAXl5es+Np7H0sDV9wFRUVRu1PPvkk3nzzTfzvf//DxIkTAejf+fP555/D39+/0e6n\nxuzduxfLly/Hvn37cPr0aZOrt3Q6XaNf6Ob66aefAOhfidyhQweT+SNGjMDatWuRk5NjcqFAc/LR\nkPemnu7s5+eHLVu2mLR///33eO+997Bx40Y4OTkhPDwcZ86cwfr161FdXY2nn34aFy5cwCeffGKy\nrqurKwDgjz/+aHSfZH9YfMhuPPbYY5gzZw6+/vprrFq1CsHBwQgKCmpy+Yaxhm3btplcXNBAJpPh\n3LlzzY7FycnJpK3hcu+/XpXn7e2NUaNG4ZtvvsHvv/+O3r17IzU1FTU1NWa/juHzzz/HpEmT0KVL\nF0RERMDHxwddu3aFXC7Hjh07sGvXLly6dKnZx3G1qqoqAMCtt97a6Hx3d3cA+mL+V83JR8M4k2jG\nFYUXLlxAfHw8oqOjMXHiRGzbtg05OTlYu3atNE6Xn5+Pl19+Wcrx1erq6oz2TfaPxYfsxrRp0zB/\n/nw88cQTOHnyZKNnM1druEjg7bffNrmR0dpmzZqFzMxMrFixAklJSfjggw/QuXNnTJ8+3az1X3rp\nJXTq1AnZ2dno06eP0TydToddu3a1OMaGfJ06darR+SUlJUbLXa9bbrkFgP4qRHO99NJLqKiowLvv\nvgvAcLY6cOBAaZmGn/Py8kyKT8O+unfvfv2Bk1VxzIfshqOjIyZNmgSdTodu3bph6tSp11w+LCwM\nAJr1KJyG7qbG7ilqifvvvx+9evXCqlWrsHXrVmg0GsTExJj9RX78+HH4+/ubFJ66ujrs2bOn0XVk\nMlmzjqPhy3vPnj2Nrrdjxw6j5a5XQ2Ewd4zqwIEDWL58OZYvX25SPK4+27vWlWw6nc5o32T/WHzI\nrixevBibNm3CN998g65du15z2UGDBmHo0KH47LPPmnxz4i+//CKNQQCGsYGm7su5XjKZDE888QT+\n+OMPxMfHA9CPBZnL29sbx44dk84+AH231cKFC5GXl9dod5KrqyuKiorM3oeHhwciIiKg1Wrx1ltv\nGc3LysrCunXr4OLiggceeMDsbTbmnnvuAQCz3npbU1ODGTNm4L777sNDDz0ktTdc7LF582ap7Ysv\nvgCARu/5OnDgADp27IjQ0NAWxU7Ww243siuenp7w9PQ0e/l169ZhxIgRiI+Px9tvv42QkBA4OTmh\nuLgYhw8fxtGjR7F//37pf9R33nknunTpgrfeegtlZWXSEwDmzJkDBweHFsUeHx+PhQsXQqfT4Y47\n7sCQIUPMXjcxMRFPPvkkBgwYgIkTJ0KpVGLv3r3Iy8vDuHHjpC/eq40cORJpaWmIiorCgAEDoFQq\nce+992Lo0KFN7uf999/HXXfdheeeew5bt27FoEGDUFRUhI0bN0KhUGDVqlV/W/T/TlhYGLp169bk\nGdvVXnnlFZSUlODbb781ag8PD8egQYPwyiuvoKCgANXV1di4cSNiYmJMHspaXV2Nw4cPY9iwYejY\nsWOLYicrsvGl3tSOXX2fz9958cUXhVwub/T+nOrqavHaa6+JQYMGiW7duonOnTuL3r17i7Fjx4oV\nK1aIc+fOGS2fmZkpwsLCRLdu3YRMJhNyuVwUFBQIIfT3qMjlcrFr1y6T/TR178zVHnjgASGTyURy\ncnKTy3h5eZnc5yOEEKtXrxb9+/cXXbt2Fd27dxcTJ04UR44caTKmP/74Qzz00EPCzc1NdOjQQcjl\nctMMgE8AAAFkSURBVLFo0aK/jVWn04mEhATRq1cvcdNNN4nu3buLBx54QGRnZzcaU1N5F6Lpe40e\nf/xxIZPJxIkTJ5rMw08//SSUSqVISUlpdH5xcbGYMGGC6Natm3B2dhYzZswQ1dXVJss13Iu0bt26\nJvdF9kcmxA3+kCsiK6mrq4OPjw/OnDmDkpISdOvWzdYh2UxOTg4GDhyIBQsWSE9jaC3h4eE4cuQI\niouLoVQqW3VfZDkc8yGykI0bN6KgoADTp09v14UHAPr374/o6Gi8++67Tb4OwRKys7OxY8cOvPji\niyw8bQzPfIhaaMmSJSgvL8cHH3yAuro65Obmtuhm0BvFiRMn4O/vj8WLF+O5555rlX2MHTsWx44d\nQ25urnTfEbUNLD5ELSSXy3HTTTfB398fS5cuRXh4uK1DIrJ7LD5ERGR1HPMhIiKrY/EhIiKrY/Eh\nIiKrY/EhIiKrY/EhIiKrY/EhIiKr+/9btYkjDSIK5gAAAABJRU5ErkJggg==\n", "text": [ "" ] } ], "prompt_number": 12 }, { "cell_type": "heading", "level": 4, "metadata": {}, "source": [ "little R" ] }, { "cell_type": "code", "collapsed": false, "input": [ "#37 million lines in methratio file" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 122 }, { "cell_type": "markdown", "metadata": {}, "source": [ "`T3D3_cg5<-T3D3[grepl(\".{2}CG.{1}\",T3D3$context) & T3D3$CT_count >= 5, ]`" ] }, { "cell_type": "code", "collapsed": false, "input": [ "#107k CG \n", "#42k CC\n", "#129k CA\n", "#74k CT\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 124 }, { "cell_type": "code", "collapsed": false, "input": [ "#checking out methylation\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 125 }, { "cell_type": "markdown", "metadata": {}, "source": [ "```R\n", "T3D3_Mcg5<-T3D3[grepl(\".{2}Cg.{1}\",T3D3$context) & T3D3$CT_count >= 5 & T3D3$ratio > 0, ]\n", "```" ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "T3D5" ] }, { "cell_type": "code", "collapsed": false, "input": [ "!wc /Volumes/web/cnidarian/BiGo_lar_T3D5_methratio_v9_A.txt" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ " 37156095 445873112 2157638420 /Volumes/web/cnidarian/BiGo_lar_T3D5_methratio_v9_A.txt\r\n" ] } ], "prompt_number": 18 }, { "cell_type": "code", "collapsed": false, "input": [ "!python /Users/sr320/sqlshare-pythonclient/tools/singleupload.py -d BiGo_lar_T3D5 /Volumes/web/cnidarian/BiGo_lar_T3D5_methratio_v9_A.txt" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "processing chunk line 0 to 1912976 (9.52025008202 s elapsed)\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "pushing /Volumes/web/cnidarian/BiGo_lar_T3D5_methratio_v9_A.txt...\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "parsing 3BECDFF8...\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "processing chunk line 1912976 to 3706333 (261.892167091 s elapsed)\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "pushing /Volumes/web/cnidarian/BiGo_lar_T3D5_methratio_v9_A.txt...\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "parsing 7B0BB388...\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "processing chunk line 3706333 to 5497779 (504.070580006 s elapsed)\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "pushing /Volumes/web/cnidarian/BiGo_lar_T3D5_methratio_v9_A.txt...\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "parsing 22A7DEA7...\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "processing chunk line 5497779 to 7289476 (753.540595055 s elapsed)\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "pushing /Volumes/web/cnidarian/BiGo_lar_T3D5_methratio_v9_A.txt...\r\n" ] }, { "output_type": 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/Volumes/web/cnidarian/BiGo_lar_M1_methratio_CG.txt" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 13 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "TID3" ] }, { "cell_type": "code", "collapsed": false, "input": [ "!wc /Volumes/web/cnidarian/BiGo_lar_TID3_methratio_v9_A.txt" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ " 27890025 334680276 1619321169 /Volumes/web/cnidarian/BiGo_lar_TID3_methratio_v9_A.txt\r\n" ] } ], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "#Creating GFF of CG 5x\n", "!python /Users/sr320/sqlshare-pythonclient/tools/fetchdata.py -s \"SELECT chr as seqname,'methratio' as source,'CpG' as feature, pos as start, pos + 1 as [end], ratio as score, strand, '.' as frame, '.' as attribute FROM [sr320@washington.edu].[BiGO_lar_T1D3] where context like '__CG_' and CT_Count > 5\" -f tsv -o 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"cell_type": "code", "collapsed": false, "input": [ "#Creating GFF of CG \n", "!python /Users/sr320/sqlshare-pythonclient/tools/fetchdata.py -s \"SELECT chr as seqname,'methratio' as source,'CpG' as feature, pos as start, pos + 1 as [end], ratio as score, strand, '.' as frame, '.' as attribute FROM [sr320@washington.edu].[BiGo_lar_T1D5] where context like '__CG_' and CT_Count > 5\" -f tsv -o /Volumes/web/cnidarian/BiGo_lar_T1D5_methratio_CG.txt" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 11 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "misc" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from IPython.display import HTML\n", "HTML('')" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "" ], "metadata": {}, "output_type": "pyout", "prompt_number": 16, "text": [ "" ] } ], "prompt_number": 16 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }