{ "metadata": { "name": "", "signature": "sha256:607642007e4a0ea2041ba3f865c4b32b1a2d1c39ee300635663d2aa3007eac1e" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "DNA Methylation during early development in oysters" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook includes procedures for analyzing data desribed in ---\n", "In theory anyone should be able to reproduce our work in IPython, if they have the following software installed.\n", "* BSMAP\n", "* R module for IPython\n", "* SQLShare python client\n", "\n", "The general premise is you simply provide minimal information including a working directory and location of software. The notebook is divided into X sections \n", "-\n", "-\n", "0\n", "\n", "At the beginning of each section specfic instructions on user actions will be described.\n", "Please post any comments and questions in issues ." ] }, { "cell_type": "code", "collapsed": false, "input": [ "!rsync -avz /Volumes/Bay3_scratch/BiGo_manny /Volumes/web/whale" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "building file list ... " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "done\r\n", "BiGo_manny/\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "BiGo_manny/BiGo_lar_fastq.tgz\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "^C\r\n" ] } ], "prompt_number": 414 }, { "cell_type": "code", "collapsed": false, "input": [ "#set locaiton of sqlshare python \n", "pt=\"/Users/sr320/sqlshare-pythonclient/tools/\"" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 13 }, { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Downloading Files" ] }, { "cell_type": "code", "collapsed": false, "input": [ "#The first thing you will need to do is set a working directory. Simply include the path to your working directory within quotes.\n", "lwd=\"/Volumes/Bay3_scratch/BiGo_manny\"" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "cd {lwd}\n" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "/Volumes/Bay3_scratch/BiGo_manny\n" ] } ], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "#This command downloads a archived file including six BS-seq libraries.\n", "!wget http://eagle.fish.washington.edu/trilobite/Crassostrea_gigas_HTSdata/BiGo_larvae_cc/BiGo_lar_fastq.tgz" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "ls" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "BiGo_lar_fastq.tgz\r\n" ] } ], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "!ls mcf*" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "mcf_M1_R1.fastq mcf_M3_R2.fastq mcf_T1D5_R1.fastq mcf_T3D3_R2.fastq\r\n", "mcf_M1_R2.fastq mcf_T1D3_R1.fastq mcf_T1D5_R2.fastq mcf_T3D5_R1.fastq\r\n", "mcf_M3_R1.fastq mcf_T1D3_R2.fastq mcf_T3D3_R1.fastq mcf_T3D5_R2.fastq\r\n" ] } ], "prompt_number": 424 }, { "cell_type": "code", "collapsed": false, "input": [ "!tar -zcvf BiGo_lar_fastq_mcf.tgz mcf*" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "a mcf_M1_R1.fastq" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r\n", "a mcf_M1_R2.fastq" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r\n", "a mcf_M3_R1.fastq" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r\n", "a mcf_M3_R2.fastq" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r\n", "a mcf_T1D3_R1.fastq" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "a mcf_T1D3_R2.fastq" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r\n", "a mcf_T1D5_R1.fastq" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r\n", "a mcf_T1D5_R2.fastq" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r\n", "a mcf_T3D3_R1.fastq" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r\n", "a mcf_T3D3_R2.fastq" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r\n", "a mcf_T3D5_R1.fastq" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r\n", "a mcf_T3D5_R2.fastq" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r\n" ] } ], "prompt_number": 425 }, { "cell_type": "code", "collapsed": false, "input": [ "#oyster genome \n", "!wget http://eagle.fish.washington.edu/trilobite/Crassostrea_gigas_ensembl_tracks/Crassostrea_gigas.GCA_000297895.1.22.dna_sm.genome.fa" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "--2014-07-08 10:20:04-- http://eagle.fish.washington.edu/trilobite/Crassostrea_gigas_ensembl_tracks/Crassostrea_gigas.GCA_000297895.1.22.dna_sm.genome.fa\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Resolving eagle.fish.washington.edu... 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"99% [=====================================> ] 562,318,536 10.7M/s eta 1s " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "99% [=====================================> ] 564,562,936 10.7M/s eta 0s " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "99% [=====================================> ] 566,798,648 10.7M/s eta 0s " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "100%[======================================>] 567,608,307 10.7M/s in 51s \r\n", "\r\n", "2014-07-08 10:20:56 (10.5 MB/s) - `Crassostrea_gigas.GCA_000297895.1.22.dna_sm.genome.fa' saved [567608307/567608307]\r\n", "\r\n" ] } ], "prompt_number": 6 }, { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Running BSMAP" ] }, { "cell_type": "code", "collapsed": false, "input": [ "#setting some variables for files but also \n", "\n", "#location of BSMAP (if not in PATH)\n", "bsmaploc=\"/Volumes/Bay3/Software/BSMAP/bsmap-2.74/\"" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 5 }, { "cell_type": "markdown", "metadata": {}, "source": [ "_Another option is running BSMAP on iPlant_" ] }, { "cell_type": "code", "collapsed": false, "input": [ "lib=\"M1\"" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [ "! {bsmaploc}bsmap \\\n", "-a mcf_{lib}_R1.fastq \\\n", "-b mcf_{lib}_R2.fastq \\\n", "-d Crassostrea_gigas.GCA_000297895.1.22.dna_sm.genome.fa \\\n", "-o bsmap_out_{lib}.sam \\\n", "-p 2" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "\r\n", "BSMAP v2.74\r\n", "Start at: Wed Jul 9 15:11:17 2014\r\n", "\r\n", "Input reference file: Crassostrea_gigas.GCA_000297895.1.22.dna_sm.genome.fa" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " \t(format: FASTA)\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Load in 7658 db seqs, total size 557717710 bp. 18 secs passed\r\n", "total_kmers: 43046721\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Create seed table. 70 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(2 threads)\r\n", "Input read file #1: mcf_M1_R1.fastq \t(format: FASTQ)\r\n", "Input read file #2: mcf_M1_R2.fastq" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " \t(format: FASTQ)\r\n", "Output file: bsmap_out_M1.sam\t (format: SAM)\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #0: \t50000 read pairs 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"text": [ "Thread #0: \t7950000 read pairs finished. 2025 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #1: \t8000000 read pairs finished. 2026 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #0: \t8023643 read pairs finished. 2035 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Total number of aligned reads: \r\n", "pairs: 3982552 (50%)\r\n", "single a: 1489441 (19%)\r\n", "single b: 1430645 (18%)\r\n", "Done.\r\n", "Finished at Wed Jul 9 15:45:12 2014\r\n", "Total time consumed: 2035 secs\r\n" ] } ], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [ "lib=\"T1D3\"" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 8 }, { "cell_type": "code", "collapsed": false, "input": [ "! {bsmaploc}bsmap \\\n", "-a mcf_{lib}_R1.fastq \\\n", "-b mcf_{lib}_R2.fastq \\\n", "-d Crassostrea_gigas.GCA_000297895.1.22.dna_sm.genome.fa \\\n", "-o bsmap_out_{lib}.sam \\\n", "-p 4" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "\r\n", "BSMAP v2.74\r\n", "Start at: Wed Jul 9 15:45:14 2014\r\n", "\r\n", "Input reference file: Crassostrea_gigas.GCA_000297895.1.22.dna_sm.genome.fa" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " \t(format: FASTA)\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Load in 7658 db seqs, total size 557717710 bp. 15 secs passed\r\n", "total_kmers: 43046721\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Create seed table. 61 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(4 threads)\r\n", "Input read file #1: mcf_T1D3_R1.fastq" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " \t(format: FASTQ)\r\n", "Input read file #2: mcf_T1D3_R2.fastq \t(format: FASTQ)\r\n", "Output file: bsmap_out_T1D3.sam\t (format: SAM)\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #2: \t50000 read pairs finished. 97 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #0: \t100000 read pairs finished. 98 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #1: \t200000 read pairs finished. 99 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #3: \t150000 read pairs finished. 99 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #2: \t250000 read pairs finished. 129 secs 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"stream", "stream": "stdout", "text": [ "Thread #2: \t4250000 read pairs finished. 591 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #0: \t4300000 read pairs finished. 595 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #1: \t4350000 read pairs finished. 595 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #3: \t4400000 read pairs finished. 597 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #2: \t4450000 read pairs finished. 614 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #0: \t4500000 read pairs finished. 618 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #1: \t4550000 read pairs finished. 619 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #3: \t4600000 read pairs finished. 621 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #2: \t4650000 read pairs finished. 638 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #1: \t4750000 read pairs finished. 641 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #0: \t4700000 read pairs finished. 641 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #3: \t4800000 read pairs finished. 643 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #2: \t4850000 read pairs finished. 660 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #1: \t4900000 read pairs finished. 664 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #0: \t4950000 read pairs finished. 664 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #3: \t5000000 read pairs finished. 665 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #2: \t5016400 read pairs finished. 667 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Total number of aligned reads: \r\n", "pairs: 2717832 (54%)\r\n", "single a: 1117376 (22%)\r\n", "single b: 1178713 (23%)\r\n", "Done.\r\n", "Finished at Wed Jul 9 15:56:21 2014\r\n", "Total time consumed: 667 secs\r\n" ] } ], "prompt_number": 9 }, { "cell_type": "code", "collapsed": false, "input": [ "lib=\"T1D5\"" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 10 }, { "cell_type": "code", "collapsed": true, "input": [ "! {bsmaploc}bsmap \\\n", "-a mcf_{lib}_R1.fastq \\\n", "-b mcf_{lib}_R2.fastq \\\n", "-d Crassostrea_gigas.GCA_000297895.1.22.dna_sm.genome.fa \\\n", "-o bsmap_out_{lib}.sam \\\n", "-p 4" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "\r\n", "BSMAP v2.74\r\n", "Start at: Wed Jul 9 15:56:22 2014\r\n", "\r\n", "Input reference file: Crassostrea_gigas.GCA_000297895.1.22.dna_sm.genome.fa \t(format: FASTA)\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Load in 7658 db seqs, total size 557717710 bp. 13 secs passed\r\n", "total_kmers: 43046721\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Create seed table. 52 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(4 threads)\r\n", "Input read file #1: mcf_T1D5_R1.fastq \t(format: FASTQ)\r\n", "Input read file #2: mcf_T1D5_R2.fastq" ] }, { "output_type": 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"Thread #2: \t5900000 read pairs finished. 878 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #3: \t5950000 read pairs finished. 881 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #0: \t6000000 read pairs finished. 882 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #0: \t6179274 read pairs finished. 898 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #1: \t6050000 read pairs finished. 903 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #2: \t6100000 read pairs finished. 903 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #3: \t6150000 read pairs finished. 905 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Total number of aligned reads: \r\n", "pairs: 2725017 (44%)\r\n", "single a: 1704213 (28%)\r\n", "single b: 1415455 (23%)\r\n", "Done.\r\n", "Finished at Wed Jul 9 16:11:28 2014\r\n", "Total time consumed: 906 secs\r\n" ] } ], "prompt_number": 11 }, { "cell_type": "code", "collapsed": false, "input": [ "lib=\"M3\"" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 12 }, { "cell_type": "code", "collapsed": false, "input": [ "! {bsmaploc}bsmap \\\n", "-a mcf_{lib}_R1.fastq \\\n", "-b mcf_{lib}_R2.fastq \\\n", "-d Crassostrea_gigas.GCA_000297895.1.22.dna_sm.genome.fa \\\n", "-o bsmap_out_{lib}.sam \\\n", "-p 4" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "\r\n", "BSMAP v2.74\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Start at: Wed Jul 9 16:11:29 2014\r\n", "\r\n", "Input reference file: Crassostrea_gigas.GCA_000297895.1.22.dna_sm.genome.fa \t(format: FASTA)\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Load in 7658 db seqs, total size 557717710 bp. 12 secs passed\r\n", "total_kmers: 43046721\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Create seed table. 52 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(4 threads)\r\n", "Input read file #1: mcf_M3_R1.fastq" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " \t(format: FASTQ)\r\n", "Input read file #2: mcf_M3_R2.fastq \t(format: FASTQ)\r\n", "Output file: bsmap_out_M3.sam\t (format: SAM)\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #0: \t50000 read pairs finished. 75 secs passed\r\n" 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(16%)\r\n", "Done.\r\n", "Finished at Wed Jul 9 16:28:44 2014\r\n", "Total time consumed: 1035 secs\r\n" ] } ], "prompt_number": 13 }, { "cell_type": "code", "collapsed": false, "input": [ "lib=\"T3D3\"" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 14 }, { "cell_type": "code", "collapsed": false, "input": [ "! {bsmaploc}bsmap \\\n", "-a mcf_{lib}_R1.fastq \\\n", "-b mcf_{lib}_R2.fastq \\\n", "-d Crassostrea_gigas.GCA_000297895.1.22.dna_sm.genome.fa \\\n", "-o bsmap_out_{lib}.sam \\\n", "-p 4" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "\r\n", "BSMAP v2.74\r\n", "Start at: Wed Jul 9 16:28:45 2014\r\n", "\r\n", "Input reference file: Crassostrea_gigas.GCA_000297895.1.22.dna_sm.genome.fa \t(format: FASTA)\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Load in 7658 db seqs, total size 557717710 bp. 13 secs passed\r\n", "total_kmers: 43046721\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Create seed table. 51 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(4 threads)\r\n", "Input read file #1: mcf_T3D3_R1.fastq" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " \t(format: FASTQ)\r\n", "Input read file #2: mcf_T3D3_R2.fastq \t(format: FASTQ)\r\n", "Output file: bsmap_out_T3D3.sam\t (format: SAM)\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #2: \t50000 read pairs finished. 75 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", 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of aligned reads: \r\n", "pairs: 4047152 (57%)\r\n", "single a: 1332491 (19%)\r\n", "single b: 1205102 (17%)\r\n", "Done.\r\n", "Finished at Wed Jul 9 16:44:54 2014\r\n", "Total time consumed: 969 secs\r\n" ] } ], "prompt_number": 15 }, { "cell_type": "code", "collapsed": false, "input": [ "lib=\"T3D5\"" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 16 }, { "cell_type": "code", "collapsed": false, "input": [ "! {bsmaploc}bsmap \\\n", "-a mcf_{lib}_R1.fastq \\\n", "-b mcf_{lib}_R2.fastq \\\n", "-d Crassostrea_gigas.GCA_000297895.1.22.dna_sm.genome.fa \\\n", "-o bsmap_out_{lib}.sam \\\n", "-p 4" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "\r\n", "BSMAP v2.74\r\n", "Start at: Wed Jul 9 16:44:56 2014\r\n", "\r\n", "Input reference file: Crassostrea_gigas.GCA_000297895.1.22.dna_sm.genome.fa \t(format: FASTA)\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Load in 7658 db seqs, total size 557717710 bp. 12 secs passed\r\n", "total_kmers: 43046721\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Create seed table. 50 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(4 threads)\r\n", "Input read file #1: mcf_T3D5_R1.fastq \t(format: FASTQ)\r\n", "Input read file #2: mcf_T3D5_R2.fastq \t(format: FASTQ)\r\n", "Output file: bsmap_out_T3D5.sam\t (format: SAM)\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #3: \t50000 read pairs finished. 74 secs passed\r\n" ] }, { "output_type": 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"Thread #0: \t6400000 read pairs finished. 869 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #2: \t6450000 read pairs finished. 889 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #3: \t6500000 read pairs finished. 891 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #1: \t6550000 read pairs finished. 893 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #0: \t6600000 read pairs finished. 894 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #2: \t6650000 read pairs finished. 914 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #3: \t6700000 read pairs finished. 916 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #1: \t6750000 read pairs finished. 918 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #0: \t6800000 read pairs finished. 919 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #2: \t6850000 read pairs finished. 939 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #3: \t6900000 read pairs finished. 940 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #1: \t6950000 read pairs finished. 942 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #0: \t7000000 read pairs finished. 944 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #1: \t7125800 read pairs finished. 953 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #2: \t7050000 read pairs finished. 959 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Thread #3: \t7100000 read pairs finished. 960 secs passed\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Total number of aligned reads: \r\n", "pairs: 4092568 (57%)\r\n", "single a: 1250715 (18%)\r\n", "single b: 1133306 (16%)\r\n", "Done.\r\n", "Finished at Wed Jul 9 17:00:56 2014\r\n", "Total time consumed: 960 secs\r\n" ] } ], "prompt_number": 17 }, { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "_methratio_" ] }, { "cell_type": "code", "collapsed": false, "input": [ "lib=\"M1\"" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 18 }, { "cell_type": "code", "collapsed": false, "input": [ "!python {bsmaploc}methratio.py \\\n", "-d Crassostrea_gigas.GCA_000297895.1.22.dna_sm.genome.fa \\\n", "-u -z -g \\\n", "-o methratio_out_{lib}.txt \\\n", "-s {bsmaploc}samtools \\\n", "bsmap_out_{lib}.sam \\\n" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "@ Wed Jul 9 17:00:58 2014: reading reference Crassostrea_gigas.GCA_000297895.1.22.dna_sm.genome.fa ...\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "@ Wed Jul 9 17:01:50 2014: reading bsmap_out_M1.sam ...\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "[samopen] SAM header is present: 7658 sequences.\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\t@ Wed Jul 9 17:09:01 2014: read 10000000 lines\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "@ Wed Jul 9 17:09:38 2014: combining CpG methylation from both strands ...\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "@ Wed Jul 9 17:10:11 2014: writing methratio_out_M1.txt ...\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "@ Wed Jul 9 17:22:53 2014: done.\r\n", "total 8716465 valid mappings, 48671759 covered cytosines, average coverage: 1.78 fold.\r\n" ] } ], "prompt_number": 19 }, { "cell_type": "code", "collapsed": false, "input": [ "lib=\"T1D3\"" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 20 }, { "cell_type": "code", "collapsed": false, "input": [ "!python {bsmaploc}methratio.py \\\n", "-d Crassostrea_gigas.GCA_000297895.1.22.dna_sm.genome.fa \\\n", "-u -z -g \\\n", "-o methratio_out_{lib}.txt \\\n", "-s {bsmaploc}samtools \\\n", "bsmap_out_{lib}.sam \\" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "@ Wed Jul 9 17:22:55 2014: reading reference Crassostrea_gigas.GCA_000297895.1.22.dna_sm.genome.fa ...\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "@ Wed Jul 9 17:23:46 2014: reading bsmap_out_T1D3.sam ...\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "[samopen] SAM header is present: 7658 sequences.\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "@ Wed Jul 9 17:27:44 2014: combining CpG methylation from both strands ...\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "@ Wed Jul 9 17:28:17 2014: writing methratio_out_T1D3.txt ...\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "@ Wed Jul 9 17:35:59 2014: done.\r\n", "total 5759215 valid mappings, 26507310 covered cytosines, average coverage: 1.32 fold.\r\n" ] } ], "prompt_number": 21 }, { "cell_type": "code", "collapsed": false, "input": [ "lib=\"T1D5\"" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 22 }, { "cell_type": "code", "collapsed": false, "input": [ "!python {bsmaploc}methratio.py \\\n", "-d Crassostrea_gigas.GCA_000297895.1.22.dna_sm.genome.fa \\\n", "-u -z -g \\\n", "-o methratio_out_{lib}.txt \\\n", "-s {bsmaploc}samtools \\\n", "bsmap_out_{lib}.sam \\" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "@ Wed Jul 9 17:36:01 2014: reading reference Crassostrea_gigas.GCA_000297895.1.22.dna_sm.genome.fa ...\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "@ Wed Jul 9 17:36:51 2014: reading bsmap_out_T1D5.sam ...\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "[samopen] SAM header is present: 7658 sequences.\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "@ Wed Jul 9 17:43:03 2014: combining CpG methylation from both strands ...\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "@ Wed Jul 9 17:43:37 2014: writing methratio_out_T1D5.txt ...\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "@ Wed Jul 9 17:56:54 2014: done.\r\n", "total 6974208 valid mappings, 45446465 covered cytosines, average coverage: 1.54 fold.\r\n" ] } ], "prompt_number": 23 }, { "cell_type": "code", "collapsed": false, "input": [ "lib=\"M3\"" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 24 }, { "cell_type": "code", "collapsed": false, "input": [ "!python {bsmaploc}methratio.py \\\n", "-d Crassostrea_gigas.GCA_000297895.1.22.dna_sm.genome.fa \\\n", "-u -z -g \\\n", "-o methratio_out_{lib}.txt \\\n", "-s {bsmaploc}samtools \\\n", "bsmap_out_{lib}.sam \\" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "@ Wed Jul 9 17:56:56 2014: reading reference Crassostrea_gigas.GCA_000297895.1.22.dna_sm.genome.fa ...\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "@ Wed Jul 9 17:57:47 2014: reading bsmap_out_M3.sam ...\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "[samopen] SAM header is present: 7658 sequences.\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\t@ Wed Jul 9 18:05:09 2014: read 10000000 lines\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "@ Wed Jul 9 18:06:22 2014: combining CpG methylation from both strands ...\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "@ Wed Jul 9 18:06:55 2014: writing methratio_out_M3.txt ...\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "@ Wed Jul 9 18:20:40 2014: done.\r\n", "total 9773223 valid mappings, 53389886 covered cytosines, average coverage: 1.78 fold.\r\n" ] } ], "prompt_number": 25 }, { "cell_type": "code", "collapsed": false, "input": [ "lib=\"T3D3\"" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 26 }, { "cell_type": "code", "collapsed": false, "input": [ "!python {bsmaploc}methratio.py \\\n", "-d Crassostrea_gigas.GCA_000297895.1.22.dna_sm.genome.fa \\\n", "-u -z -g \\\n", "-o methratio_out_{lib}.txt \\\n", "-s {bsmaploc}samtools \\\n", "bsmap_out_{lib}.sam \\" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "@ Wed Jul 9 18:20:42 2014: reading reference Crassostrea_gigas.GCA_000297895.1.22.dna_sm.genome.fa ...\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "@ Wed Jul 9 18:21:33 2014: reading bsmap_out_T3D3.sam ...\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "[samopen] SAM header is present: 7658 sequences.\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\t@ Wed Jul 9 18:28:52 2014: read 10000000 lines\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "@ Wed Jul 9 18:29:19 2014: combining CpG methylation from both strands ...\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "@ Wed Jul 9 18:29:51 2014: writing methratio_out_T3D3.txt ...\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "@ Wed Jul 9 18:43:22 2014: done.\r\n", "total 8847902 valid mappings, 52255860 covered cytosines, average coverage: 1.65 fold.\r\n" ] } ], "prompt_number": 27 }, { "cell_type": "code", "collapsed": false, "input": [ "lib=\"T3D5\"" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 28 }, { "cell_type": "code", "collapsed": false, "input": [ "!python {bsmaploc}methratio.py \\\n", "-d Crassostrea_gigas.GCA_000297895.1.22.dna_sm.genome.fa \\\n", "-u -z -g \\\n", "-o methratio_out_{lib}.txt \\\n", "-s {bsmaploc}samtools \\\n", "bsmap_out_{lib}.sam \\" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "@ Wed Jul 9 18:43:23 2014: reading reference Crassostrea_gigas.GCA_000297895.1.22.dna_sm.genome.fa ...\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "@ Wed Jul 9 18:44:14 2014: reading bsmap_out_T3D5.sam ...\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "[samopen] SAM header is present: 7658 sequences.\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\t@ Wed Jul 9 18:52:58 2014: read 10000000 lines\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "@ Wed Jul 9 18:53:22 2014: combining CpG methylation from both strands ...\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "@ Wed Jul 9 18:53:55 2014: writing methratio_out_T3D5.txt ...\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "@ Wed Jul 9 19:07:18 2014: done.\r\n", "total 8808414 valid mappings, 51732152 covered cytosines, average coverage: 1.69 fold.\r\n" ] } ], "prompt_number": 29 }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Converting methratio files for methylkit**" ] }, { "cell_type": "code", "collapsed": false, "input": [ "!cat ./scripts/mr3x.awk" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "#!/usr/bin/awk -f\r\n", "\r\n", "!awk {if ($8 >= 3) print $1,$2,$3,$4,$5,$6,$7,$8,$9,$10,$11,$12}" ] } ], "prompt_number": 39 }, { "cell_type": "code", "collapsed": false, "input": [ "!cat ./scripts/mr_gg.awk.sh" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "#!/usr/bin/awk -f\r\n", "\r\n", "BEGIN{ print \"chr.Base\\tchr\\tbase\\tstrand\\tcoverage\\tfreqC\\tfreqT\" }\r\n", "{\r\n", "\tif ($3 == \"+\") {\r\n", "\t\tstrand=\"F\"\r\n", " \t} else {\r\n", "\t\tstrand=\"R\"\r\n", "\t}\r\n", "\r\n", "\tFC=($7/$8)*100\r\n", "\tFT=(1-($7/$8))*100\r\n", "\tchrbase=$1\".\"$2\r\n", "\tprintf \"%s\\t%s\\t%s\\t%s\\t%d\\t%.2f\\t%.2f\\n\",\r\n", "\t\tchrbase, $1, $2, strand, $8, FC, FT\r\n", "}\r\n" ] } ], "prompt_number": 40 }, { "cell_type": "code", "collapsed": true, "input": [ "for i in (\"M1\",\"T1D3\",\"T1D5\", \"M3\", \"T3D3\", \"T3D5\"):\n", " !echo {i}\n", " !grep \"[A-Z][A-Z]CG[A-Z]\" methratio_out_{i}CG.txt\n", " !awk -f ./scripts/mr3x.awk methratio_out_{i}CG.txt > mr3x.{i}.txt\n", " !awk -f ./scripts/mr_gg.awk.sh mr3x.{i}.txt > mkfmt_{i}.txt\n", "\n" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "M1\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "T1D3\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "T1D5\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "M3\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "T3D3\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "T3D5\r\n" ] } ], "prompt_number": 30 }, { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Methylkit" ] }, { "cell_type": "code", "collapsed": false, "input": [ "%pylab inline" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "prompt_number": 31 }, { "cell_type": "code", "collapsed": false, "input": [ "%load_ext rpy2.ipython\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 32 }, { "cell_type": "code", "collapsed": false, "input": [ "%R library(methylKit)\n" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 33, "text": [ "\n", "[str, str, str, ..., str, str, str]" ] } ], "prompt_number": 33 }, { "cell_type": "code", "collapsed": false, "input": [ "%R library(data.table)\n" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "text": [ "data.table 1.9.2 For help type: help(\"data.table\")\n" ] } ], "prompt_number": 34 }, { "cell_type": "code", "collapsed": false, "input": [ "%R library(GenomicRanges)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "text": [ "Loading required package: BiocGenerics\n", "Loading required package: parallel\n", "\n", "Attaching package: \u2018BiocGenerics\u2019\n", "\n", "The following objects are masked from \u2018package:parallel\u2019:\n", "\n", " clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,\n", " clusterExport, clusterMap, parApply, parCapply, parLapply,\n", " parLapplyLB, parRapply, parSapply, parSapplyLB\n", "\n", "The following object is masked from \u2018package:stats\u2019:\n", "\n", " xtabs\n", "\n", "The following objects are masked from \u2018package:base\u2019:\n", "\n", " anyDuplicated, append, as.data.frame, as.vector, cbind, colnames,\n", " duplicated, eval, evalq, Filter, Find, get, intersect, is.unsorted,\n", " lapply, Map, mapply, match, mget, order, paste, pmax, pmax.int,\n", " pmin, pmin.int, Position, rank, rbind, Reduce, rep.int, rownames,\n", " sapply, setdiff, sort, table, tapply, union, unique, unlist\n", "\n", "Loading required package: IRanges\n", "Loading required package: XVector\n", "\n", "Attaching package: \u2018GenomicRanges\u2019\n", "\n", "The following object is masked from \u2018package:data.table\u2019:\n", "\n", " last\n", "\n" ] } ], "prompt_number": 35 }, { "cell_type": "code", "collapsed": false, "input": [ "ls mkfmt*" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "mkfmt_CgM1.txt mkfmt_CgM3.txt mkfmt_CgT1D3.txt mkfmt_CgT1D5.txt mkfmt_CgT3D3.txt mkfmt_CgT3D5.txt\r\n", "mkfmt_CgM1txt mkfmt_CgM3txt mkfmt_CgT1D3txt mkfmt_CgT1D5txt mkfmt_CgT3D3txt mkfmt_CgT3D5txt\r\n" ] } ], "prompt_number": 29 }, { "cell_type": "code", "collapsed": false, "input": [ "%%R file.list <- list \n", "('mkfmt_M1.txt',\n", " 'mkfmt_T1D3.txt',\n", " 'mkfmt_T1D5.txt',\n", " 'mkfmt_M3.txt',\n", " 'mkfmt_T3D3.txt',\n", " 'mkfmt_T3D5.txt'\n", ")\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 36 }, { "cell_type": "code", "collapsed": false, "input": [ "%R myobj=read(file.list,sample.id=list(\"1_sperm\",\"1_72hpf\",\"1_120hpf\",\"2_sperm\",\"2_72hpf\",\"2_120hpf\"),assembly=\"v9\",treatment=c(0,0,0,1,1,1))\n" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 37, "text": [ "\n", "[ListV..., ListV..., ListV..., ListV..., ListV..., ListV...]\n", "\n", "[ListV..., ListV..., ListV..., ListV..., ListV..., ListV...]\n", "\n", "[ListV..., ListV..., ListV..., ListV..., ListV..., ListV...]\n", "\n", "[ListV..., ListV..., ListV..., ListV..., ListV..., ListV...]\n", "\n", "[ListV..., ListV..., ListV..., ListV..., ListV..., ListV...]\n", "\n", "[ListV..., ListV..., ListV..., ListV..., ListV..., ListV...]\n", "\n", "[ListV..., ListV..., ListV..., ListV..., ListV..., ListV...]" ] } ], "prompt_number": 37 }, { "cell_type": "code", "collapsed": false, "input": [ "%%R\n", "meth<-unite(myobj)\n", "head(meth)\n", "nrow(meth)\n", "getCorrelation(meth,plot=T)\n", "hc<- clusterSamples(meth, dist=\"correlation\", method=\"ward\", plot=T)\n", "PCA<-PCASamples(meth)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "text": [ " 1_sperm 1_72hpf 1_120hpf 2_sperm 2_72hpf 2_120hpf\n", "1_sperm 1.0000000 0.8185855 0.8381244 0.8028188 0.8048533 0.8064576\n", "1_72hpf 0.8185855 1.0000000 0.8162468 0.8077710 0.8160557 0.8195972\n", "1_120hpf 0.8381244 0.8162468 1.0000000 0.8260072 0.8346218 0.8373328\n", "2_sperm 0.8028188 0.8077710 0.8260072 1.0000000 0.8813808 0.8815682\n", "2_72hpf 0.8048533 0.8160557 0.8346218 0.8813808 1.0000000 0.8799352\n", "2_120hpf 0.8064576 0.8195972 0.8373328 0.8815682 0.8799352 1.0000000\n", "KernSmooth 2.23 loaded\n", "Copyright M. P. Wand 1997-2009\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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/joAj4Ag4As2CAMoRA7bYLrnkElPGjjzyyLLVpeI1YBxXNFYgRPINGDDAJoSv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\nRYcYwBTfBa8GnGNgwqugGapa8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265QTawDRcADp0g1tFctxtFpR6uPQDzADplOOc7aiqA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\ncQDySbr++uu9aw7QSt6juRsrOkzbSYZZF+c8f3VlgSXdGVl0l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c6doMYBFZqawaKw9CTz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3FviqCyJbY83L5ZbWWvMyNdE0DRBfs+fb1yAlz3PlJIMs38i82Olb8XIanlk5rnEJ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\n+HECLJRktW5KJmpmy03fuokuGlmI+cKkfWYM0S3Bhwj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QiYFODHRiYG0x8KFswlrbTHaG\n78RAJwY6MdCJgU4M/LdhoJMB/7fVaGd5OjHQiYFODHRiYIPAQMeLLutJMXbb5XB7+ZWp0u2uIxnS\nl5yWNizUTaT0hFEE+mHzskeNAYfjCyD7pOo63PCLHeGTkJe9a9pKWsqMfTIOnP3KdDTWh8Tit9uj\nrStAnEheSoojrb6xgzKe2lhl6SK/FIKML1usS3feCCErvu+//377/LHfUbJDC2UgOPiojfMplRKF\nbGGmvFx1mLSXdcVyBfzijv8AlXCAW166HFN1KkNoTQqUb5IduAkI9kzIvgwH5NvzVFcrndOqDzR3\nxSeQsvPn2j2/+L6h+as9qK3ZylpyRZV+JAkuMnFdevz69izEduXxlZQtWV/JeowDeXmDfahzcEB5\nPWGVwd9qj9xoQcKKU/o8/O36QbFXWA+ues+vUvhcZOc4k4pT9JOne6o9yj6/stmGb9rfJrz4cHnW\nS75HjTzExr85R/0jUsxNUXhImnqlfwR8YAX4HdSyJy88of+EPoKfJBBfh/0kJBwHzNeofK5SK7aI\ncZGXohHvV3EmeQF+ezD4kIWrU5UeFNfs1kVlEC6sYbE1zW2/n4wdO9a+eNwlip8LQYt4IOka4QIa\ngr23i/iNLnZwhH2AkrYRLPWu2j7GheOgRnkP7YCEXQ9aZOdlj/16ocGX/LieHCoF/yp/pqv6idqH\nq1BVRrPzZ9q9v/ihsWO+PUBBxX87oHzls9J0dsopp2wQRQ0kc73P7KQps9X+bldr3N7zioJ82iNk\nJuh/QV3d8mTPcZ/xTwV7FM5UsG7DUtZJz+xl2lx2YBDl8yi2Qu0gICbi9ihPSmJYqvryqGmcr0cd\nqGYH3cWjPlGzrfqkFE+1yH7xBYfJsX144IEH1PtOVWSne57IVsDFAJkzspDWQNeQiFpDdWPPWlMy\n/7ILkGc/GUyPfMXqhsFvCcR7t/KLS2yjD5WjbguVQZcUAKjzbNHJGQgEzBQC4rfsoPJT4fOcqonj\nga4A9R8X2g5QPLvp2fpBXSF5tC2+4icmxScdMuCWHNrOHosi0i9ZB+3oz8rpA1xQrJBW5UpXOAKg\nUpJ6o06lotHVdcoY8GKbyCwtj6j15L5cr3fchR8uX6AeM4qHJ6VvcIEKx4h5RHbEnSczeqPaET/N\nM/UG/7P1DFLzkJbRLnvq2UffgpVPTbSZt3wr+mjnd9q0ecr+r5XANu6LvkFxeICVMT5oL2SBcYL3\nn0p4kltJm9c3AH4dCoZgEVvTnmgLShT1pAxGuOTC1VvGXp15CIdp9RVXYSo/3l6oOD2uIlYNF7sa\n9bO6kXpoZ8MPtlUT/2Lzjz8kjqnt13333ae8fl1t4NSCJ8qqJHXvsrKn/IMDod/7B92X/lIsIB8R\nVMIDLq3sVef07UK7AQ8c45ZdrfDi7UJ9wkGJ0w7Aj7ePuOw+eMMsyKvh+u1UqEOVuX5r4WBXvUfw\nHKz0t7KF5xxolLUjBsymyt/85jdRxP+lv2+99ZbdI3WhGwrE1bx+Zffaa6+1q6++upApdlM2NED5\nyG5M5WWSwjfXIRw8dvWewxCyDcoQPFb7LlCtsgB0KBHcGhFZCAtENAslo/MBEB1lNRO/ndjIuuDP\nh+ByVxwZEdu6LeVfhBudzOkuIRKPyRjRfeITn4g+9MvO5/HjpTg/LwqdwAUeIKo+itYbDEzXF2N/\nzOgVrogXysIDiCHohoQEJMIof47+mHEmPDkevCxUkaBlET8xURHFc8KrsqFPuVmZ8RF9WTwefqCI\n1BA9uiyjdvBPdavTF0y38JXAxRdfLPWUz7gd7QL8SAONvovtAkeKAuDC01Qof6JMeAhlx6w8FkZ0\njFpAaKgOp8yRey3MVeUq1Kes3U5tAWbCzVFp1a0TVYUvMGDFH9qC40SZTAknPkgR9W+K00wp/kwf\nPcJH3Q4PKHa5vbmV2hQNpwhPPPGE/eAHP3ALcMAu82XSY1ynilqu4QeXGYTyExJmE74Xxy1Ft2u7\nHZG0iyc8lEACjwGH1Dt9hnJShtD+Y7y0YsAwZvmlD4Er2pfrK5cx3MSEXY3wYM0PiFGN0SAvbV13\nO0x4DlxMbgKU7X9SepGT8Morr6hMH5EVbSPKL2UEF3RXHnACYE91B39ubKvNRI6J3xgXlEHl9sFs\naDf6RhjBVZ0+MBOuArP1QQZtIMaTlx8zfWbJPZav205SkF28fSA9SWkgl9bIoXaw0LzXVzSzV+Ox\n12UnbiwG3RGwm79nz9J+0lGYDc2dnfRcmLGhABxtvYPvfOc7xgN8/etf9yM/P7j6wQKhCBlGey6j\nWbVX70yMZKOu5Ia1/2GGUwkgNOpQfl2fjE4sIJ48MeXXdbHuDhEK1xu6f3p8TMwZ3aIA368AlLXf\n0ctdegng6NCTTz7pNtzSwTEsRnkRGUl4lJFmF0g05i2EH5LCrAv7CpM4fRZBgwfPNzaByRRcIUsx\n4NbKPXKjfMwG0zG+vFyUk++YwuEOAS4Q1iho4dfdlZdo5gD1kt7bxlCagrfCwIyjK1LR6Eewnnvu\ntaKH2AQWk6FpKxGBTZQJv9Sl6qgAoUeEeqLM4Iiy4E/1Tl5TKrPXJ/YC7DIQTsqIf/kLBBZG7Xkh\n7tA+eCceZ060nzh98ET4lnmfk6XMTa9oJheouluZVIb6g95lxPTbb7+9vfzCo54WjBWg7nmAYOZd\n7k5xyWOxnZThSW6lkHAHhwHAB3lXmYnTmbC+wY8DZRYw82Ow4jM8fRfwQ/ljPFF+zOAvt+DfYsA7\n6prRydY0VddK5A+SYxE4mhj6SbDdZ5997Nlnw1eU32SbwAVcYBdwoxxhHUMwh3ewL3/H7uRXgwmH\n0FeUd2gB5ad/gBcGGd5nZA7twQft+vQyC0ct838pxn20puu7FP2AUHApZts86Xar22p3WeiGNy33\n/F8GbgWj/qs5Nrm+4Ymmvt4C50FRSYiWpGuvHuvjUWgUACliUgZNDM2PToRZfK0Vs8E/HQ0ijPta\nw3R1Fj1ZRv90JDpfGdDxnN6ETlnmT5NZa9xcfmapU9JJRchyi0IJyyLT51NPPWXSqWx///vfbfwr\nuQI/pFyi9y49DUmBCyoXfIQYMSNmixiRDAD5nuKmkh/wBRRwNSf6rvTbIv7X0qD0ZkeuflMORDlk\nAGvKDlFqFU80e1j5roigcAkBaprwrDOy7LQb/K5hgpfDbboJhtnfjjvuaLfcskeJM/hgYEbbCGWl\n7BSVbLSCUHe8k8yk3CPuqvMWJh5MwChP7L9lgOyxUwUw46lVORFBO4NRoj7L03dyBpwUQ/vtU8Sr\nB+Lc9E7UJpyZ6XvVS4pjRaE25LEIqPL89Kc/rYsaTrYbr/2D9w3qH6Dc4IGsBTve2AOYgz12wa1V\nO8FzDKEfhe9W7+mRjbd/4cMTJwMAOMMctw1wlZwpuzjWMyek5X9tmbqzrUnXTabrGiRBGSDcjtWg\npP1+QjIBaMchvwEX4COUFX/4oZ3A33jAcmg3Mq4elLcfvoWPFrXtFkl9kuX1waicYcjeLnirAjCn\na3Vv80oNG5Uh+kRoRyEz0Iqm18d5W2l+99oi4w8eqnhDT6QL2SVK0ulup59+uu2im5aqhRtvvNGQ\nMtxxxx2FIO3F+fbbb9ull17q6e2m25ykK9w22WQT78dc5cgEY3UAic+hhx6qQdazdoVutbrgggtW\nJ/h64ZdusN4Ch/51SYHnDzINw10oNszonX4NkeUN0aDjANwxi9g1dCbeXPmnu4sKhIYOB1Oik3H3\naCS6LhIiwpR3wtCJ5VQK5R0u6Zok7En7YBaRbnxDeZ+vTieqwFogTyVAxBYUU/CmDBASgPJ31wMT\nDnRORgfyDeAXf7wDbihjJZJO3BAowvKIFJZAiDMiUqoZFhbf0kBiQOxtid6h7CUh2/mYKIIisS0z\nwcwm8iemk52rd5/KYWgb4YLtZH4wB+ZLeXkA7DFTrmLd62NNoFKdqy4D5IWHJpgpCYJsEiRhIT+I\nWPUVSQMw4A6+CEPcQnhzTJR9RiSrTd6caw2NldkCuBih6/EcVB1pVUcoL1mgk/MObYNvskPWANyA\nEIasYCY7IUW+Q5sIxSrvI/JSCivjtlFqW/wSPlh3z4YKUwKBGddsfKIY0Tj3m31fZdh4iWaPK2zl\n88qTGFmOEUIVQFsO5SbftH/KgV2y3NjhBmAObYRvyg4esAuAHyDYBTxFthV+qVc9eTHiLB01QKgM\nMqNIaR9pPXWbtmjvnaRAGrDQJ5hBI3EJTBscsMzBd8tMhR0eIqzuzdKNLsFwpgcjvP322+3444/3\n5a2OYkCRzY9+9CO75JJL7Nhjjy14by9OlLig8e4zn/mM3Xzzzfbd737Xvxk8QtuYxa4uvP766647\nHubN3cobIlD96y1wj2e4SFkXwnmnGRiLiOgsMF8egIE1bXiY3OkMgclg1s2n+i12OPyFDlPOfN1j\n2Q8d8IOC/Ptx3lUgnwU1hq5dmiJMlwYOsPaZEVmAuACBOIaQoey8Ay7wh3vwEwhHkQEzxInwFOxC\nuQMBIg6AOIIftwg/MCEqBgpeNURpEqZ5nsoCcRJRAVqWkafKgJYnOm2k2avO80SZyFvIdyg/MVDn\nyQe/5eWqWCYCC8r9RrZt/NIYxUS9cfJOgG5aLIVQIUlb8CcC2yR8MIseteQlO//NH9nJTCkrQLKf\nmMTU1DmYC20cdJIMSdNfeJNFHnAU2gJmIGSxUj2HOPGHO2GDXWscxXWL50pAZgJ3jBPPwYBkndeU\nMVW3wlpW7erSlUwPyV0F2WkaXC9JWS8yXwUQPQN3gHLxzYOZJImG/GPGLlkdoVzBLuBH3gphKuEI\n9zYhHmB5RYSyy3OqfoIKralFdrMIn5r65nVFqS9N4C688CBpCoO4tMww5v1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o6HShszkx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6FQICnxHggxt13wgAhceAO8E/eHbXlXfrPiQbmjF1Z5F+1rknfvOnqgBOs48dkoGzeUK05d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\ne32QbR1y1SnDko7qA/IJw3G0qz4gL5OGyIf1QbZ59aZTg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AtHAHNoz9gw/Gy2HgBw0aJLiHXF+B0Hzw260n3F+oI3zwwoB60lZq8HDkhzHfgiSa/ign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\n1txuWfcYfGOuJ7pBsYQdjkGXJbqjXEG6Kxg3Qlcv8oGDjSvoSkX3Frr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EEECgNgmkRQAuDDpv3jyNHTvWfxhH165dNX78eC1cuLDwLiwjgAACCCCQ8QJp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\nWS7w/wFwtCrLXCa59QAAAABJRU5ErkJggg==\n" } ], "prompt_number": 38 }, { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "ID target loci" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Summary: methration files filtered for CG and 3x coverage will be sligthly modified and uploaded to SQLshare for querying." ] }, { "cell_type": "code", "collapsed": false, "input": [ "#example file generated above that will be used\n", "!head mr3x.M1.txt" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "C12768 103 + TTCGT 0.167 6.00 1 6 6 6 0.030 0.564\r\n", "C12768 119 + TTCGT 0.250 4.00 1 4 4 4 0.046 0.699\r\n", "C12768 145 + TTCGT 0.000 3.00 0 3 3 3 0.000 0.562\r\n", "C12806 56 + AACGA 0.000 4.00 0 4 4 4 0.000 0.490\r\n", "C12806 76 + ATCGC 0.200 5.00 1 5 5 5 0.036 0.624\r\n", "C12806 78 + CGCGT 0.200 5.00 1 5 5 5 0.036 0.624\r\n", "C12806 105 + ATCGG 0.250 4.00 1 4 5 5 0.046 0.699\r\n", "C12806 142 + ATCGG 0.375 8.00 3 8 8 8 0.137 0.694\r\n", "C12924 19 + AACGA 0.000 4.00 0 4 4 4 0.000 0.490\r\n", "C12924 30 + ATCGT 0.000 4.00 0 5 4 5 0.000 0.490\r\n" ] } ], "prompt_number": 40 }, { "cell_type": "code", "collapsed": false, "input": [ "pwd" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 200, "text": [ "u'/Volumes/Bay3 scratch/BiGo_manny'" ] } ], "prompt_number": 200 }, { "cell_type": "code", "collapsed": false, "input": [ "for i in (\"M1\",\"T1D3\",\"T1D5\", \"M3\", \"T3D3\", \"T3D5\"):\n", " !echo {i}\n", " !awk -f ./scripts/psqls.awk mr3x.{i}.txt > short.{i}.txt\n", " !echo \"loci {i}ratio\" >> {i}.head\n", " !cat {i}.head short.{i}.txt > sql_{i}.txt\n", " !sed '/NA/d' sql_{i}.txt > sqlr_{i}.txt" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "M1\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "T1D3\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "T1D5\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "M3\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "T3D3\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "T3D5\r\n" ] } ], "prompt_number": 232 }, { "cell_type": "code", "collapsed": false, "input": [ "#fyi\n", "!cat ./scripts/psqls.awk" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "#!/usr/bin/awk -f\r\n", "\r\n", "!awk {print ($1\".\"$2),$5}" ] } ], "prompt_number": 60 }, { "cell_type": "code", "collapsed": false, "input": [ "!head sqlr_M1.txt" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "loci M1ratio\r\n", "C12768.103 0.167\r\n", "C12768.119 0.250\r\n", "C12768.145 0.000\r\n", "C12806.56 0.000\r\n", "C12806.76 0.200\r\n", "C12806.78 0.200\r\n", "C12806.105 0.250\r\n", "C12806.142 0.375\r\n", "C12924.19 0.000\r\n" ] } ], "prompt_number": 344 }, { "cell_type": "code", "collapsed": false, "input": [ "!wc -l sqlr_M1.txt" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ " 1212738 sqlr_M1.txt\r\n" ] } ], "prompt_number": 447 }, { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "SQLShare" ] }, { "cell_type": "code", "collapsed": false, "input": [ "#set locaiton of sqlshare python \n", "pt=\"/Users/sr320/sqlshare-pythonclient/tools/\"" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 43 }, { "cell_type": "code", "collapsed": false, "input": [ "!python {pt}multiupload.py sqlr_*" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "uploading sT3D3\r\n", "uploading sT3D3 into []\r\n", "uploading sqlr_T3D3.txt\r\n", "uploading sqlr_T3D3.txt into ['sqlr_T3D3.txt']\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "processing chunk line 0 to 1157463 (4.96696996689 s elapsed)\r\n", "pushing sqlr_T3D3.txt...\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "parsing 567A0318...\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "finished sqlr_T3D3.txt\r\n", "Successfully uploaded sqlr_T3D3.txt\r\n" ] } ], "prompt_number": 258 }, { "cell_type": "code", "collapsed": false, "input": [ "!python {pt}fetchdata.py -s \"SELECT *, ([M1ratio]+[T1D3ratio]+[T1D5ratio])/3 as mean1 FROM [sr320@washington.edu].[sqlr_M1.txt]m1 inner join [sr320@washington.edu].[sqlr_T1D5.txt]t1d5 on m1.loci=t1d5.loci inner join [sr320@washington.edu].[sqlr_T1D3.txt]t1d3 on m1.loci=t1d3.loci where [M1ratio]-(([M1ratio]+[T1D3ratio]+[T1D5ratio])/3) < abs(.2) and [T1D3ratio]-(([M1ratio]+[T1D3ratio]+[T1D5ratio])/3) < abs(.2) and [T1D5ratio]-(([M1ratio]+[T1D3ratio]+[T1D5ratio])/3) < abs(.2)\" -f tsv -o cglarv_lineage_1.txt" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 426 }, { "cell_type": "code", "collapsed": false, "input": [ "!python {pt}fetchdata.py -s \"SELECT *, ([M1ratio]+[T1D3ratio]+[T1D5ratio])/3 as mean1 FROM [sr320@washington.edu].[sqlr_M1.txt]m1 inner join [sr320@washington.edu].[sqlr_T1D5.txt]t1d5 on m1.loci=t1d5.loci inner join [sr320@washington.edu].[sqlr_T1D3.txt]t1d3 on m1.loci=t1d3.loci\" -f tsv -o tocountlarv_lineage_1.txt" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 427 }, { "cell_type": "code", "collapsed": false, "input": [ "!head tocountlarv_lineage_1.txt" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "loci\tM1ratio\tloci\tT1D5ratio\tloci\tT1D3ratio\tmean1\r", "\r\n", "C12960.123\t0\tC12960.123\t0\tC12960.123\t0\t0\r", "\r\n", "C13766.126\t0\tC13766.126\t0\tC13766.126\t0\t0\r", "\r\n", "C13766.145\t0\tC13766.145\t0\tC13766.145\t0\t0\r", "\r\n", "C13766.42\t0\tC13766.42\t0\tC13766.42\t0\t0\r", "\r\n", "C14220.143\t0\tC14220.143\t0\tC14220.143\t0\t0\r", "\r\n", "C14220.96\t0\tC14220.96\t0\tC14220.96\t0\t0\r", "\r\n", "C14468.198\t0.111\tC14468.198\t0\tC14468.198\t0\t0.037\r", "\r\n", "C14724.194\t0\tC14724.194\t0\tC14724.194\t0\t0\r", "\r\n", "C14796.69\t0.688\tC14796.69\t0.857\tC14796.69\t1\t0.848333333333333\r", "\r\n" ] } ], "prompt_number": 428 }, { "cell_type": "code", "collapsed": false, "input": [ "!wc -l tocountlarv_lineage_1.txt" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ " 69313 tocountlarv_lineage_1.txt\r\n" ] } ], "prompt_number": 429 }, { "cell_type": "code", "collapsed": false, "input": [ "!head cglarv_lineage_1.txt" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "loci\tM1ratio\tloci\tT1D5ratio\tloci\tT1D3ratio\tmean1\r", "\r\n", "C12960.123\t0\tC12960.123\t0\tC12960.123\t0\t0\r", "\r\n", "C13766.126\t0\tC13766.126\t0\tC13766.126\t0\t0\r", "\r\n", "C13766.145\t0\tC13766.145\t0\tC13766.145\t0\t0\r", "\r\n", "C13766.42\t0\tC13766.42\t0\tC13766.42\t0\t0\r", "\r\n", "C14220.143\t0\tC14220.143\t0\tC14220.143\t0\t0\r", "\r\n", "C14220.96\t0\tC14220.96\t0\tC14220.96\t0\t0\r", "\r\n", "C14468.198\t0.111\tC14468.198\t0\tC14468.198\t0\t0.037\r", "\r\n", "C14724.194\t0\tC14724.194\t0\tC14724.194\t0\t0\r", "\r\n", "C14796.69\t0.688\tC14796.69\t0.857\tC14796.69\t1\t0.848333333333333\r", "\r\n" ] } ], "prompt_number": 430 }, { "cell_type": "code", "collapsed": false, "input": [ "!python {pt}fetchdata.py -s \"SELECT *, ([M3ratio]+[T3D3ratio]+[T3D5ratio])/3 as mean3 FROM [sr320@washington.edu].[sqlr_M3.txt]M3 inner join [sr320@washington.edu].[sqlr_T3D5.txt]T3D5 on M3.loci=T3D5.loci inner join [sr320@washington.edu].[sqlr_T3D3.txt]T3D3 on M3.loci=T3D3.loci where [M3ratio]-(([M3ratio]+[T3D3ratio]+[T3D5ratio])/3) < abs(.2) and [T3D3ratio]-(([M3ratio]+[T3D3ratio]+[T3D5ratio])/3) < abs(.2) and [T3D5ratio]-(([M3ratio]+[T3D3ratio]+[T3D5ratio])/3) < abs(.2)\" -f tsv -o cglarv_lineage_3.txt" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 431 }, { "cell_type": "code", "collapsed": false, "input": [ "!head cglarv_lineage_3.txt" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "loci\tM3ratio\tloci\tT3D5ratio\tloci\tT3D3ratio\tmean3\r", "\r\n", "C12960.123\t0\tC12960.123\t0\tC12960.123\t0\t0\r", "\r\n", "C13208.148\t0.5\tC13208.148\t0.25\tC13208.148\t0.333\t0.361\r", "\r\n", "C14220.143\t0\tC14220.143\t0\tC14220.143\t0\t0\r", "\r\n", "C14220.96\t0\tC14220.96\t0\tC14220.96\t0\t0\r", "\r\n", "C14450.166\t0\tC14450.166\t0\tC14450.166\t0\t0\r", "\r\n", "C14450.187\t0\tC14450.187\t0.167\tC14450.187\t0\t0.0556666666666667\r", "\r\n", "C14450.189\t0\tC14450.189\t0.167\tC14450.189\t0\t0.0556666666666667\r", "\r\n", "C14450.192\t0\tC14450.192\t0\tC14450.192\t0\t0\r", "\r\n", "C14724.194\t0\tC14724.194\t0\tC14724.194\t0\t0\r", "\r\n" ] } ], "prompt_number": 432 }, { "cell_type": "code", "collapsed": false, "input": [ "for i in (\"1\",\"3\"):\n", " !echo {i}\n", " !awk -f ./scripts/joinr.awk cglarv_lineage_{i}.txt > cglarv_lineage_{i}trm.txt\n", " !sed 's/loci/loci{i}/g' sq_cglarv_lineage_{i}.txt\n" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "1\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "3\r\n" ] } ], "prompt_number": 433 }, { "cell_type": "code", "collapsed": false, "input": [ "!head sq_cglarv_lineage_1.txt" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "loci1 M1ratio T1D5ratio T1D3ratio mean1\r", "\r\n", "C12960.123 0 0 0 0\r", "\r\n", "C13766.126 0 0 0 0\r", "\r\n", "C13766.145 0 0 0 0\r", "\r\n", "C13766.42 0 0 0 0\r", "\r\n", "C14220.143 0 0 0 0\r", "\r\n", "C14220.96 0 0 0 0\r", "\r\n", "C14468.198 0.111 0 0 0.037\r", "\r\n", "C14724.194 0 0 0 0\r", "\r\n", "C14796.69 0.688 0.857 1 0.848333333333333\r", "\r\n" ] } ], "prompt_number": 434 }, { "cell_type": "code", "collapsed": false, "input": [ "!head sq_cglarv_lineage_3.txt" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "loci3 M3ratio T3D5ratio T3D3ratio mean3\r", "\r\n", "C12960.123 0 0 0 0\r", "\r\n", "C13208.148 0.5 0.25 0.333 0.361\r", "\r\n", "C14220.143 0 0 0 0\r", "\r\n", "C14220.96 0 0 0 0\r", "\r\n", "C14450.166 0 0 0 0\r", "\r\n", "C14450.187 0 0.167 0 0.0556666666666667\r", "\r\n", "C14450.189 0 0.167 0 0.0556666666666667\r", "\r\n", "C14450.192 0 0 0 0\r", "\r\n", "C14724.194 0 0 0 0\r", "\r\n" ] } ], "prompt_number": 435 }, { "cell_type": "code", "collapsed": false, "input": [ "!python {pt}multiupload.py sq_cglarv_*" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "uploading sq_cglarv_lineage_1.txt\r\n", "uploading sq_cglarv_lineage_1.txt into ['sq_cglarv_lineage_1.txt']\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "processing chunk line 0 to 63043 (0.0331981182098 s elapsed)\r\n", "pushing sq_cglarv_lineage_1.txt...\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "parsing 19EC8DBE...\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "finished sq_cglarv_lineage_1.txt\r\n", "Successfully uploaded sq_cglarv_lineage_1.txt\r\n", "uploading sq_cglarv_lineage_3.txt\r\n", "uploading sq_cglarv_lineage_3.txt into ['sq_cglarv_lineage_3.txt']\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "processing chunk line 0 to 245244 (0.118955850601 s elapsed)\r\n", "pushing sq_cglarv_lineage_3.txt...\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "parsing B1F7682A...\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "finished sq_cglarv_lineage_3.txt\r\n", "Successfully uploaded sq_cglarv_lineage_3.txt\r\n" ] } ], "prompt_number": 436 }, { "cell_type": "code", "collapsed": false, "input": [ "!python {pt}fetchdata.py -s \"SELECT *, abs(mean1 - mean3) as meandiff FROM [sr320@washington.edu].[sq_cglarv_lineage_1.txt]c1 inner join [sr320@washington.edu].[sq_cglarv_lineage_3.txt]c3 on c1.loci1=c3.loci3 where abs(mean1 - mean3) >= 0.3\" -f tsv -o cglarv_lineage_differ.txt" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 437 }, { "cell_type": "code", "collapsed": false, "input": [ "#QC check commmon loci\n", "!python {pt}fetchdata.py -s \"SELECT *, abs(mean1 - mean3) as meandiff FROM [sr320@washington.edu].[sq_cglarv_lineage_1.txt]c1 inner join [sr320@washington.edu].[sq_cglarv_lineage_3.txt]c3 on c1.loci1=c3.loci3\" -f tsv -o commonloci.txt\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 443 }, { "cell_type": "code", "collapsed": false, "input": [ "!head commonloci.txt" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "loci1\tM1ratio\tT1D5ratio\tT1D3ratio\tmean1\tloci3\tM3ratio\tT3D5ratio\tT3D3ratio\tmean3\tmeandiff\r", "\r\n", "C12960.123\t0\t0\t0\t0\tC12960.123\t0\t0\t0\t0\t0\r", "\r\n", "C14220.143\t0\t0\t0\t0\tC14220.143\t0\t0\t0\t0\t0\r", "\r\n", "C14220.96\t0\t0\t0\t0\tC14220.96\t0\t0\t0\t0\t0\r", "\r\n", "C14724.194\t0\t0\t0\t0\tC14724.194\t0\t0\t0\t0\t0\r", "\r\n", "C14796.69\t0.688\t0.857\t1\t0.848333333333333\tC14796.69\t1\t0.786\t1\t0.928666666666667\t0.0803333333333339\r", "\r\n", "C15066.82\t0\t0\t0\t0\tC15066.82\t0\t0\t0\t0\t0\r", "\r\n", "C15080.156\t0\t0\t0\t0\tC15080.156\t0\t0\t0\t0\t0\r", "\r\n", "C15082.128\t0\t0\t0\t0\tC15082.128\t0\t0\t0\t0\t0\r", "\r\n", "C15294.101\t0\t0\t0\t0\tC15294.101\t0\t0\t0\t0\t0\r", "\r\n" ] } ], "prompt_number": 445 }, { "cell_type": "code", "collapsed": false, "input": [ "!wc -l commonloci.txt" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ " 35475 commonloci.txt\r\n" ] } ], "prompt_number": 446 }, { "cell_type": "code", "collapsed": false, "input": [ "!head cglarv_lineage_differ.txt" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "loci1\tM1ratio\tT1D5ratio\tT1D3ratio\tmean1\tloci3\tM3ratio\tT3D5ratio\tT3D3ratio\tmean3\tmeandiff\r", "\r\n", "C22094.1004\t0.143\t0\t0.167\t0.103333333333333\tC22094.1004\t0.457\t0.673\t0.467\t0.532333333333333\t0.429\r", "\r\n", "C25394.2981\t0.538\t0.333\t0.571\t0.480666666666667\tC25394.2981\t0.778\t0.889\t0.909\t0.858666666666667\t0.378\r", "\r\n", "C25670.2403\t0.533\t0.615\t0.5\t0.549333333333333\tC25670.2403\t1\t1\t0.667\t0.889\t0.339666666666667\r", "\r\n", "C25670.2410\t0.2\t0.462\t0.5\t0.387333333333333\tC25670.2410\t0.857\t0.889\t0.8\t0.848666666666667\t0.461333333333334\r", "\r\n", "C26040.898\t0.286\t0.333\t0.333\t0.317333333333333\tC26040.898\t1\t0.75\t0.667\t0.805666666666667\t0.488333333333334\r", "\r\n", "C26156.2111\t0\t0\t0\t0\tC26156.2111\t0.25\t0.333\t0.333\t0.305333333333333\t0.305333333333333\r", "\r\n", "C26256.553\t0\t0.5\t0.5\t0.333333333333333\tC26256.553\t0.714\t0.8\t0.667\t0.727\t0.393666666666667\r", "\r\n", "C26536.2616\t0.25\t0\t0.25\t0.166666666666667\tC26536.2616\t0.333\t0.6\t0.667\t0.533333333333333\t0.366666666666666\r", "\r\n", "C26646.2199\t0.667\t0.667\t0.4\t0.578\tC26646.2199\t0.25\t0.25\t0.333\t0.277666666666667\t0.300333333333333\r", "\r\n" ] } ], "prompt_number": 438 }, { "cell_type": "code", "collapsed": false, "input": [ "!awk '{print $1}' cglarv_lineage_differ.txt > cglarv_lin_a\n", "!tail -n +2 cglarv_lin_a > cglarv_lin_b\n", "!tr '.' \"\\t\" cglarv_lin_c\n", "!awk '{print $1, $2, ($2+1), \"DML_lin\" }' cglarv_lin_c > cglarv_lin_d\n", "!tr ' ' \"\\t\" cglarv_lineage_differ.bed" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 439 }, { "cell_type": "code", "collapsed": false, "input": [ "!head cglarv_lineage_differ.bed" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "C22094\t1004\t1005\tDML_lin\r\n", "C25394\t2981\t2982\tDML_lin\r\n", "C25670\t2403\t2404\tDML_lin\r\n", "C25670\t2410\t2411\tDML_lin\r\n", "C26040\t898\t899\tDML_lin\r\n", "C26156\t2111\t2112\tDML_lin\r\n", "C26256\t553\t554\tDML_lin\r\n", "C26536\t2616\t2617\tDML_lin\r\n", "C26646\t2199\t2200\tDML_lin\r\n", "C26812\t1897\t1898\tDML_lin\r\n" ] } ], "prompt_number": 440 }, { "cell_type": "code", "collapsed": false, "input": [ "!wc -l cglarv_lineage_differ.bed" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ " 483 cglarv_lineage_differ.bed\r\n" ] } ], "prompt_number": 441 }, { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Intersect bed" ] }, { "cell_type": "code", "collapsed": false, "input": [ "Download oyster genome tracks\n" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "mkdir genome_tracks" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "for i in (\"exon\",\"intron\",\"TE\",\"gene\",\"1k5p_gene_promoter\",\"CG\"):\n", " !wget -q -P genome_tracks http://eagle.fish.washington.edu/trilobite/Crassostrea_gigas_v9_tracks/Cgigas_v9_{i}.gff \n", "\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 394 }, { "cell_type": "code", "collapsed": false, "input": [ "for i in (\"exon\",\"intron\",\"TE\",\"gene\",\"1k5p_gene_promoter\"):\n", " !intersectbed \\\n", " -wb \\\n", " -a cglarv_lineage_differ.bed \\\n", " -b ./genome_tracks/Cgigas_v9_{i}.gff \\\n", " > {i}_intersect_DML_lin_wb.txt \n", " !intersectbed \\\n", " -u \\\n", " -a cglarv_lineage_differ.bed \\\n", " -b ./genome_tracks/Cgigas_v9_{i}.gff \\\n", " > {i}_intersect_DML_lin_u.txt\n", " !wc -l {i}_intersect_DML_lin_u.txt\n" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ " 46 exon_intersect_DML_lin_u.txt\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " 152 intron_intersect_DML_lin_u.txt\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " 80 TE_intersect_DML_lin_u.txt\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " 198 gene_intersect_DML_lin_u.txt\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " 24 1k5p_gene_promoter_intersect_DML_lin_u.txt\r\n" ] } ], "prompt_number": 442 }, { "cell_type": "code", "collapsed": false, "input": [ "!wc -l ./genome_tracks/Cgigas_v9_CG.gff" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ " 10035701 ./genome_tracks/Cgigas_v9_CG.gff\r\n" ] } ], "prompt_number": 20 }, { "cell_type": "code", "collapsed": false, "input": [ "for i in (\"exon\",\"intron\",\"TE\",\"gene\",\"1k5p_gene_promoter\"):\n", " !intersectbed \\\n", " -wb \\\n", " -a ./genome_tracks/Cgigas_v9_CG.gff \\\n", " -b ./genome_tracks/Cgigas_v9_{i}.gff \\\n", " > {i}_intersect_CG_wb.txt \n", " !intersectbed \\\n", " -u \\\n", " -a ./genome_tracks/Cgigas_v9_CG.gff \\\n", " -b ./genome_tracks/Cgigas_v9_{i}.gff \\\n", " > {i}_intersect_CG_u.txt\n", " !wc -l {i}_intersect_CG_u.txt" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ " 1129658 exon_intersect_CG_u.txt\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " 2815997 intron_intersect_CG_u.txt\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " 589509 TE_intersect_CG_u.txt\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " 3938356 gene_intersect_CG_u.txt\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " 593081 1k5p_gene_promoter_intersect_CG_u.txt\r\n" ] } ], "prompt_number": 22 }, { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Developmental Differences" ] }, { "cell_type": "code", "collapsed": false, "input": [ "ID similarities in Biological Reps" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "out=\"sperm\"\n", "r1=\"M1\"\n", "r2=\"M3\"\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 466 }, { "cell_type": "code", "collapsed": false, "input": [ "!python {pt}fetchdata.py -s \"SELECT *, ([{r2}ratio]+[{r1}ratio])/2 as mean_{out} FROM [sr320@washington.edu].[sqlr_{r1}.txt]{r1} inner join [sr320@washington.edu].[sqlr_{r2}.txt]{r2} on {r1}.loci={r2}.loci where [{r1}ratio]-(([{r2}ratio]+[{r1}ratio])/2) < abs(.2) and [{r2}ratio]-(([{r2}ratio]+[{r1}ratio])/2) < abs(.2)\" -f tsv -o br_cglarv_{out}.txt\n", "!wc -l br_cglarv_{out}.txt\n", "!head br_cglarv_{out}.txt\n", "!python {pt}singleupload.py br_cglarv_{out}.txt" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ " 421561 br_cglarv_sperm.txt\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "loci\tM1ratio\tloci\tM3ratio\tmean_sperm\r", "\r\n", "C12806.142\t0.375\tC12806.142\t0\t0.1875\r", "\r\n", "C12960.123\t0\tC12960.123\t0\t0\r", "\r\n", "C13128.87\t0\tC13128.87\t0\t0\r", "\r\n", "C13208.83\t0.4\tC13208.83\t0.429\t0.4145\r", "\r\n", "C13274.128\t0\tC13274.128\t0\t0\r", "\r\n", "C13274.135\t0\tC13274.135\t0\t0\r", "\r\n", "C14180.84\t0\tC14180.84\t0\t0\r", "\r\n", "C14220.143\t0\tC14220.143\t0\t0\r", "\r\n", "C14220.96\t0\tC14220.96\t0\t0\r", "\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "processing chunk line 0 to 421561 (0.26978302002 s elapsed)\r\n", "pushing br_cglarv_sperm.txt...\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "parsing 2396B0BE...\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "finished br_cglarv_sperm.txt\r\n" ] } ], "prompt_number": 468 }, { "cell_type": "code", "collapsed": false, "input": [ "out=\"day3\"\n", "r1=\"T1D3\"\n", "r2=\"T3D3\"" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 469 }, { "cell_type": "code", "collapsed": false, "input": [ "!python {pt}fetchdata.py -s \"SELECT *, ([{r2}ratio]+[{r1}ratio])/2 as mean_{out} FROM [sr320@washington.edu].[sqlr_{r1}.txt]{r1} inner join [sr320@washington.edu].[sqlr_{r2}.txt]{r2} on {r1}.loci={r2}.loci where [{r1}ratio]-(([{r2}ratio]+[{r1}ratio])/2) < abs(.2) and [{r2}ratio]-(([{r2}ratio]+[{r1}ratio])/2) < abs(.2)\" -f tsv -o br_cglarv_{out}.txt\n", "!wc -l br_cglarv_{out}.txt\n", "!head br_cglarv_{out}.txt\n", "!python {pt}singleupload.py br_cglarv_{out}.txt" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ " 108870 br_cglarv_day3.txt\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "loci\tT1D3ratio\tloci\tT3D3ratio\tmean_day3\r", "\r\n", "C12960.123\t0\tC12960.123\t0\t0\r", "\r\n", "C13874.46\t0\tC13874.46\t0\t0\r", "\r\n", "C14220.143\t0\tC14220.143\t0\t0\r", "\r\n", "C14220.96\t0\tC14220.96\t0\t0\r", "\r\n", "C14308.63\t0\tC14308.63\t0\t0\r", "\r\n", "C14724.194\t0\tC14724.194\t0\t0\r", "\r\n", "C14724.99\t0\tC14724.99\t0\t0\r", "\r\n", "C14796.69\t1\tC14796.69\t1\t1\r", "\r\n", "C14944.199\t0\tC14944.199\t0\t0\r", "\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "processing chunk line 0 to 108870 (0.104866027832 s elapsed)\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "pushing br_cglarv_day3.txt...\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "parsing 876A9D68...\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "finished br_cglarv_day3.txt\r\n" ] } ], "prompt_number": 470 }, { "cell_type": "code", "collapsed": false, "input": [ "out=\"day5\"\n", "r1=\"T1D5\"\n", "r2=\"T3D5\"" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 471 }, { "cell_type": "code", "collapsed": false, "input": [ "!python {pt}fetchdata.py -s \"SELECT *, ([{r2}ratio]+[{r1}ratio])/2 as mean_{out} FROM [sr320@washington.edu].[sqlr_{r1}.txt]{r1} inner join [sr320@washington.edu].[sqlr_{r2}.txt]{r2} on {r1}.loci={r2}.loci where [{r1}ratio]-(([{r2}ratio]+[{r1}ratio])/2) < abs(.2) and [{r2}ratio]-(([{r2}ratio]+[{r1}ratio])/2) < abs(.2)\" -f tsv -o br_cglarv_{out}.txt\n", "!wc -l br_cglarv_{out}.txt\n", "!head br_cglarv_{out}.txt\n", "!python {pt}singleupload.py br_cglarv_{out}.txt" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ " 302492 br_cglarv_day5.txt\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "loci\tT1D5ratio\tloci\tT3D5ratio\tmean_day5\r", "\r\n", "C12960.123\t0\tC12960.123\t0\t0\r", "\r\n", "C12960.67\t0\tC12960.67\t0\t0\r", "\r\n", "C13128.87\t0\tC13128.87\t0\t0\r", "\r\n", "C13208.148\t0.333\tC13208.148\t0.25\t0.2915\r", "\r\n", "C13442.65\t0\tC13442.65\t0\t0\r", "\r\n", "C13442.79\t0\tC13442.79\t0\t0\r", "\r\n", "C13546.84\t0\tC13546.84\t0\t0\r", "\r\n", "C13712.105\t0\tC13712.105\t0\t0\r", "\r\n", "C13766.126\t0\tC13766.126\t0\t0\r", "\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "processing chunk line 0 to 302492 (0.181667089462 s elapsed)\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "pushing br_cglarv_day5.txt...\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "parsing A941BD6C...\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "finished br_cglarv_day5.txt\r\n" ] } ], "prompt_number": 477 }, { "cell_type": "code", "collapsed": false, "input": [ "!python {pt}fetchdata.py -s \"SELECT * FROM [sr320@washington.edu].[br_cglarv_sperm.txt]sperm inner join [sr320@washington.edu].[br_cglarv_day3.txt]d3 on sperm.loci=d3.loci inner join [sr320@washington.edu].[br_cglarv_day5.txt]d5 on sperm.loci=d5.loci\" -f tsv -o br_joinall.txt\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 473 }, { "cell_type": "code", "collapsed": false, "input": [ "!head -2 br_joinall.txt" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "loci\tM1ratio\tloci1\tM3ratio\tmean_sperm\tloci\tT1D3ratio\tloci1\tT3D3ratio\tmean_day3\tloci\tT1D5ratio\tloci1\tT3D5ratio\tmean_day5\r", "\r\n", "C12960.123\t0\tC12960.123\t0\t0\tC12960.123\t0\tC12960.123\t0\t0\tC12960.123\t0\tC12960.123\t0\t0\r", "\r\n" ] } ], "prompt_number": 476 }, { "cell_type": "code", "collapsed": false, "input": [ "!wc -l br_joinall.txt" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ " 36670 br_joinall.txt\r\n" ] } ], "prompt_number": 475 }, { "cell_type": "code", "collapsed": false, "input": [ "!python {pt}fetchdata.py -s \"SELECT *, sperm.mean_sperm-d3.mean_day3 as 's-3', sperm.mean_sperm-d5.mean_day5 as 's-5', d3.mean_day3-d5.mean_day5 as '3-5' FROM [sr320@washington.edu].[br_cglarv_sperm.txt]sperm inner join [sr320@washington.edu].[br_cglarv_day3.txt]d3 on sperm.loci=d3.loci inner join [sr320@washington.edu].[br_cglarv_day5.txt]d5 on sperm.loci=d5.loci where abs(sperm.mean_sperm-d3.mean_day3) > 0.4 or abs(sperm.mean_sperm-d5.mean_day5) > 0.4 or abs(d3.mean_day3-d5.mean_day5) > 0.4\" -f tsv -o DML_development.txt\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 478 }, { "cell_type": "code", "collapsed": false, "input": [ "!head DML_development.txt" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "loci\tM1ratio\tloci1\tM3ratio\tmean_sperm\tloci\tT1D3ratio\tloci1\tT3D3ratio\tmean_day3\tloci\tT1D5ratio\tloci1\tT3D5ratio\tmean_day5\ts-3\ts-5\t3-5\r", "\r\n", "C22666.609\t0.727\tC22666.609\t0.444\t0.5855\tC22666.609\t0.333\tC22666.609\t0\t0.1665\tC22666.609\t0.5\tC22666.609\t0.625\t0.5625\t0.419\t0.023\t-0.396\r", "\r\n", "C22666.624\t0.467\tC22666.624\t0.417\t0.442\tC22666.624\t0.25\tC22666.624\t0\t0.125\tC22666.624\t0.538\tC22666.624\t0.636\t0.587\t0.317\t-0.145\t-0.462\r", "\r\n", "C24478.2098\t1\tC24478.2098\t1\t1\tC24478.2098\t0.667\tC24478.2098\t0.5\t0.5835\tC24478.2098\t0.571\tC24478.2098\t0.5\t0.5355\t0.4165\t0.4645\t0.048\r", "\r\n", "C24478.2120\t0.667\tC24478.2120\t0.536\t0.6015\tC24478.2120\t0.333\tC24478.2120\t0\t0.1665\tC24478.2120\t0.8\tC24478.2120\t0.667\t0.7335\t0.435\t-0.132\t-0.567\r", "\r\n", "C24628.2236\t0.636\tC24628.2236\t0.5\t0.568\tC24628.2236\t1\tC24628.2236\t1\t1\tC24628.2236\t0.667\tC24628.2236\t0.875\t0.771\t-0.432\t-0.203\t0.229\r", "\r\n", "C24628.473\t0.917\tC24628.473\t0.586\t0.7515\tC24628.473\t0.385\tC24628.473\t0.684\t0.5345\tC24628.473\t0.25\tC24628.473\t0.273\t0.2615\t0.217\t0.49\t0.273\r", "\r\n", "C26614.3295\t0.889\tC26614.3295\t0.667\t0.778\tC26614.3295\t0.25\tC26614.3295\t0.5\t0.375\tC26614.3295\t0.5\tC26614.3295\t0.714\t0.607\t0.403\t0.171\t-0.232\r", "\r\n", "C26938.1051\t0.667\tC26938.1051\t0.8\t0.7335\tC26938.1051\t0\tC26938.1051\t0.333\t0.1665\tC26938.1051\t0.5\tC26938.1051\t0.6\t0.55\t0.567\t0.1835\t-0.3835\r", "\r\n", "C27500.1245\t0.333\tC27500.1245\t0.417\t0.375\tC27500.1245\t0.8\tC27500.1245\t0.571\t0.6855\tC27500.1245\t0.2\tC27500.1245\t0.133\t0.1665\t-0.3105\t0.2085\t0.519\r", "\r\n" ] } ], "prompt_number": 479 }, { "cell_type": "code", "collapsed": false, "input": [ "!wc -l DML_development.txt" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ " 388 DML_development.txt\r\n" ] } ], "prompt_number": 481 }, { "cell_type": "code", "collapsed": false, "input": [ "!awk '{print $1}' DML_development.txt > cglarv_dd_a\n", "!tail -n +2 cglarv_dd_a > cglarv_dd_b\n", "!tr '.' \"\\t\" cglarv_dd_c\n", "!awk '{print $1, $2, ($2+1), \"DML_dev\" }' cglarv_dd_c > cglarv_dd_d\n", "!tr ' ' \"\\t\" cglarv_development_differ.bed\n", "!head cglarv_development_differ.bed\n", "!wc -l cglarv_development_differ.bed" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "C22666\t609\t610\tDML_dev\r\n", "C22666\t624\t625\tDML_dev\r\n", "C24478\t2098\t2099\tDML_dev\r\n", "C24478\t2120\t2121\tDML_dev\r\n", "C24628\t2236\t2237\tDML_dev\r\n", "C24628\t473\t474\tDML_dev\r\n", "C26614\t3295\t3296\tDML_dev\r\n", "C26938\t1051\t1052\tDML_dev\r\n", "C27500\t1245\t1246\tDML_dev\r\n", "C27928\t1457\t1458\tDML_dev\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " 387 cglarv_development_differ.bed\r\n" ] } ], "prompt_number": 482 }, { "cell_type": "code", "collapsed": false, "input": [ "for i in (\"exon\",\"intron\",\"TE\",\"gene\",\"1k5p_gene_promoter\"):\n", " !intersectbed \\\n", " -wb \\\n", " -a cglarv_development_differ.bed \\\n", " -b ./genome_tracks/Cgigas_v9_{i}.gff \\\n", " > {i}_intersect_DML_dev_wb.txt \n", " !intersectbed \\\n", " -u \\\n", " -a cglarv_development_differ.bed \\\n", " -b ./genome_tracks/Cgigas_v9_{i}.gff \\\n", " > {i}_intersect_DML_dev_u.txt\n", " !wc -l {i}_intersect_DML_dev_u.txt" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ " 30 exon_intersect_DML_dev_u.txt\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " 137 intron_intersect_DML_dev_u.txt\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " 57 TE_intersect_DML_dev_u.txt\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " 167 gene_intersect_DML_dev_u.txt\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " 18 1k5p_gene_promoter_intersect_DML_dev_u.txt\r\n" ] } ], "prompt_number": 483 }, { "cell_type": "markdown", "metadata": {}, "source": [ "\"Screenshot_7_17_14__5_49_AM_1977FDE4.png\"/" ] }, { "cell_type": "code", "collapsed": false, "input": [ "!head TE_intersect_DML_dev_wb.txt" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "C30526\t2573\t2574\tDML_dev\tC30526\tWUBlastX\tDNA_TcMar-Tc2\t2452\t2679\t91\t-\t.\t.\r\n", "C30526\t2573\t2574\tDML_dev\tC30526\tWUBlastX\tDNA_TcMar-Tc2\t2452\t2640\t106\t-\t.\t.\r\n", "C34310\t1254\t1255\tDML_dev\tC34310\tWUBlastX\tDNA_IS4EU\t775\t1458\t20\t-\t.\t.\r\n", "scaffold1171\t48436\t48437\tDML_dev\tscaffold1171\tWUBlastX\tDNA_hAT-hATw\t47463\t48491\t36\t+\t.\t.\r\n", "scaffold1171\t48436\t48437\tDML_dev\tscaffold1171\tWUBlastX\tDNA_hAT-hATw\t47790\t48476\t102\t+\t.\t.\r\n", "scaffold1171\t48436\t48437\tDML_dev\tscaffold1171\tWUBlastX\tDNA_hAT-hATw\t48375\t49889\t213\t+\t.\t.\r\n", "scaffold1188\t80972\t80973\tDML_dev\tscaffold1188\tWUBlastX\tLINE_R2-Hero\t78711\t81929\t2619\t-\t.\t.\r\n", "scaffold1188\t81327\t81328\tDML_dev\tscaffold1188\tWUBlastX\tLINE_R2-Hero\t78711\t81929\t2619\t-\t.\t.\r\n", "scaffold1227\t137921\t137922\tDML_dev\tscaffold1227\tTRF\tTandem_Repeat\t137882\t138064\t330\t+\t.\t.\r\n", "scaffold1232\t318833\t318834\tDML_dev\tscaffold1232\tWUBlastX\tLTR_Pao\t318601\t319908\t47\t-\t.\t.\r\n" ] } ], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [ "!wc -l TE_intersect_DML_lin_wb.txt" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ " 112 TE_intersect_DML_lin_wb.txt\r\n" ] } ], "prompt_number": 119 }, { "cell_type": "code", "collapsed": false, "input": [ "!awk '{print $7}' TE_intersect_DML_dev_wb.txt > TE_intersect_DML_dev_wb_TEonly.txt\n", "! TE_intersect_DML_lin_wb_TEonly.txt\n", "! TE_intersect_CG_wb_TEonly.txt\n", "! gene_intersect_{i}_wb_a.txt \n", " #!sed 's/;//g' gene_intersect_{i}_wb_b.txt \n", " !head -3 gene_intersect_{i}_wb_b.txt\n", " !wc -l gene_intersect_{i}_wb_b.txt\n", " #!python {pt}multiupload.py gene_intersect_{i}_wb_b.txt\n", " \n", "\n", " \n", " \n", " \n", " \n" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "C30046\t1305\t1306\tDML_dev\tC30046\tGLEAN\tmRNA\t202\t6592\t0.480335\t+\t.\tCGI_10000591\r\n", "C30046\t522\t523\tDML_dev\tC30046\tGLEAN\tmRNA\t202\t6592\t0.480335\t+\t.\tCGI_10000591\r\n", "scaffold101\t283044\t283045\tDML_dev\tscaffold101\tGLEAN\tmRNA\t269476\t308543\t0.957882\t+\t.\tCGI_10021644\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " 175 gene_intersect_DML_dev_wb_b.txt\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "C30046\t1952\t1953\tDML_lin\tC30046\tGLEAN\tmRNA\t202\t6592\t0.480335\t+\t.\tCGI_10000591\r\n", "C33148\t7907\t7908\tDML_lin\tC33148\tGLEAN\tmRNA\t5142\t11739\t0.999994\t-\t.\tCGI_10001003\r\n", "C33148\t7925\t7926\tDML_lin\tC33148\tGLEAN\tmRNA\t5142\t11739\t0.999994\t-\t.\tCGI_10001003\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " 208 gene_intersect_DML_lin_wb_b.txt\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "scaffold350\tfuzznuc\tnucleotide_motif\t1161\t1162\t2\t+\t.\tID=scaffold350.25note=*pat pattern:CG\tscaffold350\tGLEAN\tmRNA\t1105\t3206\t0.996578\t-\t.\tCGI_10000780\r\n", "scaffold350\tfuzznuc\tnucleotide_motif\t1183\t1184\t2\t+\t.\tID=scaffold350.26note=*pat pattern:CG\tscaffold350\tGLEAN\tmRNA\t1105\t3206\t0.996578\t-\t.\tCGI_10000780\r\n", "scaffold350\tfuzznuc\tnucleotide_motif\t1200\t1201\t2\t+\t.\tID=scaffold350.27note=*pat pattern:CG\tscaffold350\tGLEAN\tmRNA\t1105\t3206\t0.996578\t-\t.\tCGI_10000780\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " 4046625 gene_intersect_CG_wb_b.txt\r\n" ] } ], "prompt_number": 10 }, { "cell_type": "code", "collapsed": false, "input": [ "!python {pt}multiupload.py gene_intersect_CG_wb_b.txt" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "uploading gene_intersect_CG_wb_b.txt\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "uploading gene_intersect_CG_wb_b.txt into ['gene_intersect_CG_wb_b.txt']\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "processing chunk line 0 to 658002 (2.16350102425 s elapsed)\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "pushing gene_intersect_CG_wb_b.txt...\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "parsing 1E6747F5...\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "processing chunk line 658002 to 1315922 (190.930948973 s elapsed)\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "pushing gene_intersect_CG_wb_b.txt...\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "parsing C2CCD007...\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "processing chunk line 1315922 to 1974653 (384.863775969 s elapsed)\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "pushing gene_intersect_CG_wb_b.txt...\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "parsing 3D4A4B12...\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "processing chunk line 1974653 to 2632485 (569.311421871 s elapsed)\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "pushing gene_intersect_CG_wb_b.txt...\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "parsing 4897A41D...\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Traceback (most recent call last):\r\n", " File \"/Users/sr320/sqlshare-pythonclient/tools/multiupload.py\", line 40, in \r\n", " main()\r\n", " File \"/Users/sr320/sqlshare-pythonclient/tools/multiupload.py\", line 37, in main\r\n", " multiupload(args, options.username, options.password)\r\n", " File \"/Users/sr320/sqlshare-pythonclient/tools/multiupload.py\", line 24, in multiupload\r\n", " for response in conn.upload(globexpr):\r\n", " File \"build/bdist.macosx-10.5-x86_64/egg/sqlshare/__init__.py\", line 158, in upload\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " \r\n", " File \"build/bdist.macosx-10.5-x86_64/egg/sqlshare/__init__.py\", line 183, in uploadone\r\n", " \r\n", " File \"build/bdist.macosx-10.5-x86_64/egg/sqlshare/__init__.py\", line 202, in upload_chunk\r\n", " \r\n", " File \"build/bdist.macosx-10.5-x86_64/egg/sqlshare/__init__.py\", line 244, in poll_selector\r\n", " \r\n", " File \"/Users/sr320/anaconda/lib/python2.7/httplib.py\", line 1045, in getresponse\r\n", " response.begin()\r\n", " File \"/Users/sr320/anaconda/lib/python2.7/httplib.py\", line 409, in begin\r\n", " version, status, reason = self._read_status()\r\n", " File \"/Users/sr320/anaconda/lib/python2.7/httplib.py\", line 365, in _read_status\r\n", " line = self.fp.readline(_MAXLINE + 1)\r\n", " File \"/Users/sr320/anaconda/lib/python2.7/socket.py\", line 476, in readline\r\n", " data = self._sock.recv(self._rbufsize)\r\n", " File \"/Users/sr320/anaconda/lib/python2.7/ssl.py\", line 241, in recv\r\n", " return self.read(buflen)\r\n", " File \"/Users/sr320/anaconda/lib/python2.7/ssl.py\", line 160, in read\r\n", " return self._sslobj.read(len)\r\n", "socket.error: [Errno 54] Connection reset by peer\r\n" ] } ], "prompt_number": 30 }, { "cell_type": "code", "collapsed": false, "input": [ "!python {pt}fetchdata.py -s \"SELECT * FROM [sr320@washington.edu].[gene_intersect_DML_dev_wb_b.txt]dev left join [sr320@washington.edu].[qDOD Cgigas Gene Descriptions (Swiss-prot)]des on dev.Column13=des.CGI_ID\" -f tsv -o DML_dev_SPID.txt" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 14 }, { "cell_type": "code", "collapsed": false, "input": [ "!head -2 DML_dev_SPID.txt\n", "!wc -l DML_dev_SPID.txt" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Column1\tColumn2\tColumn3\tColumn4\tColumn5\tColumn6\tColumn7\tColumn8\tColumn9\tColumn10\tColumn11\tColumn12\tColumn13\tCGI_ID\tSPID\tevalue\tDescription\r", "\r\n", "C30046\t1305\t1306\tDML_dev\tC30046\tGLEAN\tmRNA\t202\t6592\t0.480335\t+\t.\tCGI_10000591\tCGI_10000591\tQ71LX4\t4E-69\tTalin-2\r", "\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " 176 DML_dev_SPID.txt\r\n" ] } ], "prompt_number": 16 }, { "cell_type": "code", "collapsed": false, "input": [ "!python {pt}fetchdata.py -s \"SELECT * FROM [sr320@washington.edu].[gene_intersect_DML_lin_wb_b.txt]lin left join [sr320@washington.edu].[qDOD Cgigas Gene Descriptions (Swiss-prot)]des on lin.Column13=des.CGI_ID\" -f tsv -o DML_lin_SPID.txt" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 18 }, { "cell_type": "code", "collapsed": false, "input": [ "!head -2 DML_lin_SPID.txt\n", "!wc -l DML_lin_SPID.txt" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Column1\tColumn2\tColumn3\tColumn4\tColumn5\tColumn6\tColumn7\tColumn8\tColumn9\tColumn10\tColumn11\tColumn12\tColumn13\tCGI_ID\tSPID\tevalue\tDescription\r", "\r\n", "scaffold29336\t4348\t4349\tDML_lin\tscaffold29336\tGLEAN\tmRNA\t2565\t5479\t0.999993\t-\t.\tCGI_10000522\tCGI_10000522\tP86410\t8E-40\tRal GTPase-activating protein subunit beta\r", "\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " 209 DML_lin_SPID.txt\r\n" ] } ], "prompt_number": 19 }, { "cell_type": "code", "collapsed": false, "input": [ "!python {pt}fetchdata.py -d \"[sr320@washington.edu].[qDOD Cgigas Gene Descriptions (Swiss-prot)]\" -f tsv -o qdod_sp.txt" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 52 }, { "cell_type": "code", "collapsed": false, "input": [ "!wc -l qdod_sp.txt" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ " 18159 qdod_sp.txt\r\n" ] } ], "prompt_number": 17 }, { "cell_type": "code", "collapsed": false, "input": [ "!awk '{print $2}' qdod_sp.txt | sort | uniq | pbcopy" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 27 }, { "cell_type": "code", "collapsed": false, "input": [ "!awk '{print $15}' DML_lin_SPID.txt | sort | uniq | pbcopy" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 28 }, { "cell_type": "code", "collapsed": false, "input": [ "!awk '{print $15}' DML_dev_SPID.txt | sort | uniq | pbcopy" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 29 }, { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "DAVID" ] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Lineage" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\"DAVID__Database_for_Annotation__Visualization__and_Integrated_Discovery__Laboratory_of_Immunopathogenesis_and_Bioinformatics__LIB___National_Institute_of_Allergies_and_Infectious_Diseases__NIAID___Science_Applications_International_Corporat_197D5ECA.png\"/" ] }, { "cell_type": "code", "collapsed": false, "input": [ "!wget http://david.abcc.ncifcrf.gov/data/download/chart_E961EBC140A01405953792021.txt" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "--2014-07-21 07:44:52-- http://david.abcc.ncifcrf.gov/data/download/chart_E961EBC140A01405953792021.txt\r\n", "Resolving david.abcc.ncifcrf.gov... " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "129.43.1.164\r\n", "Connecting to david.abcc.ncifcrf.gov|129.43.1.164|:80... " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "connected.\r\n", "HTTP request sent, awaiting response... " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "200 OK\r\n", "Length: 6284 (6.1K) [text/plain]\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Saving to: `chart_E961EBC140A01405953792021.txt'\r\n", "\r\n", "\r", " 0% [ ] 0 --.-K/s \r", "100%[======================================>] 6,284 --.-K/s in 0.02s \r\n", "\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "2014-07-21 07:44:52 (267 KB/s) - `chart_E961EBC140A01405953792021.txt' saved [6284/6284]\r\n", "\r\n" ] } ], "prompt_number": 69 }, { "cell_type": "markdown", "metadata": {}, "source": [ "\"DAVID__Database_for_Annotation__Visualization__and_Integrated_Discovery__Laboratory_of_Immunopathogenesis_and_Bioinformatics__LIB___National_Institute_of_Allergies_and_Infectious_Diseases__NIAID___Science_Applications_International_Corporat_197D5F19.png\"/" ] }, { "cell_type": "code", "collapsed": false, "input": [ "!wget http://david.abcc.ncifcrf.gov/data/download/chart_E961EBC140A01405953960577.txt" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "--2014-07-21 07:46:12-- http://david.abcc.ncifcrf.gov/data/download/chart_E961EBC140A01405953960577.txt\r\n", "Resolving david.abcc.ncifcrf.gov... 129.43.1.164\r\n", "Connecting to david.abcc.ncifcrf.gov|129.43.1.164|:80... " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "connected.\r\n", "HTTP request sent, awaiting response... " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "200 OK\r\n", "Length: 2804 (2.7K) [text/plain]\r\n", "Saving to: `chart_E961EBC140A01405953960577.txt'\r\n", "\r\n", "\r", " 0% [ ] 0 --.-K/s \r", "100%[======================================>] 2,804 --.-K/s in 0s \r\n", "\r\n", "2014-07-21 07:46:12 (149 MB/s) - `chart_E961EBC140A01405953960577.txt' saved [2804/2804]\r\n", "\r\n" ] } ], "prompt_number": 70 }, { "cell_type": "markdown", "metadata": {}, "source": [ "\"DAVID__Database_for_Annotation__Visualization__and_Integrated_Discovery__Laboratory_of_Immunopathogenesis_and_Bioinformatics__LIB___National_Institute_of_Allergies_and_Infectious_Diseases__NIAID___Science_Applications_International_Corporat_197D5F4F.png\"/" ] }, { "cell_type": "code", "collapsed": false, "input": [ "!wget http://david.abcc.ncifcrf.gov/data/download/chart_E961EBC140A01405953986581.txt" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "--2014-07-21 07:47:02-- http://david.abcc.ncifcrf.gov/data/download/chart_E961EBC140A01405953986581.txt\r\n", "Resolving david.abcc.ncifcrf.gov... 129.43.1.164\r\n", "Connecting to david.abcc.ncifcrf.gov|129.43.1.164|:80... " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "connected.\r\n", "HTTP request sent, awaiting response... " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "200 OK\r\n", "Length: 1480 (1.4K) [text/plain]\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Saving to: `chart_E961EBC140A01405953986581.txt'\r\n", "\r\n", "\r", " 0% [ ] 0 --.-K/s \r", "100%[======================================>] 1,480 --.-K/s in 0s \r\n", "\r\n", "2014-07-21 07:47:03 (54.3 MB/s) - `chart_E961EBC140A01405953986581.txt' saved [1480/1480]\r\n", "\r\n" ] } ], "prompt_number": 72 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Development " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\"DAVID__Database_for_Annotation__Visualization__and_Integrated_Discovery__Laboratory_of_Immunopathogenesis_and_Bioinformatics__LIB___National_Institute_of_Allergies_and_Infectious_Diseases__NIAID___Science_Applications_International_Corporat_197D6031.png\"/" ] }, { "cell_type": "code", "collapsed": false, "input": [ "!wget http://david.abcc.ncifcrf.gov/data/download/chart_E961EBC140A01405954167593.txt" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "--2014-07-21 07:50:44-- http://david.abcc.ncifcrf.gov/data/download/chart_E961EBC140A01405954167593.txt\r\n", "Resolving david.abcc.ncifcrf.gov... 129.43.1.164\r\n", "Connecting to david.abcc.ncifcrf.gov|129.43.1.164|:80... " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "connected.\r\n", "HTTP request sent, awaiting response... " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "200 OK\r\n", "Length: 2147 (2.1K) [text/plain]\r\n", "Saving to: `chart_E961EBC140A01405954167593.txt'\r\n", "\r\n", "\r", " 0% [ ] 0 --.-K/s \r", "100%[======================================>] 2,147 --.-K/s in 0s \r\n", "\r\n", "2014-07-21 07:50:44 (56.9 MB/s) - `chart_E961EBC140A01405954167593.txt' saved [2147/2147]\r\n", "\r\n" ] } ], "prompt_number": 73 }, { "cell_type": "markdown", "metadata": {}, "source": [ "\"DAVID__Database_for_Annotation__Visualization__and_Integrated_Discovery__Laboratory_of_Immunopathogenesis_and_Bioinformatics__LIB___National_Institute_of_Allergies_and_Infectious_Diseases__NIAID___Science_Applications_International_Corporat_197D6071.png\"/" ] }, { "cell_type": "code", "collapsed": false, "input": [ "!wget http://david.abcc.ncifcrf.gov/data/download/chart_E961EBC140A01405954304119.txt" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "--2014-07-21 07:51:55-- http://david.abcc.ncifcrf.gov/data/download/chart_E961EBC140A01405954304119.txt\r\n", "Resolving david.abcc.ncifcrf.gov... 129.43.1.164\r\n", "Connecting to david.abcc.ncifcrf.gov|129.43.1.164|:80... " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "connected.\r\n", "HTTP request sent, awaiting response... " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "200 OK\r\n", "Length: 3968 (3.9K) [text/plain]\r\n", "Saving to: `chart_E961EBC140A01405954304119.txt'\r\n", "\r\n", "\r", " 0% [ ] 0 --.-K/s \r", "100%[======================================>] 3,968 --.-K/s in 0s \r\n", "\r\n", "2014-07-21 07:51:55 (84.1 MB/s) - `chart_E961EBC140A01405954304119.txt' saved [3968/3968]\r\n", "\r\n" ] } ], "prompt_number": 74 }, { "cell_type": "markdown", "metadata": {}, "source": [ "\"DAVID__Database_for_Annotation__Visualization__and_Integrated_Discovery__Laboratory_of_Immunopathogenesis_and_Bioinformatics__LIB___National_Institute_of_Allergies_and_Infectious_Diseases__NIAID___Science_Applications_International_Corporat_197D609F.png\"/" ] }, { "cell_type": "code", "collapsed": false, "input": [ "!wget http://david.abcc.ncifcrf.gov/data/download/chart_E961EBC140A01405954319262.txt" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "--2014-07-21 07:52:35-- http://david.abcc.ncifcrf.gov/data/download/chart_E961EBC140A01405954319262.txt\r\n", "Resolving david.abcc.ncifcrf.gov... 129.43.1.164\r\n", "Connecting to david.abcc.ncifcrf.gov|129.43.1.164|:80... " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "connected.\r\n", "HTTP request sent, awaiting response... " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "200 OK\r\n", "Length: 4310 (4.2K) [text/plain]\r\n", "Saving to: `chart_E961EBC140A01405954319262.txt'\r\n", "\r\n", "\r", " 0% [ ] 0 --.-K/s " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "100%[======================================>] 4,310 --.-K/s in 0.07s \r\n", "\r\n", "2014-07-21 07:52:35 (62.3 KB/s) - `chart_E961EBC140A01405954319262.txt' saved [4310/4310]\r\n", "\r\n" ] } ], "prompt_number": 75 }, { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Are TEs likely to be found in certain classes of genes?" ] }, { "cell_type": "code", "collapsed": false, "input": [ "intersect bed genes on TEs" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ " !intersectbed \\\n", " -wb \\\n", " -a ./genome_tracks/Cgigas_v9_gene.gff \\\n", " -b ./genome_tracks/Cgigas_v9_TE.gff \\\n", " > gene_intersect_TE_wb.txt " ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 96 }, { "cell_type": "code", "collapsed": false, "input": [ "!head gene_intersect_TE_wb.txt " ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "C22430\tGLEAN\tmRNA\t263\t493\t0.555898\t+\t.\tID=CGI_10000081;\tC22430\tWUBlastX\tDNA_TcMar-Tc2\t230\t493\t60\t+\t.\t.\r\n", "C23316\tGLEAN\tmRNA\t929\t1111\t0.999999\t-\t.\tID=CGI_10000116;\tC23316\tWUBlastX\tLTR_Ngaro\t929\t1675\t117\t-\t.\t.\r\n", "C23316\tGLEAN\tmRNA\t953\t1111\t0.999999\t-\t.\tID=CGI_10000116;\tC23316\tWUBlastX\tLTR_Ngaro\t953\t1489\t157\t-\t.\t.\r\n", "C23316\tGLEAN\tmRNA\t644\t660\t0.999999\t-\t.\tID=CGI_10000116;\tC23316\tWUBlastX\tLTR_Ngaro\t547\t660\t32\t-\t.\t.\r\n", "C23734\tGLEAN\tmRNA\t512\t712\t1\t-\t.\tID=CGI_10000149;\tC23734\tWUBlastX\tLTR_Gypsy\t23\t724\t246\t-\t.\t.\r\n", "C23734\tGLEAN\tmRNA\t512\t712\t1\t-\t.\tID=CGI_10000149;\tC23734\tWUBlastX\tLTR_Gypsy\t26\t964\t217\t-\t.\t.\r\n", "C24320\tGLEAN\tmRNA\t1545\t1904\t0.999372\t-\t.\tID=CGI_10000190;\tC24320\tWUBlastX\tLTR_ERV1\t1545\t1904\t27\t-\t.\t.\r\n", "C24604\tGLEAN\tmRNA\t792\t989\t0.997918\t-\t.\tID=CGI_10000200;\tC24604\tWUBlastX\tDNA_MuDR\t792\t989\t42\t-\t.\t.\r\n", "C25496\tGLEAN\tmRNA\t2006\t2074\t0.998748\t+\t.\tID=CGI_10000243;\tC25496\tWUBlastX\tLINE_CR1\t2006\t2536\t27\t+\t.\t.\r\n", "C26320\tGLEAN\tmRNA\t2740\t2964\t0.481188\t+\t.\tID=CGI_10000297;\tC26320\tWUBlastX\tLINE_L2\t2710\t2964\t75\t+\t.\t.\r\n" ] } ], "prompt_number": 97 }, { "cell_type": "code", "collapsed": false, "input": [ " !intersectbed \\\n", " -c \\\n", " -a ./genome_tracks/Cgigas_v9_gene.gff \\\n", " -b ./genome_tracks/Cgigas_v9_TE.gff \\\n", " > gene_intersect_TE_c.txt\n", " !head gene_intersect_TE_c.txt " ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "C16582\tGLEAN\tmRNA\t35\t385\t0.555898\t-\t.\tID=CGI_10000001;\t0\r\n", "C17212\tGLEAN\tmRNA\t31\t363\t0.999572\t+\t.\tID=CGI_10000002;\t0\r\n", "C17316\tGLEAN\tmRNA\t30\t257\t0.555898\t+\t.\tID=CGI_10000003;\t0\r\n", "C17476\tGLEAN\tmRNA\t34\t257\t0.998947\t-\t.\tID=CGI_10000004;\t0\r\n", "C17998\tGLEAN\tmRNA\t196\t387\t1\t-\t.\tID=CGI_10000005;\t0\r\n", "C18346\tGLEAN\tmRNA\t174\t551\t1\t+\t.\tID=CGI_10000009;\t0\r\n", "C18428\tGLEAN\tmRNA\t286\t546\t0.555898\t-\t.\tID=CGI_10000010;\t0\r\n", "C18964\tGLEAN\tmRNA\t203\t658\t0.999572\t-\t.\tID=CGI_10000011;\t0\r\n", "C18980\tGLEAN\tmRNA\t30\t674\t0.555898\t+\t.\tID=CGI_10000012;\t0\r\n", "C19100\tGLEAN\tmRNA\t160\t681\t0.999955\t-\t.\tID=CGI_10000013;\t0\r\n" ] } ], "prompt_number": 99 }, { "cell_type": "code", "collapsed": false, "input": [ "!awk '{print $9,$10}' gene_intersect_TE_c.txt > gene_intersect_TE_c2.txt\n", "!sed 's/ID=C/C/g' gene_intersect_TE_c2c.txt \n", "!sed 's/;//g' gene_intersect_TE_c2ge.txt \n", "!echo \"CGI_ID Num_TEs\" >> gTE.head\n", "!cat gTE.head gene_intersect_TE_c2ge.txt > gene_intersect_TE_c2g.txt \n", "!head -3 gene_intersect_TE_c2g.txt \n", "!python {pt}singleupload.py gene_intersect_TE_c2g.txt " ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "CGI_ID Num_TEs\r\n", "CGI_10000001 0\r\n", "CGI_10000002 0\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "processing chunk line 0 to 28028 (0.00581192970276 s elapsed)\r\n", "pushing gene_intersect_TE_c2g.txt...\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "parsing 4AD03B75...\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "finished gene_intersect_TE_c2g.txt\r\n" ] } ], "prompt_number": 111 }, { "cell_type": "code", "collapsed": false, "input": [ "!python {pt}fetchdata.py -s \"SELECT * FROM [sr320@washington.edu].[gene_intersect_TE_c2g.txt]te left join [sr320@washington.edu].[qDOD_Cgigas_GOslim_DISTINCT]slim on te.CGI_ID=slim.CGI_ID where [aspect] = 'P'\" -f tsv -o gene_i_TE_geneGOslim.txt" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 116 }, { "cell_type": "code", "collapsed": false, "input": [ "!head gene_i_TE_geneGOslim.txt\n" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "CGI_ID\tNum_TEs\tCGI_ID\tGOslim_bin\taspect\r", "\r\n", "CGI_10000001\t0\tCGI_10000001\tother biological processes\tP\r", "\r\n", "CGI_10000003\t0\tCGI_10000003\tsignal transduction\tP\r", "\r\n", "CGI_10000011\t0\tCGI_10000011\tdeath\tP\r", "\r\n", "CGI_10000011\t0\tCGI_10000011\tprotein metabolism\tP\r", "\r\n", "CGI_10000012\t0\tCGI_10000012\tprotein metabolism\tP\r", "\r\n", "CGI_10000012\t0\tCGI_10000012\tRNA metabolism\tP\r", "\r\n", "CGI_10000013\t0\tCGI_10000013\tother metabolic processes\tP\r", "\r\n", "CGI_10000013\t0\tCGI_10000013\tprotein metabolism\tP\r", "\r\n", "CGI_10000015\t0\tCGI_10000015\tcell organization and biogenesis\tP\r", "\r\n" ] } ], "prompt_number": 117 }, { "cell_type": "markdown", "metadata": {}, "source": [ "\"Screenshot_7_21_14_8_52_AM_197D6F5C.png\"/" ] }, { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "GO Slim - DML" ] }, { "cell_type": "code", "collapsed": false, "input": [ "!python {pt}fetchdata.py -s \"SELECT * FROM [sr320@washington.edu].[gene_intersect_DML_lin_wb_b.txt]lin left join [sr320@washington.edu].[qDOD_Cgigas_GOslim_DISTINCT]slim on lin.Column13=slim.CGI_ID where [aspect] = 'P'\" -f tsv -o DML_lin_geneGOslimDistinct.txt" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 120 }, { "cell_type": "code", "collapsed": false, "input": [ "!head -2 DML_lin_geneGOslimDistinct.txt" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Column1\tColumn2\tColumn3\tColumn4\tColumn5\tColumn6\tColumn7\tColumn8\tColumn9\tColumn10\tColumn11\tColumn12\tColumn13\tCGI_ID\tGOslim_bin\taspect\r", "\r\n", "scaffold29336\t4348\t4349\tDML_lin\tscaffold29336\tGLEAN\tmRNA\t2565\t5479\t0.999993\t-\t.\tCGI_10000522\tCGI_10000522\tother biological processes\tP\r", "\r\n" ] } ], "prompt_number": 122 }, { "cell_type": "code", "collapsed": false, "input": [ "!wc -l DML_lin_geneGOslimDistinct.txt" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ " 358 DML_lin_geneGOslimDistinct.txt\r\n" ] } ], "prompt_number": 123 }, { "cell_type": "code", "collapsed": false, "input": [ "!awk '{print $15}' DML_lin_geneGOslimDistinct.txt > DML_lin_geneGOslimDistinct_15.txt\n", "! DML_dev_geneGOslimDistinct_15.txt\n", "!