{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Calculating CpG ratio for the *Acropora palmata* transcriptome" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This workflow calculates CpG ratio, or CpG O/E, for contigs in the *Acropora palmata* [transcriptome](https://usegalaxy.org/datasets/cb51c4a06d7ae94e/display?to_ext=fasta). CpG ratio is an estimate of germline DNA methylation.\n", "\n", "This workflow is an extension of another IPython notebook workflow, `Apalm_blast_anno.ipynb`, that generates an annotation of the same transcriptome. This workflow assumes that you have created the directories and files specified in the annotation workflow." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/Users/jd/Documents/Projects/Coral-CpG-ratio-MS/data/Apalm\n" ] } ], "source": [ "cd .data/Apalm" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ ">AOKF1013_g2_c length=710 Acc=Q9DE13 Description=Bromodomainadjacenttozincfingerdomainprotein2B\n", "AGGGCAATTGAGTCGCAAAGAAAACAAGAGGAGCGTGAAAGATTAAAGGAAGAGAAAAAAATGGAAAAGGAACTTCAAAGAGAGAAAAAGCTTGAGCAAAAGAGAAGGGAGATGATTTTAGCCCGTGAACTGAAAAAGCCAGTAGAAGATATGGTTTTAAAGGATAGCAAGACACTTCCTGCTTTCTCCAGAGTTGTGGGCCTTAAAATACCAGGGGACGCATTTGCTGACTTGTTGATGGTTCAGGAATTTGTGCACAATTTTAGTGAAGCCTTGGAACTTGATTCCAACGAAGTCCCTTCCTTGTGGGAAATGCAGTTGTCATTGTTAAATGACAGCAGTGAGGATGTCCTCGTGCCACTTTGTCAGAGTCTTCTGATGTCTGCATTAGAGGATCCTGGCTGTGAGGGGCCTGATTCATTCACAATGCTTGGAGTTGCATTAGCCAAAGTGGAATTGAATGAAACAAACTTCTCTGAAGTCTTGAGGCTGTTTATAATTTCAAGAAATGCTGGTGACCCTCATCCTTTGGCAGAAGCTTTCATCAGTACACCTTTCCAAGCACTCACCATGTCAGCTAAGGCTGGAGTCTTGGGTTACCTGTGCAATGAACTGCTGTGCAGTAGAACAATATGCAAGGAAATAGAGAATAGTATTGAACACATGTCAAATTTACGTCGAGATAAGTGGGTTGTGGAAGGCAAGTTTGG\n", "\n", "number of seqs =\n", "88020\n" ] } ], "source": [ "#fasta file generated in Apalm_blast_anno.ipynb\n", "!head -2 Apalmata_assembled.fasta\n", "!echo \n", "!echo number of seqs =\n", "!fgrep -c \">\" Apalmata_assembled.fasta" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ ">AOKF1013_g2_c\n", "AGGGCAATTGAGTCGCAAAGAAAACAAGAGGAGCGTGAAAGATTAAAGGAAGAGAAAAAAATGGAAAAGGAACTTCAAAGAGAGAAAAAGCTTGAGCAAAAGAGAAGGGAGATGATTTTAGCCCGTGAACTGAAAAAGCCAGTAGAAGATATGGTTTTAAAGGATAGCAAGACACTTCCTGCTTTCTCCAGAGTTGTGGGCCTTAAAATACCAGGGGACGCATTTGCTGACTTGTTGATGGTTCAGGAATTTGTGCACAATTTTAGTGAAGCCTTGGAACTTGATTCCAACGAAGTCCCTTCCTTGTGGGAAATGCAGTTGTCATTGTTAAATGACAGCAGTGAGGATGTCCTCGTGCCACTTTGTCAGAGTCTTCTGATGTCTGCATTAGAGGATCCTGGCTGTGAGGGGCCTGATTCATTCACAATGCTTGGAGTTGCATTAGCCAAAGTGGAATTGAATGAAACAAACTTCTCTGAAGTCTTGAGGCTGTTTATAATTTCAAGAAATGCTGGTGACCCTCATCCTTTGGCAGAAGCTTTCATCAGTACACCTTTCCAAGCACTCACCATGTCAGCTAAGGCTGGAGTCTTGGGTTACCTGTGCAATGAACTGCTGTGCAGTAGAACAATATGCAAGGAAATAGAGAATAGTATTGAACACATGTCAAATTTACGTCGAGATAAGTGGGTTGTGGAAGGCAAGTTTGG\n", ">AOKF1022_b2_c\n", "GGGCAAAACGAACAAATTTTGACAATAATCTCTCAAATCTGTCAAGTCACGGCAGGGCTGCAAATAGCTATCGGGGAGGCGCCGGTCACGTCCGGTCAAACATGATTTTGCTCGGACAAGACCCGCTTTTGGCCGGTCAAATTTTAACAGTCGTAACTCTTACGATAGTGAACCCAGATTGCGCAGTAATCCTTTTATAACTACAAAACAATTGAATCCAAGTCGGTTTGGCAATAAAAGGTACTACTTTTACCACTCTTTGCTTTTCGCACTTTGCAATAAATTCTACGTAGAGGATTCTTGGTGTAGCGAGATTATTCTTCGTGGGAGTGCTTTCCGATCATTCAATCAATCAATCAATCACTTTATTTGTGAGTCAATCACGGTATCTCTCCAAAGATAAAACCCTCTACCAAGTGGGAACACCTAAGGCTAATAAAAATAACGGACGACTCGATGATTTGCCGTCGTGACAGGACTTGATGACATCGTGGAAATTTTCTAGTACCGGGAATTTCACTACCAAGAATTTGTCTAGTTTTATATTCGTTTTTTTTTATCATACATGTCCCTCGTGATTATCAAATAGTTAAAACTTAAAACTTGTCTGAACGAGTGAATAAAGGGTT\n", ">AOKF1022_g2_c\n", "TTTGGGGGGGGGCCGGTCCCGTCCGGTCAAACATGATTTTGCTCGGACAAGACCCGCTTTTGGCCGGTCAAATTTTAACAGTGGTAACTCTTTCGATAGGGAACCCAGATTGGGCCGTAATCCTTTTTTAACTTCAAAACAATTGAATCCAAGTCGGTTTGGCAATAAAAGGGACTACTTTTACCCCTCTTTGCTTTTCGCCCTTTGCAAAAAATTTTACGTAGGGGGTTTTTGGGGTAGGGGGATTTTTTTTTGGGGGGGGGGTTTCCGGTCATTCAATCAATCAATCAATCCCTTTTTTTGGGGGGCAATCCCGGGATTTCTCCAAAGATAAAACCCTTTTCCAAGGGGGGACCCCTAAGGGTAATAAAAAAAACGGGCGGCCCGATGATTTGCCGTCGTGACAGGACTTGAAGACATCGGGGAAATTTTTTAGTTCCGGGAATTTCCCTCCCAAGAATTTGTCTAGTTTTAAATTCGTTTTTTTTTATCAAACAAGTCCCCCGGGGTTTTCAAAAAGTTAAAACTTAAAACTTGTTTGAACGGGGGAATAAAGGGTTTaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaagccgg\n", ">AOKF1024_g2_c\n", "TTTTTTTAAACCCCTTTTTTTAAACGGTAGGGGGCCAAAAAATGTTGTTAAAAATTCCTTTAACTAAGGGTTTTTTTTGGGAAAAAAAAAAAAGGGGGGCCTTGTCCTTTTTTTTTTTTTTTTTTTTTTaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaccc\n", ">AOKF1029_g2_c\n", "TAAGCTGCTCCAACCACTGGTACATACATTAAATTTTATTTCCACTGAAAGGGTATAAATGACCCGATCAATTTTCATGTTTTTTTCCCCTCAAAGACAGTATGCCAACTATGGTATTTTCCATTTTACACGATTCCTTGTTCTTTTTTTTTTTGAGAGACCTTGTTTCTGTAACATGCAAATTGTCCCCAAGCTGAGGTAGGCATAAGTGTCCTACTGTTGTGATGATTTTCTCTTAATATATTTTAACTGGACATCATTGTATAGTTGCATATAGTTGTTTGGCTTTGCCTAGAACAAGAGGGTAAGACATTTTCTAACCTACCGACCTATAATCTGACTTTTAATATAGAAGAATTTTCATGAATAAACTGTTTCATGTCTCTAGTTCACTAACATAACATGTTTCATAAAAAAGTTCTTTTGAAAGTAAAATAAAGCCATTATTGACTTCTTTCATAATTTTAAAAATTAATCCAGGAAAAATTTATTTGCAAAAAGAGAAAATGGAAACATCAATAAATCACCATCAGCTACTTTCTTTTAATCTCTTTATGCAAAACCAAAATTTGCATTGGTTGTAAATTAGTCTGGTAACTAAAGTTTCTACCAGTTTCAAAACTGGCAGCTTTGAGTACCAACTCGATACCAATAGTAAATCTGTTTAGATCTAATCCAGCTGTAATGATTGTCGAAGACCAGAGCGTACTGCCCTGCATCAACTAACCCCC\n", ">F66KHFO02JZYYU\n", "ACTAAGTCTGGATATTCTAGCTTGGACTGCAGGGATATTATTAATCAACACAATAGATAATAATAATAATAATATTATTACTTCAACATTTGACTGACTGATTGGCTGACAAATGGGAAATAGTACCCATAATAGTCTGTAACATGATTTACACAGATTTCTCCTCAGGGACAATACAGTTGCGAAAATGATTCAGTTTCATGGGTGGGAGCAGTCTCCATCTGAGAGATGGCTTCCAGTAGTGGAAGCTCACGACGTTGTTTCTCAGGAATCAAACCTTCACACTGCAACTCAGCTGTTAAACCAAAACATTAGCATGCATGCAATTCCTGACCTCAATGAAAATAATAACCTTGAATCAACTTTGTATCTGGAAACTGCCATTTCAAATTCCTCTGGCAGTAAAAGAACAACAGCACATGCAGTCTTCGACATCTGATTCACATTTGTNCAGATGACTTGGTCAATC\n", ">F66KHFO02JZZ42\n", "ATTTAACTCAGTATCAGAATTCATCCGCTCTACTTGTTTTGCACCGTGACAAGTTGTTTTCGGGTGAAAGGATTTCAAGCGGAACTGAGCTTCATCTCGCTTTCAACCCACTACACAGTGGCTGAAAGACGAGCCTTAAGCTCTCTAATAACGCAACAGCAATAAGGAGAAATGAACGCTGGGAGAACAGAAGACGAGTTTTTCTGCGAAGGAGATTCAAGAAGGCACCACCGCTCCGTCTCCCATTTGTGGCGCGTCTGGCACTTATACGAGGAATTTGGCTTCCTCTAATGTCCCTCATCTACTCAGCTTGTCGGGTGGCGAGGAAACACTATTATAAGAGCCCCCACATCAACTCAACAGGTGGTTCCTCTTTGTTTAACTTGGCAACAACTTTGTGGACTCATACCCGTAATTTTAGCTCTTTAGTAGAATAAACATCATGTCCACCACTTATGACATATGATTGATATTATTCTGGTATTT\n", ">F66KHFO02JZZ9J\n", "GCAACAATGAAGTGGACAAAAACACTGACAAGTTTGCGACTTGTCCAGGAATGCACTTTAACGAAATAAAAAACATTCAATAAGCATAATCAAATGAAGTCCAAGAAGGGCCGTGAATGAGATTGCGAATACTACAAAATTGCAAAATTTACAATAAATAGGATCAGGACAGTTAGGTAAATAGTCTGAGAAAAAGAACAAAAGTTATACAAAACCGAAATAAGGCCCTGAAGGGAAGGCTATACCTCTTGATACTTGATAAATGGTGTACATAGCAGCTGGTACTCCGGAGTTCAAACGACATGAATGATTAAAGCAATTTTGGGTAGGTTTATTTAAGCAAGGACTTTCTTGGGTGTTAAAAAAAATGAGAGGATTCAGGATAGTTTCCACGTATCCCTGAGTG\n", ">F66KHFO02JZZML\n", "ACTTTCATTCAAGGGAGNACCAAAGACAGCCTTCCACACAGTAAGATGTAAGACTGCATCTCTATGTTTCATAAAACTATTGCAATTTAACTGCCGCATTCCTCTCAATTTCCGCGGTCAAAAACACAGCTGTTTATAACCCCTTATATACTTTCTGTGAAATTCACACGCAAAGTTAAATTCTTGATCTTTGTATTGAAATTTACTTGTAAGTGGACGGGTATTCTGCAATCTAGCCTTTGCTTCTATTCATTTTAGATGTGAGAACTTGTCTACCGGCGAAGAAGATAATTTTACATTATATTTTTTCCGGCGTTTAAGCATAATTAGGAATAATTATGGTGAAAGATGAAACAGGATGTTGATCCGACAAAGCAGGAGCCATCTGGCTGTTCAGCGAGACC\n", ">F66KHFO02JZZVO\n", "GTGAATTTCCAACTGATAAGCTCTTCCTTTATCACTCAAAGTTCACTCGTGTGAGGCACACAGTCCTGGATGACCGCATTGTATGTCCGGAAGACTTTGTTCTTGATCTAAACCAAAGTTTCTAAGATCATGTTACCAATTTGAAATTTCGGCATTTGGAGCTTCTTTATCTTCTGCCACAAGAATCTTTGGAACTGTAACTCGAGGTGAAGGGGTACGACTAACACCCATCTTTGCAAACTCAAATTTAAACCCAGGCGTGTGGTGAAAGGGTACCACTAACACTCATCTTTGACAAACTCAAATTCAAACCCAGGTGTCTATTACTATATTTTACTCTAGCGGCAAGCGAACATCAAAGCCGAATGTATTGCCAAGAAAATCGCACCAAGCAAAATACCAAAAGGGGCGTACTATACAGAGACCAAAGTGACATTTAAATCGTGTTTATTGATTGATCTCGTAAACTGCAAATAAACGATAAAACGCAGGTATAAGACGTCTA\n" ] } ], "source": [ "#I remembered that this fasta is full of \" marks before some of the \">\"\n", "#Removing \" from fasta and printing first line w/out comments and looking at contig names\n", "!sed 's/\"//g' Apalmata_assembled.fasta | awk '{print $1}' > Apalm.fasta\n", "!head -10 Apalm.fasta\n", "!tail -10 Apalm.fasta" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\r\n", "Converted 88020 FASTA records in 176040 lines to tabular format\r\n", "Total sequence length: 63829400\r\n", "\r\n" ] } ], "source": [ "#Converting FASTA to tabular format and placing output file in analyses directory\n", "!perl -e '$count=0; $len=0; while(<>) {s/\\r?\\n//; s/\\t/ /g; if (s/^>//) { if ($. != 1) {print \"\\n\"} s/ |$/\\t/; $count++; $_ .= \"\\t\";} else {s/ //g; $len += length($_)} print $_;} print \"\\n\"; warn \"\\nConverted $count FASTA records in $. lines to tabular format\\nTotal sequence length: $len\\n\\n\";' \\\n", "Apalm.fasta > ../../analyses/Apalm/fasta2tab" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/Users/jd/Documents/Projects/Coral-CpG-ratio-MS/analyses/Apalm\n" ] } ], "source": [ "cd ../../analyses/Apalm" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "AOKF1013_g2_c\t\tAGGGCAATTGAGTCGCAAAGAAAACAAGAGGAGCGTGAAAGATTAAAGGAAGAGAAAAAAATGGAAAAGGAACTTCAAAGAGAGAAAAAGCTTGAGCAAAAGAGAAGGGAGATGATTTTAGCCCGTGAACTGAAAAAGCCAGTAGAAGATATGGTTTTAAAGGATAGCAAGACACTTCCTGCTTTCTCCAGAGTTGTGGGCCTTAAAATACCAGGGGACGCATTTGCTGACTTGTTGATGGTTCAGGAATTTGTGCACAATTTTAGTGAAGCCTTGGAACTTGATTCCAACGAAGTCCCTTCCTTGTGGGAAATGCAGTTGTCATTGTTAAATGACAGCAGTGAGGATGTCCTCGTGCCACTTTGTCAGAGTCTTCTGATGTCTGCATTAGAGGATCCTGGCTGTGAGGGGCCTGATTCATTCACAATGCTTGGAGTTGCATTAGCCAAAGTGGAATTGAATGAAACAAACTTCTCTGAAGTCTTGAGGCTGTTTATAATTTCAAGAAATGCTGGTGACCCTCATCCTTTGGCAGAAGCTTTCATCAGTACACCTTTCCAAGCACTCACCATGTCAGCTAAGGCTGGAGTCTTGGGTTACCTGTGCAATGAACTGCTGTGCAGTAGAACAATATGCAAGGAAATAGAGAATAGTATTGAACACATGTCAAATTTACGTCGAGATAAGTGGGTTGTGGAAGGCAAGTTTGG\n", "AOKF1022_b2_c\t\tGGGCAAAACGAACAAATTTTGACAATAATCTCTCAAATCTGTCAAGTCACGGCAGGGCTGCAAATAGCTATCGGGGAGGCGCCGGTCACGTCCGGTCAAACATGATTTTGCTCGGACAAGACCCGCTTTTGGCCGGTCAAATTTTAACAGTCGTAACTCTTACGATAGTGAACCCAGATTGCGCAGTAATCCTTTTATAACTACAAAACAATTGAATCCAAGTCGGTTTGGCAATAAAAGGTACTACTTTTACCACTCTTTGCTTTTCGCACTTTGCAATAAATTCTACGTAGAGGATTCTTGGTGTAGCGAGATTATTCTTCGTGGGAGTGCTTTCCGATCATTCAATCAATCAATCAATCACTTTATTTGTGAGTCAATCACGGTATCTCTCCAAAGATAAAACCCTCTACCAAGTGGGAACACCTAAGGCTAATAAAAATAACGGACGACTCGATGATTTGCCGTCGTGACAGGACTTGATGACATCGTGGAAATTTTCTAGTACCGGGAATTTCACTACCAAGAATTTGTCTAGTTTTATATTCGTTTTTTTTTATCATACATGTCCCTCGTGATTATCAAATAGTTAAAACTTAAAACTTGTCTGAACGAGTGAATAAAGGGTT\n", "F66KHFO02JZZML\t\tACTTTCATTCAAGGGAGNACCAAAGACAGCCTTCCACACAGTAAGATGTAAGACTGCATCTCTATGTTTCATAAAACTATTGCAATTTAACTGCCGCATTCCTCTCAATTTCCGCGGTCAAAAACACAGCTGTTTATAACCCCTTATATACTTTCTGTGAAATTCACACGCAAAGTTAAATTCTTGATCTTTGTATTGAAATTTACTTGTAAGTGGACGGGTATTCTGCAATCTAGCCTTTGCTTCTATTCATTTTAGATGTGAGAACTTGTCTACCGGCGAAGAAGATAATTTTACATTATATTTTTTCCGGCGTTTAAGCATAATTAGGAATAATTATGGTGAAAGATGAAACAGGATGTTGATCCGACAAAGCAGGAGCCATCTGGCTGTTCAGCGAGACC\n", "F66KHFO02JZZVO\t\tGTGAATTTCCAACTGATAAGCTCTTCCTTTATCACTCAAAGTTCACTCGTGTGAGGCACACAGTCCTGGATGACCGCATTGTATGTCCGGAAGACTTTGTTCTTGATCTAAACCAAAGTTTCTAAGATCATGTTACCAATTTGAAATTTCGGCATTTGGAGCTTCTTTATCTTCTGCCACAAGAATCTTTGGAACTGTAACTCGAGGTGAAGGGGTACGACTAACACCCATCTTTGCAAACTCAAATTTAAACCCAGGCGTGTGGTGAAAGGGTACCACTAACACTCATCTTTGACAAACTCAAATTCAAACCCAGGTGTCTATTACTATATTTTACTCTAGCGGCAAGCGAACATCAAAGCCGAATGTATTGCCAAGAAAATCGCACCAAGCAAAATACCAAAAGGGGCGTACTATACAGAGACCAAAGTGACATTTAAATCGTGTTTATTGATTGATCTCGTAAACTGCAAATAAACGATAAAACGCAGGTATAAGACGTCTA\n" ] } ], "source": [ "#Checking header on new tabular format file\n", "!head -2 fasta2tab\n", "!tail -2 fasta2tab" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\r\n", "Added column with length of column 2 for 88020 lines.\r\n", "\r\n" ] } ], "source": [ "#Add column with length of sequence\n", "!perl -e '$col = 2;' -e 'while (<>) { s/\\r?\\n//; @F = split /\\t/, $_; $len = length($F[$col]); print \"$_\\t$len\\n\" } warn \"\\nAdded column with length of column $col for $. lines.\\n\\n\";' \\\n", "fasta2tab > tab_1\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " 88020 264060 65591859 tab_1\r\n" ] } ], "source": [ "!wc tab_1" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#The file used to count Cs and Gs will only include the sequence\n", "!awk '{print $2}' tab_1 > tab_2" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#This counts CGs - both cases\n", "!echo \"CG\" | awk -F\\[Cc][Gg] '{print NF-1}' tab_2 > CG " ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#Counts Cs\n", "!echo \"C\" | awk -F\\[Cc] '{print NF-1}' tab_2 > C " ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#Counts Gs\n", "!echo \"G\" | awk -F\\[Gg] '{print NF-1}' tab_2 > G " ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "AOKF1013_g2_c\t\tAGGGCAATTGAGTCGCAAAGAAAACAAGAGGAGCGTGAAAGATTAAAGGAAGAGAAAAAAATGGAAAAGGAACTTCAAAGAGAGAAAAAGCTTGAGCAAAAGAGAAGGGAGATGATTTTAGCCCGTGAACTGAAAAAGCCAGTAGAAGATATGGTTTTAAAGGATAGCAAGACACTTCCTGCTTTCTCCAGAGTTGTGGGCCTTAAAATACCAGGGGACGCATTTGCTGACTTGTTGATGGTTCAGGAATTTGTGCACAATTTTAGTGAAGCCTTGGAACTTGATTCCAACGAAGTCCCTTCCTTGTGGGAAATGCAGTTGTCATTGTTAAATGACAGCAGTGAGGATGTCCTCGTGCCACTTTGTCAGAGTCTTCTGATGTCTGCATTAGAGGATCCTGGCTGTGAGGGGCCTGATTCATTCACAATGCTTGGAGTTGCATTAGCCAAAGTGGAATTGAATGAAACAAACTTCTCTGAAGTCTTGAGGCTGTTTATAATTTCAAGAAATGCTGGTGACCCTCATCCTTTGGCAGAAGCTTTCATCAGTACACCTTTCCAAGCACTCACCATGTCAGCTAAGGCTGGAGTCTTGGGTTACCTGTGCAATGAACTGCTGTGCAGTAGAACAATATGCAAGGAAATAGAGAATAGTATTGAACACATGTCAAATTTACGTCGAGATAAGTGGGTTGTGGAAGGCAAGTTTGG\t710\t8\t119\t183\r\n" ] } ], "source": [ "#Combining counts\n", "!paste tab_1 \\\n", "CG \\\n", "C \\\n", "G \\\n", "> comb\n", "!head -1 comb" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Calculating CpGo/e based on [Gavery and Roberts (2010)](http://www.biomedcentral.com/1471-2164/11/483)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\"BMC_Genomics___Full_text___DNA_methylation_patterns_provide_insight_into_epigenetic_regulation_in_the_Pacific_oyster__Crassostrea_gigas__1A0683A5.png\"/" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#Calculation of CpG o/e\n", "!awk '{print $1, \"\\t\", (($4)/($5*$6))*(($3^2)/($3-1))}' comb > ID_CpG #use ^ instead of ** for exponent" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "AOKF1013_g2_c \t 0.261194\r\n", "AOKF1022_b2_c \t 1.21084\r\n", "AOKF1022_g2_c \t 0.933676\r\n", "AOKF1024_g2_c \t 0.46793\r\n", "AOKF1029_g2_c \t 0.305319\r\n", "AOKF1031_g2_c \t 0.476647\r\n", "AOKF1034_g2_c \t 0.250371\r\n", "AOKF1040_g2_c \t 1.11148\r\n", "AOKF1045_g2_c \t 0.415524\r\n", "AOKF1046_g2_c \t 0.278746\r\n" ] } ], "source": [ "!head ID_CpG" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Now joining CpG to annotation, but first must sort files." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "AOKF1031_g2_c\tsp\tQ66I12\tCCD47_DANRE\t46.03\t239\t126\t3\t8\t721\t229\t465\t3e-48\t 171\r\n", "AOKF1045_g2_c\tsp\tB5DFQ4\tRHG26_XENTR\t45.99\t237\t126\t1\t2\t712\t74\t308\t1e-60\t 208\r\n", "AOKF1050_b2_c\tsp\tP81004\tVDAC2_XENLA\t67.41\t135\t44\t0\t315\t719\t3\t137\t5e-60\t 197\r\n", "AOKF1057_b2_c\tsp\tP56616\tUBE2C_XENLA\t55.48\t146\t60\t4\t109\t537\t34\t177\t3e-49\t 166\r\n", "AOKF1062_g2_c\tsp\tQ5PR73\tDIRA2_MOUSE\t37.35\t166\t99\t4\t4\t501\t7\t167\t2e-29\t 113\r\n", "AOKF1091_g2_c\tsp\tA2RRV3\tPATL1_DANRE\t38.69\t168\t83\t4\t195\t695\t402\t550\t4e-26\t 111\r\n", "AOKF1100_g2_c\tsp\tL0N7N1\tKIF14_MOUSE\t54.02\t87\t40\t0\t397\t657\t424\t510\t1e-26\t 112\r\n", "AOKF1114_g2_c\tsp\tQ9BX66\tSRBS1_HUMAN\t47.17\t53\t28\t0\t188\t346\t798\t850\t2e-10\t63.9\r\n", "AOKF1132_g2_c\tsp\tQ8VDS4\tRPR1A_MOUSE\t53.04\t247\t111\t2\t2\t727\t20\t266\t8e-84\t 259\r\n", "AOKF1164_g2_c\tsp\tQ9VHH9\tJHD1_DROME\t67.72\t127\t41\t0\t2\t382\t250\t376\t1e-59\t 209\r\n" ] } ], "source": [ "#Sorting Apalm Uniprot/Swissprot annotation file. This file was the result of work done in another notebook: \n", "#Apalm_blast_anno.ipynb\n", "!sort Apalm_blastx_uniprot_sql.tab | tail -n +2 > Apalm_blastx_uniprot_sql.tab.sorted\n", "!head Apalm_blastx_uniprot_sql.tab.sorted" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "AOKF1045_g2_c\tcell organization and biogenesis\r", "\r\n", "AOKF1045_g2_c\tother biological processes\r", "\r\n", "AOKF1045_g2_c\tsignal transduction\r", "\r\n", "AOKF1050_b2_c\ttransport\r", "\r\n", "AOKF1057_b2_c\tcell cycle and proliferation\r", "\r\n", "AOKF1057_b2_c\tcell organization and biogenesis\r", "\r\n", "AOKF1057_b2_c\tother biological processes\r", "\r\n", "AOKF1057_b2_c\tprotein metabolism\r", "\r\n", "AOKF1062_g2_c\tother biological processes\r", "\r\n", "AOKF1062_g2_c\tother metabolic processes\r", "\r\n" ] } ], "source": [ "#Sorting Ahya GOSlim annotation file. This file was the result of work done in another notebook: Apalm_blast_anno.ipynb\n", "!sort Apalm_GOSlim.tab | tail -n +2 > Apalm_GOSlim.sorted\n", "!head Apalm_GOSlim.sorted" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "AOKF1013_g2_c \t 0.261194\r\n", "AOKF1022_b2_c \t 1.21084\r\n", "AOKF1022_g2_c \t 0.933676\r\n", "AOKF1024_g2_c \t 0.46793\r\n", "AOKF1029_g2_c \t 0.305319\r\n", "AOKF1031_g2_c \t 0.476647\r\n", "AOKF1034_g2_c \t 0.250371\r\n", "AOKF1040_g2_c \t 1.11148\r\n", "AOKF1045_g2_c \t 0.415524\r\n", "AOKF1046_g2_c \t 0.278746\r\n" ] } ], "source": [ "#Sorting Ahya CpG file\n", "!sort ID_CpG > ID_CpG.sorted\n", "!head ID_CpG.sorted" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "!join ID_CpG.sorted Apalm_blastx_uniprot_sql.tab.sorted | awk '{print $1, \"\\t\", $2}' > Apalm_cpg_anno" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "AOKF1031_g2_c \t 0.476647\r\n", "AOKF1045_g2_c \t 0.415524\r\n", "AOKF1050_b2_c \t 0.405247\r\n", "AOKF1057_b2_c \t 0.337031\r\n", "AOKF1062_g2_c \t 1.00104\r\n", "AOKF1091_g2_c \t 0.503552\r\n", "AOKF1100_g2_c \t 0.616876\r\n", "AOKF1114_g2_c \t 0.964931\r\n", "AOKF1132_g2_c \t 0.228244\r\n", "AOKF1164_g2_c \t 0.905474\r\n" ] } ], "source": [ "!head Apalm_cpg_anno" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": true }, "outputs": [], "source": [ "!join ID_CpG.sorted Apalm_GOSlim.sorted > Apalm_cpg_GOslim" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "AOKF1045_g2_c 0.415524 cell organization and biogenesis\r", "\r\n", "AOKF1045_g2_c 0.415524 other biological processes\r", "\r\n", "AOKF1045_g2_c 0.415524 signal transduction\r", "\r\n", "AOKF1050_b2_c 0.405247 transport\r", "\r\n", "AOKF1057_b2_c 0.337031 cell cycle and proliferation\r", "\r\n", "AOKF1057_b2_c 0.337031 cell organization and biogenesis\r", "\r\n", "AOKF1057_b2_c 0.337031 other biological processes\r", "\r\n", "AOKF1057_b2_c 0.337031 protein metabolism\r", "\r\n", "AOKF1062_g2_c 1.00104 other biological processes\r", "\r\n", "AOKF1062_g2_c 1.00104 other metabolic processes\r", "\r\n" ] } ], "source": [ "!head Apalm_cpg_GOslim" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "AOKF1045_g2_c \t 0.415524 \t cell organization and biogenesis\r", "\r\n", "AOKF1045_g2_c \t 0.415524 \t other biological processes\r", " \r\n", "AOKF1045_g2_c \t 0.415524 \t signal transduction\r", " \r\n", "AOKF1050_b2_c \t 0.405247 \t transport\r", " \r\n", "AOKF1057_b2_c \t 0.337031 \t cell cycle and proliferation\r", "\r\n", "AOKF1057_b2_c \t 0.337031 \t cell organization and biogenesis\r", "\r\n", "AOKF1057_b2_c \t 0.337031 \t other biological processes\r", " \r\n", "AOKF1057_b2_c \t 0.337031 \t protein metabolism\r", " \r\n", "AOKF1062_g2_c \t 1.00104 \t other biological processes\r", " \r\n", "AOKF1062_g2_c \t 1.00104 \t other metabolic processes\r", " \r\n" ] } ], "source": [ "#Putting tabs in between columns\n", "!awk '{print $1, \"\\t\", $2, \"\\t\", $3, $4, $5, $6}' Apalm_cpg_GOslim > Apalm_cpg_GOslim.tab\n", "!head Apalm_cpg_GOslim.tab" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Now time to plot data using pandas and matplot" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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............
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biogenesis\n", "25 AOKF1114_g2_c 0.964931 other biological processes\n", "26 NaN NaN\n", "27 AOKF1114_g2_c 0.964931 other metabolic processes\n", "28 NaN NaN\n", "29 AOKF1114_g2_c 0.964931 signal transduction\n", "... ... ... ...\n", "133709 F66KHFO02JZND7 0.645784 protein metabolism\n", "133710 NaN NaN\n", "133711 F66KHFO02JZND7 0.645784 signal transduction\n", "133712 NaN NaN\n", "133713 F66KHFO02JZO8X 0.858204 RNA metabolism\n", "133714 NaN NaN\n", "133715 F66KHFO02JZO8X 0.858204 other biological processes\n", "133716 NaN NaN\n", "133717 F66KHFO02JZO8X 0.858204 stress response\n", "133718 NaN NaN\n", "133719 F66KHFO02JZOT7 0.657806 other metabolic processes\n", "133720 NaN NaN\n", "133721 F66KHFO02JZQBN 0.852378 protein metabolism\n", "133722 NaN NaN\n", "133723 F66KHFO02JZTL8 0.713145 death\n", "133724 NaN NaN\n", "133725 F66KHFO02JZTL8 0.713145 transport\n", "133726 NaN NaN\n", "133727 F66KHFO02JZV0J 0.375928 protein metabolism\n", "133728 NaN NaN\n", "133729 F66KHFO02JZWQ6 0.488831 transport\n", "133730 NaN NaN\n", "133731 F66KHFO02JZX3R 0.916672 other metabolic processes\n", "133732 NaN NaN\n", "133733 F66KHFO02JZX9S 0.545160 other biological processes\n", "133734 NaN NaN\n", "133735 F66KHFO02JZXKF 0.926412 other biological processes\n", "133736 NaN NaN\n", "133737 F66KHFO02JZXKF 0.926412 other metabolic processes\n", "133738 NaN NaN\n", "\n", "[133739 rows x 3 columns]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "jData = pd.read_table('Apalm_cpg_GOslim.tab', header=None)\n", "jData" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import matplotlib.pyplot as plt " ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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IkgEfDfxQ0lukoOrLtf9EQRAEQX8T2hZ1RNIQ0qTCVyWNIvWobOA+eF1S0onA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\n4OOkgKByrecBh2efH0taRrr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6 AOKF1100_g2_c 0.616876
7 AOKF1114_g2_c 0.964931
8 AOKF1132_g2_c 0.228244
9 AOKF1164_g2_c 0.905474
10 AOKF1206_g2_c 0.820971
11 AOKF1221_g2_c 0.424964
12 AOKF1230_g2_c 0.716592
13 AOKF1238_g2_c 0.487211
14 AOKF1251_g2_c 0.253120
15 AOKF1269_g2_c 0.330159
16 AOKF1336_g2_c 0.368121
17 AOKF1356_g2_c 0.461341
18 AOKF1427_b2_c 0.764298
19 AOKF1478_g2_c 0.936591
20 AOKF1514_g2_c 0.295433
21 AOKF1531_b2_c 0.237932
22 AOKF1549_b2_c 0.881497
23 AOKF1585_g2_c 0.720776
24 AOKF1597_g2_c 0.435898
25 AOKF1614_g2_c 0.720420
26 AOKF1658_g2_c 0.285014
27 AOKF1670_g2_c 0.326164
28 AOKF1737_g2_c 0.563409
29 AOKF1741_g2_c 0.272728
.........
35273 F66KHFO02JZ9IQ 0.481931
35274 F66KHFO02JZ9XY 1.224440
35275 F66KHFO02JZCNS 0.779820
35276 F66KHFO02JZCTR 0.524576
35277 F66KHFO02JZD9L 0.242735
35278 F66KHFO02JZDTN 0.583960
35279 F66KHFO02JZFC0 0.668225
35280 F66KHFO02JZG27 0.846390
35281 F66KHFO02JZG3R 0.852755
35282 F66KHFO02JZG9W 0.825320
35283 F66KHFO02JZH8B 0.594062
35284 F66KHFO02JZJET 0.320926
35285 F66KHFO02JZJKP 0.548232
35286 F66KHFO02JZK7V 0.438391
35287 F66KHFO02JZL1Y 0.429955
35288 F66KHFO02JZLMO 0.494197
35289 F66KHFO02JZMKS 0.631184
35290 F66KHFO02JZMWV 0.838499
35291 F66KHFO02JZND4 0.282860
35292 F66KHFO02JZND7 0.645784
35293 F66KHFO02JZO8X 0.858204
35294 F66KHFO02JZOT7 0.657806
35295 F66KHFO02JZQBN 0.852378
35296 F66KHFO02JZRK4 0.620927
35297 F66KHFO02JZTL8 0.713145
35298 F66KHFO02JZV0J 0.375928
35299 F66KHFO02JZWQ6 0.488831
35300 F66KHFO02JZX3R 0.916672
35301 F66KHFO02JZX9S 0.545160
35302 F66KHFO02JZXKF 0.926412
\n", "

35303 rows × 2 columns

\n", "
" ], "text/plain": [ " 0 1\n", "0 AOKF1031_g2_c 0.476647\n", "1 AOKF1045_g2_c 0.415524\n", "2 AOKF1050_b2_c 0.405247\n", "3 AOKF1057_b2_c 0.337031\n", "4 AOKF1062_g2_c 1.001040\n", "5 AOKF1091_g2_c 0.503552\n", "6 AOKF1100_g2_c 0.616876\n", "7 AOKF1114_g2_c 0.964931\n", "8 AOKF1132_g2_c 0.228244\n", "9 AOKF1164_g2_c 0.905474\n", "10 AOKF1206_g2_c 0.820971\n", "11 AOKF1221_g2_c 0.424964\n", "12 AOKF1230_g2_c 0.716592\n", "13 AOKF1238_g2_c 0.487211\n", "14 AOKF1251_g2_c 0.253120\n", "15 AOKF1269_g2_c 0.330159\n", "16 AOKF1336_g2_c 0.368121\n", "17 AOKF1356_g2_c 0.461341\n", "18 AOKF1427_b2_c 0.764298\n", "19 AOKF1478_g2_c 0.936591\n", "20 AOKF1514_g2_c 0.295433\n", "21 AOKF1531_b2_c 0.237932\n", "22 AOKF1549_b2_c 0.881497\n", "23 AOKF1585_g2_c 0.720776\n", "24 AOKF1597_g2_c 0.435898\n", "25 AOKF1614_g2_c 0.720420\n", "26 AOKF1658_g2_c 0.285014\n", "27 AOKF1670_g2_c 0.326164\n", "28 AOKF1737_g2_c 0.563409\n", "29 AOKF1741_g2_c 0.272728\n", "... ... ...\n", "35273 F66KHFO02JZ9IQ 0.481931\n", "35274 F66KHFO02JZ9XY 1.224440\n", "35275 F66KHFO02JZCNS 0.779820\n", "35276 F66KHFO02JZCTR 0.524576\n", "35277 F66KHFO02JZD9L 0.242735\n", "35278 F66KHFO02JZDTN 0.583960\n", "35279 F66KHFO02JZFC0 0.668225\n", "35280 F66KHFO02JZG27 0.846390\n", "35281 F66KHFO02JZG3R 0.852755\n", "35282 F66KHFO02JZG9W 0.825320\n", "35283 F66KHFO02JZH8B 0.594062\n", "35284 F66KHFO02JZJET 0.320926\n", "35285 F66KHFO02JZJKP 0.548232\n", "35286 F66KHFO02JZK7V 0.438391\n", "35287 F66KHFO02JZL1Y 0.429955\n", "35288 F66KHFO02JZLMO 0.494197\n", "35289 F66KHFO02JZMKS 0.631184\n", "35290 F66KHFO02JZMWV 0.838499\n", "35291 F66KHFO02JZND4 0.282860\n", "35292 F66KHFO02JZND7 0.645784\n", "35293 F66KHFO02JZO8X 0.858204\n", "35294 F66KHFO02JZOT7 0.657806\n", "35295 F66KHFO02JZQBN 0.852378\n", "35296 F66KHFO02JZRK4 0.620927\n", "35297 F66KHFO02JZTL8 0.713145\n", "35298 F66KHFO02JZV0J 0.375928\n", "35299 F66KHFO02JZWQ6 0.488831\n", "35300 F66KHFO02JZX3R 0.916672\n", "35301 F66KHFO02JZX9S 0.545160\n", "35302 F66KHFO02JZXKF 0.926412\n", "\n", "[35303 rows x 2 columns]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#To plot density curve, must use CpG data WITHOUT annotation\n", "CpG = pd.read_table('Apalm_cpg_anno', header=None)\n", "CpG" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[-0.3, 1.7, 0, 1.7]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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G1nV2GfAr/t7wZlpJfyXwXwljcQ1nxl8YXOv/rMT6qeJxnfPE30VdmL+juCD2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\nzjnXY/4/5MyngKgJIZkAAAAASUVORK5CYII=\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# pandas density plot\n", "CpG[1].plot(kind='kde', linewidth=3);\n", "plt.axis([-0.3, 1.7, 0, 1.7])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Now looking only at differentially expressed contigs, joining via common contig IDs" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "day1and2.txt is a file listing contigs that were differentially expressed in response to *either* one or two days of thermal stress. The samples were also *A. palmata* larvae. The file was derived from an [Excel file containing supplementary data](http://datadryad.org/bitstream/handle/10255/dryad.39350/SuppTableS3_Final.xlsx?sequence=1) that was presented in [Polato et al. (2013)](http://onlinelibrary.wiley.com.offcampus.lib.washington.edu/doi/10.1111/mec.12163/abstract). Contig IDs are the same as in the transcriptome and can be joined using common contig IDs." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "AOKF1013_g2_c\n", "AOKF1050_b2_c\n", "AOKF1050_b2_c\n", "AOKF1140_b2_c\n", "AOKF1501_b2_c\n", "AOKF1952_b2_c\n", "AOKF386_g2_c\n", "AOKG1730_b2_c\n", "AOKG1840_b2_c\n", "CAFB1171_b2_c\n", " 2581 2582 34046 day1and2.txt\n" ] } ], "source": [ "!head day1and2.txt\n", "!wc day1and2.txt" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "AOKF1013_g2_c\n", "AOKF1050_b2_c\n", "AOKF1140_b2_c\n", "AOKF1501_b2_c\n", "AOKF1952_b2_c\n", "AOKF386_g2_c\n", "AOKG1730_b2_c\n", "AOKG1840_b2_c\n", "CAFB1171_b2_c\n", "CAFB1297_b2_c\n", " 2002 2002 26622 day1and2_uniq.txt\n" ] } ], "source": [ "!uniq day1and2.txt > day1and2_uniq.txt\n", "!head day1and2_uniq.txt\n", "!wc day1and2_uniq.txt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Now joining with CpG file" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "AOKF1013_g2_c\r\n", "AOKF1050_b2_c\r\n", "AOKF1140_b2_c\r\n", "AOKF1501_b2_c\r\n", "AOKF1952_b2_c\r\n", "AOKF386_g2_c\r\n", "AOKG1730_b2_c\r\n", "AOKG1840_b2_c\r\n", "CAFB1171_b2_c\r\n", "CAFB1297_b2_c\r\n" ] } ], "source": [ "!sort day1and2_uniq.txt > day1and2_uniq.txt.sorted\n", "!head day1and2_uniq.txt.sorted" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "AOKF1013_g2_c \t 0.261194\n", "AOKF1050_b2_c \t 0.405247\n", "AOKF1140_b2_c \t 0.828815\n", "AOKF1501_b2_c \t 0.730739\n", "AOKF1952_b2_c \t 1.26506\n", "AOKF386_g2_c \t 1.02326\n", "AOKG1730_b2_c \t 0.852003\n", "AOKG1840_b2_c \t 0.623692\n", "CAFB1171_b2_c \t 0.638914\n", "CAFB1297_b2_c \t 0\n", " 2002 4004 48074 day1and2temp_CpG\n" ] } ], "source": [ "!join day1and2_uniq.txt.sorted ID_CpG.sorted | awk '{print $1, \"\\t\", $2}' > day1and2temp_CpG\n", "!head day1and2temp_CpG\n", "!wc day1and2temp_CpG" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "AOKF1050_b2_c \t 0.405247\n", "AOKF386_g2_c \t 1.02326\n", "AOKG1730_b2_c \t 0.852003\n", "AOKG1840_b2_c \t 0.623692\n", "CAOG977_b1_c \t 0.424244\n", "CAOH2044_b1_c \t 0.720682\n", "CAOH2436_g1_c \t 0.546901\n", "CAOH2554_b1_c \t 0.589779\n", "CAOI2629_b1_c \t 1.07011\n", "CAOI641_b2_c \t 0.898445\n", " 994 1988 23814 Apalm_diff_cpg_anno\n" ] } ], "source": [ "#Joining with annotation file\n", "!join day1and2temp_CpG Apalm_blastx_uniprot_sql.tab.sorted | awk '{print $1, \"\\t\", $2}' > Apalm_diff_cpg_anno\n", "!head Apalm_diff_cpg_anno\n", "!wc Apalm_diff_cpg_anno" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " \n", "AOKF386_g2_c \t 1.02326 \t cell organization and biogenesis\n", " \n", "AOKG1730_b2_c \t 0.852003 \t cell organization and biogenesis\n", " \n", " \n", " \n", "CAOI2629_b1_c \t 1.07011 \t cell organization and biogenesis\n", " \n", " \n", " 1909 8565 96254 Apalm_diff_cpg_GOslim\n" ] } ], "source": [ "#Joining with GOslim annotation file\n", "!join day1and2temp_CpG Apalm_GOSlim.sorted | awk '{print $1, \"\\t\", $2, \"\\t\", $3, $4, $5, $6}' > Apalm_diff_cpg_GOslim\n", "!head Apalm_diff_cpg_GOslim\n", "!wc Apalm_diff_cpg_GOslim" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Now plotting data using pandas and matplot" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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012
0 AOKF1050_b2_c 0.405247 transport
1 NaN NaN
2 AOKF386_g2_c 1.023260 cell organization and biogenesis
3 AOKG1730_b2_c 0.852003 RNA metabolism
4 NaN NaN
5 AOKG1730_b2_c 0.852003 cell organization and biogenesis
6 CAOG977_b1_c 0.424244 protein metabolism
7 NaN NaN
8 CAOH2436_g1_c 0.546901 other biological processes
9 NaN NaN
10 CAOI2629_b1_c 1.070110 cell adhesion
11 NaN NaN
12 CAOI2629_b1_c 1.070110 cell organization and biogenesis
13 CAOI2629_b1_c 1.070110 developmental processes
14 NaN NaN
15 CAOI2629_b1_c 1.070110 other biological processes
16 NaN NaN
17 CAOI2629_b1_c 1.070110 other metabolic processes
18 NaN NaN
19 CAOI2629_b1_c 1.070110 protein metabolism
20 NaN NaN
21 CAOI2629_b1_c 1.070110 signal transduction
22 NaN NaN
23 CAOI2629_b1_c 1.070110 stress response
24 NaN NaN
25 CAOI641_b2_c 0.898445 other metabolic processes
26 NaN NaN
27 CAOI641_b2_c 0.898445 protein metabolism
28 NaN NaN
29 CAWS1371_b2_c 0.331062 RNA metabolism
............
3531 NaN NaN
3532 F66KHFO02JQPWC 0.436226 other biological processes
3533 NaN NaN
3534 F66KHFO02JQPWC 0.436226 other metabolic processes
3535 NaN NaN
3536 F66KHFO02JQPWC 0.436226 transport
3537 NaN NaN
3538 F66KHFO02JRJIB 0.379576 cell-cell signaling
3539 NaN NaN
3540 F66KHFO02JRJIB 0.379576 developmental processes
3541 NaN NaN
3542 F66KHFO02JRJIB 0.379576 other metabolic processes
3543 NaN NaN
3544 F66KHFO02JSUBP 0.724542 other metabolic processes
3545 NaN NaN
3546 F66KHFO02JUJ1P 0.259713 other biological processes
3547 NaN NaN
3548 F66KHFO02JUJ1P 0.259713 stress response
3549 NaN NaN
3550 F66KHFO02JUJ1P 0.259713 transport
3551 NaN NaN
3552 F66KHFO02JUUKW 0.593258 RNA metabolism
3553 NaN NaN
3554 F66KHFO02JVQ28 0.469748 cell organization and biogenesis
3555 F66KHFO02JVQ28 0.469748 other biological processes
3556 NaN NaN
3557 F66KHFO02JVQ28 0.469748 other metabolic processes
3558 NaN NaN
3559 F66KHFO02JVXP5 0.203597 other metabolic processes
3560 NaN NaN
\n", "

3561 rows × 3 columns

\n", "
" ], "text/plain": [ " 0 1 2\n", "0 AOKF1050_b2_c 0.405247 transport\n", "1 NaN NaN\n", "2 AOKF386_g2_c 1.023260 cell organization and biogenesis\n", "3 AOKG1730_b2_c 0.852003 RNA metabolism\n", "4 NaN NaN\n", "5 AOKG1730_b2_c 0.852003 cell organization and biogenesis\n", "6 CAOG977_b1_c 0.424244 protein metabolism\n", "7 NaN NaN\n", "8 CAOH2436_g1_c 0.546901 other biological processes\n", "9 NaN NaN\n", "10 CAOI2629_b1_c 1.070110 cell adhesion\n", "11 NaN NaN\n", "12 CAOI2629_b1_c 1.070110 cell organization and biogenesis\n", "13 CAOI2629_b1_c 1.070110 developmental processes\n", "14 NaN NaN\n", "15 CAOI2629_b1_c 1.070110 other biological processes\n", "16 NaN NaN\n", "17 CAOI2629_b1_c 1.070110 other metabolic processes\n", "18 NaN NaN\n", "19 CAOI2629_b1_c 1.070110 protein metabolism\n", "20 NaN NaN\n", "21 CAOI2629_b1_c 1.070110 signal transduction\n", "22 NaN NaN\n", "23 CAOI2629_b1_c 1.070110 stress response\n", "24 NaN NaN\n", "25 CAOI641_b2_c 0.898445 other metabolic processes\n", "26 NaN NaN\n", "27 CAOI641_b2_c 0.898445 protein metabolism\n", "28 NaN NaN\n", "29 CAWS1371_b2_c 0.331062 RNA metabolism\n", "... ... ... ...\n", "3531 NaN NaN\n", "3532 F66KHFO02JQPWC 0.436226 other biological processes\n", "3533 NaN NaN\n", "3534 F66KHFO02JQPWC 0.436226 other metabolic processes\n", "3535 NaN NaN\n", "3536 F66KHFO02JQPWC 0.436226 transport\n", "3537 NaN NaN\n", "3538 F66KHFO02JRJIB 0.379576 cell-cell signaling\n", "3539 NaN NaN\n", "3540 F66KHFO02JRJIB 0.379576 developmental processes\n", "3541 NaN NaN\n", "3542 F66KHFO02JRJIB 0.379576 other metabolic processes\n", "3543 NaN NaN\n", "3544 F66KHFO02JSUBP 0.724542 other metabolic processes\n", "3545 NaN NaN\n", "3546 F66KHFO02JUJ1P 0.259713 other biological processes\n", "3547 NaN NaN\n", "3548 F66KHFO02JUJ1P 0.259713 stress response\n", "3549 NaN NaN\n", "3550 F66KHFO02JUJ1P 0.259713 transport\n", "3551 NaN NaN\n", "3552 F66KHFO02JUUKW 0.593258 RNA metabolism\n", "3553 NaN NaN\n", "3554 F66KHFO02JVQ28 0.469748 cell organization and biogenesis\n", "3555 F66KHFO02JVQ28 0.469748 other biological processes\n", "3556 NaN NaN\n", "3557 F66KHFO02JVQ28 0.469748 other metabolic processes\n", "3558 NaN NaN\n", "3559 F66KHFO02JVXP5 0.203597 other metabolic processes\n", "3560 NaN NaN\n", "\n", "[3561 rows x 3 columns]" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Apalm_diff_CpG_GOSlim = pd.read_table('Apalm__diff_cpg_GOslim', header=None)\n", "Apalm_diff_CpG_GOSlim" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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axaSudRocUzx2FnAFyQ76QttzisfYvlLS+0iBhYGjbT8paXSh3rNy2zWXyv1t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\nO0i/p+/YvixfsyNILprnA+P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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "Apalm_diff_CpG_GOSlim.groupby(2)[1].mean().plot(kind='barh', color=list('myb'))\n", "plt.axis([0.5, 0.85, -1, 14])\n", "plt.xlabel('')\n", "plt.ylabel('')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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01
0 AOKF1050_b2_c 0.405247
1 AOKF386_g2_c 1.023260
2 AOKG1730_b2_c 0.852003
3 AOKG1840_b2_c 0.623692
4 CAOG977_b1_c 0.424244
5 CAOH2044_b1_c 0.720682
6 CAOH2436_g1_c 0.546901
7 CAOH2554_b1_c 0.589779
8 CAOI2629_b1_c 1.070110
9 CAOI641_b2_c 0.898445
10 CAWS1371_b2_c 0.331062
11 CAWS1482_b2_c 0.706679
12 CCHX11969_b1_c 0.932186
13 CCHX12472_b1_c 0.844341
14 CCHX12647_b1_c 0.909217
15 CCHX1314_b1_c 0.788706
16 CCHX13764_b1_c 1.162150
17 CCHX14952_b1_c 0.363122
18 CCHX15242_b1_c 0.408760
19 CCHX16619_b1_c 0.783179
20 CCHX2147_b1_c 0.287108
21 CCHX3889_b1_c 0.041551
22 CCHX4084_b1_c 0.752735
23 CCHX5422_b1_c 0.549186
24 CCHX7039_b1_c 0.308911
25 CCHX7275_b1_c 0.905557
26 CCHX8585_b1_c 0.209281
27 CCHX9155_b1_c 0.750502
28 CCHX9618_b1_c 0.726452
29 Contig_10042 0.886648
.........
964 F66KHFO02IGNHO 0.558197
965 F66KHFO02IHEKI 0.691784
966 F66KHFO02II348 0.215927
967 F66KHFO02IJ17R 0.797105
968 F66KHFO02IKGMI 0.962609
969 F66KHFO02ILIKA 0.256732
970 F66KHFO02ILJOW 0.829998
971 F66KHFO02ILW21 0.873883
972 F66KHFO02IN4WY 0.301022
973 F66KHFO02INB9A 0.708336
974 F66KHFO02INJ32 0.753148
975 F66KHFO02IO4B5 0.737794
976 F66KHFO02IOCNQ 0.982825
977 F66KHFO02IRSL9 0.862706
978 F66KHFO02J2KQP 0.859879
979 F66KHFO02J2R55 0.703131
980 F66KHFO02J3DCX 0.777464
981 F66KHFO02J3VJ6 1.039200
982 F66KHFO02JCQIY 0.831741
983 F66KHFO02JEH8U 0.473770
984 F66KHFO02JH11M 0.380762
985 F66KHFO02JH6QK 0.813869
986 F66KHFO02JLTK7 0.677554
987 F66KHFO02JQPWC 0.436226
988 F66KHFO02JRJIB 0.379576
989 F66KHFO02JSUBP 0.724542
990 F66KHFO02JUJ1P 0.259713
991 F66KHFO02JUUKW 0.593258
992 F66KHFO02JVQ28 0.469748
993 F66KHFO02JVXP5 0.203597
\n", "

994 rows × 2 columns

\n", "
" ], "text/plain": [ " 0 1\n", "0 AOKF1050_b2_c 0.405247\n", "1 AOKF386_g2_c 1.023260\n", "2 AOKG1730_b2_c 0.852003\n", "3 AOKG1840_b2_c 0.623692\n", "4 CAOG977_b1_c 0.424244\n", "5 CAOH2044_b1_c 0.720682\n", "6 CAOH2436_g1_c 0.546901\n", "7 CAOH2554_b1_c 0.589779\n", "8 CAOI2629_b1_c 1.070110\n", "9 CAOI641_b2_c 0.898445\n", "10 CAWS1371_b2_c 0.331062\n", "11 CAWS1482_b2_c 0.706679\n", "12 CCHX11969_b1_c 0.932186\n", "13 CCHX12472_b1_c 0.844341\n", "14 CCHX12647_b1_c 0.909217\n", "15 CCHX1314_b1_c 0.788706\n", "16 CCHX13764_b1_c 1.162150\n", "17 CCHX14952_b1_c 0.363122\n", "18 CCHX15242_b1_c 0.408760\n", "19 CCHX16619_b1_c 0.783179\n", "20 CCHX2147_b1_c 0.287108\n", "21 CCHX3889_b1_c 0.041551\n", "22 CCHX4084_b1_c 0.752735\n", "23 CCHX5422_b1_c 0.549186\n", "24 CCHX7039_b1_c 0.308911\n", "25 CCHX7275_b1_c 0.905557\n", "26 CCHX8585_b1_c 0.209281\n", "27 CCHX9155_b1_c 0.750502\n", "28 CCHX9618_b1_c 0.726452\n", "29 Contig_10042 0.886648\n", ".. ... ...\n", "964 F66KHFO02IGNHO 0.558197\n", "965 F66KHFO02IHEKI 0.691784\n", "966 F66KHFO02II348 0.215927\n", "967 F66KHFO02IJ17R 0.797105\n", "968 F66KHFO02IKGMI 0.962609\n", "969 F66KHFO02ILIKA 0.256732\n", "970 F66KHFO02ILJOW 0.829998\n", "971 F66KHFO02ILW21 0.873883\n", "972 F66KHFO02IN4WY 0.301022\n", "973 F66KHFO02INB9A 0.708336\n", "974 F66KHFO02INJ32 0.753148\n", "975 F66KHFO02IO4B5 0.737794\n", "976 F66KHFO02IOCNQ 0.982825\n", "977 F66KHFO02IRSL9 0.862706\n", "978 F66KHFO02J2KQP 0.859879\n", "979 F66KHFO02J2R55 0.703131\n", "980 F66KHFO02J3DCX 0.777464\n", "981 F66KHFO02J3VJ6 1.039200\n", "982 F66KHFO02JCQIY 0.831741\n", "983 F66KHFO02JEH8U 0.473770\n", "984 F66KHFO02JH11M 0.380762\n", "985 F66KHFO02JH6QK 0.813869\n", "986 F66KHFO02JLTK7 0.677554\n", "987 F66KHFO02JQPWC 0.436226\n", "988 F66KHFO02JRJIB 0.379576\n", "989 F66KHFO02JSUBP 0.724542\n", "990 F66KHFO02JUJ1P 0.259713\n", "991 F66KHFO02JUUKW 0.593258\n", "992 F66KHFO02JVQ28 0.469748\n", "993 F66KHFO02JVXP5 0.203597\n", "\n", "[994 rows x 2 columns]" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Apalm_diff_CpG_Data = pd.read_table('Apalm_diff_cpg_anno', header=None)\n", "Apalm_diff_CpG_Data " ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[-0.3, 1.7, 0, 2.0]" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# pandas density plot\n", "Apalm_diff_CpG_Data[1].plot(kind='kde', linewidth=3);\n", "CpG[1].plot(kind='kde', linewidth=3);\n", "plt.axis([-0.3, 1.7, 0, 2.0])" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy as np" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "1 0.626061\n", "dtype: float64" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.mean(CpG)" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "1 0.683799\n", "dtype: float64" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.mean(Apalm_diff_CpG_Data)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.9" } }, "nbformat": 4, "nbformat_minor": 0 }