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{
"cells": [
{
"cell_type": "heading",
"level": 1,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"Transcriptome Exploration"
]
},
{
"cell_type": "heading",
"level": 3,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"First lets get set with a working directory"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"pwd"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 11,
"text": [
"u'/Users/sr320/Dropbox/Steven/eimd/ipynbs'"
]
}
],
"prompt_number": 11
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"cd "
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"/Users/sr320\n"
]
}
],
"prompt_number": 13
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"ls"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\u001b[34mApplications\u001b[m\u001b[m/ \u001b[34mGeneious 5.5 Data\u001b[m\u001b[m/ \u001b[34manaconda\u001b[m\u001b[m/\r\n",
"BiGo_RNAseq.ipynb HOBOwareInstall.txt \u001b[34mapollo-webstart\u001b[m\u001b[m/\r\n",
"BlackAb_Annot.ipynb \u001b[34mLibrary\u001b[m\u001b[m/ asample.html\r\n",
"\u001b[34mCLC_Data\u001b[m\u001b[m/ \u001b[34mMovies\u001b[m\u001b[m/ \u001b[34mblast2go\u001b[m\u001b[m/\r\n",
"\u001b[34mClueGOConfiguration\u001b[m\u001b[m/ \u001b[34mMusic\u001b[m\u001b[m/ \u001b[34migv\u001b[m\u001b[m/\r\n",
"\u001b[34mCytoscapeConfiguration\u001b[m\u001b[m/ \u001b[34mPictures\u001b[m\u001b[m/ outputfile\r\n",
"\u001b[34mDesktop\u001b[m\u001b[m/ \u001b[34mPublic\u001b[m\u001b[m/ \u001b[34msqlshare-pythonclient\u001b[m\u001b[m/\r\n",
"\u001b[34mDocuments\u001b[m\u001b[m/ StencylWorks.prefs \u001b[34mstencylworks\u001b[m\u001b[m/\r\n",
"\u001b[34mDownloads\u001b[m\u001b[m/ TJGR_pearl.ipynb test\r\n",
"\u001b[34mDropbox\u001b[m\u001b[m/ \u001b[34mVirtualBox VMs\u001b[m\u001b[m/\r\n"
]
}
],
"prompt_number": 14
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"cd Desktop/"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"/Users/sr320/Desktop\n"
]
}
],
"prompt_number": 15
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"mkdir myawesome_dir"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [],
"prompt_number": 16
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"ls -d my*"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\u001b[34mmyawesome_dir\u001b[m\u001b[m/\r\n"
]
}
],
"prompt_number": 19
},
{
"cell_type": "heading",
"level": 1,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"Download the fasta file"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"!curl -O http://eagle.fish.washington.edu/whale/eimd_14/Phel_transcriptome_clc.fa"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
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" % Total % Received % Xferd Average Speed Time Time Time Current\r\n",
" Dload Upload Total Spent Left Speed\r\n",
"\r",
" 0 0 0 0 0 0 0 0 --:--:-- --:--:-- --:--:-- 0"
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},
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"text": [
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]
},
{
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"stream": "stdout",
"text": [
"\r",
" 59 43.2M 59 25.8M 0 0 718k 0 0:01:01 0:00:36 0:00:25 1247k"
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" 64 43.2M 64 28.0M 0 0 740k 0 0:00:59 0:00:38 0:00:21 1198k"
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" 82 43.2M 82 35.8M 0 0 801k 0 0:00:55 0:00:45 0:00:10 1089k"
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},
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},
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"\r",
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"\r",
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"output_type": "stream",
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"\r",
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},
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"output_type": "stream",
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"\r",
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"\r",
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"output_type": "stream",
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"text": [
"\r",
" 98 43.2M 98 42.6M 0 0 756k 0 0:00:58 0:00:57 0:00:01 371k"
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},
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"output_type": "stream",
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"text": [
"\r",
"100 43.2M 100 43.2M 0 0 756k 0 0:00:58 0:00:58 --:--:-- 417k\r\n"
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"prompt_number": 20
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{
"cell_type": "code",
"collapsed": false,
"input": [
"!head Phel_transcriptome_clc.fa"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
">Phel_clc_contig_1\r\n",
"CAAATATATGAACGGTTGATTGTCAACGATTAGTACATGTTTTCATTGTTCCCCACGCCC\r\n",
"GCCCCCCCCCACTCAAACATTTAAAGTGTGAAATATTATTTATCCACAAATTTCCTTAAA\r\n",
"CCTGCAAACTTGTCTGCTGTCTCTTATTGGAAGTTATGAAAAAGAACAACGGGTTTTCTT\r\n",
"TAAAGGGTCTGCGTGCGATTTTCAACCTTTTGAGTAATAGCAGTTATTTTGATAACCGAT\r\n",
"TTTTTTCAAAGCTCAACAGCTTTTTAAAATAAGGAATCCTATAATGGCCAAACGAATACT\r\n",
"ATAAAAATAAGGGTTCTCTTAATTGTATAAAACGTATAATTTTATCAATTTTGGGACCGT\r\n",
"GTAATTTTTTAAAGACCACAAGAATGTTACATACAACAAATAGACGAAACTCGTAGCTTT\r\n",
"GGAAACTACGTCATGGGCGTTTGGTCAAAAGCTGGAGAGAAAGAGAGGTGGGGTGCCAGA\r\n",
"CTTAAGTAGTCACGTGATCTGACCAACGCACATCGGAAGCTCGATCGGATGAAATCTTCT\r\n"
]
}
],
"prompt_number": 21
},
{
"cell_type": "heading",
"level": 2,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"Question - How many Sequences?"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"!fgrep -c \">\" Phel_transcriptome_clc.fa"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"30578\r\n"
]
}
],
"prompt_number": 25
},
{
"cell_type": "heading",
"level": 1,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"How Long are these sequences?"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"Convert fasta to tabular"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"!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: $len\\n\\n\";' Phel_transcriptome_clc.fa > Phel_transcriptome_clc.tab"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r\n",
"Converted 30578 FASTA records in 778053 lines to tabular format\r\n",
"Total sequence length: 43945151\r\n",
"\r\n"
]
}
],
"prompt_number": 26
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"!head -1 Phel_transcriptome_clc.tab"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Phel_clc_contig_1\t\tCAAATATATGAACGGTTGATTGTCAACGATTAGTACATGTTTTCATTGTTCCCCACGCCCGCCCCCCCCCACTCAAACATTTAAAGTGTGAAATATTATTTATCCACAAATTTCCTTAAACCTGCAAACTTGTCTGCTGTCTCTTATTGGAAGTTATGAAAAAGAACAACGGGTTTTCTTTAAAGGGTCTGCGTGCGATTTTCAACCTTTTGAGTAATAGCAGTTATTTTGATAACCGATTTTTTTCAAAGCTCAACAGCTTTTTAAAATAAGGAATCCTATAATGGCCAAACGAATACTATAAAAATAAGGGTTCTCTTAATTGTATAAAACGTATAATTTTATCAATTTTGGGACCGTGTAATTTTTTAAAGACCACAAGAATGTTACATACAACAAATAGACGAAACTCGTAGCTTTGGAAACTACGTCATGGGCGTTTGGTCAAAAGCTGGAGAGAAAGAGAGGTGGGGTGCCAGACTTAAGTAGTCACGTGATCTGACCAACGCACATCGGAAGCTCGATCGGATGAAATCTTCTCTATCGTTCTTGCGTCTATACGTGCTACGAAGAGCTGACAGAAGTTTGGACTTGTTTACTTCTTGCACCTGTTGATGGAACGGCCACGGACCTTGTCGCACGCACACCTGGAGCCAGTGCTCGGATCGACGCAACGGATGTACTGTCTTCCCCTTCCGCGTTTCTCAAGTAGGTACTCAAAGTCGTCCGCGTCGAAGTTGGCCTCGGCGTCCCTCTTCTCCAGCTCCTCCATGTCCTCCTCTGTGTAGTACGGGGTGACGAGCACCACCAGGGCGGCCACAATGGCCAGTGCTAGAAGACACTTCGTATTCATTCTGCTGGTGGTTGGATGTGCGCAAACAAGACAGGAGAGACTTATTAGAATC\r\n"
]
}
],
"prompt_number": 28
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"!perl -e '$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\";' Phel_transcriptome_clc.tab > Phel_transcriptome_clc_length.tab\n"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r\n",
"Added column with length of column 2 for 30578 lines.\r\n",
"\r\n"
]
}
],
"prompt_number": 29
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"!head -1 Phel_transcriptome_clc_length.tab"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Phel_clc_contig_1\t\tCAAATATATGAACGGTTGATTGTCAACGATTAGTACATGTTTTCATTGTTCCCCACGCCCGCCCCCCCCCACTCAAACATTTAAAGTGTGAAATATTATTTATCCACAAATTTCCTTAAACCTGCAAACTTGTCTGCTGTCTCTTATTGGAAGTTATGAAAAAGAACAACGGGTTTTCTTTAAAGGGTCTGCGTGCGATTTTCAACCTTTTGAGTAATAGCAGTTATTTTGATAACCGATTTTTTTCAAAGCTCAACAGCTTTTTAAAATAAGGAATCCTATAATGGCCAAACGAATACTATAAAAATAAGGGTTCTCTTAATTGTATAAAACGTATAATTTTATCAATTTTGGGACCGTGTAATTTTTTAAAGACCACAAGAATGTTACATACAACAAATAGACGAAACTCGTAGCTTTGGAAACTACGTCATGGGCGTTTGGTCAAAAGCTGGAGAGAAAGAGAGGTGGGGTGCCAGACTTAAGTAGTCACGTGATCTGACCAACGCACATCGGAAGCTCGATCGGATGAAATCTTCTCTATCGTTCTTGCGTCTATACGTGCTACGAAGAGCTGACAGAAGTTTGGACTTGTTTACTTCTTGCACCTGTTGATGGAACGGCCACGGACCTTGTCGCACGCACACCTGGAGCCAGTGCTCGGATCGACGCAACGGATGTACTGTCTTCCCCTTCCGCGTTTCTCAAGTAGGTACTCAAAGTCGTCCGCGTCGAAGTTGGCCTCGGCGTCCCTCTTCTCCAGCTCCTCCATGTCCTCCTCTGTGTAGTACGGGGTGACGAGCACCACCAGGGCGGCCACAATGGCCAGTGCTAGAAGACACTTCGTATTCATTCTGCTGGTGGTTGGATGTGCGCAAACAAGACAGGAGAGACTTATTAGAATC\t905\r\n"
]
}
],
"prompt_number": 30
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#lets add header\n",
"!echo \"contig\\tsequence\\tseq_len\" >> fa.head\n",
"!cat fa.head Phel_transcriptome_clc_length.tab > Phel_transcriptome_clc_length2.tab"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [],
"prompt_number": 90
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"!head -2 Phel_transcriptome_clc_length2.tab"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"contig\tsequence\tseq_len\r\n",
"Phel_clc_contig_1\t\tCAAATATATGAACGGTTGATTGTCAACGATTAGTACATGTTTTCATTGTTCCCCACGCCCGCCCCCCCCCACTCAAACATTTAAAGTGTGAAATATTATTTATCCACAAATTTCCTTAAACCTGCAAACTTGTCTGCTGTCTCTTATTGGAAGTTATGAAAAAGAACAACGGGTTTTCTTTAAAGGGTCTGCGTGCGATTTTCAACCTTTTGAGTAATAGCAGTTATTTTGATAACCGATTTTTTTCAAAGCTCAACAGCTTTTTAAAATAAGGAATCCTATAATGGCCAAACGAATACTATAAAAATAAGGGTTCTCTTAATTGTATAAAACGTATAATTTTATCAATTTTGGGACCGTGTAATTTTTTAAAGACCACAAGAATGTTACATACAACAAATAGACGAAACTCGTAGCTTTGGAAACTACGTCATGGGCGTTTGGTCAAAAGCTGGAGAGAAAGAGAGGTGGGGTGCCAGACTTAAGTAGTCACGTGATCTGACCAACGCACATCGGAAGCTCGATCGGATGAAATCTTCTCTATCGTTCTTGCGTCTATACGTGCTACGAAGAGCTGACAGAAGTTTGGACTTGTTTACTTCTTGCACCTGTTGATGGAACGGCCACGGACCTTGTCGCACGCACACCTGGAGCCAGTGCTCGGATCGACGCAACGGATGTACTGTCTTCCCCTTCCGCGTTTCTCAAGTAGGTACTCAAAGTCGTCCGCGTCGAAGTTGGCCTCGGCGTCCCTCTTCTCCAGCTCCTCCATGTCCTCCTCTGTGTAGTACGGGGTGACGAGCACCACCAGGGCGGCCACAATGGCCAGTGCTAGAAGACACTTCGTATTCATTCTGCTGGTGGTTGGATGTGCGCAAACAAGACAGGAGAGACTTATTAGAATC\t905\r\n"
]
}
],
"prompt_number": 110
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import pandas as pd\n",
"\n",
"# read data from data file into a pandas DataFrame \n",
"Ph = read_table(\"Phel_transcriptome_clc_length2.tab\", # name of the data file\n",
" #sep=\",\", # what character separates each column?\n",
" na_values=[\"\", \" \"]) # what values should be considered \"blank\" values?"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [],
"prompt_number": 95
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"Ph.head()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"\n",
"<class 'pandas.core.frame.DataFrame'>\n",
"Index: 5 entries, Phel_clc_contig_1 to Phel_clc_contig_5\n",
"Data columns (total 3 columns):\n",
"contig 0 non-null values\n",
"sequence 5 non-null values\n",
"seq_len 5 non-null values\n",
"dtypes: float64(1), int64(1), object(1)\n",
""
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 96,
"text": [
" contig sequence \\\n",
"Phel_clc_contig_1 NaN CAAATATATGAACGGTTGATTGTCAACGATTAGTACATGTTTTCAT... \n",
"Phel_clc_contig_2 NaN ATACTTCTAATTATTGTTCGCTGCAAAGGCACGTGTGAACATCTAC... \n",
"Phel_clc_contig_3 NaN TGTGGATGAATTAGATACTAGCCCCCCCCCCGCCCACTGGCCGTCA... \n",
"Phel_clc_contig_4 NaN AGCTTGAGCCTAATCCCTCTGTTTCCTATCAACGAAGTTTCGGATA... \n",
"Phel_clc_contig_5 NaN AATAAAGTACCACTACGTGCACGCCAGTGTTACCACGCTCAGCTTG... \n",
"\n",
" seq_len \n",
"Phel_clc_contig_1 905 \n",
"Phel_clc_contig_2 735 \n",
"Phel_clc_contig_3 4761 \n",
"Phel_clc_contig_4 11084 \n",
"Phel_clc_contig_5 3274 "
]
}
],
"prompt_number": 96
},
{
"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"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"WARNING: pylab import has clobbered these variables: ['load', 'info', 'save', 'datetime', 'set_printoptions', 'unique']\n",
"`%matplotlib` prevents importing * from pylab and numpy\n"
]
}
],
"prompt_number": 53
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"Ph ['seq_len'].hist(bins=1000);\n",
"#Axis limits are changed using the axis([xmin, xmax, ymin, ymax]) function.\n",
"plt.axis([0, 10000, 0, 5000])"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 94,
"text": [
"[0, 10000, 0, 5000]"
]
},
{
"metadata": {},
"output_type": "display_data",
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"text": [
""
]
}
],
"prompt_number": 94
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Other tricks"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"!head -2 Phel_transcriptome_clc_length2.tab"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"contig\tsequence\tseq_len\r\n",
"Phel_clc_contig_1\t\tCAAATATATGAACGGTTGATTGTCAACGATTAGTACATGTTTTCATTGTTCCCCACGCCCGCCCCCCCCCACTCAAACATTTAAAGTGTGAAATATTATTTATCCACAAATTTCCTTAAACCTGCAAACTTGTCTGCTGTCTCTTATTGGAAGTTATGAAAAAGAACAACGGGTTTTCTTTAAAGGGTCTGCGTGCGATTTTCAACCTTTTGAGTAATAGCAGTTATTTTGATAACCGATTTTTTTCAAAGCTCAACAGCTTTTTAAAATAAGGAATCCTATAATGGCCAAACGAATACTATAAAAATAAGGGTTCTCTTAATTGTATAAAACGTATAATTTTATCAATTTTGGGACCGTGTAATTTTTTAAAGACCACAAGAATGTTACATACAACAAATAGACGAAACTCGTAGCTTTGGAAACTACGTCATGGGCGTTTGGTCAAAAGCTGGAGAGAAAGAGAGGTGGGGTGCCAGACTTAAGTAGTCACGTGATCTGACCAACGCACATCGGAAGCTCGATCGGATGAAATCTTCTCTATCGTTCTTGCGTCTATACGTGCTACGAAGAGCTGACAGAAGTTTGGACTTGTTTACTTCTTGCACCTGTTGATGGAACGGCCACGGACCTTGTCGCACGCACACCTGGAGCCAGTGCTCGGATCGACGCAACGGATGTACTGTCTTCCCCTTCCGCGTTTCTCAAGTAGGTACTCAAAGTCGTCCGCGTCGAAGTTGGCCTCGGCGTCCCTCTTCTCCAGCTCCTCCATGTCCTCCTCTGTGTAGTACGGGGTGACGAGCACCACCAGGGCGGCCACAATGGCCAGTGCTAGAAGACACTTCGTATTCATTCTGCTGGTGGTTGGATGTGCGCAAACAAGACAGGAGAGACTTATTAGAATC\t905\r\n"
]
}
],
"prompt_number": 101
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"!awk '{print \">\"$1,$2}' /Users/sr320/Desktop/Phel_transcriptome_clc_length2.tab | sort -g -r"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"!fgrep \"Phel_clc_contig_2671\" Phel_transcriptome_clc_length2.tab | pbcopy"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 113
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"Another way to get some basic statistics"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"mkdir scripts"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [],
"prompt_number": 75
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"cd scripts"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"/Users/sr320/Desktop/scripts\n"
]
}
],
"prompt_number": 76
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"!curl -O https://raw.githubusercontent.com/sr320/eimd/master/scripts/count_fasta.pl"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
" % Total % Received % Xferd Average Speed Time Time Time Current\r\n",
" Dload Upload Total Spent Left Speed\r\n",
"\r",
" 0 0 0 0 0 0 0 0 --:--:-- --:--:-- --:--:-- 0"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
"100 909 0 909 0 0 2081 0 --:--:-- --:--:-- --:--:-- 2094"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
"100 15426 0 15426 0 0 22283 0 --:--:-- --:--:-- --:--:-- 22356\r\n"
]
}
],
"prompt_number": 79
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"cd .."
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"/Users/sr320/Desktop\n"
]
}
],
"prompt_number": 80
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"!perl /Users/sr320/Dropbox/Steven/eimd/scripts/count_fasta.pl -i 10000 Phel_transcriptome_clc.fa"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r\n",
"0:9999 \t30486\r\n",
"10000:19999 \t58\r\n",
"20000:29999 \t11\r\n",
"30000:39999 \t9\r\n",
"40000:49999 \t7\r\n",
"50000:59999 \t3\r\n",
"60000:69999 \t2\r\n",
"70000:79999 \t1\r\n",
"80000:89999 \t0\r\n",
"90000:99999 \t0\r\n",
"100000:109999 \t1\r\n",
"\r\n",
"Total length of sequence:\t43945151 bp\r\n",
"Total number of sequences:\t30578\r\n",
"N25 stats:\t\t\t25% of total sequence length is contained in the 1930 sequences >= 3393 bp\r\n",
"N50 stats:\t\t\t50% of total sequence length is contained in the 6425 sequences >= 1848 bp\r\n",
"N75 stats:\t\t\t75% of total sequence length is contained in the 14670 sequences >= 977 bp\r\n",
"Total GC count:\t\t\t18236282 bp\r\n",
"GC %:\t\t\t\t41.50 %\r\n",
"\r\n"
]
}
],
"prompt_number": 111
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"!perl /Users/sr320/Dropbox/Steven/eimd/scripts/count_fasta.pl -i 1000 ../data/Phel_transcriptome_clc_v3.fasta"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r\n",
"0:999 \t15505\r\n",
"1000:1999 \t8559\r\n",
"2000:2999 \t3003\r\n",
"3000:3999 \t1240\r\n",
"4000:4999 \t580\r\n",
"5000:5999 \t312\r\n",
"6000:6999 \t129\r\n",
"7000:7999 \t72\r\n",
"8000:8999 \t31\r\n",
"9000:9999 \t19\r\n",
"10000:10999 \t9\r\n",
"11000:11999 \t7\r\n",
"12000:12999 \t2\r\n",
"13000:13999 \t4\r\n",
"14000:14999 \t3\r\n",
"15000:15999 \t0\r\n",
"16000:16999 \t1\r\n",
"\r\n",
"Total length of sequence:\t40747496 bp\r\n",
"Total number of sequences:\t29476\r\n",
"N25 stats:\t\t\t25% of total sequence length is contained in the 2260 sequences >= 3085 bp\r\n",
"N50 stats:\t\t\t50% of total sequence length is contained in the 6715 sequences >= 1757 bp\r\n",
"N75 stats:\t\t\t75% of total sequence length is contained in the 14612 sequences >= 959 bp\r\n",
"Total GC count:\t\t\t16459121 bp\r\n",
"GC %:\t\t\t\t40.39 %\r\n",
"\r\n"
]
}
],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"Sequence Similarity Search "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Blast - lets get comfortable with commandline; maybe add to our PATH"
]
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Making a database "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Download fasta http://www.uniprot.org/downloads"
]
},
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"BIG Seqs"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
}