{
 "metadata": {
  "name": "BiGO_core_analysis"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "#Examining basic parameters of sperm methylation based on methratio file."
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "This is an analysis of the basic information provided in the methratio output detailed elsewhere\n\n<https://github.com/uwescience/sqlshare/wiki/Workflow:-Analysis-of-BSMAP-data>\n\n<hr>\n    <img src=\"https://www.evernote.com/shard/s10/sh/8a33d7a0-b05a-491b-a445-9ec0345056ae/5207ebd0608264195e9bb99b5f042f37/deep/0/Screenshot%205/28/13%2012:51%20PM.png\" alt=\"Screenshot%205/28/13%2012:51%20PM\" width=30% />\n    <hr>\n    Initially sequencing reads, in this case from oyster sperm, are mapped to the genome using BS-MAP.\n\n```\n./bsmap -a /Volumes/betty/filtered_174gm_A_NoIndex_L006_R1.fastq.gz -b /Volumes/betty/filtered_174gm_A_NoIndex_L006_R2.fastq.gz -d /Volumes/betty/oyster_v9.fa -o /Volumes/betty/BiGO_Betty_plain.sam -p 4\n```\n\n_output_:  \nTotal number of aligned reads:  \npairs:       90102067 (53%)  \nsingle a:    17033002 (9.9%)  \nsingle b:    15975991 (9.3%)  \n\n<http://eagle.fish.washington.edu/cnidarian/BiGO_Betty_plain.sam>\n\n\nResulting SAM file then subjected to methratio python script\n\n    python methratio.py -d /Volumes/betty/oyster_v9.fa -u -z -g -o  /Volumes/betty/BiGO_betty_plain_methratio_v1.txt -s /Volumes/Bay3/Software/BSMAP/bsmap-2.73/samtools /Volumes/betty/BiGO_Betty_plain.sam\n    \n_output_:  \ntotal 167774088 valid mappings, 127192776 covered cytosines, average coverage: 13.23 fold.\n\n<http://eagle.fish.washington.edu/cnidarian/BiGO_betty_plain_methratio_v1.txt>\n\n**See also [this evernote page](https://www.evernote.com/shard/s10/sh/7764b2a3-2c69-4a92-a4c2-a251fc93b987/de649b2cd0decda57e0400644dc891f5)**\n    \n    \nThe file was uploaded to SQLShare using python script  \n    \n    python singleupload.py sr320@washington.edu c8APIKEYAPIKEYAPIKEY5c15c /Volumes/web/cnidarian/BiGO_betty_plain_methratio_v1.txt"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "!head /Volumes/web/cnidarian/BiGO_betty_plain_methratio_v1.txt",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "chr\tpos\tstrand\tcontext\tratio\teff_CT_count\tC_count\tCT_count\trev_G_count\trev_GA_count\tCI_lower\tCI_upper\r\nC10003\t71\t-\tGAGAA\t0.000\t1.00\t0\t1\t1\t1\t0.000\t0.793\r\nC10003\t76\t-\tAAGGT\t0.000\t1.00\t0\t1\t1\t1\t0.000\t0.793\r\nC10003\t77\t-\tAGGTA\t0.000\t1.00\t0\t1\t1\t1\t0.000\t0.793\r\nC10003\t80\t+\tTACAT\t0.000\t1.00\t0\t1\t1\t1\t0.000\t0.793\r\nC10003\t83\t-\tATGTA\t0.000\t2.00\t0\t2\t1\t1\t0.000\t0.658\r\nC10003\t90\t+\tAACTA\t0.000\t1.00\t0\t1\t2\t2\t0.000\t0.793\r\nC10003\t94\t+\tATCAT\t0.000\t1.00\t0\t1\t2\t2\t0.000\t0.793\r\nC10003\t98\t-\tTTGAG\t0.000\t2.00\t0\t2\t1\t1\t0.000\t0.658\r\nC10003\t100\t-\tGAGAA\t0.000\t2.00\t0\t2\t1\t1\t0.000\t0.658\r\n"
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "<hr>\n<https://sqlshare.escience.washington.edu/sqlshare#s=query/sr320%40washington.edu/BiGO_betty_plain_methratio_v1.txt>"
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "<hr>\n<img src=\"https://www.evernote.com/shard/s10/sh/f1c4796b-c52d-4814-b2a6-b8063eb08612/25a5f2aedc71fd2813bd10c81f5a40ba/deep/0/Screenshot%205/28/13%2012:56%20PM.png\" alt=\"Screenshot%205/28/13%2012:56%20PM\" width=30% />\n\n\nThere are 127,192,776 rows of data\n\n\n"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "import pandas as pd",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "!tr ',' \"\\t\" </Volumes/web/cnidarian/BiGO_betty_plain_methratio_v1.txt> /Volumes/web/cnidarian/BiGO_betty_plain_methratio_v1.csv",
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "<hr>\n\n```\nSelect count(*) from\n  (\nSELECT \n  * \n  FROM [sr320@washington.edu].[BiGO_betty_plain_methratio_v1.txt]\n  Where CT_count >=10\n  \u200b) pls\u200b\n```\n<hr>\n74,663,926\n<hr>\n```\nSelect count(*) from\n  (\nSELECT \n  * \n  FROM [sr320@washington.edu].[BiGO_betty_plain_methratio_v1.txt]\n  Where CT_count >=5\n  \u200b) pls\u200b\n```\n<hr>\n104,500,062\n<hr>\n```\nSelect count(*) from\n  (\nSELECT \n  * \n  FROM [sr320@washington.edu].[BiGO_betty_plain_methratio_v1.txt]\n  Where CT_count >=5\n  and context like '__CG_'\n  \u200b) pls\u200b\u200b\u200b\u200b\u200b\n```\n<hr>\n7,648,304\n<hr>\n```\nSelect count(*) from\n  (\nSELECT \n  * \n  FROM [sr320@washington.edu].[BiGO_betty_plain_methratio_v1.txt]\n  Where CT_count >=5\n  and context like '__CA_'\n  and strand = '+'\n  ) pls\u200b\u200b\u200b\n```\n<hr>\n21678289\n<hr>\n"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "",
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}