[FEATURE] added DBAdapter usage ipython notebook draft.

.gitignore

@@ -8,3 +8,4 @@ dist/*
 .pythoscope
 .idea
 *.pyc
+Fred2/tutorials/.ipynb_checkpoints/*

Fred2/tutorials/DBAdapterUsage.ipynb

@@ -0,0 +1,250 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h1> DBAdapter usage </h1>\n",
+    "\n",
+    "This tutorial illustrates the use of Fred2 to map gene names and retrieve database accessions and genetic or transcript sequences from a database source like BioMart. Fred2 can connect to a variety of DB sources both online and offline.\n",
+    "Here, we will cover the use of Fred2 MartsAdapter as and example for online access and EnsemblAdapter for offline access."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "<h2> Chapter 1: The basics </h2>\n",
+    "<br/>\n",
+    "We first start with importing the needed packages."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "%load_ext autoreload\n",
+    "%autoreload 2\n",
+    "import sys\n",
+    "sys.path.extend(['/home/walzer/immuno-tools/Fred2'])\n",
+    "from Fred2.IO.MartsAdapter import MartsAdapter\n",
+    "from Fred2.IO.EnsemblAdapter import EnsemblDB"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For starters we will connect to the BioMart:\n",
+    "\n",
+    "Initializing the MartsAdapter, you can specify the URL under which the BioMart of your choice is to be reached by supporting the attribute biomart. If you do not choose a specific BioMart it will default to <a href=\"http://biomart.org\">http://biomart.org</a>. Here however, we will use: <a href=\"http://grch37.ensembl.org\">http://grch37.ensembl.org</a>. Please refer to the documentation of your BioMart to find the correct URL."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "mart_adapter = MartsAdapter(biomart=\"http://grch37.ensembl.org\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can start using the BioMart. For a comprehensive list of methods implemented to the adapter, you can refer to the <a href=\"http://fred2.readthedocs.org/en/latest/Fred2.IO.html#module-Fred2.IO.MartsAdapter\">documentation</a>.\n",
+    "\n",
+    "You can fetch all different kinds of sequences with the adapter. We will start with a transcript sequence to the glucagon gene. You have to provide an identifier that will be known by the BioMart and identifies the <i>transcript</i>, in our <a href=\"http://www.ensembl.org/\">ensembl</a> case in the form \"ENST...\".\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<type 'str'>\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "TRANSCRIPT: ENST00000375497\n",
+       "\tVARIANTS:\n",
+       "\tSEQUENCE: ATGAAAAGCATTTACTTTGTGGCTGGATTATTTGTAATGCTGGTACAAGGCAGCTGGCAACGTTCCCTTCAAGACACAGAGGAGAAATCCAGATCATTCTCAGCTTCCCAGGCAGACCCACTCAGTGATCCTGATCAGATGAACGAGGACAAGCGCCATTCACAGGGCACATTCACCAGTGACTACAGCAAGTATCTGGACTCCAGGCGTGCCCAAGATTTTGTGCAGTGGTTGATGAATACCAAGAGGAACAGGAATAACATTGCCAAACGTCACGATGAATTTGAGAGACATGCTGAAGGGACCTTTACCAGTGATGTAAGTTCTTATTTGGAAGGCCAAGCTGCCAAGGAATTCATTGCTTGGCTGGTGAAAGGCCGAGGAAGGCGAGATTTCCCAGAAGAGGTCGCCATTGTTGAAGAACTTGGCCGCAGACATGCTGATGGTTCTTTCTCTGATGAGATGAACACCATTCTTGATAATCTTGCCGCCAGGGACTTTATAAACTGGTTGATTCAGACCAAAATCACTGACAGGAAATAA (mRNA)"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "transcript = mart_adapter.get_transcript_sequence('ENST00000375497')\n",
+    "print type(transcript)\n",
+    "from Fred2.Core import Transcript\n",
+    "fred2_transcript = Transcript(transcript, 'glucagon', 'ENST00000375497')\n",
+    "fred2_transcript"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The adapter will yield a simple string. You can use this string to contruct your <a href=\"http://fred2.readthedocs.org/en/latest/Fred2.Core.html#module-Fred2.Core.Transcript\">transcript object</a>."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(539, 539)"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mart_adapter.get_transcript_position('163002078', '163002078', 'ENSG00000115263', 'ENST00000375497')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'P53'"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mart_adapter.get_variant_gene(17, 7565101, 7565101)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2> Chapter 2: Connecting to offline databases</h2>\n",
+    "<br/>\n",
+    "Fred2 Also supports the read from offline databases such as fasta and dat files as you can download from Ensebl, UniProt or RefSeq.\n",
+    "To connect, you will have to initialize the corresponding adapter and feed it the location of your database file.\n",
+    "\n",
+    "As example, we will use the EnsemblAdapter. You can get the official sequence ressources from ensemble <a href=\"http://www.ensembl.org/info/data/ftp/index.html\">here</a>. However for this tutorial, we will use a small test excerpt from the ensembl Protein sequence (FASTA)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "ed = EnsemblDB()\n",
+    "ed.read_seqs(\"data/Homo_sapiens.GRCh38.pep.test_stub.fa\")\n",
+    "ed.read_seqs(\"data/Homo_sapiens.GRCh38.cds.test_stub.fa\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ID: ENSP00000400926\n",
+      "Name: ENSP00000400926\n",
+      "Description: ENSP00000400926 pep:known chromosome:GRCh38:4:82818662:82891266:-1 gene:ENSG00000138674 transcript:ENST00000448323 gene_biotype:protein_coding transcript_biotype:protein_coding\n",
+      "Number of features: 0\n",
+      "Seq('MKLKEVDRTAMQAWSPAQNHPIYLATGTSAQQLDATFSTNASLEIFELDLSDPS...LGV', SingleLetterAlphabet())\n",
+      "ATGAAGTTAAAGGAAGTAGATCGTACAGCCATGCAGGCATGGAGCCCTGCCCAGAATCACCCCATTTACCTAGCAACAGGAACATCTGCTCAGCAATTGGATGCAACATTTAGTACGAATGCTTCCCTTGAGATATTTGAATTAGACCTCTCTGATCCATCCTTGGATATGAAATCTTGTGCCACATTCTCCTCTTCTCACAGGTACCACAAGTTGATTTGGGGGCCTTATAAAATGGATTCCAAAGGAGATGTCTCTGGAGTTCTGATTGCAGGTGGTGAAAATGGAAATATTATTCTCTATGATCCTTCTAAAATTATAGCTGGAGACAAGGAAGTTGTGATTGCCCAGAATGACAAGCATACTGGCCCAGTGAGAGCCTTGGATGTGAACATTTTCCAGACTAATCTGGTAGCTTCTGGTGCTAATGAATCTGAAATCTACATATGGGATCTAAATAATTTTGCAACCCCAATGACACCAGGAGCCAAAACACAGCCGCCAGAAGATATCAGCTGCATTGCATGGAACAGACAAGTTCAGCATATTTTAGCATCAGCCAGTCCCAGTGGCCGGGCCACTGTATGGGATCTTAGAAAAAATGAGCCAATCATCAAAGTCAGTGACCATAGTAACAGAATGCATTGTTCTGGGTTGGCATGGCATCCTGATGTTGCTACTCAGATGGTCCTTGCCTCCGAGGATGACCGGTTACCAGTGATCCAGATGTGGGATCTTCGATTTGCTTCCTCTCCACTTCGTGTCCTGGAAAACCATGCCAGGGGGATTTTGGCAATTGCTTGGAGCATGGCAGATCCTGAATTGTTACTGAGCTGTGGAAAAGATGCTAAGATTCTCTGCTCCAATCCAAACACAGGAGAGGTGTTATATGAACTTCCCACCAACACACAGTGGTGCTTCGATATTCAGTGGTGTCCCCGAAATCCTGCTGTCTTATCAGCTGCTTCGTTTGATGGGCGTATCAGTGTTTATTCTATCATGGGAGGTAGCACAGATGGTTTAAGACAGAAACAAGTTGACAAGCTTTCATCATCTTTTGGGAATCTTGATCCCTTTGGCACAGGACAGCCCCTTCCTCCGTTACAAATTCCACAGCAGACTGCTCAGCATAGTATAGTGCTGCCTCTGAAGAAGCCGCCCAAGTGGATTCGAAGGCCTGTTGGTGCTTCTTTTTCATTTGGAGGCAAACTGGTTACGTTTGAGAATGTCAGAATGCCTTCTCATCAGGGAGCTGAGCAGCAGCAGCAGCAGCACCATGTGTTCATTAGTCAGGTTGTAACAGAAAAGGAGTTCCTCAGCCGATCAGACCAACTTCAGCAGGCTGTGCAGTCACAAGGATTTATCAATTATTGCCAAAAAAAAATTGATGCTTCTCAGACTGAATTTGAGAAAAATGTGTGGTCCTTTTTGAAGGTAAACTTTGAGGATGATTCTCGTGGAAAATACCTTGAACTTCTAGGATACAGAAAAGAAGATCTAGGAAAGAAGATTGCTTTGGCCTTGAACAAAGTGGATGGAGCCAATGTGGCTCTTAAAGACTCTGACCAAGTAGCACAGAGTGATGGGGAGGAGAGCCCTGCTGCTGAAGAGCAGCTCTTGGGAGAGCACATTAAAGAGGAAAAAGAAGAATCTGAATTTCTACCCTCATCTGGAGGAACATTTAATATCTCTGTCAGTGGGGACATTGATGGTTTAATTACTCAGGCTTTGCTGACGGGCAATTTTGAGAGTGCTGTTGACCTTTGTTTACATGATAACCGCATGGCCGATGCCATTATATTGGCCATAGCAGGTGGACAAGAACTCTTGGCTCGAACCCAGAAAAAATACTTCGCAAAATCCCAAAGCAAAATTACCAGGCTCATCACTGCAGTGGTGATGAAGAACTGGAAAGAGATTGTTGAGTCTTGTGATCTTAAAAATTGGAGAGAGGCTTTAGCTGCAGTATTGACTTATGCAAAGCCGGATGAATTTTCAGCCCTTTGTGATCTTTTGGGAACCAGGCTTGAAAATGAAGGAGATAGCCTCCTGCAGACTCAAGCATGTCTCTGCTATATTTGTGCAGGGAATGTAGAGAAATTAGTTGCATGTTGGACTAAAGCTCAAGATGGAAGCCACCCTTTGTCACTTCAGGATCTGATTGAGAAAGTTGTCATCCTGCGAAAAGCTGTGCAACTCACTCAAGCCATGGACACTAGTACTGTAGGAGTTCTCTTGGCTGCGAAGATGAGTCAGTATGCCAATTTGTTGGCAGCTCAGGGCAGTATTGCTGCAGCCTTGGCTTTTCTTCCTGACAACACCAACCAGCCAAATATCATGCAGCTTCGTGACAGACTTTGTAGAGCACAAGGAGAGCCTGTAGCAGGACATGAATCACCTAAAATTCCGTACGAGAAACAGCAGCTCCCCAAGGGCAGGCCTGGACCAGTTGCTGGCCACCACCAGATGCCAAGAGTTCAAACTCAACAATATTATCCCCATGGAGAAAATCCTCCACCTCCGGGTTTCATAATGCATGGAAATGTTAATCCAAATGCTGCTGGTCAGCTTCCCACATCTCCAGGTCATATGCACACCCAGGTACCACCTTATCCACAGCCACAGCCTTATCAACCAGCCCAGCCGTATCCCTTCGGAACAGGGGGGTCAGCAATGTATCGACCTCAGCAGCCTGTTGCTCCTCCTACTTCAAACGCTTACCCTAACACCCCTTACATATCTTCTGCTTCTTCCTATACTGGGCAGTCTCAGCTGTACGCAGCACAGCACCAGGCCTCTTCACCTACCTCCAGCCCTGCTACTTCTTTCCCTCCTCCCCCTTCCTCTGGAGCATCCTTCCAGCATGGCGGACCAGGAGCTCCACCATCATCTTCAGCTTATGCACTGCCTCCTGGAACAACAGGTACACTGCCTGCTGCCAGTGAGCTGCCTGCGTCCCAAAGAACAGGTCCTCAGAATGGTTGGAATGACCCTCCAGCTTTGAACAGAGTACCCAAAAAGAAGAAGATGCCTGAAAACTTCATGCCTCCTGTTCCCATCACATCACCAATCATGAACCCGTTGGGTGACCCCCAGTCACAAATGCTGCAGCAACAGCCTTCAGCTCCAGTACCACTGTCAAGCCAGTCTTCATTCCCACAGCCACATCTTCCAGGTGGCCAGCCCTTCCATGGCGTACAGCAACCTCTTGGTCAAACAGGCATGCCACCATCTTTTTCAAAGCCCAATATTGAAGGTGCCCCAGGGGCTCCTATTGGAAATACCTTCCAGCATGTGCAGTCTTTGCCAACAAAAAAAATTACCAAGAAACCTATTCCAGATGAGCACCTCATTCTAAAGACCACATTTGAGGATCTTATTCAGCGCTGCCTTTCTTCAGCAACAGACCCTCAAACCAAGAGGAAGCTAGATGATGCCAGCAAACGTTTGGAGTTTCTGTATGATAAACTTAGGGAACAGACACTTTCACCAACAATCACCAGTGGTTTACACAACATTGCAAGGAGCATTGAAACTCGAAACTACTCAGAAGGATTGACCATGCATACCCACATAGTTAGCACCAGCAACTTCAGTGAGACCTCTGCTTTCATGCCAGTTCTCAAAGTTGTTCTCACCCAGGCCAATAAGCTGGGTGTCTAA\n"
+     ]
+    }
+   ],
+   "source": [
+    "print ed.get_product_sequence('ENSP00000400926')\n",
+    "print ed.get_transcript_sequence('ENST00000395310')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To combine prediction results we can use `merge_results` from `Fred2.Core`. In addition to the result object we want to merge, also have to specify the type of these objects (here `EpitopePredictionResult`). The function will return a merged results object of the same type."
+   ]
+  }
+ ],
+ "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": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}