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@hardingnj
Created April 12, 2016 16:28
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Worked examples with Ag 1000 Genomes data\n",
"\n",
"## Preamble"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import h5py\n",
"import allel\n",
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"fn = (\"/data/coluzzi/ag1000g/data/phase1/release/AR3/variation/main/hdf5/\" +\n",
" \"ag1000g.phase1.ar3.pass.3L.h5\")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# open filehandle\n",
"fh = h5py.File(fn, \"r\")"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['AB0085-C',\n",
" 'AB0087-C',\n",
" 'AB0088-C',\n",
" 'AB0089-C',\n",
" 'AB0090-C',\n",
" 'AB0091-C',\n",
" 'AB0092-C',\n",
" 'AB0094-C',\n",
" 'AB0095-C',\n",
" 'AB0097-C']"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# samples are held at the first level after chrom\n",
"# we decode them from the byte literal format\n",
"samples = [s.decode() for s in fh[\"3L\"][\"samples\"][:]]\n",
"samples[:10]"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# read in the metadata using the pandas module\n",
"metadata = pd.read_csv(\n",
" \"/data/coluzzi/ag1000g/data/phase1/release/AR3/samples/samples.meta.txt\", \n",
" index_col=0,\n",
" sep=\"\\t\")"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>ox_code</th>\n",
" <th>src_code</th>\n",
" <th>sra_sample_accession</th>\n",
" <th>population</th>\n",
" <th>country</th>\n",
" <th>region</th>\n",
" <th>contributor</th>\n",
" <th>contact</th>\n",
" <th>year</th>\n",
" <th>m_s</th>\n",
" <th>sex</th>\n",
" <th>n_sequences</th>\n",
" <th>mean_coverage</th>\n",
" <th>latitude</th>\n",
" <th>longitude</th>\n",
" </tr>\n",
" <tr>\n",
" <th>index</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>AB0085-C</td>\n",
" <td>BF2-4</td>\n",
" <td>ERS223996</td>\n",
" <td>BFS</td>\n",
" <td>Burkina Faso</td>\n",
" <td>Pala</td>\n",
" <td>Austin Burt</td>\n",
" <td>Sam O'Loughlin</td>\n",
" <td>2012</td>\n",
" <td>S</td>\n",
" <td>F</td>\n",
" <td>89905852</td>\n",
" <td>28.01</td>\n",
" <td>11.150</td>\n",
" <td>-4.235</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>AB0087-C</td>\n",
" <td>BF3-3</td>\n",
" <td>ERS224013</td>\n",
" <td>BFM</td>\n",
" <td>Burkina Faso</td>\n",
" <td>Bana</td>\n",
" <td>Austin Burt</td>\n",
" <td>Sam O'Loughlin</td>\n",
" <td>2012</td>\n",
" <td>M</td>\n",
" <td>F</td>\n",
" <td>116706234</td>\n",
" <td>36.76</td>\n",
" <td>11.233</td>\n",
" <td>-4.472</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>AB0088-C</td>\n",
" <td>BF3-5</td>\n",
" <td>ERS223991</td>\n",
" <td>BFM</td>\n",
" <td>Burkina Faso</td>\n",
" <td>Bana</td>\n",
" <td>Austin Burt</td>\n",
" <td>Sam O'Loughlin</td>\n",
" <td>2012</td>\n",
" <td>M</td>\n",
" <td>F</td>\n",
" <td>112090460</td>\n",
" <td>23.30</td>\n",
" <td>11.233</td>\n",
" <td>-4.472</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>AB0089-C</td>\n",
" <td>BF3-8</td>\n",
" <td>ERS224031</td>\n",
" <td>BFM</td>\n",
" <td>Burkina Faso</td>\n",
" <td>Bana</td>\n",
" <td>Austin Burt</td>\n",
" <td>Sam O'Loughlin</td>\n",
" <td>2012</td>\n",
" <td>M</td>\n",
" <td>F</td>\n",
" <td>145350454</td>\n",
" <td>41.36</td>\n",
" <td>11.233</td>\n",
" <td>-4.472</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>AB0090-C</td>\n",
" <td>BF3-10</td>\n",
" <td>ERS223936</td>\n",
" <td>BFM</td>\n",
" <td>Burkina Faso</td>\n",
" <td>Bana</td>\n",
" <td>Austin Burt</td>\n",
" <td>Sam O'Loughlin</td>\n",
" <td>2012</td>\n",
" <td>M</td>\n",
" <td>F</td>\n",
" <td>105012254</td>\n",
" <td>34.64</td>\n",
" <td>11.233</td>\n",
" <td>-4.472</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" ox_code src_code sra_sample_accession population country region \\\n",
"index \n",
"0 AB0085-C BF2-4 ERS223996 BFS Burkina Faso Pala \n",
"1 AB0087-C BF3-3 ERS224013 BFM Burkina Faso Bana \n",
"2 AB0088-C BF3-5 ERS223991 BFM Burkina Faso Bana \n",
"3 AB0089-C BF3-8 ERS224031 BFM Burkina Faso Bana \n",
"4 AB0090-C BF3-10 ERS223936 BFM Burkina Faso Bana \n",
"\n",
" contributor contact year m_s sex n_sequences mean_coverage \\\n",
"index \n",
"0 Austin Burt Sam O'Loughlin 2012 S F 89905852 28.01 \n",
"1 Austin Burt Sam O'Loughlin 2012 M F 116706234 36.76 \n",
"2 Austin Burt Sam O'Loughlin 2012 M F 112090460 23.30 \n",
"3 Austin Burt Sam O'Loughlin 2012 M F 145350454 41.36 \n",
"4 Austin Burt Sam O'Loughlin 2012 M F 105012254 34.64 \n",
"\n",
" latitude longitude \n",
"index \n",
"0 11.150 -4.235 \n",
"1 11.233 -4.472 \n",
"2 11.233 -4.472 \n",
"3 11.233 -4.472 \n",
"4 11.233 -4.472 "
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"metadata.head()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"SortedIndex((9643193,), dtype=int32)\n",
"[ 9790 9798 9812 ..., 41956541 41956551 41956556]"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# positions dataset is in the \"variants\" group\n",
"positions = allel.SortedIndex(fh[\"/3L/variants/POS\"][:])\n",
"positions"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<table class='petl'>\n",
"<caption>GenotypeChunkedArray((9643193, 765, 2), int8, nbytes=13.7G, cbytes=548.0M, cratio=25.7, cname=gzip, clevel=3, shuffle=False, chunks=(6553, 10, 2), data=h5py._hl.dataset.Dataset)</caption>\n",
"<thead>\n",
"<tr>\n",
"<th></th>\n",
"<th>0</th>\n",
"<th>1</th>\n",
"<th>2</th>\n",
"<th>3</th>\n",
"<th>4</th>\n",
"<th>...</th>\n",
"<th>760</th>\n",
"<th>761</th>\n",
"<th>762</th>\n",
"<th>763</th>\n",
"<th>764</th>\n",
"</tr>\n",
"</thead>\n",
"<tbody>\n",
"<tr>\n",
"<td style='font-weight: bold'>0</td>\n",
"<td>0/0</td>\n",
"<td>0/0</td>\n",
"<td>0/0</td>\n",
"<td>0/0</td>\n",
"<td>0/0</td>\n",
"<td>...</td>\n",
"<td>0/0</td>\n",
"<td>0/0</td>\n",
"<td>0/0</td>\n",
"<td>0/0</td>\n",
"<td>0/0</td>\n",
"</tr>\n",
"<tr>\n",
"<td style='font-weight: bold'>1</td>\n",
"<td>0/0</td>\n",
"<td>0/0</td>\n",
"<td>0/0</td>\n",
"<td>0/0</td>\n",
"<td>0/0</td>\n",
"<td>...</td>\n",
"<td>0/0</td>\n",
"<td>0/0</td>\n",
"<td>0/0</td>\n",
"<td>0/0</td>\n",
"<td>0/0</td>\n",
"</tr>\n",
"<tr>\n",
"<td style='font-weight: bold'>2</td>\n",
"<td>0/0</td>\n",
"<td>0/0</td>\n",
"<td>0/0</td>\n",
"<td>0/0</td>\n",
"<td>0/0</td>\n",
"<td>...</td>\n",
"<td>0/0</td>\n",
"<td>0/0</td>\n",
"<td>0/0</td>\n",
"<td>0/0</td>\n",
"<td>0/0</td>\n",
"</tr>\n",
"<tr>\n",
"<td style='font-weight: bold'>3</td>\n",
"<td>0/0</td>\n",
"<td>0/0</td>\n",
"<td>0/0</td>\n",
"<td>0/0</td>\n",
"<td>0/0</td>\n",
"<td>...</td>\n",
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"<td>0/0</td>\n",
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"</tr>\n",
"<tr>\n",
"<td style='font-weight: bold'>4</td>\n",
"<td>0/0</td>\n",
"<td>0/0</td>\n",
"<td>0/0</td>\n",
"<td>0/0</td>\n",
"<td>0/0</td>\n",
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"</tr>\n",
"</tbody>\n",
"</table>\n",
"<p><strong>...</strong></p>"
],
"text/plain": [
"GenotypeChunkedArray((9643193, 765, 2), int8, nbytes=13.7G, cbytes=548.0M, cratio=25.7, cname=gzip, clevel=3, shuffle=False, chunks=(6553, 10, 2), data=h5py._hl.dataset.Dataset)"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# genotype data is in the \"calldata\" group\n",
"g = allel.GenotypeChunkedArray(fh[\"3L\"][\"calldata\"][\"genotype\"])\n",
"g"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Plot mean mq0 in windows across the genome"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# scikit allel has a built in function that allows us to calculate statistics within windows\n",
"values, windows, counts = allel.stats.windowed_statistic(\n",
" positions, fh[\"3L\"][\"variants/CoverageMQ0\"][:], np.mean, \n",
" size=100000, start=0)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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OjigoKFAry8o6e3t7FN/i3GBlumSbh7LOGirTxpJp7cqXufuhrCmZNnesrAXH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\nCyGEEEIIYaf/B+tR766z3yVLAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f3f988ed550>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# using matplotlib here\n",
"fig = plt.figure(figsize=(12, 3))\n",
"\n",
"ax = fig.add_subplot(111)\n",
"\n",
"ax.plot(windows.mean(1), values, \"k-\")\n",
"ax.set_ylabel(\"Coverage MQ0\")\n",
"ax.grid(True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Plot count of variants across the genome\n",
"\n",
"NOTE: Accessibility likely to differ, this plot shows accessibility and diversity. ie if low we cannot tell if inaccessible or not diverse."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# again scikit-allel function\n",
"values, windows, counts = allel.stats.windowed_statistic(\n",
" positions, positions, np.size, \n",
" size=100000, start=0)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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oieBlUZba2lqPO0fV1dU4evQofv/733udOIq9XnEHQPc7Dew1udfz8OHDePbZ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\n7u6TwjEnhJzyOXMlwjyVUroOQB+l9DtK6S0ALg5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m1BDm9gTKMfckzH0VZ/5co5KSkqS/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\ngiAIgggB/j8iHRBxdOWzBQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f3f988a3fd0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize=(12, 3))\n",
"\n",
"ax = fig.add_subplot(111)\n",
"\n",
"ax.plot(windows.mean(1), values, \"k-\")\n",
"ax.set_ylabel(\"n variants\")\n",
"ax.grid(True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Calculate Fst between two populations\n",
"\n",
"For this we will need some metadata.\n",
"We compute the allele counts and use these to calculate Fst. "
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# create a dictionary containing the indices of each subpopulation\n",
"subpops_ix = dict()\n",
"pop_groups = metadata.groupby(\"population\")\n",
"for grp in pop_groups.groups:\n",
" subpops_ix[grp] = [samples.index(x) for x in pop_groups.get_group(grp).ox_code]"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# remove non biallelic variants\n",
"qq = np.array(g.max(axis=(1, 2)))"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"((9643193,), array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1]))"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"qq.shape, qq[:10]"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"g_bial = g.compress(condition=qq < 2, axis=0)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# count alleles within subpops\n",
"allele_counts = g_bial.count_alleles_subpops(subpops_ix)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# scikit allel has a built in function for windowed Fst\n",
"fst, windows, counts = allel.stats.windowed_patterson_fst(\n",
" positions.compress(qq < 2), \n",
" allele_counts[\"BFM\"], allele_counts[\"BFS\"], size=100000, start=0)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x7f3f92c5b978>"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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RURE+/PBDrFixorOnYnZWm1xnZGS0ui3Ew8MDhYWFzW5z6NAhvPLKK+2Znsl1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\nAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f3f92c52518>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize=(12, 3))\n",
"\n",
"ax = fig.add_subplot(111)\n",
"ax.plot(windows.mean(1), fst, \"k-\")\n",
"ax.grid(True)\n",
"ax.set_ylabel(\"Fst\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Plot number of variants in a KES sample vs CMS sample"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# take the first sample from each population\n",
"kes_ix = metadata.loc[metadata.population==\"KES\"].index[0]\n",
"cms_ix = metadata.loc[metadata.population==\"CMS\"].index[0]"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# use the is_het method from scikit allel\n",
"kes_hz = g.take([kes_ix], axis=1).is_het()\n",
"cms_hz = g.take([cms_ix], axis=1).is_het()"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# and the windowed statistic function...\n",
"kes_count, windows, _ = allel.stats.windowed_statistic(\n",
" pos=fh[\"/3L/variants/POS\"][:], values=kes_hz,\n",
" statistic=np.sum, start=0, size=100000)"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"cms_count, windows, _ = allel.stats.windowed_statistic(\n",
" pos=fh[\"/3L/variants/POS\"][:], values=cms_hz,\n",
" statistic=np.sum, start=0, size=100000)"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x7f3f92be21d0>"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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52KGoJYBtVRPReM4H5Y5U87rg05SVZWLofT7YuhXfcIIaa2pe9nC7oaEhI1pd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\ngg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f3f92bd6d30>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize=(12, 3))\n",
"\n",
"ax = fig.add_subplot(111)\n",
"\n",
"ax.plot(windows.mean(1), kes_count, \"r-\")\n",
"ax.plot(windows.mean(1), cms_count, \"b-\")\n",
"ax.grid(True)\n",
"ax.set_ylabel(\"n Heterozygotes\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Select a gene and list the NS variants"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# start end of gene from vectorbase\n",
"tep1 = 11202091, 11206882"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# locate the indices of the start/end\n",
"ix = positions.locate_range(*tep1)"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# load the annotation object from hdf5\n",
"annotation = fh[\"3L\"][\"variants\"][\"ANN\"][ix]"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# create a table, but only keep rows in the gene\n",
"var_tab = allel.VariantChunkedTable(fh[\"3L\"][\"variants\"], names=[\"POS\", \"ANN\"], index=\"POS\")\n",
"var_tab = var_tab[ix]"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"21"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# define non synonymous mutations\n",
"non_syn = np.in1d(var_tab[\"ANN\"][\"Annotation_Impact\"], (b\"MODERATE\", b\"HIGH\"))\n",
"non_syn.sum()"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<table class='petl'>\n",
"<caption>VariantTable((21,), dtype=(numpy.record, [('Annotation', 'S34'), ('Feature_Type', 'S20'), ('HGVS_c', 'S12'), ('HGVS_p', 'S14'), ('cDNA_pos', '&lt;i4'), ('cDNA_length', '&lt;i4'), ('CDS_pos', '&lt;i4')]))</caption>\n",
"<thead>\n",
"<tr>\n",
"<th>Annotation</th>\n",
"<th>Feature_Type</th>\n",
"<th>HGVS_c</th>\n",
"<th>HGVS_p</th>\n",
"<th>cDNA_pos</th>\n",
"<th>cDNA_length</th>\n",
"<th>CDS_pos</th>\n",
"</tr>\n",
"</thead>\n",
"<tbody>\n",
"<tr>\n",
"<td>b'missense_variant'</td>\n",
"<td>b'transcript'</td>\n",
"<td>b'n.3117C>A'</td>\n",
"<td>b'p.His1039Gln'</td>\n",
"<td>3117</td>\n",
"<td>4023</td>\n",
"<td>3117</td>\n",
"</tr>\n",
"<tr>\n",
"<td>b'missense_variant'</td>\n",
"<td>b'transcript'</td>\n",
"<td>b'n.2210A>G'</td>\n",
"<td>b'p.Asn737Ser'</td>\n",
"<td>2210</td>\n",
"<td>4023</td>\n",
"<td>2210</td>\n",
"</tr>\n",
"<tr>\n",
"<td>b'missense_variant'</td>\n",
"<td>b'transcript'</td>\n",
"<td>b'n.2207C>T'</td>\n",
"<td>b'p.Pro736Leu'</td>\n",
"<td>2207</td>\n",
"<td>4023</td>\n",
"<td>2207</td>\n",
"</tr>\n",
"<tr>\n",
"<td>b'missense_variant'</td>\n",
"<td>b'transcript'</td>\n",
"<td>b'n.2122A>T'</td>\n",
"<td>b'p.Thr708Ser'</td>\n",
"<td>2122</td>\n",
"<td>4023</td>\n",
"<td>2122</td>\n",
"</tr>\n",
"<tr>\n",
"<td>b'missense_variant'</td>\n",
"<td>b'transcript'</td>\n",
"<td>b'n.2044A>T'</td>\n",
"<td>b'p.Thr682Ser'</td>\n",
"<td>2044</td>\n",
"<td>4023</td>\n",
"<td>2044</td>\n",
"</tr>\n",
"<tr>\n",
"<td>b'missense_variant'</td>\n",
"<td>b'transcript'</td>\n",
"<td>b'n.2040G>C'</td>\n",
"<td>b'p.Gln680His'</td>\n",
"<td>2040</td>\n",
"<td>4023</td>\n",
"<td>2040</td>\n",
"</tr>\n",
"<tr>\n",
"<td>b'missense_variant'</td>\n",
"<td>b'transcript'</td>\n",
"<td>b'n.1948G>A'</td>\n",
"<td>b'p.Glu650Lys'</td>\n",
"<td>1948</td>\n",
"<td>4023</td>\n",
"<td>1948</td>\n",
"</tr>\n",
"<tr>\n",
"<td>b'missense_variant'</td>\n",
"<td>b'transcript'</td>\n",
"<td>b'n.1942T>A'</td>\n",
"<td>b'p.Leu648Met'</td>\n",
"<td>1942</td>\n",
"<td>4023</td>\n",
"<td>1942</td>\n",
"</tr>\n",
"<tr>\n",
"<td>b'missense_variant'</td>\n",
"<td>b'transcript'</td>\n",
"<td>b'n.1939A>C'</td>\n",
"<td>b'p.Lys647Gln'</td>\n",
"<td>1939</td>\n",
"<td>4023</td>\n",
"<td>1939</td>\n",
"</tr>\n",
"<tr>\n",
"<td>b'missense_variant'</td>\n",
"<td>b'transcript'</td>\n",
"<td>b'n.1902G>C'</td>\n",
"<td>b'p.Leu634Phe'</td>\n",
"<td>1902</td>\n",
"<td>4023</td>\n",
"<td>1902</td>\n",
"</tr>\n",
"<tr>\n",
"<td>b'missense_variant'</td>\n",
"<td>b'transcript'</td>\n",
"<td>b'n.1853C>G'</td>\n",
"<td>b'p.Pro618Arg'</td>\n",
"<td>1853</td>\n",
"<td>4023</td>\n",
"<td>1853</td>\n",
"</tr>\n",
"<tr>\n",
"<td>b'missense_variant'</td>\n",
"<td>b'transcript'</td>\n",
"<td>b'n.1852C>T'</td>\n",
"<td>b'p.Pro618Ser'</td>\n",
"<td>1852</td>\n",
"<td>4023</td>\n",
"<td>1852</td>\n",
"</tr>\n",
"<tr>\n",
"<td>b'missense_variant'</td>\n",
"<td>b'transcript'</td>\n",
"<td>b'n.1840C>A'</td>\n",
"<td>b'p.Gln614Lys'</td>\n",
"<td>1840</td>\n",
"<td>4023</td>\n",
"<td>1840</td>\n",
"</tr>\n",
"<tr>\n",
"<td>b'missense_variant'</td>\n",
"<td>b'transcript'</td>\n",
"<td>b'n.1838T>A'</td>\n",
"<td>b'p.Leu613Gln'</td>\n",
"<td>1838</td>\n",
"<td>4023</td>\n",
"<td>1838</td>\n",
"</tr>\n",
"<tr>\n",
"<td>b'missense_variant'</td>\n",
"<td>b'transcript'</td>\n",
"<td>b'n.1823C>T'</td>\n",
"<td>b'p.Thr608Met'</td>\n",
"<td>1823</td>\n",
"<td>4023</td>\n",
"<td>1823</td>\n",
"</tr>\n",
"<tr>\n",
"<td>b'missense_variant'</td>\n",
"<td>b'transcript'</td>\n",
"<td>b'n.1810G>A'</td>\n",
"<td>b'p.Ala604Thr'</td>\n",
"<td>1810</td>\n",
"<td>4023</td>\n",
"<td>1810</td>\n",
"</tr>\n",
"<tr>\n",
"<td>b'missense_variant'</td>\n",
"<td>b'transcript'</td>\n",
"<td>b'n.1792G>A'</td>\n",
"<td>b'p.Asp598Asn'</td>\n",
"<td>1792</td>\n",
"<td>4023</td>\n",
"<td>1792</td>\n",
"</tr>\n",
"<tr>\n",
"<td>b'missense_variant'</td>\n",
"<td>b'transcript'</td>\n",
"<td>b'n.1723T>C'</td>\n",
"<td>b'p.Phe575Leu'</td>\n",
"<td>1723</td>\n",
"<td>4023</td>\n",
"<td>1723</td>\n",
"</tr>\n",
"<tr>\n",
"<td>b'missense_variant'</td>\n",
"<td>b'transcript'</td>\n",
"<td>b'n.1517T>A'</td>\n",
"<td>b'p.Phe506Tyr'</td>\n",
"<td>1517</td>\n",
"<td>4023</td>\n",
"<td>1517</td>\n",
"</tr>\n",
"<tr>\n",
"<td>b'missense_variant'</td>\n",
"<td>b'transcript'</td>\n",
"<td>b'n.32C>T'</td>\n",
"<td>b'p.Thr11Met'</td>\n",
"<td>32</td>\n",
"<td>4023</td>\n",
"<td>32</td>\n",
"</tr>\n",
"<tr>\n",
"<td>b'missense_variant'</td>\n",
"<td>b'transcript'</td>\n",
"<td>b'n.4T>A'</td>\n",
"<td>b'p.Trp2Arg'</td>\n",
"<td>4</td>\n",
"<td>4023</td>\n",
"<td>4</td>\n",
"</tr>\n",
"</tbody>\n",
"</table>\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# show some columns from the data\n",
"xx = var_tab[non_syn]\n",
"\n",
"xx[\"ANN\"][\"Annotation Feature_Type HGVS_c HGVS_p cDNA_pos cDNA_length CDS_pos\".split()].display(limit=30)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Select a gene and find variants > 5%"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# ix, positions and g are defined above\n",
"# here we count alleles for a specific region in tep1, convert to frequencies \n",
"# and take the maximum across rows, the MAF is 1 - this value.\n",
"maf = 1 - g[ix].count_alleles().to_frequencies().max(axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"SortedIndex((10,), dtype=int32)\n",
"[11204271 11204298 11204330 11204373 11204436 11204442 11204659 11205025\n",
" 11205026 11205037]"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# given the positions in the gene, just show those above 5%\n",
"positions[ix][maf > 0.05]"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.0+"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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