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2019-nCov.ipynb
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| { | |
| "nbformat": 4, | |
| "nbformat_minor": 0, | |
| "metadata": { | |
| "kernelspec": { | |
| "name": "python3", | |
| "display_name": "Python 3" | |
| }, | |
| "language_info": { | |
| "codemirror_mode": { | |
| "name": "ipython", | |
| "version": 3 | |
| }, | |
| "file_extension": ".py", | |
| "mimetype": "text/x-python", | |
| "name": "python", | |
| "nbconvert_exporter": "python", | |
| "pygments_lexer": "ipython3", | |
| "version": "3.6.9" | |
| }, | |
| "colab": { | |
| "name": "2019-nCov.ipynb", | |
| "provenance": [], | |
| "collapsed_sections": [], | |
| "toc_visible": true, | |
| "include_colab_link": true | |
| } | |
| }, | |
| "cells": [ | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "view-in-github", | |
| "colab_type": "text" | |
| }, | |
| "source": [ | |
| "<a href=\"https://colab.research.google.com/gist/cjw85/a60b8d892a0977343cdef6e44fd4d988/2019-ncov.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "T2v_huWyiwmt", | |
| "colab_type": "text" | |
| }, | |
| "source": [ | |
| "Coronavirus demo\n", | |
| "----------------\n", | |
| "\n", | |
| "To use this demo first open a terminal and run:\n", | |
| "```\n", | |
| "mkdir labfolder\n", | |
| "docker run --rm -p 8888:8888 -e JUPYTER_ENABLE_LAB=yes -v \"$PWD/labfolder\":/home/jovyan/work ontresearch/nanolabs-notebook\n", | |
| "```\n", | |
| "This first creates a new directory on your computer which will be used as an output folder for the notebook. The second command starts a notebook server on your local computer.\n", | |
| "\n", | |
| "When it is running you will see a message like:\n", | |
| "```\n", | |
| "or http://127.0.0.1:8888/?token=c0bc8ed20519537bd2f817834f235473a85a7ef20f7a31b9\n", | |
| "```\n", | |
| "copy the long string after `token=`. In the colab interface find the connect to local runtime option:\n", | |
| "\n", | |
| "\n", | |
| "\n", | |
| "and enter the following in the text box:\n", | |
| "```\n", | |
| "http://localhost:8888/?token=<tokenfromabove>\n", | |
| "```\n", | |
| "so for the example above one would enter:\n", | |
| "```\n", | |
| "http://localhost:8888/?token=c0bc8ed20519537bd2f817834f235473a85a7ef20f7a31b9\n", | |
| "```\n", | |
| "\n", | |
| "This will connect colab to your local jupyter server running via docker. Once connected we are ready to start our data analysis.\n", | |
| "\n", | |
| "# Downloading the data\n", | |
| "\n", | |
| "First we will download some nanopore data from the SRA:\n", | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "YzyLGtCMiwmw", | |
| "colab_type": "code", | |
| "colab": {} | |
| }, | |
| "source": [ | |
| "!wget https://sra-download.ncbi.nlm.nih.gov/traces/sra45/SRZ/010948/SRR10948550/HKU-SZ-002a.fastq" | |
| ], | |
| "execution_count": 0, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "_gz7PkZCs0qD", | |
| "colab_type": "text" | |
| }, | |
| "source": [ | |
| "and also download a reference genome:" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "064l8qkHZCL-", | |
| "colab_type": "code", | |
| "colab": {} | |
| }, | |
| "source": [ | |
| "!wget https://ftp.ncbi.nlm.nih.gov/genomes/refseq/viral/Wuhan_seafood_market_pneumonia_virus/all_assembly_versions/GCF_009858895.2_ASM985889v3/GCF_009858895.2_ASM985889v3_genomic.fna.gz\n", | |
| "!gunzip -f GCF_009858895.2_ASM985889v3_genomic.fna.gz" | |
| ], | |
| "execution_count": 0, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "ajKvfuUkeTi-", | |
| "colab_type": "text" | |
| }, | |
| "source": [ | |
| "# A little look at the data\n", | |
| "\n", | |
| "First we can align the reads to the reference," | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "LTxXwNiZiwn2", | |
| "colab_type": "code", | |
| "outputId": "39a2dec8-09bd-4ddb-bb34-5a02e1414311", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 187 | |
| } | |
| }, | |
| "source": [ | |
| "!mini_align -r GCF_009858895.2_ASM985889v3_genomic.fna -i HKU-SZ-002a.fastq -p reads2ref -t 4" | |
| ], | |
| "execution_count": 0, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "Found minimap files.\n", | |
| "[M::main::0.072*0.33] loaded/built the index for 1 target sequence(s)\n", | |
| "[M::mm_mapopt_update::0.074*0.34] mid_occ = 3\n", | |
| "[M::mm_idx_stat] kmer size: 15; skip: 10; is_hpc: 0; #seq: 1\n", | |
| "[M::mm_idx_stat::0.075*0.35] distinct minimizers: 5587 (99.93% are singletons); average occurrences: 1.004; average spacing: 5.332\n", | |
| "[M::worker_pipeline::7.475*1.30] mapped 425717 sequences\n", | |
| "[M::main] Version: 2.17-r941\n", | |
| "[M::main] CMD: minimap2 -x map-ont -t 4 -a GCF_009858895.2_ASM985889v3_genomic.fna.mmi HKU-SZ-002a.fastq\n", | |
| "[M::main] Real time: 7.537 sec; CPU: 9.751 sec; Peak RSS: 0.353 GB\n", | |
| "[bam_sort_core] merging from 0 files and 4 in-memory blocks...\n" | |
| ], | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "fba-TEuGIVBs", | |
| "colab_type": "text" | |
| }, | |
| "source": [ | |
| "As a first analysis, lets plot a graph dipicting the coverage of the reads against the reference. To do this we run the following program," | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "pWdpfIxUiwnV", | |
| "colab_type": "code", | |
| "outputId": "64495db3-ae32-4ff9-f3e9-535a038ac12e", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 34 | |
| } | |
| }, | |
| "source": [ | |
| "!coverage_from_bam reads2ref.bam -s 10" | |
| ], | |
| "execution_count": 0, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "[10:49:26 - root] Processing region NC_045512.2:0-29900\r\n" | |
| ], | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "mpU0lrSGIg-m", | |
| "colab_type": "text" | |
| }, | |
| "source": [ | |
| "which produces a text file with coverage data. The `-s` option here asks for the coverage to be calculated at 10-base intervals along the reference. The data output can be read using the `pandas` library, and plotted using the interactive `plotly` library:" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "Do4rWTEliwnZ", | |
| "colab_type": "code", | |
| "outputId": "f4aebeff-f0f2-40c3-de41-b9fb66398d0e", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 542 | |
| } | |
| }, | |
| "source": [ | |
| "import pandas\n", | |
| "import plotly.graph_objects as go\n", | |
| "import plotly.io as pio\n", | |
| "pio.templates.default = 'plotly_white'\n", | |
| "pio.renderers.default = 'colab'\n", | |
| "\n", | |
| "# read the data from text file using pandas\n", | |
| "df1 = pandas.read_table(\"reads2ref_NC_045512.2_0_29903.depth.txt\")\n", | |
| "# create a new figure\n", | |
| "fig = go.Figure()\n", | |
| "# add a line representing the total depth...\n", | |
| "fig.add_trace(\n", | |
| " go.Scatter(x=df1['pos'], y=df1['depth'], mode='lines', name='total'))\n", | |
| "# ...another for the + strand depth\n", | |
| "fig.add_trace(\n", | |
| " go.Scatter(x=df1['pos'], y=df1['depth_fwd'], mode='lines', name='+'))\n", | |
| "# ...and one for the - strand\n", | |
| "fig.add_trace(\n", | |
| " go.Scatter(x=df1['pos'], y=df1['depth_rev'], mode='lines', name='-'))\n", | |
| "# Add some nice titles\n", | |
| "fig.update_layout(\n", | |
| " title='Genome Coverage',\n", | |
| " xaxis={'title':'genome position'},\n", | |
| " yaxis={'title':'genome coverage'})\n", | |
| "\n", | |
| "fig.show()" | |
| ], | |
| "execution_count": 0, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/html": [ | |
| "<html>\n", | |
| "<head><meta charset=\"utf-8\" /></head>\n", | |
| "<body>\n", | |
| " <div>\n", | |
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| " <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n", | |
| " <script src=\"https://cdn.plot.ly/plotly-latest.min.js\"></script> \n", | |
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| " <script type=\"text/javascript\">\n", | |
| " \n", | |
| " window.PLOTLYENV=window.PLOTLYENV || {};\n", | |
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| " Plotly.newPlot(\n", | |
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| }, | |
| "metadata": { | |
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| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "sryD-eikiwnL", | |
| "colab_type": "text" | |
| }, | |
| "source": [ | |
| "Next we can calculate some further statistics of the alignments, with a second program:" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
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| "id": "BMPKL5gLiwnM", | |
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| "source": [ | |
| "!stats_from_bam reads2ref.bam > reads.stats" | |
| ], | |
| "execution_count": 0, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "Mapped/Unmapped/Short/Masked: 1001/0/0/0\r\n" | |
| ], | |
| "name": "stdout" | |
| } | |
| ] | |
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| { | |
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| "metadata": { | |
| "id": "hMBXAzp_iwnR", | |
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| "To plot a histogram dipicting the accuracy of the reads with respect to the reference sequence we can run:" | |
| ] | |
| }, | |
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| "df2 = pandas.read_table(\"reads.stats\")\n", | |
| "fig = px.histogram(df2, x=\"acc\", title=\"Read accuracy\")\n", | |
| "fig.update_layout(xaxis={'title':'accuracy'})\n", | |
| "fig.show()" | |
| ], | |
| "execution_count": 0, | |
| "outputs": [ | |
| { | |
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| " {\"responsive\": true}\n", | |
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| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| } | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "bm8LasSbc5wL", | |
| "colab_type": "text" | |
| }, | |
| "source": [ | |
| "# Variant Calling with medaka\n", | |
| "\n", | |
| "The following little workflow demonstrates minimal SNP calling by using the alignments of the reads to the reference sequence created above." | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "colab_type": "text", | |
| "id": "D_buLVyIdg2S" | |
| }, | |
| "source": [ | |
| "First we run `medaka`'s RNN on the alignments produced above to create a set of base probabilities in an `.hdf` file:" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "vrBdx1LGdIAA", | |
| "colab_type": "code", | |
| "outputId": "ac78c1b4-6ccb-4f7d-cf89-d2f479d6fa3e", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 748 | |
| } | |
| }, | |
| "source": [ | |
| "!rm -rf reads2ref.hdf\n", | |
| "!medaka consensus reads2ref.bam reads2ref.hdf --threads 4" | |
| ], | |
| "execution_count": 0, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "[10:49:48 - Predict] Processing region(s): NC_045512.2:0-29903\n", | |
| "[10:49:48 - Predict] Setting tensorflow threads to 4.\n", | |
| "[10:49:48 - Predict] Processing 1 long region(s) with batching.\n", | |
| "[10:49:48 - Predict] Using model: /opt/conda/lib/python3.6/site-packages/medaka/data/r941_min_high_g344_model.hdf5.\n", | |
| "[10:49:48 - ModelLoad] Building model with cudnn optimization: False\n", | |
| "OMP: Info #212: KMP_AFFINITY: decoding x2APIC ids.\n", | |
| "OMP: Info #213: KMP_AFFINITY: x2APIC ids not unique - decoding legacy APIC ids.\n", | |
| "OMP: Info #149: KMP_AFFINITY: Affinity capable, using global cpuid info\n", | |
| "OMP: Info #154: KMP_AFFINITY: Initial OS proc set respected: 0-3\n", | |
| "OMP: Info #156: KMP_AFFINITY: 4 available OS procs\n", | |
| "OMP: Info #157: KMP_AFFINITY: Uniform topology\n", | |
| "OMP: Info #159: KMP_AFFINITY: 4 packages x 1 cores/pkg x 1 threads/core (4 total cores)\n", | |
| "OMP: Info #214: KMP_AFFINITY: OS proc to physical thread map:\n", | |
| "OMP: Info #171: KMP_AFFINITY: OS proc 0 maps to package 0 \n", | |
| "OMP: Info #171: KMP_AFFINITY: OS proc 1 maps to package 1 \n", | |
| "OMP: Info #171: KMP_AFFINITY: OS proc 2 maps to package 2 \n", | |
| "OMP: Info #171: KMP_AFFINITY: OS proc 3 maps to package 3 \n", | |
| "OMP: Info #250: KMP_AFFINITY: pid 257 tid 277 thread 0 bound to OS proc set 0\n", | |
| "OMP: Info #250: KMP_AFFINITY: pid 257 tid 277 thread 1 bound to OS proc set 1\n", | |
| "OMP: Info #250: KMP_AFFINITY: pid 257 tid 278 thread 2 bound to OS proc set 2\n", | |
| "OMP: Info #250: KMP_AFFINITY: pid 257 tid 280 thread 3 bound to OS proc set 3\n", | |
| "OMP: Info #250: KMP_AFFINITY: pid 257 tid 284 thread 4 bound to OS proc set 0\n", | |
| "OMP: Info #250: KMP_AFFINITY: pid 257 tid 286 thread 6 bound to OS proc set 2\n", | |
| "OMP: Info #250: KMP_AFFINITY: pid 257 tid 289 thread 9 bound to OS proc set 1\n", | |
| "OMP: Info #250: KMP_AFFINITY: pid 257 tid 288 thread 8 bound to OS proc set 0\n", | |
| "OMP: Info #250: KMP_AFFINITY: pid 257 tid 285 thread 5 bound to OS proc set 1\n", | |
| "OMP: Info #250: KMP_AFFINITY: pid 257 tid 291 thread 11 bound to OS proc set 3\n", | |
| "OMP: Info #250: KMP_AFFINITY: pid 257 tid 287 thread 7 bound to OS proc set 3\n", | |
| "OMP: Info #250: KMP_AFFINITY: pid 257 tid 279 thread 13 bound to OS proc set 1\n", | |
| "OMP: Info #250: KMP_AFFINITY: pid 257 tid 290 thread 10 bound to OS proc set 2\n", | |
| "OMP: Info #250: KMP_AFFINITY: pid 257 tid 292 thread 12 bound to OS proc set 0\n", | |
| "OMP: Info #250: KMP_AFFINITY: pid 257 tid 293 thread 14 bound to OS proc set 2\n", | |
| "OMP: Info #250: KMP_AFFINITY: pid 257 tid 295 thread 16 bound to OS proc set 0\n", | |
| "OMP: Info #250: KMP_AFFINITY: pid 257 tid 294 thread 15 bound to OS proc set 3\n", | |
| "[10:49:49 - DLoader] Initializing data loader\n", | |
| "[10:49:49 - PWorker] Running inference for 0.0M draft bases.\n", | |
| "[10:49:49 - Sampler] Initializing sampler for consensus of region NC_045512.2:0-29903.\n", | |
| "[10:49:50 - Feature] Pileup counts do not span requested region, requested NC_045512.2:0-29903, received 32-29869.\n", | |
| "[10:49:50 - Feature] Processed NC_045512.2:32.0-29869.0 (median depth 22.0)\n", | |
| "[10:49:50 - Sampler] Took 0.09s to make features.\n", | |
| "[10:50:13 - PWorker] 100.0% Done (0.0/0.0 Mbases) in 23.7s\n", | |
| "[10:50:13 - PWorker] All done, 0 remainder regions.\n", | |
| "[10:50:13 - Predict] Finished processing all regions.\n" | |
| ], | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "colab_type": "text", | |
| "id": "ds_JmA41duWA" | |
| }, | |
| "source": [ | |
| "These probabilities can be processed into a Variant Call Format file:" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "gx1vrRb2dxEh", | |
| "colab_type": "code", | |
| "outputId": "5d165183-e87c-4c60-a076-5e366302c267", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 51 | |
| } | |
| }, | |
| "source": [ | |
| "!medaka snp GCF_009858895.2_ASM985889v3_genomic.fna reads2ref.hdf ont.snp.vcf --threshold 0.9" | |
| ], | |
| "execution_count": 0, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "[10:50:20 - DataIndex] Loaded 1/1 (100.00%) sample files.\n", | |
| "[10:50:21 - SNPs] Processing NC_045512.2:0-.\n" | |
| ], | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "aWGSqbj8d088", | |
| "colab_type": "text" | |
| }, | |
| "source": [ | |
| "The threshold parameter here controls the reporting of minor variants. Setting a value close to one will filter most minor calls. To view the variants we can simply inspect the file:" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "iR3vbS-adz5G", | |
| "colab_type": "code", | |
| "outputId": "ab65bb63-1b1b-44bb-be5d-ac85e11b6d19", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 68 | |
| } | |
| }, | |
| "source": [ | |
| "from medaka.vcf import VCFReader\n", | |
| "v = VCFReader(\"ont.snp.vcf\")\n", | |
| "for variant in v.fetch():\n", | |
| " if variant.qual > 10:\n", | |
| " print(variant.chrom, variant.pos, variant.ref, \"->\", variant.alt[0])" | |
| ], | |
| "execution_count": 0, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "NC_045512.2 8781 C -> T\n", | |
| "NC_045512.2 28143 T -> C\n", | |
| "NC_045512.2 29094 C -> T\n" | |
| ], | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "d-XaaXpgoPZF", | |
| "colab_type": "code", | |
| "colab": {} | |
| }, | |
| "source": [ | |
| "" | |
| ], | |
| "execution_count": 0, | |
| "outputs": [] | |
| } | |
| ] | |
| } |
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