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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ref_png = '/scratch/tranchant/rice-output/04-stats/04-plots/00_ref.png'\n",
"ref_csv = '/scratch/tranchant/rice-output/04-stats/04-summary/00_ref.txt'\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"***\n",
"\n",
"[<img src=\"Images/up-arrow.png\" alt=\"Top\" width=2% align=\"right\">](#home \"Go back to the top\")\n",
"\n",
"\n",
"# <span style=\"color: #3987C4;\">I - Workflow configuration <a class=\"anchor\" id=\"workflow\"></a></span>\n",
"\n",
"### <span style=\"color: #919395\"> _Parameters_ <a class=\"anchor\" id=\"configinput\"></a></span>\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(out_dir, ref_file, vec_file, group_file, fastq_dir) #,cpus)"
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]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### <span style=\"color: #919395\">_Preparing Genome Reference for next analysis_\n",
"\n",
"#### __Genome indexation__ and __Genome dashboard__\n",
"\n",
"This step is done with `bwa index` if index are absent. Indexation is required before performing reads mapping against genome reference."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#from pathlib import Path\n",
"import sys\n",
"\n",
"sys.path.append(\"/home/christine/Documents/Dev/frangiPANe_snake/workflow\")\n",
"from scripts import generate_stats as gs\n",
"gs.dashboard_genome2(\"400\",png,csv)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### <span style=\"color: #919395\">_Analyzing Group File_</span> "
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'read_group_file' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-1-e7c314ec7bb8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# Reading group file\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mid_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdf_group\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mread_group_file\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgroup_file\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlogger\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;31m# Group file dashboard\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mdashboard_group\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf_group\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'read_group_file' is not defined"
]
}
],
"source": [
"# Reading group file\n",
"id_dict, df_group = read_group_file(group_file.value,logger)\n",
"\n",
"# Group file dashboard\n",
"dashboard_group(df_group)\n",
"bgc('LightBlue')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"***\n",
"\n",
"[<img src=\"Images/up-arrow.png\" alt=\"Top\" width=2% align=\"right\">](#home \"Go back to the top\")\n",
"\n",
"\n",
"# <span style=\"color: #3987C4;\">II - frangiPANe Workflow <a class=\"anchor\" id=\"workflow\"></a></span>\n",
"\n",
"### <span style=\"color: #919395\"> _1 - Stats about raw data (fastq files)_\n",
"\n",
"#### __Generating fastq statistics with `fastq_stats`__\n",
" \n",
"After this stat analysis, several files have been created and saved into 00_fastq_stats directory :\n",
"* one file (fastq-stat) by fastq file\n",
"* one file with all stats : all_fastq-stats.csv"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#Raw data dashboard\n",
"dashboard_fastq(fastqstat_csv,total_genome_size,df_group)"
]
},
{
"cell_type": "markdown",
"metadata": {
"tags": []
},
"source": [
"### <span style=\"color: #919395\">_2 - Mapping the individuals reads against the reference genome_ <a class=\"anchor\" id=\"mapping\"></a></span>"
]
},
{
"cell_type": "markdown",
"metadata": {
"tags": []
},
"source": [
"#### __Generating mapping stats <a class=\"anchor\" id=\"mappingstat\">__\n",
" \n",
"Statistics are generated by `samtools flagstat` and they are saved into the directory _01_mapping-against_reference_ and the subdirectory _stat_\n",
"\n",
"* One \"flagtstat file\" is generated for each bam file (http://www.htslib.org/doc/samtools-flagstat.html).\n",
"\n",
"* _all_flagstat.csv_ file compiling all the stats\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"### Dashboard\n",
"dashboard_flagstat(stat_file,df_group)\n",
"\n",
"bgc('LightBlue')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[<img src=\"Images/up-arrow.png\" alt=\"Top\" width=2% align=\"right\">](#home \"Go back to the top\")\n",
"\n",
"\n",
"### <span style=\"color: #919395\">3 - Assembly of the individuals' reads that do not map (properly) on the reference genome <a class=\"anchor\" id=\"assembly\"></a></span>\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"dashboard_ab(stat_len,stats_N,stats_L,output_assembly_testplots)\n",
"\n",
"bgc('LightBlue')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### __Assembly step 2 : assembly with the final k value__\n",
"\n",
"### Running ABySS for each individual\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"dashboard_assembly(stat_file,df_group)\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"tags": []
},
"source": [
"[<img src=\"Images/up-arrow.png\" alt=\"Top\" width=2% align=\"right\">](#home \"Go back to the top\")\n",
" \n",
"### <span style=\"color: #919395\"> 4 - Removing contamination<a class=\"anchor\" id=\"contamination\"></a></span>\n",
"\n",
"#### __VecScreen__\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"dashboard_ass(final_stat_file,df_group)\n",
"\n",
"bgc('LightBlue')\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"tags": []
},
"source": [
"[<img src=\"Images/up-arrow.png\" alt=\"Top\" width=2% align=\"right\">](#home \"Go back to the top\")\n",
" \n",
"### <span style=\"color: #919395\"> 5 - Reducing Sequence Redundancy<a class=\"anchor\" id=\"redundancy\"></a></span>\n",
"\n",
"frangiPANe uses CD-HIT to cluster sequences and to reduce sequence redundancy (inter and intra-species).\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#Dashboard\n",
"dashboard_cdhit(df_cdhit)\n",
"\n",
"bgc('LightBlue')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[<img src=\"Images/up-arrow.png\" alt=\"Top\" width=2% align=\"right\">](#home \"Go back to the top\")\n",
" \n",
"### <span style=\"color: #919395\"> 6 - Anchoring Clusters on Reference Genome<a class=\"anchor\" id=\"anchoring\"></a></span>\n",
"\n",
"#### __Generating panreference__\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"dashboard_flagstat(stat2_file,df_group)\n",
"\n",
"\n",
"bgc('LightBlue')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### __Panreference dashboard__\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"dashboard_anchoring(cdhit_fasta,panref_keep_file,panref_bed_file, output_dir, anc_stat_dict)\n",
"\n",
"bgc('LightBlue')"
]
}
],
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