Skip to content

Instantly share code, notes, and snippets.

@AvantiShri

AvantiShri/homer-test.ipynb

Created August 5, 2018 22:01
Show Gist options
  • Star 0 You must be signed in to star a gist
  • Fork 0 You must be signed in to fork a gist
  • Save AvantiShri/45cd0034f8b7f1d5b1a7ac115d3fc084 to your computer and use it in GitHub Desktop.
Save AvantiShri/45cd0034f8b7f1d5b1a7ac115d3fc084 to your computer and use it in GitHub Desktop.
Download ZIP
HOMER test.ipynb
Display the source blob
Display the rendered blob
Raw
homer-test.ipynb
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "HOMER test.ipynb",
"version": "0.3.2",
"provenance": [],
"collapsed_sections": [],
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"accelerator": "GPU"
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"[View in Colaboratory](https://colab.research.google.com/gist/AvantiShri/45cd0034f8b7f1d5b1a7ac115d3fc084/homer-test.ipynb)"
]
},
{
"metadata": {
"id": "Icptd_-9aMFM",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 51
},
"outputId": "d958b280-30fd-4a34-97c1-8b46e47fc527"
},
"cell_type": "code",
"source": [
"!mkdir HOMER\n",
"%cd HOMER"
],
"execution_count": 12,
"outputs": [
{
"output_type": "stream",
"text": [
"mkdir: cannot create directory ‘HOMER’: File exists\r\n",
"/content/HOMER\n"
],
"name": "stdout"
}
]
},
{
"metadata": {
"id": "mr9DczQFcOks",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 3267
},
"outputId": "bbea7687-1e1d-44aa-dce8-b69adb417206"
},
"cell_type": "code",
"source": [
"!wget http://homer.ucsd.edu/homer/configureHomer.pl -O configureHomer.pl\n",
"!perl configureHomer.pl -install homer"
],
"execution_count": 15,
"outputs": [
{
"output_type": "stream",
"text": [
"--2018-08-05 18:55:28-- http://homer.ucsd.edu/homer/configureHomer.pl\r\n",
"Resolving homer.ucsd.edu (homer.ucsd.edu)... 169.228.63.226\r\n",
"Connecting to homer.ucsd.edu (homer.ucsd.edu)|169.228.63.226|:80... connected.\n",
"HTTP request sent, awaiting response... 200 OK\n",
"Length: 26464 (26K) [application/x-perl]\n",
"Saving to: ‘configureHomer.pl’\n",
"\n",
"configureHomer.pl 100%[===================>] 25.84K --.-KB/s in 0.04s \n",
"\n",
"2018-08-05 18:55:28 (678 KB/s) - ‘configureHomer.pl’ saved [26464/26464]\n",
"\n",
"\n",
"\tCurrent base directory for HOMER is /content/HOMER/./\n",
"\n",
"\tWill install homer\n",
"--2018-08-05 18:55:30-- http://homer.ucsd.edu/homer/update.txt\n",
"Resolving homer.ucsd.edu (homer.ucsd.edu)... 169.228.63.226\n",
"Connecting to homer.ucsd.edu (homer.ucsd.edu)|169.228.63.226|:80... connected.\n",
"HTTP request sent, awaiting response... 200 OK\n",
"Length: 16187 (16K) [text/plain]\n",
"Saving to: ‘/content/HOMER/.//update.txt’\n",
"\n",
"/content/HOMER/.//u 100%[===================>] 15.81K --.-KB/s in 0.04s \n",
"\n",
"2018-08-05 18:55:30 (415 KB/s) - ‘/content/HOMER/.//update.txt’ saved [16187/16187]\n",
"\n",
"\tUpdating Settings...\n",
"`wget -O 0.841106168766604.tmp http://homer.ucsd.edu/homer/configureHomer.pl`;\n",
"--2018-08-05 18:55:30-- http://homer.ucsd.edu/homer/configureHomer.pl\n",
"Resolving homer.ucsd.edu (homer.ucsd.edu)... 169.228.63.226\n",
"Connecting to homer.ucsd.edu (homer.ucsd.edu)|169.228.63.226|:80... connected.\n",
"HTTP request sent, awaiting response... 200 OK\n",
"Length: 26464 (26K) [application/x-perl]\n",
"Saving to: ‘0.841106168766604.tmp’\n",
"\n",
"0.841106168766604.t 100%[===================>] 25.84K --.-KB/s in 0.04s \n",
"\n",
"2018-08-05 18:55:30 (675 KB/s) - ‘0.841106168766604.tmp’ saved [26464/26464]\n",
"\n",
"\tconfigureHomer.pl script is up-to-date\n",
"\tPackages to Install...\n",
"\t\thomer -> \n",
"\n",
"\tChecking for standard utilities and 3rd party software:\n",
"\n",
"\t\t(Note: seqlogo, gs, and blat no longer required)\n",
"\tChecking for wget... /usr/bin/wget\n",
"\tChecking for cut... /usr/bin/cut\n",
"\tChecking for gcc... /usr/bin/gcc\n",
"\tChecking for g++... /usr/bin/g++\n",
"\tChecking for zip... /usr/bin/zip\n",
"\tChecking for unzip... /usr/bin/unzip\n",
"\tChecking for make... /usr/bin/make\n",
"\tChecking for tar... /bin/tar\n",
"\tChecking for gunzip... /bin/gunzip\n",
"\tChecking for gzip... /bin/gzip\n",
"\n",
"\tAll auxilary programs found.\n",
"\n",
"\tInstalling: homer\n",
"\t\tDownloading...\n",
"--2018-08-05 18:55:30-- http://homer.ucsd.edu/homer/data/software/homer.v4.10.3.zip\n",
"Resolving homer.ucsd.edu (homer.ucsd.edu)... 169.228.63.226\n",
"Connecting to homer.ucsd.edu (homer.ucsd.edu)|169.228.63.226|:80... connected.\n",
"HTTP request sent, awaiting response... 200 OK\n",
"Length: 49121188 (47M) [application/zip]\n",
"Saving to: ‘homer.package.zip’\n",
"\n",
"homer.package.zip 100%[===================>] 46.84M 50.6MB/s in 0.9s \n",
"\n",
"2018-08-05 18:55:31 (50.6 MB/s) - ‘homer.package.zip’ saved [49121188/49121188]\n",
"\n",
"\t\tUnzipping...\n",
"\t\tFinished Installing homer\n",
"\n",
"\u001b[01m\u001b[KHashtable.cpp:\u001b[m\u001b[K In destructor ‘\u001b[01m\u001b[KFloatlist::~Floatlist()\u001b[m\u001b[K’:\n",
"\u001b[01m\u001b[KHashtable.cpp:209:4:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kthis ‘\u001b[01m\u001b[Kif\u001b[m\u001b[K’ clause does not guard... [\u001b[01;35m\u001b[K-Wmisleading-indentation\u001b[m\u001b[K]\n",
" \u001b[01;35m\u001b[Kif\u001b[m\u001b[K (hashstrs[i] != NULL)\n",
" \u001b[01;35m\u001b[K^~\u001b[m\u001b[K\n",
"\u001b[01m\u001b[KHashtable.cpp:211:5:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[K...this statement, but the latter is misleadingly indented as if it were guarded by the ‘\u001b[01m\u001b[Kif\u001b[m\u001b[K’\n",
" \u001b[01;36m\u001b[Khashstrs\u001b[m\u001b[K[i] = NULL;\n",
" \u001b[01;36m\u001b[K^~~~~~~~\u001b[m\u001b[K\n",
"\u001b[01m\u001b[KHashtable.cpp:\u001b[m\u001b[K In destructor ‘\u001b[01m\u001b[KDoublelist::~Doublelist()\u001b[m\u001b[K’:\n",
"\u001b[01m\u001b[KHashtable.cpp:224:4:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kthis ‘\u001b[01m\u001b[Kif\u001b[m\u001b[K’ clause does not guard... [\u001b[01;35m\u001b[K-Wmisleading-indentation\u001b[m\u001b[K]\n",
" \u001b[01;35m\u001b[Kif\u001b[m\u001b[K (hashstrs[i] != NULL)\n",
" \u001b[01;35m\u001b[K^~\u001b[m\u001b[K\n",
"\u001b[01m\u001b[KHashtable.cpp:226:5:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[K...this statement, but the latter is misleadingly indented as if it were guarded by the ‘\u001b[01m\u001b[Kif\u001b[m\u001b[K’\n",
" \u001b[01;36m\u001b[Khashstrs\u001b[m\u001b[K[i] = NULL;\n",
" \u001b[01;36m\u001b[K^~~~~~~~\u001b[m\u001b[K\n",
"\u001b[01m\u001b[Khomer.cpp:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kint main(int, char**)\u001b[m\u001b[K’:\n",
"\u001b[01m\u001b[Khomer.cpp:71:3:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kignoring return value of ‘\u001b[01m\u001b[Kint system(const char*)\u001b[m\u001b[K’, declared with attribute warn_unused_result [\u001b[01;35m\u001b[K-Wunused-result\u001b[m\u001b[K]\n",
" \u001b[01;35m\u001b[K(void)system(c)\u001b[m\u001b[K;\n",
" \u001b[01;35m\u001b[K^~~~~~~~~~~~~~~\u001b[m\u001b[K\n",
"\u001b[01m\u001b[Khomer.cpp:82:4:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kignoring return value of ‘\u001b[01m\u001b[Kint system(const char*)\u001b[m\u001b[K’, declared with attribute warn_unused_result [\u001b[01;35m\u001b[K-Wunused-result\u001b[m\u001b[K]\n",
" \u001b[01;35m\u001b[K(void)system(c)\u001b[m\u001b[K;\n",
" \u001b[01;35m\u001b[K^~~~~~~~~~~~~~~\u001b[m\u001b[K\n",
"\u001b[01m\u001b[KSeqTag.cpp:\u001b[m\u001b[K In member function ‘\u001b[01m\u001b[Kvoid TagLibrary::makeDirectory()\u001b[m\u001b[K’:\n",
"\u001b[01m\u001b[KSeqTag.cpp:6519:2:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kignoring return value of ‘\u001b[01m\u001b[Kint system(const char*)\u001b[m\u001b[K’, declared with attribute warn_unused_result [\u001b[01;35m\u001b[K-Wunused-result\u001b[m\u001b[K]\n",
" \u001b[01;35m\u001b[K(void)system(filename)\u001b[m\u001b[K;\n",
" \u001b[01;35m\u001b[K^~~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n",
"\u001b[01m\u001b[KSeqTag.cpp:6523:2:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kignoring return value of ‘\u001b[01m\u001b[Kint system(const char*)\u001b[m\u001b[K’, declared with attribute warn_unused_result [\u001b[01;35m\u001b[K-Wunused-result\u001b[m\u001b[K]\n",
" \u001b[01;35m\u001b[K(void)system(filename)\u001b[m\u001b[K;\n",
" \u001b[01;35m\u001b[K^~~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n",
"\u001b[01m\u001b[KSeqTag.cpp:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kchar* unzipFileIfNeeded(char*, int&, int&)\u001b[m\u001b[K’:\n",
"\u001b[01m\u001b[KSeqTag.cpp:6555:4:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kignoring return value of ‘\u001b[01m\u001b[Kint system(const char*)\u001b[m\u001b[K’, declared with attribute warn_unused_result [\u001b[01;35m\u001b[K-Wunused-result\u001b[m\u001b[K]\n",
" \u001b[01;35m\u001b[K(void)system(command)\u001b[m\u001b[K;\n",
" \u001b[01;35m\u001b[K^~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n",
"\u001b[01m\u001b[KSeqTag.cpp:6562:4:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kignoring return value of ‘\u001b[01m\u001b[Kint system(const char*)\u001b[m\u001b[K’, declared with attribute warn_unused_result [\u001b[01;35m\u001b[K-Wunused-result\u001b[m\u001b[K]\n",
" \u001b[01;35m\u001b[K(void)system(command)\u001b[m\u001b[K;\n",
" \u001b[01;35m\u001b[K^~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n",
"\u001b[01m\u001b[KSeqTag.cpp:6569:4:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kignoring return value of ‘\u001b[01m\u001b[Kint system(const char*)\u001b[m\u001b[K’, declared with attribute warn_unused_result [\u001b[01;35m\u001b[K-Wunused-result\u001b[m\u001b[K]\n",
" \u001b[01;35m\u001b[K(void)system(command)\u001b[m\u001b[K;\n",
" \u001b[01;35m\u001b[K^~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n",
"\u001b[01m\u001b[KSeqTag.cpp:6577:4:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kignoring return value of ‘\u001b[01m\u001b[Kint system(const char*)\u001b[m\u001b[K’, declared with attribute warn_unused_result [\u001b[01;35m\u001b[K-Wunused-result\u001b[m\u001b[K]\n",
" \u001b[01;35m\u001b[K(void)system(command)\u001b[m\u001b[K;\n",
" \u001b[01;35m\u001b[K^~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n",
"\u001b[01m\u001b[KSeqTag.cpp:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kvoid rezipFileIfNeeded(char*, int)\u001b[m\u001b[K’:\n",
"\u001b[01m\u001b[KSeqTag.cpp:6592:3:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kignoring return value of ‘\u001b[01m\u001b[Kint system(const char*)\u001b[m\u001b[K’, declared with attribute warn_unused_result [\u001b[01;35m\u001b[K-Wunused-result\u001b[m\u001b[K]\n",
" \u001b[01;35m\u001b[K(void)system(command)\u001b[m\u001b[K;\n",
" \u001b[01;35m\u001b[K^~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n",
"\u001b[01m\u001b[KSeqTag.cpp:\u001b[m\u001b[K In member function ‘\u001b[01m\u001b[Kvoid TagLibrary::readPEAlignment(char*, int, int)\u001b[m\u001b[K’:\n",
"\u001b[01m\u001b[KSeqTag.cpp:6878:3:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kignoring return value of ‘\u001b[01m\u001b[Kint system(const char*)\u001b[m\u001b[K’, declared with attribute warn_unused_result [\u001b[01;35m\u001b[K-Wunused-result\u001b[m\u001b[K]\n",
" \u001b[01;35m\u001b[K(void)system(fname)\u001b[m\u001b[K;\n",
" \u001b[01;35m\u001b[K^~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n",
"\u001b[01m\u001b[KSeqTag.cpp:6880:3:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kignoring return value of ‘\u001b[01m\u001b[Kint system(const char*)\u001b[m\u001b[K’, declared with attribute warn_unused_result [\u001b[01;35m\u001b[K-Wunused-result\u001b[m\u001b[K]\n",
" \u001b[01;35m\u001b[K(void)system(fname)\u001b[m\u001b[K;\n",
" \u001b[01;35m\u001b[K^~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n",
"\u001b[01m\u001b[KSeqTag.cpp:\u001b[m\u001b[K In member function ‘\u001b[01m\u001b[Kvoid UniqMapChrs::loadData()\u001b[m\u001b[K’:\n",
"\u001b[01m\u001b[KSeqTag.cpp:18298:2:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kignoring return value of ‘\u001b[01m\u001b[Ksize_t fread(void*, size_t, size_t, FILE*)\u001b[m\u001b[K’, declared with attribute warn_unused_result [\u001b[01;35m\u001b[K-Wunused-result\u001b[m\u001b[K]\n",
" \u001b[01;35m\u001b[K(void)fread(&psize,sizeof(unsigned int),1,fpp)\u001b[m\u001b[K;\n",
" \u001b[01;35m\u001b[K^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n",
"\u001b[01m\u001b[KSeqTag.cpp:18300:2:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kignoring return value of ‘\u001b[01m\u001b[Ksize_t fread(void*, size_t, size_t, FILE*)\u001b[m\u001b[K’, declared with attribute warn_unused_result [\u001b[01;35m\u001b[K-Wunused-result\u001b[m\u001b[K]\n",
" \u001b[01;35m\u001b[K(void)fread(&nsize,sizeof(unsigned int),1,fpn)\u001b[m\u001b[K;\n",
" \u001b[01;35m\u001b[K^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n",
"\u001b[01m\u001b[KSeqTag.cpp:18310:2:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kignoring return value of ‘\u001b[01m\u001b[Ksize_t fread(void*, size_t, size_t, FILE*)\u001b[m\u001b[K’, declared with attribute warn_unused_result [\u001b[01;35m\u001b[K-Wunused-result\u001b[m\u001b[K]\n",
" \u001b[01;35m\u001b[K(void)fread(pstrand,sizeof(unsigned char),maxIndex,fpp)\u001b[m\u001b[K;\n",
" \u001b[01;35m\u001b[K^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n",
"\u001b[01m\u001b[KSeqTag.cpp:18311:2:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kignoring return value of ‘\u001b[01m\u001b[Ksize_t fread(void*, size_t, size_t, FILE*)\u001b[m\u001b[K’, declared with attribute warn_unused_result [\u001b[01;35m\u001b[K-Wunused-result\u001b[m\u001b[K]\n",
" \u001b[01;35m\u001b[K(void)fread(nstrand,sizeof(unsigned char),maxIndex,fpn)\u001b[m\u001b[K;\n",
" \u001b[01;35m\u001b[K^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n",
"\u001b[01m\u001b[KmakeUCSCfile.cpp:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kint main(int, char**)\u001b[m\u001b[K’:\n",
"\u001b[01m\u001b[KmakeUCSCfile.cpp:312:4:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kignoring return value of ‘\u001b[01m\u001b[Kint system(const char*)\u001b[m\u001b[K’, declared with attribute warn_unused_result [\u001b[01;35m\u001b[K-Wunused-result\u001b[m\u001b[K]\n",
" \u001b[01;35m\u001b[K(void)system(str)\u001b[m\u001b[K;\n",
" \u001b[01;35m\u001b[K^~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n",
"\u001b[01m\u001b[KmakeUCSCfile.cpp:323:4:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kignoring return value of ‘\u001b[01m\u001b[Kint system(const char*)\u001b[m\u001b[K’, declared with attribute warn_unused_result [\u001b[01;35m\u001b[K-Wunused-result\u001b[m\u001b[K]\n",
" \u001b[01;35m\u001b[K(void)system(str)\u001b[m\u001b[K;\n",
" \u001b[01;35m\u001b[K^~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n",
"\u001b[01m\u001b[KmakeUCSCfile.cpp:326:4:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kignoring return value of ‘\u001b[01m\u001b[Kint system(const char*)\u001b[m\u001b[K’, declared with attribute warn_unused_result [\u001b[01;35m\u001b[K-Wunused-result\u001b[m\u001b[K]\n",
" \u001b[01;35m\u001b[K(void)system(str)\u001b[m\u001b[K;\n",
" \u001b[01;35m\u001b[K^~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n",
"\u001b[01m\u001b[KhomerTools.cpp:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kvoid decontaminateProgram(int, char**)\u001b[m\u001b[K’:\n",
"\u001b[01m\u001b[KhomerTools.cpp:1683:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kignoring return value of ‘\u001b[01m\u001b[Kint system(const char*)\u001b[m\u001b[K’, declared with attribute warn_unused_result [\u001b[01;35m\u001b[K-Wunused-result\u001b[m\u001b[K]\n",
" \u001b[01;35m\u001b[K(void)system(\"sleep 1\")\u001b[m\u001b[K;\n",
" \u001b[01;35m\u001b[K^~~~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n",
"\u001b[01m\u001b[KhomerTools.cpp:1691:3:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kignoring return value of ‘\u001b[01m\u001b[Kint system(const char*)\u001b[m\u001b[K’, declared with attribute warn_unused_result [\u001b[01;35m\u001b[K-Wunused-result\u001b[m\u001b[K]\n",
" \u001b[01;35m\u001b[K(void)system(command)\u001b[m\u001b[K;\n",
" \u001b[01;35m\u001b[K^~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n",
"\u001b[01m\u001b[KhomerTools.cpp:1693:3:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kignoring return value of ‘\u001b[01m\u001b[Kint system(const char*)\u001b[m\u001b[K’, declared with attribute warn_unused_result [\u001b[01;35m\u001b[K-Wunused-result\u001b[m\u001b[K]\n",
" \u001b[01;35m\u001b[K(void)system(command)\u001b[m\u001b[K;\n",
" \u001b[01;35m\u001b[K^~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n",
"\u001b[01m\u001b[KhomerTools.cpp:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kvoid specialUniqMapProgram(int, char**)\u001b[m\u001b[K’:\n",
"\u001b[01m\u001b[KhomerTools.cpp:1768:2:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kignoring return value of ‘\u001b[01m\u001b[Kint system(const char*)\u001b[m\u001b[K’, declared with attribute warn_unused_result [\u001b[01;35m\u001b[K-Wunused-result\u001b[m\u001b[K]\n",
" \u001b[01;35m\u001b[K(void)system(command)\u001b[m\u001b[K;\n",
" \u001b[01;35m\u001b[K^~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n",
"\u001b[01m\u001b[KanalyzeHiC.cpp:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kint main(int, char**)\u001b[m\u001b[K’:\n",
"\u001b[01m\u001b[KanalyzeHiC.cpp:1375:4:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kignoring return value of ‘\u001b[01m\u001b[Kint system(const char*)\u001b[m\u001b[K’, declared with attribute warn_unused_result [\u001b[01;35m\u001b[K-Wunused-result\u001b[m\u001b[K]\n",
" \u001b[01;35m\u001b[K(void)system(cmd)\u001b[m\u001b[K;\n",
" \u001b[01;35m\u001b[K^~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n",
"\u001b[01m\u001b[KanalyzeHiC.cpp:1380:4:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kignoring return value of ‘\u001b[01m\u001b[Kint system(const char*)\u001b[m\u001b[K’, declared with attribute warn_unused_result [\u001b[01;35m\u001b[K-Wunused-result\u001b[m\u001b[K]\n",
" \u001b[01;35m\u001b[K(void)system(cmd)\u001b[m\u001b[K;\n",
" \u001b[01;35m\u001b[K^~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n",
"\u001b[01m\u001b[KanalyzeHiC.cpp:1385:4:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kignoring return value of ‘\u001b[01m\u001b[Kint system(const char*)\u001b[m\u001b[K’, declared with attribute warn_unused_result [\u001b[01;35m\u001b[K-Wunused-result\u001b[m\u001b[K]\n",
" \u001b[01;35m\u001b[K(void)system(cmd)\u001b[m\u001b[K;\n",
" \u001b[01;35m\u001b[K^~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n",
"\u001b[01m\u001b[KanalyzeHiC.cpp:1390:4:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kignoring return value of ‘\u001b[01m\u001b[Kint system(const char*)\u001b[m\u001b[K’, declared with attribute warn_unused_result [\u001b[01;35m\u001b[K-Wunused-result\u001b[m\u001b[K]\n",
" \u001b[01;35m\u001b[K(void)system(cmd)\u001b[m\u001b[K;\n",
" \u001b[01;35m\u001b[K^~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n",
"\u001b[01m\u001b[KanalyzeHiC.cpp:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kvoid printCircosFiles(char*, int, TagLibrary*, PeakLibrary*, PeakLibrary*, int, int, int, int, char**, int, char**, int, char**, int, TagLibrary*)\u001b[m\u001b[K’:\n",
"\u001b[01m\u001b[KanalyzeHiC.cpp:1796:3:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kignoring return value of ‘\u001b[01m\u001b[Kint system(const char*)\u001b[m\u001b[K’, declared with attribute warn_unused_result [\u001b[01;35m\u001b[K-Wunused-result\u001b[m\u001b[K]\n",
" \u001b[01;35m\u001b[K(void)system(command)\u001b[m\u001b[K;\n",
" \u001b[01;35m\u001b[K^~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"\r\n",
"\tSoftware Installed. If not done so already, add the homer programs to your executable path.\r\n",
"\r\n",
"\tAdd this line to your .bash_profile or .bashrc file (or other depending on your shell):\r\n",
"\t\tPATH=$PATH:/content/HOMER/.//bin/\r\n",
"\r\n",
"\r\n",
"\tSimply typing \"findMotifs.pl\" should work before running Homer.\r\n",
"\r\n"
],
"name": "stdout"
}
]
},
{
"metadata": {
"id": "yJ4nq-bBcqYK",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 54
},
"outputId": "30294f1d-fa49-4f2d-9b2e-de34da72669b"
},
"cell_type": "code",
"source": [
"import os\n",
"os.environ[\"PATH\"] = os.environ[\"PATH\"]+\":/content/HOMER/.//bin/\"\n",
"print(os.environ[\"PATH\"])"
],
"execution_count": 18,
"outputs": [
{
"output_type": "stream",
"text": [
"/usr/local/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/tools/node/bin:/tools/google-cloud-sdk/bin:/opt/bin:/content/HOMER/.//bin/\n"
],
"name": "stdout"
}
]
},
{
"metadata": {
"id": "trblg4kKdm-H",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 428
},
"outputId": "ea2a6080-6b58-46a6-c92d-7b0f52cf4d79"
},
"cell_type": "code",
"source": [
"%cd ~\n",
"!wget https://raw.githubusercontent.com/AvantiShri/file_hosting/master/yamda_test/pos_seqs_concat.fa.gz -O pos_seqs_concat.fa.gz\n",
"!wget https://raw.githubusercontent.com/AvantiShri/file_hosting/master/yamda_test/neg_seqs_concat.fa.gz -O neg_seqs_concat.fa.gz\n",
"!zcat pos_seqs_concat.fa.gz | perl -lane 'if ($.%2==1) {print $_} else {print substr($_,300,400)}' > central400_pos_seqs_concat.fa\n",
"!zcat neg_seqs_concat.fa.gz | perl -lane 'if ($.%2==1) {print $_} else {print substr($_,300,400)}' > central400_neg_seqs_concat.fa"
],
"execution_count": 23,
"outputs": [
{
"output_type": "stream",
"text": [
"/content\n",
"--2018-08-05 20:13:23-- https://raw.githubusercontent.com/AvantiShri/file_hosting/master/yamda_test/pos_seqs_concat.fa.gz\n",
"Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.0.133, 151.101.64.133, 151.101.128.133, ...\n",
"Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.0.133|:443... connected.\n",
"HTTP request sent, awaiting response... 200 OK\n",
"Length: 1641890 (1.6M) [application/octet-stream]\n",
"Saving to: ‘pos_seqs_concat.fa.gz’\n",
"\n",
"pos_seqs_concat.fa. 100%[===================>] 1.57M 7.41MB/s in 0.2s \n",
"\n",
"2018-08-05 20:13:23 (7.41 MB/s) - ‘pos_seqs_concat.fa.gz’ saved [1641890/1641890]\n",
"\n",
"--2018-08-05 20:13:24-- https://raw.githubusercontent.com/AvantiShri/file_hosting/master/yamda_test/neg_seqs_concat.fa.gz\n",
"Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.0.133, 151.101.64.133, 151.101.128.133, ...\n",
"Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.0.133|:443... connected.\n",
"HTTP request sent, awaiting response... 200 OK\n",
"Length: 1573876 (1.5M) [application/octet-stream]\n",
"Saving to: ‘neg_seqs_concat.fa.gz’\n",
"\n",
"neg_seqs_concat.fa. 100%[===================>] 1.50M --.-KB/s in 0.1s \n",
"\n",
"2018-08-05 20:13:25 (10.4 MB/s) - ‘neg_seqs_concat.fa.gz’ saved [1573876/1573876]\n",
"\n"
],
"name": "stdout"
}
]
},
{
"metadata": {
"id": "Lgu5_KaA7_mI",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 15048
},
"outputId": "d15c407c-4a82-4950-8363-a36724f6b005"
},
"cell_type": "code",
"source": [
"!findMotifs.pl central400_pos_seqs_concat.fa fasta homer_out -fasta central400_neg_seqs_concat.fa"
],
"execution_count": 29,
"outputs": [
{
"output_type": "stream",
"text": [
"\r\n",
"Selected Options:\r\n",
"\tInput file = central400_pos_seqs_concat.fa\r\n",
"\tPromoter Set = fasta\r\n",
"\tOutput Directory = homer_out\r\n",
"\tWill use FASTA files for motif finding\r\n",
"\t\tTarget Sequences = central400_pos_seqs_concat.fa\r\n",
"\t\tBackground Sequences = central400_neg_seqs_concat.fa\r\n",
"\tUsing custom gene IDs for GO analysis\r\n",
"\tParsing FASTA format files...\n",
"\tFound 5000 sequences\n",
"\tFound 5000 sequences\n",
"\n",
"\tProgress: Step4 - removing redundant promoters\n",
"\n",
"\tProgress: Step5 - adjusting background sequences for GC/CpG content...\n",
"\n",
"\tSequences processed:\n",
"\t\tAuto detected maximum sequence length of 400 bp\n",
"\t\t10000 total\n",
"\n",
"\tFrequency Bins: 0.2 0.25 0.3 0.35 0.4 0.45 0.5 0.6 0.7 0.8\n",
"\tFreq\tBin\tCount\n",
"\t0.2\t0\t3\n",
"\t0.25\t1\t18\n",
"\t0.3\t2\t310\n",
"\t0.35\t3\t897\n",
"\t0.4\t4\t1588\n",
"\t0.45\t5\t1841\n",
"\t0.5\t6\t1757\n",
"\t0.6\t7\t2078\n",
"\t0.7\t8\t933\n",
"\t0.8\t9\t337\n",
"\t10\t10\t29\n",
"\tBin\t# Targets\t# Background\tBackground Weight\n",
"\t1\t1\t17\t0.056\n",
"\t2\t57\t253\t0.216\n",
"\t3\t277\t620\t0.428\n",
"\t4\t685\t903\t0.726\n",
"\t5\t891\t950\t0.898\n",
"\t6\t962\t795\t1.159\n",
"\t7\t1224\t854\t1.372\n",
"\t8\t633\t300\t2.021\n",
"\t9\t248\t89\t2.668\n",
"\t10\t22\t7\t3.010\n",
"\n",
"\tNormalizing lower order oligos using homer2\n",
"\n",
"\tReading input files...\n",
"\t9788 total sequences read\n",
"\tAutonormalization: 1-mers (4 total)\n",
"\t\tA\t25.28%\t25.39%\t0.996\n",
"\t\tC\t24.72%\t24.61%\t1.004\n",
"\t\tG\t24.72%\t24.61%\t1.004\n",
"\t\tT\t25.28%\t25.39%\t0.996\n",
"\tAutonormalization: 2-mers (16 total)\n",
"\t\tAA\t7.48%\t7.43%\t1.006\n",
"\t\tCA\t7.27%\t7.34%\t0.991\n",
"\t\tGA\t5.98%\t6.07%\t0.985\n",
"\t\tTA\t4.55%\t4.55%\t1.000\n",
"\t\tAC\t4.95%\t4.93%\t1.004\n",
"\t\tCC\t7.42%\t7.41%\t1.002\n",
"\t\tGC\t6.37%\t6.21%\t1.026\n",
"\t\tTC\t5.98%\t6.07%\t0.985\n",
"\t\tAG\t7.48%\t7.49%\t0.999\n",
"\t\tCG\t2.55%\t2.38%\t1.069\n",
"\t\tGG\t7.42%\t7.41%\t1.002\n",
"\t\tTG\t7.27%\t7.34%\t0.991\n",
"\t\tAT\t5.36%\t5.53%\t0.969\n",
"\t\tCT\t7.48%\t7.49%\t0.999\n",
"\t\tGT\t4.95%\t4.93%\t1.004\n",
"\t\tTT\t7.48%\t7.43%\t1.006\n",
"\tAutonormalization: 3-mers (64 total)\n",
"\tNormalization weights can be found in file: homer_out/seq.autonorm.tsv\n",
"\tConverging on autonormalization solution:\n",
"\t................................................"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"...............................\n",
"\tFinal normalization:\tAutonormalization: 1-mers (4 total)\n",
"\t\tA\t25.28%\t25.29%\t1.000\n",
"\t\tC\t24.72%\t24.71%\t1.000\n",
"\t\tG\t24.72%\t24.71%\t1.000\n",
"\t\tT\t25.28%\t25.29%\t1.000\n",
"\tAutonormalization: 2-mers (16 total)\n",
"\t\tAA\t7.48%\t7.46%\t1.002\n",
"\t\tCA\t7.27%\t7.27%\t1.000\n",
"\t\tGA\t5.98%\t6.01%\t0.994\n",
"\t\tTA\t4.55%\t4.54%\t1.002\n",
"\t\tAC\t4.95%\t4.93%\t1.004\n",
"\t\tCC\t7.42%\t7.43%\t0.999\n",
"\t\tGC\t6.37%\t6.34%\t1.005\n",
"\t\tTC\t5.98%\t6.01%\t0.994\n",
"\t\tAG\t7.48%\t7.47%\t1.002\n",
"\t\tCG\t2.55%\t2.54%\t1.002\n",
"\t\tGG\t7.42%\t7.43%\t0.999\n",
"\t\tTG\t7.27%\t7.27%\t1.000\n",
"\t\tAT\t5.36%\t5.43%\t0.989\n",
"\t\tCT\t7.48%\t7.47%\t1.002\n",
"\t\tGT\t4.95%\t4.93%\t1.004\n",
"\t\tTT\t7.48%\t7.46%\t1.002\n",
"\tAutonormalization: 3-mers (64 total)\n",
"\n",
"\tProgress: Step6 - Gene Ontology Enrichment Analysis\n",
"\tSkipping...\n",
"\n",
"\tProgress: Step7 - Known motif enrichment\n",
"\n",
"\tReading input files...\n",
"\t9788 total sequences read\n",
"\t976 motifs loaded\n",
"\tCache length = 11180\n",
"\tUsing hypergeometric scoring\n",
"\tChecking enrichment of 976 motif(s)\n",
"\t|0% 50% 100%|\n",
"\t=================================================================================\n",
"\tPreparing HTML output with sequence logos...\n",
"\t\t1 of 976 (1e-614) CTCF(Zf)/CD4+-CTCF-ChIP-Seq(Barski_et_al.)/Homer\n",
"\t\t2 of 976 (1e-560) BORIS(Zf)/K562-CTCFL-ChIP-Seq(GSE32465)/Homer\n",
"\t\t3 of 976 (1e-11) Bcl11a(Zf)/HSPC-BCL11A-ChIP-Seq(GSE104676)/Homer\n",
"\t\t4 of 976 (1e-9) SCL(bHLH)/HPC7-Scl-ChIP-Seq(GSE13511)/Homer\n",
"\t\t5 of 976 (1e-8) Tgif2(Homeobox)/mES-Tgif2-ChIP-Seq(GSE55404)/Homer\n",
"\t\t6 of 976 (1e-8) Zic3(Zf)/mES-Zic3-ChIP-Seq(GSE37889)/Homer\n",
"\t\t7 of 976 (1e-7) NeuroD1(bHLH)/Islet-NeuroD1-ChIP-Seq(GSE30298)/Homer\n",
"\t\t8 of 976 (1e-6) Unknown-ESC-element(?)/mES-Nanog-ChIP-Seq(GSE11724)/Homer\n",
"\t\t9 of 976 (1e-5) E2FA(E2FDP)/colamp-E2FA-DAP-Seq(GSE60143)/Homer\n",
"\t\t10 of 976 (1e-5) bZIP18(bZIP)/colamp-bZIP18-DAP-Seq(GSE60143)/Homer\n",
"\t\t11 of 976 (1e-5) HIC1(Zf)/Treg-ZBTB29-ChIP-Seq(GSE99889)/Homer\n",
"\t\t12 of 976 (1e-4) Erra(NR)/HepG2-Erra-ChIP-Seq(GSE31477)/Homer\n",
"\t\t13 of 976 (1e-4) SeqBias: A/T bias\n",
"\t\t14 of 976 (1e-4) PRDM9(Zf)/Testis-DMC1-ChIP-Seq(GSE35498)/Homer\n",
"\t\t15 of 976 (1e-4) Nanog(Homeobox)/mES-Nanog-ChIP-Seq(GSE11724)/Homer\n",
"\t\t16 of 976 (1e-4) Meis1(Homeobox)/MastCells-Meis1-ChIP-Seq(GSE48085)/Homer\n",
"\t\t17 of 976 (1e-4) CTCF-SatelliteElement(Zf?)/CD4+-CTCF-ChIP-Seq(Barski_et_al.)/Homer\n",
"\t\t18 of 976 (1e-4) SeqBias: CG bias\n",
"\t\t19 of 976 (1e-4) Ascl1(bHLH)/NeuralTubes-Ascl1-ChIP-Seq(GSE55840)/Homer\n",
"\t\t20 of 976 (1e-4) Tgif1(Homeobox)/mES-Tgif1-ChIP-Seq(GSE55404)/Homer\n",
"\t\t21 of 976 (1e-3) ERF13(AP2EREBP)/colamp-ERF13-DAP-Seq(GSE60143)/Homer\n",
"\t\t22 of 976 (1e-3) AT5G23930(mTERF)/col-AT5G23930-DAP-Seq(GSE60143)/Homer\n",
"\t\t23 of 976 (1e-3) AtGRF6(GRF)/col-AtGRF6-DAP-Seq(GSE60143)/Homer\n",
"\t\t24 of 976 (1e-3) THRb(NR)/HepG2-THRb.Flag-ChIP-Seq(Encode)/Homer\n",
"\t\t25 of 976 (1e-3) CELF2(RRM)/JSL1-CELF2-CLIP-Seq(GSE71264)/Homer\n",
"\t\t26 of 976 (1e-3) TCP1(TCP)/col-TCP1-DAP-Seq(GSE60143)/Homer\n",
"\t\t27 of 976 (1e-3) Tcf12(bHLH)/GM12878-Tcf12-ChIP-Seq(GSE32465)/Homer\n",
"\t\t28 of 976 (1e-3) bHLH122(bHLH)/col100-bHLH122-DAP-Seq(GSE60143)/Homer\n",
"\t\t29 of 976 (1e-3) GRF9(GRF)/colamp-GRF9-DAP-Seq(GSE60143)/Homer\n",
"\t\t30 of 976 (1e-3) Unknown3/Drosophila-Promoters/Homer\n",
"\t\t31 of 976 (1e-3) Sox9(HMG)/Limb-SOX9-ChIP-Seq(GSE73225)/Homer\n",
"\t\t32 of 976 (1e-2) AT2G33550(Trihelix)/colamp-AT2G33550-DAP-Seq(GSE60143)/Homer\n",
"\t\t33 of 976 (1e-2) AT2G15740(C2H2)/col-AT2G15740-DAP-Seq(GSE60143)/Homer\n",
"\t\t34 of 976 (1e-2) JGL(C2H2)/col-JGL-DAP-Seq(GSE60143)/Homer\n",
"\t\t35 of 976 (1e-2) IRF1(IRF)/PBMC-IRF1-ChIP-Seq(GSE43036)/Homer\n",
"\t\t36 of 976 (1e-2) ATHB34(ZFHD)/colamp-ATHB34-DAP-Seq(GSE60143)/Homer\n",
"\t\t37 of 976 (1e-2) Tbx5(T-box)/HL1-Tbx5.biotin-ChIP-Seq(GSE21529)/Homer\n",
"\t\t38 of 976 (1e-2) At5g04390(C2H2)/col200-At5g04390-DAP-Seq(GSE60143)/Homer\n",
"\t\t39 of 976 (1e-2) ERE(NR),IR3/MCF7-ERa-ChIP-Seq(Unpublished)/Homer\n",
"\t\t40 of 976 (1e-2) ERF104(AP2EREBP)/col-ERF104-DAP-Seq(GSE60143)/Homer\n",
"\t\t41 of 976 (1e-2) AT1G76870(Trihelix)/col-AT1G76870-DAP-Seq(GSE60143)/Homer\n",
"\t\t42 of 976 (1e-2) SPL1(SBP)/colamp-SPL1-DAP-Seq(GSE60143)/Homer\n",
"\t\t43 of 976 (1e-2) COUP-TFII(NR)/Artia-Nr2f2-ChIP-Seq(GSE46497)/Homer\n",
"\t\t44 of 976 (1e-2) ATHB25(ZFHD)/colamp-ATHB25-DAP-Seq(GSE60143)/Homer\n",
"\t\t45 of 976 (1e-2) CRF10(AP2EREBP)/col100-CRF10-DAP-Seq(GSE60143)/Homer\n",
"\t\t46 of 976 (1e-2) ERF105(AP2EREBP)/colamp-ERF105-DAP-Seq(GSE60143)/Homer\n",
"\t\t47 of 976 (1e-2) ERF5(AP2EREBP)/colamp-ERF5-DAP-Seq(GSE60143)/Homer\n",
"\t\t48 of 976 (1e-2) RUNX1(Runt)/Jurkat-RUNX1-ChIP-Seq(GSE29180)/Homer\n",
"\t\t49 of 976 (1e-2) SPL11(SBP)/col100-SPL11-DAP-Seq(GSE60143)/Homer\n",
"\t\t50 of 976 (1e-2) IRF3(IRF)/BMDM-Irf3-ChIP-Seq(GSE67343)/Homer\n",
"\t\t51 of 976 (1e-2) bHLH80(bHLH)/col-bHLH80-DAP-Seq(GSE60143)/Homer\n",
"\t\t52 of 976 (1e-2) ATHB23(ZFHD)/col-ATHB23-DAP-Seq(GSE60143)/Homer\n",
"\t\t53 of 976 (1e-2) At2g41835(C2H2)/col-At2g41835-DAP-Seq(GSE60143)/Homer\n",
"\t\t54 of 976 (1e-2) ERF15(AP2EREBP)/colamp-ERF15-DAP-Seq(GSE60143)/Homer\n",
"\t\t55 of 976 (1e-2) X-box(HTH)/NPC-H3K4me1-ChIP-Seq(GSE16256)/Homer\n",
"\t\t56 of 976 (1e-2) IRF2(IRF)/Erythroblas-IRF2-ChIP-Seq(GSE36985)/Homer\n",
"\t\t57 of 976 (1e-2) CUX1(Homeobox)/K562-CUX1-ChIP-Seq(GSE92882)/Homer\n",
"\t\t58 of 976 (1e-2) SPL9(SBP)/colamp-SPL9-DAP-Seq(GSE60143)/Homer\n",
"\t\t59 of 976 (1e-2) Ptf1a(bHLH)/Panc1-Ptf1a-ChIP-Seq(GSE47459)/Homer\n",
"\t\t60 of 976 (1e-2) ERF9(AP2EREBP)/colamp-ERF9-DAP-Seq(GSE60143)/Homer\n",
"\t\t61 of 976 (1e-2) NF1-halfsite(CTF)/LNCaP-NF1-ChIP-Seq(Unpublished)/Homer\n",
"\t\t62 of 976 (1e-2) SOL1(CPP)/colamp-SOL1-DAP-Seq(GSE60143)/Homer\n",
"\t\t63 of 976 (1e-2) CHR(?)/Hela-CellCycle-Expression/Homer\n",
"\t\t64 of 976 (1e-2) SPL5(SBP)/colamp-SPL5-DAP-Seq(GSE60143)/Homer\n",
"\t\t65 of 976 (1e-2) VND6(NAC)/col-VND6-DAP-Seq(GSE60143)/Homer\n",
"\t\t66 of 976 (1e-2) ERF1(AP2EREBP)/colamp-ERF1-DAP-Seq(GSE60143)/Homer\n",
"\t\t67 of 976 (1e-2) SFP1/SacCer-Promoters/Homer\n"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"\r\n",
"\tProgress: Step8 - De novo motif finding (HOMER)\r\n",
"\r\n",
"\tScanning input files...\r\n",
"\tParsing sequences...\r\n",
"\t|0% 50% 100%|\n",
"\t================================================================================\n",
"\tTotal number of Oligos: 32888\n",
"\tAutoadjustment for sequence coverage in background: 1.00x\n",
"\n",
"\tOligos: 32888 of 34497 max\n",
"\tTree : 67152 of 172485 max\n",
"\tOptimizing memory usage...\n",
"\tCache length = 11180\n",
"\tUsing hypergeometric scoring\n",
"\n",
"\tGlobal Optimization Phase: Looking for enriched oligos with up to 1 mismatches...\n",
"\n",
"\tScreening oligos 32888 (allowing 0 mismatches):\n",
"\t|0% 50% 100%|\n",
"\t================================================================================\n",
"\t\t0.03% skipped, 99.97% checked (32878 of 32888), of those checked:\n",
"\t\t0.03% not in target, 0.00% increased p-value, 0.00% high p-value\n",
"\n",
"\tScreening oligos 32888 (allowing 1 mismatches):\n",
"\t|0% 50% 100%|\n",
"\t================================================================================\n",
"\t\t0.03% skipped, 99.97% checked (32878 of 32888), of those checked:\n",
"\t\t0.00% not in target, 47.25% increased p-value, 46.50% high p-value\n",
"\tReading input files...\n",
"\t9788 total sequences read\n",
"\tCache length = 11180\n",
"\tUsing hypergeometric scoring\n",
"\n",
"\tLocal Optimization Phase:\n",
"\t1 of 25 Initial Sequence: GCCCTCTA... (-129.013)\n",
"\t\tRound 1: -312.98 GCCCTCTA T:2447.0(38.70%),B:809.0(16.20%),P:1e-135\n",
"\t\tRound 2: -323.58 GCCMCCTG T:2651.0(41.15%),B:893.4(17.75%),P:1e-140\n",
"\t\tRound 3: -323.58 GCCMCCTG T:2651.0(41.15%),B:893.4(17.75%),P:1e-140\n"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"\t\t=Final=: -432.40 GCCMCCTG T:2246.0(44.92%),B:798.9(17.48%),P:1e-187\r\n",
"\t\tPerforming exhaustive masking of motif...\n",
"\t\tReprioritizing potential motifs...\n",
"\t2 of 25 Initial Sequence: CTAGTGGC... (-52.524)\n",
"\t\tRound 1: -99.42 CTAGTGGC T:2152.0(34.98%),B:1146.4(22.18%),P:1e-43\n",
"\t\tRound 2: -99.81 CTAGTGGC T:1865.0(31.14%),B:955.4(18.86%),P:1e-43\n",
"\t\tRound 3: -99.81 CTAGTGGC T:1865.0(31.14%),B:955.4(18.86%),P:1e-43\n",
"\t\t=Final=: -122.91 CTAGTGGC T:1694.0(33.88%),B:909.7(19.91%),P:1e-53\n",
"\t\tPerforming exhaustive masking of motif...\n",
"\t\tReprioritizing potential motifs...\n",
"\t3 of 25 Initial Sequence: AGCGCCCT... (-23.080)\n",
"\t\tRound 1: -37.78 AGCGCCCT T:2341.0(37.39%),B:1587.0(29.32%),P:1e-16\n",
"\t\tRound 2: -37.78 AGCGCCCT T:2341.0(37.39%),B:1587.0(29.32%),P:1e-16\n",
"\t\t=Final=: -43.24 AGCGCCCT T:1738.0(34.76%),B:1202.1(26.30%),P:1e-18\n",
"\t\tPerforming exhaustive masking of motif...\n",
"\t\tReprioritizing potential motifs...\n",
"\t4 of 25 Initial Sequence: GTCCTGCA... (-17.464)\n",
"\t\tRound 1: -29.66 GTMCTGCA T:199.0(3.90%),B:70.0(1.50%),P:1e-12\n",
"\t\tRound 2: -29.66 GTMCTGCA T:199.0(3.90%),B:70.0(1.50%),P:1e-12\n",
"\t\t=Final=: -28.32 GTMCTGCA T:193.0(3.86%),B:69.1(1.51%),P:1e-12\n",
"\t\tPerforming exhaustive masking of motif...\n",
"\t\tReprioritizing potential motifs...\n",
"\t5 of 25 Initial Sequence: TTTCAAAG... (-17.363)\n",
"\t\tRound 1: -22.38 TTTCAAAG T:2438.0(38.59%),B:1793.0(32.44%),P:1e-9\n",
"\t\tRound 2: -23.19 TTTCAAAG T:2019.0(33.22%),B:1451.5(27.20%),P:1e-10\n",
"\t\tRound 3: -24.71 TTTCAARG T:2820.0(43.11%),B:2073.4(36.46%),P:1e-10\n",
"\t\tRound 4: -24.71 TTTCAARG T:2820.0(43.11%),B:2073.4(36.46%),P:1e-10\n",
"\t\t=Final=: -30.69 TTTCAARG T:1953.0(39.06%),B:1451.6(31.76%),P:1e-13\n",
"\t\tPerforming exhaustive masking of motif...\n",
"\t\tReprioritizing potential motifs...\n",
"\t6 of 25 Initial Sequence: TTCTGGCA... (-15.542)\n",
"\t\tRound 1: -24.51 TTCTGGCA T:3094.0(46.14%),B:2293.2(39.45%),P:1e-10\n",
"\t\tRound 2: -25.51 TTCTGGCR T:2182.0(35.37%),B:1560.4(28.92%),P:1e-11\n",
"\t\tRound 3: -25.51 TTCTGGCR T:2182.0(35.37%),B:1560.4(28.92%),P:1e-11\n",
"\t\t=Final=: -26.96 TTCTGGCR T:1762.0(35.24%),B:1307.7(28.61%),P:1e-11\n",
"\t\tPerforming exhaustive masking of motif...\n",
"\t\tReprioritizing potential motifs...\n",
"\t7 of 25 Initial Sequence: CCACCTAG... (-15.508)\n",
"\t\tRound 1: -5.17 CCACCTAG T:2195.0(35.53%),B:1834.7(33.05%),P:1e-2\n",
"\t\tRound 2: -11.45 CCAYCTAG T:1294.0(22.80%),B:977.9(19.25%),P:1e-4\n",
"\t\tRound 3: -11.52 CCMYCTAG T:2050.0(33.64%),B:1600.6(29.54%),P:1e-5\n",
"\t\tRound 4: -11.95 CCMYCTAG T:1367.0(23.92%),B:1031.2(20.19%),P:1e-5\n",
"\t\tRound 5: -11.95 CCMYCTAG T:1367.0(23.92%),B:1031.2(20.19%),P:1e-5\n",
"\t\t=Final=: -14.99 CCMYCTAG T:1214.0(24.28%),B:915.6(20.03%),P:1e-6\n",
"\t\tPerforming exhaustive masking of motif...\n",
"\t\tReprioritizing potential motifs...\n",
"\t8 of 25 Initial Sequence: CTTTGCCG... (-13.732)\n",
"\t\tRound 1: -17.64 CTTTGCCG T:50.0(1.00%),B:8.8(0.17%),P:1e-7\n",
"\t\tRound 2: -17.64 CTTTGCCG T:50.0(1.00%),B:8.8(0.17%),P:1e-7\n",
"\t\t=Final=: -16.90 CTTTGCCG T:50.0(1.00%),B:8.8(0.19%),P:1e-7\n",
"\t\tPerforming exhaustive masking of motif...\n",
"\t\tReprioritizing potential motifs...\n",
"\t9 of 25 Initial Sequence: GTGGCAAT... (-13.712)\n",
"\t\tRound 1: -26.82 GTGGCAAT T:1125.0(20.15%),B:730.8(14.76%),P:1e-11\n",
"\t\tRound 2: -26.82 GTGGCAAT T:1125.0(20.15%),B:730.8(14.76%),P:1e-11\n",
"\t\t=Final=: -28.63 GTGGCAAT T:1193.0(23.86%),B:817.6(17.89%),P:1e-12\n",
"\t\tPerforming exhaustive masking of motif...\n",
"\t\tReprioritizing potential motifs...\n",
"\t10 of 25 Initial Sequence: CGCCTGGA... (-13.328)\n",
"\t\tRound 1: -15.98 CGCCTGGA T:121.0(2.39%),B:47.2(1.02%),P:1e-6\n",
"\t\tRound 2: -15.98 CGCCTGGA T:121.0(2.39%),B:47.2(1.02%),P:1e-6\n",
"\t\t=Final=: -14.46 CGCCTGGA T:116.0(2.32%),B:47.2(1.03%),P:1e-6\n",
"\t\tPerforming exhaustive masking of motif...\n",
"\t\tReprioritizing potential motifs...\n"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"\t11 of 25 Initial Sequence: ATTCGACG... (-12.100)\n",
"\t\tRound 1: -25.90 ATTCGACG T:342.0(6.61%),B:165.4(3.55%),P:1e-11\n",
"\t\tRound 2: -25.90 ATTCGACG T:342.0(6.61%),B:165.4(3.55%),P:1e-11\n",
"\t\t=Final=: -22.24 ATTCGACG T:316.0(6.32%),B:162.8(3.56%),P:1e-9\n",
"\t\tPerforming exhaustive masking of motif...\n",
"\t\tReprioritizing potential motifs...\n",
"\t12 of 25 Initial Sequence: AAGCGCGC... (-11.898)\n",
"\t\tRound 1: -11.90 AAGCGCGC T:29.0(0.58%),B:5.0(0.09%),P:1e-5\n",
"\t\tRound 2: -11.90 AAGCGCGC T:29.0(0.58%),B:5.0(0.09%),P:1e-5\n",
"\t\t=Final=: -10.51 AAGCGCGC T:28.0(0.56%),B:5.0(0.11%),P:1e-4\n",
"\t\tPerforming exhaustive masking of motif...\n",
"\t\tReprioritizing potential motifs...\n",
"\t13 of 25 Initial Sequence: CGCCCCCC... (-11.406)\n",
"\t\tRound 1: -21.76 CGCCCCCC T:595.0(11.22%),B:358.5(7.53%),P:1e-9\n",
"\t\tRound 2: -22.31 CGCACCCC T:624.0(11.73%),B:377.4(7.92%),P:1e-9\n",
"\t\tRound 3: -22.31 CGCACCCC T:624.0(11.73%),B:377.4(7.92%),P:1e-9\n",
"\t\t=Final=: -19.62 CGCACCCC T:551.0(11.02%),B:345.9(7.57%),P:1e-8\n",
"\t\tPerforming exhaustive masking of motif...\n",
"\t\tReprioritizing potential motifs...\n",
"\t14 of 25 Initial Sequence: GAGACATT... (-11.146)\n",
"\t\tRound 1: -21.71 GAGACATT T:982.0(17.83%),B:650.6(13.26%),P:1e-9\n",
"\t\tRound 2: -21.71 GAGACATT T:982.0(17.83%),B:650.6(13.26%),P:1e-9\n",
"\t\t=Final=: -20.26 GAGACATT T:714.0(14.28%),B:470.5(10.30%),P:1e-8\n",
"\t\tPerforming exhaustive masking of motif...\n",
"\t\tReprioritizing potential motifs...\n",
"\t15 of 25 Initial Sequence: CGCTAGAC... (-11.049)\n",
"\t\tRound 1: -13.00 CGCTMGAN T:21.0(0.42%),B:0.0(0.00%),P:1e-5\n",
"\t\tRound 2: -13.00 CGCTMGAN T:21.0(0.42%),B:0.0(0.00%),P:1e-5\n",
"\t\t=Final=: -13.65 CGCTMGAN T:21.0(0.42%),B:0.0(0.00%),P:1e-5\n",
"\t\tPerforming exhaustive masking of motif...\n",
"\t\tReprioritizing potential motifs...\n",
"\t16 of 25 Initial Sequence: CCGGGGGA... (-10.828)\n",
"\t\tRound 1: -13.14 CCGGGGGA T:159.0(3.13%),B:77.7(1.67%),P:1e-5\n",
"\t\tRound 2: -13.14 CCGGGGGA T:159.0(3.13%),B:77.7(1.67%),P:1e-5\n",
"\t\t=Final=: -10.71 CCGGGGGA T:148.0(2.96%),B:77.7(1.70%),P:1e-4\n",
"\t\tPerforming exhaustive masking of motif...\n",
"\t\tReprioritizing potential motifs...\n",
"\t17 of 25 Initial Sequence: AATGGTCT... (-10.599)\n",
"\t\tRound 1: -15.34 AATGGTCT T:255.0(4.97%),B:137.7(2.95%),P:1e-6\n",
"\t\tRound 2: -17.30 AAKRGTCT T:209.0(4.09%),B:100.4(2.16%),P:1e-7\n",
"\t\tRound 3: -17.30 AAKRGTCT T:209.0(4.09%),B:100.4(2.16%),P:1e-7\n",
"\t\t=Final=: -16.10 AAKRGTCT T:201.0(4.02%),B:99.6(2.18%),P:1e-6\n",
"\t\tPerforming exhaustive masking of motif...\n",
"\t\tReprioritizing potential motifs...\n",
"\t18 of 25 Initial Sequence: TTCCGCTG... (-10.116)\n",
"\t\tRound 1: -10.40 NTCCGCTN T:286.0(5.56%),B:178.1(3.82%),P:1e-4\n",
"\t\tRound 2: -16.05 TTCCGCTS T:171.0(3.36%),B:78.0(1.69%),P:1e-6\n",
"\t\tRound 3: -21.00 GTCCGCGW T:1059.0(19.09%),B:713.2(14.44%),P:1e-9\n",
"\t\tRound 4: -21.28 GTCCGCGH T:1327.0(23.31%),B:920.3(18.23%),P:1e-9\n",
"\t\tRound 5: -21.28 GTCCGCGH T:1327.0(23.31%),B:920.3(18.23%),P:1e-9\n",
"\t\t=Final=: -16.18 GTCCGCGH T:976.0(19.52%),B:706.4(15.46%),P:1e-7\n",
"\t\tPerforming exhaustive masking of motif...\n",
"\t\tReprioritizing potential motifs...\n",
"\t19 of 25 Initial Sequence: GGATCGCC... (-11.210)\n",
"\t\tRound 1: -11.56 GGATCGCC T:38.0(0.76%),B:8.2(0.17%),P:1e-5\n",
"\t\tRound 2: -13.23 GGTWCGCC T:62.0(1.23%),B:18.8(0.39%),P:1e-5\n",
"\t\tRound 3: -13.23 GGTWCGCC T:62.0(1.23%),B:18.8(0.39%),P:1e-5\n",
"\t\t=Final=: -12.46 GGTWCGCC T:61.0(1.22%),B:18.8(0.41%),P:1e-5\n",
"\t\tPerforming exhaustive masking of motif...\n",
"\t\tReprioritizing potential motifs...\n",
"\t20 of 25 Initial Sequence: ATGGCGCG... (-10.635)\n",
"\t\tRound 1: -18.43 ATGGCGCG T:84.0(1.67%),B:23.9(0.50%),P:1e-8\n",
"\t\tRound 2: -19.62 TTACCGCG T:114.0(2.25%),B:37.5(0.81%),P:1e-8\n",
"\t\tRound 3: -19.62 TTACCGCG T:114.0(2.25%),B:37.5(0.81%),P:1e-8\n",
"\t\t=Final=: -15.57 TTACCGCG T:78.0(1.56%),B:23.9(0.52%),P:1e-6\n",
"\t\tPerforming exhaustive masking of motif...\n",
"\t\tReprioritizing potential motifs...\n"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"\t21 of 25 Initial Sequence: AGTCGTCT... (-10.039)\n",
"\t\tRound 1: -13.23 AGTCGTCT T:62.0(1.23%),B:18.0(0.39%),P:1e-5\n",
"\t\tRound 2: -13.23 AGTCGTCT T:62.0(1.23%),B:18.0(0.39%),P:1e-5\n",
"\t\t=Final=: -12.87 AGTCGTCT T:62.0(1.24%),B:18.0(0.39%),P:1e-5\n",
"\t\tPerforming exhaustive masking of motif...\n",
"\t\tReprioritizing potential motifs...\n",
"\t22 of 25 Initial Sequence: GTTTTCAA... (-9.852)\n",
"\t\tRound 1: -18.44 GTTTTCAA T:1140.0(20.39%),B:794.4(15.95%),P:1e-8\n",
"\t\tRound 2: -19.80 VTTTTCAA T:2882.0(43.81%),B:2177.9(37.89%),P:1e-8\n",
"\t\tRound 3: -20.04 STTYTCAA T:1597.0(27.34%),B:1143.6(22.13%),P:1e-8\n",
"\t\tRound 4: -20.57 VTTTTCAA T:1923.0(31.93%),B:1398.5(26.35%),P:1e-8\n",
"\t\tRound 5: -20.57 VTTTTCAA T:1923.0(31.93%),B:1398.5(26.35%),P:1e-8\n",
"\t\t=Final=: -19.44 VTTTTCAA T:1171.0(23.42%),B:849.6(18.59%),P:1e-8\n",
"\t\tPerforming exhaustive masking of motif...\n",
"\t\tReprioritizing potential motifs...\n",
"\t23 of 25 Initial Sequence: GTGGTGCT... (-9.747)\n",
"\t\tRound 1: -9.98 GTGGTGCT T:169.0(3.32%),B:93.3(2.01%),P:1e-4\n",
"\t\tRound 2: -18.85 GTAGTGCT T:440.0(8.42%),B:257.2(5.47%),P:1e-8\n",
"\t\tRound 3: -18.85 GTAGTGCT T:440.0(8.42%),B:257.2(5.47%),P:1e-8\n",
"\t\t=Final=: -18.72 GTAGTGCT T:411.0(8.22%),B:243.0(5.32%),P:1e-8\n",
"\t\tPerforming exhaustive masking of motif...\n",
"\t\tReprioritizing potential motifs...\n",
"\t24 of 25 Initial Sequence: CACCTGCT... (-9.807)\n",
"\t\tRound 1: -13.19 CACCTGCT T:451.0(8.63%),B:289.6(6.13%),P:1e-5\n",
"\t\tRound 2: -15.22 CACCTGAT T:465.0(8.88%),B:290.7(6.15%),P:1e-6\n",
"\t\tRound 3: -18.77 CGCCTGAT T:557.0(10.54%),B:344.0(7.23%),P:1e-8\n",
"\t\tRound 4: -18.77 CGCCTGAT T:557.0(10.54%),B:344.0(7.23%),P:1e-8\n",
"\t\t=Final=: -18.42 CGCCTGAT T:529.0(10.58%),B:333.5(7.30%),P:1e-8\n",
"\t\tPerforming exhaustive masking of motif...\n",
"\t\tReprioritizing potential motifs...\n",
"\tRemaining seeds don't look promising (After initial 5 motifs, logp -9.539 > -9.699)\n",
"\n",
"\tFinalizing Enrichment Statistics (new in v3.4)\n",
"\tReading input files...\n",
"\t9788 total sequences read\n",
"\tCache length = 11180\n",
"\tUsing hypergeometric scoring\n",
"\tChecking enrichment of 24 motif(s)\n",
"\t|0% 50% 100%|\n",
"\t=================================================================================\n",
"\tOutput in file: homer_out/homerMotifs.motifs8\n",
"\n",
"\n",
"\tScanning input files...\n",
"\tParsing sequences...\n",
"\t|0% 50% 100%|\n",
"\t======="
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"=========================================================================\n",
"\tTotal number of Oligos: 456521\n",
"\tAutoadjustment for sequence coverage in background: 1.00x\n",
"\n",
"\tOligos: 456521 of 551887 max\n",
"\tTree : 956116 of 2759435 max\n",
"\tOptimizing memory usage...\n",
"\tCache length = 11180\n",
"\tUsing hypergeometric scoring\n",
"\n",
"\tGlobal Optimization Phase: Looking for enriched oligos with up to 1 mismatches...\n",
"\n",
"\tScreening oligos 456521 (allowing 0 mismatches):\n",
"\t|0% 50% 100%|\n",
"\t================================================================================\n",
"\t\t10.28% skipped, 89.72% checked (409591 of 456521), of those checked:\n",
"\t\t10.28% not in target, 0.00% increased p-value, 0.00% high p-value\n",
"\n",
"\tScreening oligos 456521 (allowing 1 mismatches):\n",
"\t|0% 50% 100%|\n",
"\t================================================================================\n",
"\t\t10.28% skipped, 89.72% checked (409591 of 456521), of those checked:\n",
"\t\t0.00% not in target, 37.52% increased p-value, 51.46% high p-value\n",
"\tReading input files...\n",
"\t9788 total sequences read\n",
"\tCache length = 11180\n",
"\tUsing hypergeometric scoring\n",
"\n",
"\tLocal Optimization Phase:\n",
"\t1 of 25 Initial Sequence: CCCTCTAGTG... (-222.668)\n",
"\t\tRound 1: -553.30 CCCTCTAGTG T:2271.0(36.51%),B:421.9(8.80%),P:1e-240\n",
"\t\tRound 2: -587.63 CCMCCTRSTG T:2615.0(40.73%),B:522.6(10.79%),P:1e-255\n",
"\t\tRound 3: -599.64 CCMCCTDGHG T:2766.0(42.49%),B:569.9(11.71%),P:1e-260\n",
"\t\tRound 4: -599.64 CCMCCTDGHG T:2766.0(42.49%),B:569.9(11.71%),P:1e-260\n",
"\t\t=Final=: -882.27 CCMCCTDGHG T:2529.0(50.58%),B:540.6(11.83%),P:1e-383\n",
"\t\tPerforming exhaustive masking of motif...\n",
"\t\tReprioritizing potential motifs...\n",
"\t2 of 25 Initial Sequence: AGCGCCCCCT... (-51.094)\n",
"\t\tRound 1: -93.85 AGCGCCCCCT T:1234.0(21.87%),B:565.6(11.63%),P:1e-40\n",
"\t\tRound 2: -93.85 AGCGCCCCCT T:1234.0(21.87%),B:565.6(11.63%),P:1e-40\n",
"\t\t=Final=: -87.96 AGCGCCCCCT T:942.0(18.84%),B:438.2(9.59%),P:1e-38\n",
"\t\tPerforming exhaustive masking of motif...\n"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"\t\tReprioritizing potential motifs...\n",
"\t3 of 25 Initial Sequence: CCACTAGGTG... (-38.890)\n",
"\t\tRound 1: -62.97 CCACTAGGTG T:905.0(16.56%),B:436.8(9.10%),P:1e-27\n",
"\t\tRound 2: -62.97 CCACTAGGTG T:905.0(16.56%),B:436.8(9.10%),P:1e-27\n",
"\t\t=Final=: -68.48 CCACTAGGTG T:836.0(16.72%),B:408.9(8.95%),P:1e-29\n",
"\t\tPerforming exhaustive masking of motif...\n",
"\t\tReprioritizing potential motifs...\n",
"\t4 of 25 Initial Sequence: ACTGCCATCT... (-17.629)\n",
"\t\tRound 1: -41.22 ACTGCCATCT T:286.0(5.56%),B:100.1(2.16%),P:1e-17\n",
"\t\tRound 2: -43.16 ACTGCCATCT T:467.0(8.92%),B:204.4(4.37%),P:1e-18\n",
"\t\tRound 3: -46.06 RCTGCCMTCT T:583.0(11.01%),B:271.0(5.76%),P:1e-20\n",
"\t\tRound 4: -49.81 RCTGYCMTCT T:581.0(10.97%),B:261.2(5.55%),P:1e-21\n",
"\t\tRound 5: -49.81 RCTGYCMTCT T:581.0(10.97%),B:261.2(5.55%),P:1e-21\n",
"\t\t=Final=: -57.21 RCTGYCMTCT T:572.0(11.44%),B:253.7(5.55%),P:1e-24\n",
"\t\tPerforming exhaustive masking of motif...\n",
"\t\tReprioritizing potential motifs...\n",
"\t5 of 25 Initial Sequence: GGCCGCAGGA... (-17.611)\n",
"\t\tRound 1: -31.43 GGCCGCAGGA T:210.0(4.11%),B:72.5(1.56%),P:1e-13\n",
"\t\tRound 2: -31.43 GGCCGCAGGA T:210.0(4.11%),B:72.5(1.56%),P:1e-13\n",
"\t\t=Final=: -29.97 GGCCGCAGGA T:201.0(4.02%),B:71.2(1.56%),P:1e-13\n",
"\t\tPerforming exhaustive masking of motif...\n",
"\t\tReprioritizing potential motifs...\n",
"\t6 of 25 Initial Sequence: CTCCGCCCTT... (-15.960)\n",
"\t\tRound 1: -26.55 CTCCGCCCTT T:137.0(2.70%),B:40.5(0.87%),P:1e-11\n",
"\t\tRound 2: -31.98 CTCCGCCCTT T:149.0(2.94%),B:39.5(0.85%),P:1e-13\n",
"\t\tRound 3: -32.41 CTCCGCCCTT T:141.0(2.78%),B:35.2(0.76%),P:1e-14\n",
"\t\tRound 4: -32.41 CTCCGCCCTT T:141.0(2.78%),B:35.2(0.76%),P:1e-14\n",
"\t\t=Final=: -28.55 CTCCGCCCTT T:136.0(2.72%),B:37.5(0.82%),P:1e-12\n",
"\t\tPerforming exhaustive masking of motif...\n",
"\t\tReprioritizing potential motifs...\n",
"\t7 of 25 Initial Sequence: AAAGGCTTTG... (-15.619)\n",
"\t\tRound 1: -25.80 AAAGGCTTTG T:919.0(16.79%),B:580.3(11.92%),P:1e-11\n",
"\t\tRound 2: -26.40 AAAGGCTTTG T:1516.0(26.16%),B:1032.0(20.19%),P:1e-11\n",
"\t\tRound 3: -29.94 AAAGSCTTTG T:1268.0(22.40%),B:821.1(16.44%),P:1e-13\n",
"\t\tRound 4: -35.36 AAAGSCTTTG T:1262.0(22.31%),B:787.5(15.82%),P:1e-15\n",
"\t\tRound 5: -39.56 AAAGBCTTTG T:1300.0(22.90%),B:795.8(15.97%),P:1e-17\n",
"\t\tRound 6: -39.56 AAAGBCTTTG T:1300.0(22.90%),B:795.8(15.97%),P:1e-17\n",
"\t\t=Final=: -31.62 AAAGBCTTTG T:1019.0(20.38%),B:662.1(14.49%),P:1e-13\n",
"\t\tPerforming exhaustive masking of motif...\n",
"\t\tReprioritizing potential motifs...\n",
"\t8 of 25 Initial Sequence: CGCGTCGCCT... (-14.602)\n",
"\t\tRound 1: -26.56 CGCGTCGCCT T:89.0(1.76%),B:17.2(0.37%),P:1e-11\n",
"\t\tRound 2: -27.65 CGCGTCGCCT T:56.0(1.11%),B:4.6(0.09%),P:1e-12\n",
"\t\tRound 3: -27.65 CGCGTCGCCT T:56.0(1.11%),B:4.6(0.09%),P:1e-12\n",
"\t\t=Final=: -25.68 CGCGTCGCCT T:55.0(1.10%),B:4.6(0.10%),P:1e-11\n",
"\t\tPerforming exhaustive masking of motif...\n",
"\t\tReprioritizing potential motifs...\n",
"\t9 of 25 Initial Sequence: CGTCCGCGAA... (-14.123)\n",
"\t\tRound 1: -18.65 CGTCCGCGAA T:34.0(0.68%),B:2.1(0.04%),P:1e-8\n",
"\t\tRound 2: -22.78 CGTCCGCGAA T:36.0(0.72%),B:0.0(0.00%),P:1e-9\n",
"\t\tRound 3: -22.78 CGTCCGCGAA T:36.0(0.72%),B:0.0(0.00%),P:1e-9\n",
"\t\t=Final=: -23.43 CGTCCGCGAA T:36.0(0.72%),B:0.0(0.00%),P:1e-10\n",
"\t\tPerforming exhaustive masking of motif...\n",
"\t\tReprioritizing potential motifs...\n",
"\t10 of 25 Initial Sequence: CACAGTATCA... (-14.095)\n",
"\t\tRound 1: -18.29 CACAGTATCA T:114.0(2.25%),B:39.5(0.85%),P:1e-7\n",
"\t\tRound 2: -21.04 CACAGTATCA T:187.0(3.67%),B:77.1(1.67%),P:1e-9\n",
"\t\tRound 3: -21.58 CACAGTATCA T:160.0(3.15%),B:60.3(1.30%),P:1e-9\n",
"\t\tRound 4: -21.58 CACAGTATCA T:160.0(3.15%),B:60.3(1.30%),P:1e-9\n",
"\t\t=Final=: -21.47 CACAGTATCA T:155.0(3.10%),B:58.4(1.28%),P:1e-9\n",
"\t\tPerforming exhaustive masking of motif...\n",
"\t\tReprioritizing potential motifs...\n"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"\t11 of 25 Initial Sequence: CCTCTTGCTG... (-13.919)\n",
"\t\tRound 1: -22.92 CCTCTTGCTG T:676.0(12.65%),B:412.4(8.62%),P:1e-9\n",
"\t\tRound 2: -22.92 CCTCTTGCTG T:676.0(12.65%),B:412.4(8.62%),P:1e-9\n",
"\t\t=Final=: -23.88 CCTCTTGCTG T:624.0(12.48%),B:384.9(8.42%),P:1e-10\n",
"\t\tPerforming exhaustive masking of motif...\n",
"\t\tReprioritizing potential motifs...\n",
"\t12 of 25 Initial Sequence: GGGCGGCAGT... (-14.128)\n",
"\t\tRound 1: -30.53 GGGCGGCAGT T:159.0(3.13%),B:46.8(1.00%),P:1e-13\n",
"\t\tRound 2: -30.53 GGGCGGCAGT T:159.0(3.13%),B:46.8(1.00%),P:1e-13\n",
"\t\t=Final=: -28.56 GGGCGGCAGT T:153.0(3.06%),B:46.8(1.02%),P:1e-12\n",
"\t\tPerforming exhaustive masking of motif...\n",
"\t\tReprioritizing potential motifs...\n",
"\t13 of 25 Initial Sequence: GTGGACGCGT... (-13.398)\n",
"\t\tRound 1: -24.65 GTGGACGCGT T:51.0(1.01%),B:4.9(0.09%),P:1e-10\n",
"\t\tRound 2: -24.65 GTGGACGCGT T:51.0(1.01%),B:4.9(0.09%),P:1e-10\n",
"\t\t=Final=: -22.45 GTGGACGCGT T:58.0(1.16%),B:7.1(0.15%),P:1e-9\n",
"\t\tPerforming exhaustive masking of motif...\n",
"\t\tReprioritizing potential motifs...\n",
"\t14 of 25 Initial Sequence: CTTCGGAAGT... (-13.591)\n",
"\t\tRound 1: -24.64 CTTCGGAAGT T:63.0(1.25%),B:8.1(0.17%),P:1e-10\n",
"\t\tRound 2: -24.64 CTTCGGAAGT T:63.0(1.25%),B:8.1(0.17%),P:1e-10\n",
"\t\t=Final=: -23.54 CTTCGGAAGT T:60.0(1.20%),B:7.7(0.17%),P:1e-10\n",
"\t\tPerforming exhaustive masking of motif...\n",
"\t\tReprioritizing potential motifs...\n",
"\t15 of 25 Initial Sequence: TAGTCGTCGT... (-13.002)\n",
"\t\tRound 1: -15.90 TAGTCGTCGT T:33.0(0.66%),B:3.6(0.07%),P:1e-6\n",
"\t\tRound 2: -15.90 TAGTCGTCGT T:33.0(0.66%),B:3.6(0.07%),P:1e-6\n",
"\t\t=Final=: -16.26 TAGTCGTCGT T:25.0(0.50%),B:0.6(0.01%),P:1e-7\n",
"\t\tPerforming exhaustive masking of motif...\n",
"\t\tReprioritizing potential motifs...\n",
"\t16 of 25 Initial Sequence: GGGGGGCGTA... (-12.816)\n",
"\t\tRound 1: -17.09 GGGGGGCGTA T:170.0(3.34%),B:75.2(1.63%),P:1e-7\n",
"\t\tRound 2: -18.89 GGGGGGCGTA T:38.0(0.76%),B:3.9(0.07%),P:1e-8\n",
"\t\tRound 3: -21.37 GGGGGGCGTG T:57.0(1.13%),B:8.7(0.17%),P:1e-9\n",
"\t\tRound 4: -22.27 GGGGGGCGWG T:47.0(0.94%),B:4.6(0.09%),P:1e-9\n",
"\t\tRound 5: -22.27 GGGGGGCGWG T:47.0(0.94%),B:4.6(0.09%),P:1e-9\n",
"\t\t=Final=: -21.19 GGGGGGCGWG T:53.0(1.06%),B:6.9(0.15%),P:1e-9\n",
"\t\tPerforming exhaustive masking of motif...\n",
"\t\tReprioritizing potential motifs...\n",
"\t17 of 25 Initial Sequence: ATAGCCCCTC... (-12.582)\n",
"\t\tRound 1: -22.83 ATAGCCCCTC T:92.0(1.82%),B:22.1(0.48%),P:1e-9\n",
"\t\tRound 2: -29.83 ATAGCCCCTC T:108.0(2.14%),B:22.8(0.48%),P:1e-12\n",
"\t\tRound 3: -29.83 ATAGCCCCTC T:108.0(2.14%),B:22.8(0.48%),P:1e-12\n",
"\t\t=Final=: -29.74 ATAGCCCCTC T:108.0(2.16%),B:22.8(0.50%),P:1e-12\n",
"\t\tPerforming exhaustive masking of motif...\n",
"\t\tReprioritizing potential motifs...\n",
"\t18 of 25 Initial Sequence: TGTCCTGCAA... (-12.379)\n",
"\t\tRound 1: -23.12 TGTCCTGCAA T:141.0(2.78%),B:47.8(1.02%),P:1e-10\n",
"\t\tRound 2: -23.76 TGTCCTGCAA T:149.0(2.94%),B:50.2(1.09%),P:1e-10\n",
"\t\tRound 3: -23.76 TGTCCTGCAA T:149.0(2.94%),B:50.2(1.09%),P:1e-10\n",
"\t\t=Final=: -23.12 TGTCCTGCAA T:146.0(2.92%),B:50.2(1.10%),P:1e-10\n",
"\t\tPerforming exhaustive masking of motif...\n",
"\t\tReprioritizing potential motifs...\n",
"\t19 of 25 Initial Sequence: ACGTTCCGCG... (-12.368)\n",
"\t\tRound 1: -20.34 ACGTTCCGCG T:123.0(2.43%),B:41.4(0.89%),P:1e-8\n",
"\t\tRound 2: -21.47 ACKTTCCGCG T:201.0(3.94%),B:85.6(1.84%),P:1e-9\n",
"\t\tRound 3: -21.47 ACKTTCCGCG T:201.0(3.94%),B:85.6(1.84%),P:1e-9\n",
"\t\t=Final=: -20.69 ACKTTCCGCG T:196.0(3.92%),B:85.6(1.87%),P:1e-8\n",
"\t\tPerforming exhaustive masking of motif...\n",
"\t\tReprioritizing potential motifs...\n",
"\t20 of 25 Initial Sequence: GTATCGGGTC... (-12.368)\n",
"\t\tRound 1: -23.92 GTATCGGGTC T:88.0(1.74%),B:19.3(0.41%),P:1e-10\n",
"\t\tRound 2: -23.92 GTATCGGGTC T:88.0(1.74%),B:19.3(0.41%),P:1e-10\n"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"\t\t=Final=: -23.38 GTATCGGGTC T:88.0(1.76%),B:19.3(0.42%),P:1e-10\r\n",
"\t\tPerforming exhaustive masking of motif...\n",
"\t\tReprioritizing potential motifs...\n",
"\t21 of 25 Initial Sequence: ACCGCGATAC... (-12.351)\n",
"\t\tRound 1: -20.10 ACCGCGATAC T:52.0(1.03%),B:7.7(0.15%),P:1e-8\n",
"\t\tRound 2: -20.10 ACCGCGATAC T:52.0(1.03%),B:7.7(0.15%),P:1e-8\n",
"\t\t=Final=: -18.17 ACCGCGATAC T:50.0(1.00%),B:7.7(0.17%),P:1e-7\n",
"\t\tPerforming exhaustive masking of motif...\n",
"\t\tReprioritizing potential motifs...\n",
"\t22 of 25 Initial Sequence: CAGAGCCAAC... (-12.244)\n",
"\t\tRound 1: -21.72 CAGAGCCAAC T:139.0(2.74%),B:48.4(1.04%),P:1e-9\n",
"\t\tRound 2: -22.91 CAGAGCCAAC T:264.0(5.14%),B:122.2(2.63%),P:1e-9\n",
"\t\tRound 3: -25.03 CAGAGCCAAC T:253.0(4.93%),B:110.3(2.38%),P:1e-10\n",
"\t\tRound 4: -25.03 CAGAGCCAAC T:253.0(4.93%),B:110.3(2.38%),P:1e-10\n",
"\t\t=Final=: -24.79 CAGAGCCAAC T:239.0(4.78%),B:105.0(2.30%),P:1e-10\n",
"\t\tPerforming exhaustive masking of motif...\n",
"\t\tReprioritizing potential motifs...\n",
"\t23 of 25 Initial Sequence: GCCCCATCGA... (-12.162)\n",
"\t\tRound 1: -22.87 GCCCCATCGA T:48.0(0.96%),B:5.0(0.09%),P:1e-9\n",
"\t\tRound 2: -22.87 GCCCCATCGA T:48.0(0.96%),B:5.0(0.09%),P:1e-9\n",
"\t\t=Final=: -21.61 GCCCCATCGA T:48.0(0.96%),B:5.0(0.11%),P:1e-9\n",
"\t\tPerforming exhaustive masking of motif...\n",
"\t\tReprioritizing potential motifs...\n",
"\t24 of 25 Initial Sequence: CCGCGCTCTT... (-12.097)\n",
"\t\tRound 1: -24.09 CCGCGCTCTT T:62.0(1.23%),B:8.8(0.17%),P:1e-10\n",
"\t\tRound 2: -24.30 CCGCGCTCTT T:43.0(0.86%),B:1.1(0.02%),P:1e-10\n",
"\t\tRound 3: -24.30 CCGCGCTCTT T:43.0(0.86%),B:1.1(0.02%),P:1e-10\n",
"\t\t=Final=: -24.93 CCGCGCTCTT T:43.0(0.86%),B:1.1(0.02%),P:1e-10\n",
"\t\tPerforming exhaustive masking of motif...\n",
"\t\tReprioritizing potential motifs...\n",
"\t25 of 25 Initial Sequence: CCGTCCAAAA... (-12.076)\n",
"\t\tRound 1: -21.50 CCGTCCAAAA T:253.0(4.93%),B:118.6(2.55%),P:1e-9\n",
"\t\tRound 2: -22.55 CCGTCCAAAA T:236.0(4.61%),B:105.6(2.27%),P:1e-9\n",
"\t\tRound 3: -22.55 CCGTCCAAAA T:236.0(4.61%),B:105.6(2.27%),P:1e-9\n",
"\t\t=Final=: -19.81 CCGTCCAAAA T:222.0(4.44%),B:104.3(2.28%),P:1e-8\n",
"\t\tPerforming exhaustive masking of motif...\n",
"\t\tReprioritizing potential motifs...\n",
"\n",
"\tFinalizing Enrichment Statistics (new in v3.4)\n",
"\tReading input files...\n",
"\t9788 total sequences read\n",
"\tCache length = 11180\n",
"\tUsing hypergeometric scoring\n",
"\tChecking enrichment of 25 motif(s)\n",
"\t|0% 50% 100%|\n",
"\t=================================================================================\n"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"\tOutput in file: homer_out/homerMotifs.motifs10\r\n",
"\n",
"\n",
"\t-blen automatically set to 2\n",
"\tScanning input files...\n",
"\tParsing sequences...\n",
"\t|0% 50% 100%|\n",
"\t================================================================================\n",
"\tTotal number of Oligos: 2310136\n",
"\tAutoadjustment for sequence coverage in background: 1.00x\n",
"\n",
"\tOligos: 2310136 of 3807532 max\n",
"\tTree : 5608896 of 19037660 max\n",
"\tOptimizing memory usage...\n",
"\tCache length = 11180\n",
"\tUsing hypergeometric scoring\n",
"\n",
"\tGlobal Optimization Phase: Looking for enriched oligos with up to 1 mismatches...\n",
"\n",
"\tScreening oligos 2310136 (allowing 0 mismatches):\n",
"\t|0% 50% 100%|\n",
"\t================================================================================\n",
"\t\t39.07% skipped, 60.93% checked (1407477 of 2310136), of those checked:\n",
"\t\t39.07% not in target, 0.00% increased p-value, 0.00% high p-value\n",
"\n",
"\tScreening oligos 2310136 (allowing 1 mismatches):\n",
"\t|0% 50% 100%|\n",
"\t=================================================================="
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"==============\n",
"\t\t39.07% skipped, 60.93% checked (1407477 of 2310136), of those checked:\n",
"\t\t0.00% not in target, 24.88% increased p-value, 40.19% high p-value\n",
"\tReading input files...\n",
"\t9788 total sequences read\n",
"\tCache length = 11180\n",
"\tUsing hypergeometric scoring\n",
"\n",
"\tLocal Optimization Phase:\n",
"\t1 of 25 Initial Sequence: CCACCAGGGGGC... (-164.275)\n",
"\t\tRound 1: -706.73 CCACCAGGGGGC T:3815.0(53.38%),B:867.3(17.28%),P:1e-306\n",
"\t\tRound 2: -788.82 CCACYAGGKGGC T:2919.0(44.23%),B:448.9(9.34%),P:1e-342\n",
"\t\tRound 3: -788.82 CCACYAGGKGGC T:2919.0(44.23%),B:448.9(9.34%),P:1e-342\n",
"\t\t=Final=: -1379.25 CCACYAGGKGGC T:3890.0(77.80%),B:1166.2(25.52%),P:1e-598\n",
"\t\tPerforming exhaustive masking of motif...\n",
"\t\tReprioritizing potential motifs...\n",
"\t2 of 25 Initial Sequence: GCAGCTAGAGGG... (-22.450)\n",
"\t\tRound 1: -29.81 GCAGCTAGAGGG T:63.0(1.25%),B:5.8(0.11%),P:1e-12\n",
"\t\tRound 2: -31.41 GCAGCTAGAGGG T:78.0(1.55%),B:9.7(0.20%),P:1e-13\n",
"\t\tRound 3: -33.08 GCAGCTAGAGGG T:75.0(1.49%),B:7.6(0.15%),P:1e-14\n",
"\t\tRound 4: -33.72 GCAGCHAGAGGG T:116.0(2.29%),B:22.6(0.48%),P:1e-14\n",
"\t\tRound 5: -39.86 GCAGCHAGAGGG T:121.0(2.39%),B:19.3(0.41%),P:1e-17\n",
"\t\tRound 6: -39.86 GCAGCHAGAGGG T:121.0(2.39%),B:19.3(0.41%),P:1e-17\n",
"\t\t=Final=: -37.88 GCAGCHAGAGGG T:120.0(2.40%),B:20.5(0.45%),P:1e-16\n",
"\t\tPerforming exhaustive masking of motif...\n",
"\t\tReprioritizing potential motifs...\n",
"\t3 of 25 Initial Sequence: GAAACAGCCACC... (-20.527)\n",
"\t\tRound 1: -27.43 GAAACAGCCACC T:52.0(1.03%),B:3.3(0.07%),P:1e-11\n",
"\t\tRound 2: -31.20 GAAACAGCCACC T:104.0(2.06%),B:19.1(0.41%),P:1e-13\n",
"\t\tRound 3: -31.20 GAAACAGCCACC T:104.0(2.06%),B:19.1(0.41%),P:1e-13\n",
"\t\t=Final=: -29.09 GAAACAGCCACC T:100.0(2.00%),B:19.1(0.42%),P:1e-12\n",
"\t\tPerforming exhaustive masking of motif...\n",
"\t\tReprioritizing potential motifs...\n",
"\t4 of 25 Initial Sequence: CCTGAGAGGATG... (-17.090)\n",
"\t\tRound 1: -26.07 CCTGAGAGGATG T:81.0(1.61%),B:14.2(0.31%),P:1e-11\n",
"\t\tRound 2: -26.07 CCTGAGAGGATG T:81.0(1.61%),B:14.2(0.31%),P:1e-11\n",
"\t\t=Final=: -25.38 CCTGAGAGGATG T:81.0(1.62%),B:14.2(0.31%),P:1e-11\n",
"\t\tPerforming exhaustive masking of motif...\n",
"\t\tReprioritizing potential motifs...\n",
"\t5 of 25 Initial Sequence: GGAAAAGAAGCC... (-16.331)\n",
"\t\tRound 1: -26.70 GGAAAAGAAGCC T:42.0(0.84%),B:0.4(0.00%),P:1e-11\n",
"\t\tRound 2: -26.70 GGAAAAGAAGCC T:42.0(0.84%),B:0.4(0.00%),P:1e-11\n",
"\t\t=Final=: -27.35 GGAAAAGAAGCC T:42.0(0.84%),B:0.4(0.01%),P:1e-11\n",
"\t\tPerforming exhaustive masking of motif...\n",
"\t\tReprioritizing potential motifs...\n",
"\t6 of 25 Initial Sequence: GGCTCTCGCGCC... (-15.607)\n",
"\t\tRound 1: -24.08 GGCTCTCGCGCC T:38.0(0.76%),B:1.0(0.00%),P:1e-10\n",
"\t\tRound 2: -24.08 GGCTCTCGCGCC T:38.0(0.76%),B:1.0(0.00%),P:1e-10\n",
"\t\t=Final=: -24.08 GGCTCTCGCGCC T:37.0(0.74%),B:1.0(0.02%),P:1e-10\n",
"\t\tPerforming exhaustive masking of motif...\n",
"\t\tReprioritizing potential motifs...\n",
"\t7 of 25 Initial Sequence: GCCGCGCCCCCT... (-15.344)\n",
"\t\tRound 1: -24.16 GCCGCGCCCCCT T:97.0(1.92%),B:23.6(0.50%),P:1e-10\n",
"\t\tRound 2: -24.16 GCCGCGCCCCCT T:97.0(1.92%),B:23.6(0.50%),P:1e-10\n",
"\t\t=Final=: -20.16 GCCGCGCCCCCT T:89.0(1.78%),B:23.6(0.52%),P:1e-8\n",
"\t\tPerforming exhaustive masking of motif...\n",
"\t\tReprioritizing potential motifs...\n",
"\t8 of 25 Initial Sequence: CCCTGGGAGGTC... (-15.053)\n",
"\t\tRound 1: -23.67 CCCTGGGAGGTC T:42.0(0.84%),B:2.4(0.04%),P:1e-10\n",
"\t\tRound 2: -30.63 CCCTGGGAGGTC T:53.0(1.05%),B:2.6(0.04%),P:1e-13\n",
"\t\tRound 3: -30.63 CCCTGGGAGGTC T:53.0(1.05%),B:2.6(0.04%),P:1e-13\n",
"\t\t=Final=: -28.66 CCCTGGGAGGTC T:53.0(1.06%),B:2.6(0.06%),P:1e-12\n",
"\t\tPerforming exhaustive masking of motif...\n",
"\t\tReprioritizing potential motifs...\n",
"\t9 of 25 Initial Sequence: CCGCTCAGGCCG... (-14.956)\n",
"\t\tRound 1: -28.72 CCGCTCAGGCCG T:50.0(1.00%),B:2.2(0.04%),P:1e-12\n"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"\t\tRound 2: -28.72 CCGCTCAGGCCG T:50.0(1.00%),B:2.2(0.04%),P:1e-12\n",
"\t\t=Final=: -26.81 CCGCTCAGGCCG T:50.0(1.00%),B:2.2(0.05%),P:1e-11\n",
"\t\tPerforming exhaustive masking of motif...\n",
"\t\tReprioritizing potential motifs...\n",
"\t10 of 25 Initial Sequence: CAGGAATGGCCT... (-14.713)\n",
"\t\tRound 1: -20.50 CAGGAATGGCCT T:44.0(0.88%),B:4.6(0.09%),P:1e-8\n",
"\t\tRound 2: -20.50 CAGGAATGGCCT T:44.0(0.88%),B:4.6(0.09%),P:1e-8\n",
"\t\t=Final=: -18.23 CAGGAATGGCCT T:55.0(1.10%),B:10.0(0.22%),P:1e-7\n",
"\t\tPerforming exhaustive masking of motif...\n",
"\t\tReprioritizing potential motifs...\n",
"\t11 of 25 Initial Sequence: GACCCAGAAGCC... (-14.492)\n",
"\t\tRound 1: -23.41 GACCCAGAAGCC T:58.0(1.15%),B:7.3(0.15%),P:1e-10\n",
"\t\tRound 2: -23.41 GACCCAGAAGCC T:58.0(1.15%),B:7.3(0.15%),P:1e-10\n",
"\t\t=Final=: -21.91 GACCCAGAAGCC T:57.0(1.14%),B:7.3(0.16%),P:1e-9\n",
"\t\tPerforming exhaustive masking of motif...\n",
"\t\tReprioritizing potential motifs...\n",
"\t12 of 25 Initial Sequence: CCAACAGCGCAG... (-14.311)\n",
"\t\tRound 1: -18.89 CCAACAGCGCAG T:38.0(0.76%),B:3.7(0.07%),P:1e-8\n",
"\t\tRound 2: -18.89 CCAACAGCGCAG T:38.0(0.76%),B:3.7(0.07%),P:1e-8\n",
"\t\t=Final=: -17.01 CCAACAGCGCAG T:37.0(0.74%),B:3.7(0.08%),P:1e-7\n",
"\t\tPerforming exhaustive masking of motif...\n",
"\t\tReprioritizing potential motifs...\n",
"\t13 of 25 Initial Sequence: CCCTTGGTCTGG... (-14.280)\n",
"\t\tRound 1: -20.53 CCCTTGGTCTGG T:37.0(0.74%),B:2.5(0.04%),P:1e-8\n",
"\t\tRound 2: -22.13 CCCTTGGTCTGG T:35.0(0.70%),B:0.2(0.00%),P:1e-9\n",
"\t\tRound 3: -23.67 CCCTTGGTCTGG T:42.0(0.84%),B:2.9(0.04%),P:1e-10\n",
"\t\tRound 4: -24.30 CCMTTGGTCTKG T:43.0(0.86%),B:2.5(0.04%),P:1e-10\n",
"\t\tRound 5: -24.97 YCMYTGBTCTKG T:48.0(0.96%),B:3.7(0.07%),P:1e-10\n",
"\t\tRound 6: -24.97 YCMYTGBTCTKG T:48.0(0.96%),B:3.7(0.07%),P:1e-10\n",
"\t\t=Final=: -21.09 YCMYTGBTCTKG T:44.0(0.88%),B:3.7(0.08%),P:1e-9\n",
"\t\tPerforming exhaustive masking of motif...\n",
"\t\tReprioritizing potential motifs...\n",
"\t14 of 25 Initial Sequence: ACCCCGTCCCCC... (-14.143)\n",
"\t\tRound 1: -18.03 ACCCCGTCCCCC T:33.0(0.66%),B:1.2(0.02%),P:1e-7\n",
"\t\tRound 2: -25.56 ACCCCGTCCCCC T:45.0(0.90%),B:1.2(0.02%),P:1e-11\n",
"\t\tRound 3: -25.56 ACCCCGTCCCCC T:45.0(0.90%),B:1.2(0.02%),P:1e-11\n",
"\t\t=Final=: -26.19 ACCCCGTCCCCC T:45.0(0.90%),B:1.2(0.03%),P:1e-11\n",
"\t\tPerforming exhaustive masking of motif...\n",
"\t\tReprioritizing potential motifs...\n",
"\t15 of 25 Initial Sequence: GCGCCTGGAAGC... (-14.123)\n",
"\t\tRound 1: -19.19 NCGCCTGGANNN T:102.0(2.02%),B:31.5(0.68%),P:1e-8\n",
"\t\tRound 2: -19.19 NCGCCTGGANNN T:102.0(2.02%),B:31.5(0.68%),P:1e-8\n",
"\t\t=Final=: -17.30 NCGCCTGGANNN T:97.0(1.94%),B:31.5(0.69%),P:1e-7\n",
"\t\tPerforming exhaustive masking of motif...\n",
"\t\tReprioritizing potential motifs...\n",
"\t16 of 25 Initial Sequence: TTCTCCCGCCGC... (-14.830)\n",
"\t\tRound 1: -15.55 TTCTCCCGCCGC T:29.0(0.58%),B:1.3(0.02%),P:1e-6\n",
"\t\tRound 2: -18.03 TTCTCCCGCCGC T:33.0(0.66%),B:1.3(0.02%),P:1e-7\n",
"\t\tRound 3: -18.75 TTCTCCCGCCGC T:41.0(0.82%),B:4.5(0.09%),P:1e-8\n",
"\t\tRound 4: -18.89 TKCTCCCGCCGC T:38.0(0.76%),B:3.6(0.07%),P:1e-8\n",
"\t\tRound 5: -21.68 TTCTCCCGCCGC T:46.0(0.92%),B:4.9(0.09%),P:1e-9\n",
"\t\tRound 6: -21.68 TTCTCCCGCCGC T:46.0(0.92%),B:4.9(0.09%),P:1e-9\n",
"\t\t=Final=: -20.46 TTCTCCCGCCGC T:46.0(0.92%),B:4.9(0.11%),P:1e-8\n",
"\t\tPerforming exhaustive masking of motif...\n",
"\t\tReprioritizing potential motifs...\n",
"\t17 of 25 Initial Sequence: CCCCGAGGGCAG... (-13.959)\n",
"\t\tRound 1: -21.47 CCCCGAGGGCAG T:34.0(0.68%),B:0.0(0.00%),P:1e-9\n",
"\t\tRound 2: -27.35 CCCCGAGGGCAG T:43.0(0.86%),B:0.0(0.00%),P:1e-11\n",
"\t\tRound 3: -27.35 CCCCGAGGGCAG T:43.0(0.86%),B:0.0(0.00%),P:1e-11\n",
"\t\t=Final=: -26.70 CCCCGAGGGCAG T:41.0(0.82%),B:0.0(0.00%),P:1e-11\n",
"\t\tPerforming exhaustive masking of motif...\n",
"\t\tReprioritizing potential motifs...\n",
"\t18 of 25 Initial Sequence: CTCCCCAAAGCT... (-13.732)\n"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"\t\tRound 1: -19.28 CTCCCCAAAGCT T:35.0(0.70%),B:2.2(0.04%),P:1e-8\n",
"\t\tRound 2: -19.28 CTCCCCAAAGCT T:35.0(0.70%),B:2.2(0.04%),P:1e-8\n",
"\t\t=Final=: -18.03 CTCCCCAAAGCT T:32.0(0.64%),B:1.4(0.03%),P:1e-7\n",
"\t\tPerforming exhaustive masking of motif...\n",
"\t\tReprioritizing potential motifs...\n",
"\t19 of 25 Initial Sequence: AGTGTTGGTTCT... (-13.696)\n",
"\t\tRound 1: -16.91 AGTGTTGGTTCT T:27.0(0.54%),B:0.2(0.00%),P:1e-7\n",
"\t\tRound 2: -24.08 AGTGTTGGTTCT T:38.0(0.76%),B:0.6(0.00%),P:1e-10\n",
"\t\tRound 3: -24.08 AGTGTTGGTTCT T:38.0(0.76%),B:0.6(0.00%),P:1e-10\n",
"\t\t=Final=: -24.74 AGTGTTGGTTCT T:38.0(0.76%),B:0.6(0.01%),P:1e-10\n",
"\t\tPerforming exhaustive masking of motif...\n",
"\t\tReprioritizing potential motifs...\n",
"\t20 of 25 Initial Sequence: CCCTGTTCTCTA... (-13.696)\n",
"\t\tRound 1: -19.92 CCCTGTTCTCTA T:43.0(0.86%),B:4.1(0.09%),P:1e-8\n",
"\t\tRound 2: -19.92 CCCTGTTCTCTA T:43.0(0.86%),B:4.1(0.09%),P:1e-8\n",
"\t\t=Final=: -19.28 CCCTGTTCTCTA T:34.0(0.68%),B:1.5(0.03%),P:1e-8\n",
"\t\tPerforming exhaustive masking of motif...\n",
"\t\tReprioritizing potential motifs...\n",
"\t21 of 25 Initial Sequence: AGGGCAAGAACT... (-13.696)\n",
"\t\tRound 1: -13.70 AGGGCAAGAACT T:26.0(0.52%),B:2.7(0.04%),P:1e-5\n",
"\t\tRound 2: -13.70 AGGGCAAGAACT T:26.0(0.52%),B:2.7(0.04%),P:1e-5\n",
"\t\t=Final=: -11.79 AGGGCAAGAACT T:25.0(0.50%),B:2.7(0.06%),P:1e-5\n",
"\t\tPerforming exhaustive masking of motif...\n",
"\t\tReprioritizing potential motifs...\n",
"\t22 of 25 Initial Sequence: CTCTGCACCTCC... (-13.696)\n",
"\t\tRound 1: -20.32 CTCTGCACCTCC T:114.0(2.25%),B:36.9(0.78%),P:1e-8\n",
"\t\tRound 2: -20.32 CTCTGCACCTCC T:114.0(2.25%),B:36.9(0.78%),P:1e-8\n",
"\t\t=Final=: -20.00 CTCTGCACCTCC T:113.0(2.26%),B:36.9(0.81%),P:1e-8\n",
"\t\tPerforming exhaustive masking of motif...\n",
"\t\tReprioritizing potential motifs...\n",
"\t23 of 25 Initial Sequence: CCAAGACCTAGA... (-13.653)\n",
"\t\tRound 1: -14.96 CCAAGACCTAGA T:24.0(0.48%),B:0.4(0.00%),P:1e-6\n",
"\t\tRound 2: -14.96 CCAAGACCTAGA T:24.0(0.48%),B:0.4(0.00%),P:1e-6\n",
"\t\t=Final=: -15.61 CCAAGACCTAGA T:24.0(0.48%),B:0.4(0.01%),P:1e-6\n",
"\t\tPerforming exhaustive masking of motif...\n",
"\t\tReprioritizing potential motifs...\n",
"\t24 of 25 Initial Sequence: CTGCTTCATGGA... (-13.577)\n",
"\t\tRound 1: -18.03 CTGCTTCATGGA T:33.0(0.66%),B:3.0(0.04%),P:1e-7\n",
"\t\tRound 2: -21.15 CTGCTTCATGGA T:38.0(0.76%),B:2.4(0.04%),P:1e-9\n",
"\t\tRound 3: -21.15 CTGCTTCATGGA T:38.0(0.76%),B:2.4(0.04%),P:1e-9\n",
"\t\t=Final=: -18.89 CTGCTTCATGGA T:37.0(0.74%),B:2.4(0.05%),P:1e-8\n",
"\t\tPerforming exhaustive masking of motif...\n",
"\t\tReprioritizing potential motifs...\n",
"\t25 of 25 Initial Sequence: AGAAGTGACCAT... (-13.536)\n",
"\t\tRound 1: -17.09 AGAAGTGACCAT T:35.0(0.70%),B:3.9(0.07%),P:1e-7\n",
"\t\tRound 2: -18.65 AGAAGTGACCAT T:34.0(0.68%),B:2.6(0.04%),P:1e-8\n",
"\t\tRound 3: -21.78 AGAAGTGACCAT T:39.0(0.78%),B:2.9(0.04%),P:1e-9\n",
"\t\tRound 4: -21.78 AGAAGTGACCAT T:39.0(0.78%),B:2.9(0.04%),P:1e-9\n",
"\t\t=Final=: -20.10 AGAAGTGACCAT T:39.0(0.78%),B:2.9(0.06%),P:1e-8\n",
"\t\tPerforming exhaustive masking of motif...\n",
"\t\tReprioritizing potential motifs...\n",
"\n",
"\tFinalizing Enrichment Statistics (new in v3.4)\n",
"\tReading input files...\n",
"\t9788 total sequences read\n",
"\tCache length = 11180\n",
"\tUsing hypergeometric scoring\n",
"\tChecking enrichment of 25 motif(s)\n",
"\t|0% 50% 100%|\n",
"\t===================================="
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"=============================================\n",
"\tOutput in file: homer_out/homerMotifs.motifs12\n",
"\n",
"\t(Motifs in homer2 format)\n",
"\tDetermining similar motifs... 74 reduced to 48 motifs\n",
"\tOutputing HTML and sequence logos for motif comparison...\n",
"\tChecking de novo motifs against known motifs...\n",
"\tFormatting HTML page...\n",
"\t\t1 of 48 (1e-598) similar to BORIS(Zf)/K562-CTCFL-ChIP-Seq(GSE32465)/Homer(0.937)\n",
"\t\t2 of 48 (1e-46) similar to brk/dmmpmm(Bergman)/fly(0.748)\n",
"\t\t3 of 48 (1e-28) similar to RPN4/RPN4_H2O2Lo/[](Harbison)/Yeast(0.787)\n",
"\t\t4 of 48 (1e-17) similar to Unknown-ESC-element(?)/mES-Nanog-ChIP-Seq(GSE11724)/Homer(0.729)\n",
"\t\t5 of 48 (1e-16) similar to Sp1(Zf)/Promoter/Homer(0.847)\n",
"\t\t6 of 48 (1e-15) similar to AtGRF6(GRF)/col-AtGRF6-DAP-Seq(GSE60143)/Homer(0.764)\n",
"\t\t7 of 48 (1e-14) similar to PBF/MA0064.1/Jaspar(0.618)\n",
"\t\t8 of 48 (1e-14) similar to ERF2(AP2EREBP)/colamp-ERF2-DAP-Seq(GSE60143)/Homer(0.797)\n",
"\t\t9 of 48 (1e-13) similar to At2g41835(C2H2)/col-At2g41835-DAP-Seq(GSE60143)/Homer(0.781)\n",
"\t\t10 of 48 (1e-13) similar to CRZ1(MacIsaac)/Yeast(0.741)\n",
"\t\t11 of 48 (1e-13) similar to SOK2/SOK2_BUT14/4-SUT1(Harbison)/Yeast(0.774)\n",
"\t\t12 of 48 (1e-12) similar to sna/dmmpmm(Bigfoot)/fly(0.806)\n",
"\t\t13 of 48 (1e-12) similar to GLIS3(Zf)/Thyroid-Glis3.GFP-ChIP-Seq(GSE103297)/Homer(0.756)\n",
"\t\t14 of 48 (1e-12) similar to CHA4/MA0283.1/Jaspar(0.806)\n",
"\t\t15 of 48 (1e-11) similar to BAS1(MacIsaac)/Yeast(0.669)\n",
"\t\t16 of 48 (1e-11) similar to POL011.1_XCPE1/Jaspar(0.667)\n",
"\t\t17 of 48 (1e-10) similar to ZNF189(Zf)/HEK293-ZNF189.GFP-ChIP-Seq(GSE58341)/Homer(0.683)\n",
"\t\t18 of 48 (1e-10) similar to PB0153.1_Nr2f2_2/Jaspar(0.697)\n",
"\t\t19 of 48 (1e-10) similar to Nkx2-5(var.2)/MA0503.1/Jaspar(0.665)\n",
"\t\t20 of 48 (1e-10) similar to HINFP/MA0131.2/Jaspar(0.795)\n",
"\t\t21 of 48 (1e-10) similar to RDR1/MA0360.1/Jaspar(0.789)\n",
"\t\t22 of 48 (1e-10) similar to ERF018/MA1048.1/Jaspar(0.691)\n",
"\t\t23 of 48 (1e-10) similar to TFAP2A(var.3)/MA0872.1/Jaspar(0.792)\n",
"\t\t24 of 48 (1e-10) similar to STB4/MA0391.1/Jaspar(0.710)\n",
"\t\t25 of 48 (1e-9) similar to ZNF143|STAF(Zf)/CUTLL-ZNF143-ChIP-Seq(GSE29600)/Homer(0.695)\n",
"\t\t26 of 48 (1e-9) similar to YAP5/MA0417.1/Jaspar(0.709)\n",
"\t\t27 of 48 (1e-9) similar to UGA3/MA0410.1/Jaspar(0.753)\n",
"\t\t28 of 48 (1e-9) similar to RCS1(MacIsaac)/Yeast(0.653)\n",
"\t\t29 of 48 (1e-9) similar to Sox6/MA0515.1/Jaspar(0.611)\n",
"\t\t30 of 48 (1e-9) similar to MET31/Literature(Harbison)/Yeast(0.747)\n",
"\t\t31 of 48 (1e-8) similar to RBM4(RRM,Znf)/Homo_sapiens-RNCMPT00052-PBM/HughesRNA(0.723)\n",
"\t\t32 of 48 (1e-8) similar to OPI1/Literature(Harbison)/Yeast(0.747)\n",
"\t\t33 of 48 (1e-8) similar to MBP1(MacIsaac)/Yeast(0.804)\n",
"\t\t34 of 48 (1e-8) similar to TEC1/TEC1_YPD/[](Harbison)/Yeast(0.705)\n",
"\t\t35 of 48 (1e-8) similar to RTG3/Literature(Harbison)/Yeast(0.651)\n",
"\t\t36 of 48 (1e-8) similar to XBP1(MacIsaac)/Yeast(0.702)\n",
"\t\t37 of 48 (1e-8) similar to SRSF10(RRM)/Homo_sapiens-RNCMPT00019-PBM/HughesRNA(0.706)\n",
"\t\t38 of 48 (1e-8) similar to RAP211/MA1266.1/Jaspar(0.661)\n",
"\t\t39 of 48 (1e-7) similar to Zac1(Zf)/Neuro2A-Plagl1-ChIP-Seq(GSE75942)/Homer(0.687)\n",
"\t\t40 of 48 (1e-7) similar to RSF1(RRM)/Drosophila_melanogaster-RNCMPT00061-PBM/HughesRNA(0.770)\n",
"\t\t41 of 48 (1e-7) similar to Smad3(MAD)/NPC-Smad3-ChIP-Seq(GSE36673)/Homer(0.709)\n",
"\t\t42 of 48 (1e-6) similar to POL008.1_DCE_S_I/Jaspar(0.692)\n",
"\t\t43 of 48 (1e-6) similar to ESRP2(RRM)/Homo_sapiens-RNCMPT00150-PBM/HughesRNA(0.673)\n",
"\t\t44 of 48 (1e-6) similar to ZBTB33/MA0527.1/Jaspar(0.735)\n",
"\t\t45 of 48 (1e-6) similar to TUT1(RRM,Znf)/Homo_sapiens-RNCMPT00075-PBM/HughesRNA(0.659)\n",
"\t\t46 of 48 (1e-6) similar to GATA6/MA1396.1/Jaspar(0.637)\n",
"\t\t47 of 48 (1e-5) similar to PB0094.1_Zfp128_1/Jaspar(0.841)\n",
"\t\t48 of 48 (1e-4) similar to TBP3(MYBrelated)/col-TBP3-DAP-Seq(GSE60143)/Homer(0.637)\n",
"\tJob finished\n",
"\n"
],
"name": "stdout"
}
]
},
{
"metadata": {
"id": "nDz3dPXldwV2",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 258
},
"outputId": "46e5df86-f699-4fb4-d359-528170cdd697"
},
"cell_type": "code",
"source": [
"!cat homer_out/homerResults/motif1.motif"
],
"execution_count": 31,
"outputs": [
{
"output_type": "stream",
"text": [
">CCACYAGGKGGC\t1-CCACYAGGKGGC,BestGuess:BORIS(Zf)/K562-CTCFL-ChIP-Seq(GSE32465)/Homer(0.937)\t6.716483\t-1379.247391\t0\tT:3890.0(77.80%),B:1166.2(25.51%),P:1e-598\r\n",
"0.090\t0.772\t0.068\t0.070\r\n",
"0.010\t0.982\t0.001\t0.007\r\n",
"0.702\t0.095\t0.088\t0.115\r\n",
"0.052\t0.596\t0.323\t0.029\r\n",
"0.162\t0.427\t0.053\t0.357\r\n",
"0.772\t0.012\t0.059\t0.157\r\n",
"0.038\t0.001\t0.951\t0.010\r\n",
"0.400\t0.006\t0.584\t0.010\r\n",
"0.080\t0.024\t0.464\t0.432\r\n",
"0.028\t0.004\t0.948\t0.020\r\n",
"0.024\t0.030\t0.863\t0.083\r\n",
"0.158\t0.739\t0.012\t0.091\r\n"
],
"name": "stdout"
}
]
},
{
"metadata": {
"id": "-bkFn9t0EcIK",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 71
},
"outputId": "226e2c70-e363-46a0-fb2a-ff8bef8a9f5a"
},
"cell_type": "code",
"source": [
"!pip install deeplift"
],
"execution_count": 32,
"outputs": [
{
"output_type": "stream",
"text": [
"Requirement already satisfied: deeplift in /usr/local/lib/python3.6/dist-packages (0.6.6)\r\n",
"Requirement already satisfied: numpy>=1.9 in /usr/local/lib/python3.6/dist-packages (from deeplift) (1.14.5)\r\n"
],
"name": "stdout"
}
]
},
{
"metadata": {
"id": "V1A5XP2U74zF",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 177
},
"outputId": "5c34427a-f78a-4f04-dd23-ced29b33d285"
},
"cell_type": "code",
"source": [
"import numpy as np\n",
"motif_mat = np.array([[float(y) for y in x.split(\"\\t\")] for x in\n",
"\"\"\"0.090\t0.772\t0.068\t0.070\n",
"0.010\t0.982\t0.001\t0.007\n",
"0.702\t0.095\t0.088\t0.115\n",
"0.052\t0.596\t0.323\t0.029\n",
"0.162\t0.427\t0.053\t0.357\n",
"0.772\t0.012\t0.059\t0.157\n",
"0.038\t0.001\t0.951\t0.010\n",
"0.400\t0.006\t0.584\t0.010\n",
"0.080\t0.024\t0.464\t0.432\n",
"0.028\t0.004\t0.948\t0.020\n",
"0.024\t0.030\t0.863\t0.083\n",
"0.158\t0.739\t0.012\t0.091\"\"\".split(\"\\n\")])\n",
"from deeplift.visualization import viz_sequence\n",
"viz_sequence.plot_weights(motif_mat)"
],
"execution_count": 34,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAABH8AAACMCAYAAADlYgUmAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nwMH+BaUSa3okkkWjAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f598e9c10f0>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"metadata": {
"id": "oxJbYi278KJD",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
""
],
"execution_count": 0,
"outputs": []
}
]
}
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment