{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Transcriptomes\n",
    "==============\n",
    "\n",
    "RNAvigate has some functionality to extract transcript-coordinate data from\n",
    "genomic-coordinate data files."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import rnavigate as rnav\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Transcripts\n",
    "-----------\n",
    "\n",
    "First, we need to set up the genome and transcriptome annotations, then we can retreive information about our transcript(s) of interest, here SERPINA1 (Ensembl ID: ENST00000393087.9).\n",
    "\n",
    "As we'll see later, this `Transcript` object provides useful tools on it's own, and can be used with BED files to extract transcript-coordinate profiles or annotations.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "GRCh38 = rnav.transcriptomics.Transcriptome(\n",
    "    genome=\"GCF_000001405.26_GRCh38_genomic.fna\",\n",
    "    annotation=\"MANE.GRCh38.v1.0.ensembl_genomic.gtf\"\n",
    ")\n",
    "\n",
    "SERPINA1 = GRCh38.get_transcript(\"ENST00000393087.9\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "eCLIP Peaks\n",
    "-----------\n",
    "\n",
    "RNAvigate parses BED6 and narrowPeak (BED6+4) files, and includes specific functions to download peak files from the ENCORE eCLIP database.\n",
    "\n",
    "First, we can use `rnav.transcriptomics.download_eclip_peaks` to retreive the eCLIP peaks from\n",
    "[ENCORE](https://www.encodeproject.org/encore-matrix/?type=Experiment&status=released&internal_tags=ENCORE).\n",
    "This downloads one narrowPeak file for each combination of protein target and cell line (K562 and HepG2).\n",
    "We only need to do this once.\n",
    "The data can be saved to a central location and reused in other notebooks.\n",
    "\n",
    "With these files, we can create the eCLIP \"database\" using `rnav.transcriptomics.eCLIPDatabase`.\n",
    "\n",
    "To help us to start thinking about this data, we can display all of the proteins that bind SERPINA1. Binding sites will be displayed in transcript coordinates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K562     AATF     62-145\n",
      "HepG2    AKAP1    1424-1476 1737-1797 2092-2126 2317-2350\n",
      "HepG2    DDX3X    2-43 48-91 92-121\n",
      "K562     DHX30    190-239 309-350 398-467\n",
      "HepG2    IGF2BP1  156-163 164-185 186-190 191-207 208-216 217-236 237-240 241-248 249-254 255-272 273-280 281-284 285-289 290-299 300-320 321-330 331-360 374-383 905-948 949-964 965-980 981-997 1016-1062\n",
      "HepG2    IGF2BP3  169-219 220-238 243-248 249-267 270-288 289-299 300-321 322-348 349-366 367-389 397-413 414-443 444-482 912-929 930-952 953-961 962-964 965-968 969-979 980-994 1015-1044 1177-1236 1247-1297\n",
      "HepG2    ILF3     1426-1466 1741-1811 2250-2338\n",
      "HepG2    LARP4    1236-1279 1280-1282 1283-1288 1289-1299 1300-1310 1311-1334 1335-1370\n",
      "HepG2    LIN28B   779-798 799-819\n",
      "HepG2    NIP7     397-466\n",
      "HepG2    PCBP1    44-88 89-100 101-106 107-123 124-154 218-253 399-447 1299-1328 1329-1360\n",
      "HepG2    PCBP2    1299-1325 1326-1330 1331-1395 1412-1452 2134-2143 2144-2173 2243-2251 2252-2285 2286-2302\n",
      "HepG2    QKI      2525-2624\n",
      "HepG2    RPS3     9-42 48-91 92-105 107-119 121-170 171-185 186-191 192-202 203-226 227-236 237-259 278-293 294-339 368-390 391-469 531-577 694-709 710-753 754-778 808-844 845-870 872-912 991-1001 1027-1042 1060-1093 1094-1098 1099-1106 1107-1112 1113-1121 1122-1171 1173-1230 1239-1282 1283-1297\n",
      "HepG2    SDAD1    529-618 619-652 801-845\n",
      "HepG2    SND1     75-109 110-120 121-145 196-219 220-234 235-264 271-282 283-289 290-296 299-315 316-330 331-345 346-362 363-368 369-375 376-395 396-404 405-412 413-443 446-452 453-470 471-473 475-481 482-510 536-556 557-589 590-594 595-607 608-618 619-624 625-632 633-635 636-643 644-674 687-691 692-693 694-704 705-710 711-729 731-733 734-739 740-748 749-783 816-847 848-858 859-870 871-889 890-913 914-917 918-919 965-968 1014-1041 1042-1045 1046-1057 1058-1059 1060-1065 1066-1075 1076-1083 1084-1106 1107-1112 1113-1121 1122-1123 1124-1126 1127-1137 1138-1148 1150-1173 1175-1186 1187-1191 1192-1233 1308-1358\n",
      "HepG2    UPF1     1426-1544 1704-1818 1887-1917 1951-1988 2079-2132 2143-2192 2262-2311 2312-2372 2846-2900\n",
      "HepG2    ZNF800   737-795 796-818\n"
     ]
    }
   ],
   "source": [
    "eclip_path = \"../../../reference_data/eCLIP_downloads\"\n",
    "# rnav.transcriptomics.download_eclip_peaks(outpath=eclip_path)\n",
    "# rnav.transcriptomics.create_eclip_table(inpath=eclip_path, outpath=eclip_path)\n",
    "eclip = rnav.transcriptomics.eCLIPDatabase(inpath=eclip_path)\n",
    "\n",
    "eclip.print_all_peaks(SERPINA1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creating annotations and profiles\n",
    "---------------------------------\n",
    "\n",
    "We will use the methods of `Transcript` and `eCLIPDatabase` to create annotations and profiles, and assign these directly to data keywords.\n",
    "We can use any data keywords we like for this assignment.\n",
    "\n",
    "`eclip.get_eclip_density` will create a per-nucleotide profile.\n",
    "The value of each nucleotide is the total number of eCLIP peaks overlapping that position.\n",
    "This can be useful to get a sense of overall protein binding and which regions may be functional protein-binding scaffolds.\n",
    "\n",
    "`eclip.get_annotation` will create an annotation of protein binding regions for a given protein target and cell line.\n",
    "\n",
    "`transcript.get_cds_annotation` creates a span annotation to highlight the coding sequence.\n",
    "\n",
    "`transcript.get_junctions_annotation` creates a span annotation to highlight exon-exon junctions.\n",
    "Each span is two nucleotides: the 3' end of the 5' exon, and the 5' end of the 3' exon.\n",
    "\n",
    "`transcript.get_exon_annotation` creates a span annotation to highlight a specified exon.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "test = rnav.Sample(\n",
    "    sample=\"SERPINA1 mRNA\",\n",
    "    SERPINA1=SERPINA1,\n",
    "    eCLIP=eclip.get_eclip_density(transcript=SERPINA1, cell_line=\"HepG2\"),\n",
    "    cds=SERPINA1.get_cds_annotation(color=\"red\"),\n",
    "    ddx3x=eclip.get_annotation(SERPINA1, \"HepG2\", \"DDX3X\", color=\"blue\"),\n",
    "    junctions=SERPINA1.get_junctions_annotation(color=\"black\"),\n",
    "    exon3=SERPINA1.get_exon_annotation(3),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting\n",
    "--------\n",
    "\n",
    "With these profiles and annotations, we can start creating plots.\n",
    "\n",
    "For example, here a profile of eCLIP peak density over SERPINA1.\n",
    "\n",
    "- red bar: coding sequence\n",
    "- blue bars: DDX3X binding regions (in the 5' UTR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warning: seq-object missing expected error column\n"
     ]
    },
    {
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cFPfZZau4+QtfiJsvuii2f9vbhv/RtVek9yOxX+f9n9/Ehj1nlvwbmz7/+eX/5oT9TH728f/m9lcv+vfUfqa+g63+31hWmzfHeRduKtvPjG0m8xr/uxM+a+p7XnPRTPFnW/i/95h0Io999pu/8KXO+70o3zGL9nvCsZTar/EsFp03FedA6nse/5s52xjfr9TniIjksZT631nn5phF+SXOgYmfvfS8ybhutbgOFW8j47MXn5sVn7V0v3KuS8nzZvxvNMgi69xMHUsNPnvqO8i5npZe58evuZPqe/FnHzvG733ZBZ3r0aRtpGpHi3z7+OzL1cCJ7acGtTknr1S7Y1HbZkzyXK1pLyXyzfreK4754mt0Rr6dP3uqfTqpbVNYAyca30bq71a0wWqO1z7yLL4nyPjspe24ZP3Pqc2J6+fEtmIi30Vt74p2ybjUuTfp+llzTHe99o3/jWQWGde6mvvkzu3NjHOz9BzIOra6nlcZ9SiZZ4s+gIx71tT9ZR/3glV/o/T6mJFN1z6BnPO9tB0yqc8gJ8/Oba4G18dxNcd463byNO6XJu1XcT9DTV9aIr+ce5kW93Fd+xVz2rSlfWs5x855b/h1bNhzq5Fzs2ufX+m9dc5xUHps5dT3mu85mUVCzvfc9Z6gxbFU851MOpZKs+i6Xy3O95r7y9Rnz7nXTvWplh6fVX2VNW2bwvZmzrGVPAcyvrPi/yZxzzCpnVx8rtb0gze4hy1ue1e0HZv0exXuR9b9ZuH33mKMtMk4YepecMLvtxjfKG1DTGP8Y1yT873iWEq1uXKudX2MyxT3UWdc91PbSN0rZrXvJ1jus1T1i1XoOt5R89mzxofHJOtmxu+Ufs81+9382ldRN5uM7bToI0jV94zPWjxGWnOfUdhnNenaWHzfVtGGKO77bTCml3O+99F3UXy+V7TBiu9lKo6tmr6LcaV/o2ZuTOm4ds092Pj/rrp+VtzDdu5jyfjsqeMz53tu3e6o6bdJ/Y2sMZQGfZXj52aLuZt93Mcl97PFdb5mXKujrDknhf3vNfOs+uqrLPlOsvqCW4x7le53RRu3631yzTzVnHvD0vMopz416aNKaHGP2qJN28c86q7tzZrrfEQUf8+dP3sPfcGT8u2lX6FxH3VO2zGZZ06ff+GxlbNfpcdS1WevuC8untffw/jmovNownW/tD9kktb1J+ezt9jP5HmV2K+cftpe5gVVPHvReZ5vRnu+6372kmeDuRw57dPi8feK/Uz1XdS0S4r7rHuYU5rTvzRpntVy18uq+fMNxqRKx/hW7D6jwXN8LbZRPB+swZhUk76elZizV3p81szZrciia1un1ZyI4vm0DdrzOfOCOj87MKbJZy/sr8tpb9U8F3lLOR6T3/sUxueq+mVL2yE9zBeZpGvbO0fnvqHM57VL+xk6P2damU3X/Sw+NyvaiuP7mdOuS53vNX3UrcdQc+bbFB8HPdzbRERx27vFsdSij6DF3IOVuCaPa7KuSQ9z34q/s4pnA2vatC3mRHb97FXnUYM2Q4sx/ub3HTnPrq7AvL8mzwq1GJdJtLlq+sFrrpfNxxFXau2PFmMTY3Lmf3WdB1RzT9DHvJbWzw7VnJst+lRzPnsf/XUtxiZSWrQ/+/jsre+xavLNua4X3yO0WJ8h8b9zriml7c0W8whq1mMovkeomXOyAnM7emnP1/SPRPv74tRnr+qnrfheO49BNWijTWUdrgb3MuOy+oIT33OLubCLvqOK/uTSa13V8ZrKr491olr0KybOm9y1P7r225TOhak5j5Lr2Uxp7djO9afFupB9bLOmL20aawxWrPnSue+nhzGqqrlbDcY/ul6Tp/W8x7gW6/CU3htWrQu1EnMkKtanTbVhq57f7mHO2fjvNFn/s6Lvt3T8veZZ1pSq9mZhP03V2mgN8iy9D5vKedRg3cKs5yI7njc1/SE1z2IU951XzBOoeRaw9H6z5j6jtG+yjz6BmueLi9tPUxqXaXEOtFgzo7RfsWbsrPPcrRbrajZ450LqXOxl3LBm/eQePnuL50pTz2L08exAzXhH5zUKasbjMvoq+rh/n8Y8teK15VZo/nyLcZgW1/WkrutT97G+YoM+lhZt73E160SNq2rbFLYZWnzWFt97zTzLlVjnpMWc6HHJ62XF8wkt1sBt8lk7nos5/Xdd/2ZWPWowNp7sV6y4R+j8vrMW86xq3ncWiTZsi3c/VTxr1aR/qeu4QUW/d4u1ZZN59vFejJr11brm2WI/G5ybLd69UdOX1vm+uMXx2aCPqsWcx673703WKGiwPkOLce0+5pQWf/YG48c1/QxN1nUv/A6q1tmrGGeYxrPApc/P1OxnzTb7OBf7mFPaedygh/ZSznfUYg2N4nlrPbw3rOZ5jtQaWTXzu7uupdCij6DJGtct+mkrrq/TGD8qnmNW8Sxbi3vYZJ4Z33vnNljFGHTVPIuux3hFO7nFWn59zFderc/tt54jUTPOVdO/VPMdLLIanrmrue9okUXHOY8t2vO9tEMy+pO7zi3s4zn9iaawPthKjHeMqxnbTW2j5r2BNWt9tO5Lr3mGseo9BB3b8729O7Pj/WaO0vuQqvH4BmuK9vL8Vh/zFXsYP2qxvuI0nv0vfr6wxTzVmve0jVmJObm9PHfax7MtDa6fVWtvF9biaax13GJuTM08wJr7uJXo+615l0Fp22aSPp71rXk+bqE+5rqvyPle0Z6vma9Uur5NL+/fq2jXNTk3e3j+oHS935rnEWreG9h6DfaatXlr3q1Ten/e5F67j37vinrUYp29Fu3N1vO/qubk1zxrOYVnwFOatJcqxvQ7j7fl9C8V1vcWzwZmWYm2d0U/bee5rhnfe+m1rpc+1Zp5LBV9v53HoGvmL/XxzpqKY77zmm0Vn730PazTet9EH89rN1l7ZlyD+/cVOTcT/aE1edbMUy0931vMNUr1Uec8E1o6FtlkTn6DdR/HNZn/WdHP3cf4ZtfrUs07Eyd9lq59v33Mp21xj9BinexFprFmToNn6nvpu+hhLYUWa9H0Ma7ddR3trHvtBn3Upf20Tdo2Pcx57uP6OX4sJZ/raVA7+jg+q46lFmNpLcZhO45rtxhHHD8OasYm+njWsmYe9WqZx1I6jt2iv67zOhJ9PCNS0V9X08fSx/tlOj8bnLHNrsdBX+/v6GOt6M7zpmueP5zGZ2/xfHGUjSPUrvXTuV9sfBsV89JK132s6betWT+x9JmRmjXDk3k2WJN5fJs19+ulax3WvFes9N0QLT7HRIXP9aSO+Zp632pN5tLvpLjfJnH8tZqTX/PsVUl+VfvZ01o0zet7g+Oxj7U8ku9paFCbs3QdY87oD+n6PEIva4r20b9ceb85rvPc9gZrkFWtw9F1zLlF31qD8fnidY0qvveaftrS86hmrKJqHktqmxN+Vtpfn/o7NXON+nivevEcnppnwKewRut4vrVz9lLfSXHbZhrPF1fUitT9UR/rZvfxjtlpvP+9apyrRZthTIt3+LaYG1OcRQ/jxb3U4gZrEDV5z0CDZy1b7FfX8baceSyd798rnttt8UxT8TonU3i2Jeua3Mcc8gbPUaTaYFXPXjWYt9b1etli3mrONkv7j1u8O6/qecPC872mTpa2IWreC9z13qZmDLX2ec7WfUEtnkspbudlfAdd14GsGeee9Fm6bqOP9eoW7WdOP1ihmjm7Ld71lKPuzLkluf7aiDveJWLrbSO22jpizYSPPP47v/rFsv/N2l+N/v7V183EFdduif9x+DZx3jdvjoiILVdcEVvttlts+sIX4vpnPjM2f/3L6f1YZr+uvm6ruOJny/+NTV/60rJ/c9J+pj77ov9m/Pe3Wpvcz9R3MHPV6Of40blfK97P1DY3ffnLybwW/d3ry7/nLddcU/TZrv75lsWfNXF8bvnxjzvtd87fHN/vRcfv2Pc+ab/Gsxg/b0rPgZzvefxv5mxjfL9SnyMife6l/nfWuTlmUX6Jc2DiZy88b3KuWy2uQ6XbyPnspedmzWct3a+c61LyvBn/Gw2yyDo3U8dSg8+e+g5yakXxdX7MoiwmXOtSn338GF+0TxX1aPz3U59jUhal+W768pebf/acGpj8rBW1OZXXNb+cUBdTbZvUsTOeVU17KdUmy/jeS4/5mmt0zvHY9bOnspjYtimsgan9zvq7FW2wmvO7dZ4517rSzz5xm6nrTqr+Z9S41PVzYlsx8b2Ptw1r2iXjUufepNpRekxPvLal/j3xN5JZZFzrau6TO7c3M2pH6TmQdWx1Pa/GtlGTZ4vPmrp/TB1rk475FveCVX+j9PqYUYu79gmkzveadsj4uTrxGpK6ZlS0uVpcH8eVHuM5/R2l16Fp3C9N2q/SfoaavrRUfjn3Mi3u47r2K+a0aYv71hLHziV//tK48qc3j/z+eH41fX7F99YZx0HpsZXTB1j6PWdlkZD6nlvcE7Q4lkq/k0nHUmkWOf20pf0ONed76f1lzmdP3Wvn3CfXHJ/FfZU1bZvS/qTEd5h1DqRqXMb5nvpOUn3rNfchNf3gXe9hq+5lKtqOLfq9Svcj636z8HtvMUbaYpwwOZ45oT+k6/1mTftzGuMf41qc7zXHUqrNlWyn5NSjwvZTTR1IHb+Trm2l94o1fa7jfzfVns/qF6vQdbyj5rNnjQ8vkMoqIt2ezzlWUvuR2u+aOpm89lXUzRZjO036CFJ1MeOzlo6RVt1nFPZZTeq3Kb1vq2lDlPb9thjTy7nOt+67yNnv0rGd8c9acy9Tc2zV9F0slHNNaTE3pnRcu+YebPx/V10/C9s6LfYz9dmzjs8M02h3pKT+Rs4YSte+yknnZou5m63v43L2s8V1vmpcq6OsOSeF/e8186z66KusGUNZpI9xr8L9rmnjdr1Prpmnmro3rDmPsuZltOijSmhxj9qiTdvHPOqu7c2a63xp27DFZ++jL3hSX2Uv9b1xH3VO2zGZZ06ff+GxldqvmmOp5rOX3hfnzLvI6kNt/NknjRt2bVfk9KW1mHPfej9z7uNS+5Xqp+1tXlDhsxc5n7303q9mbCK1n33k2WIuR/KaXfHZS/czp++itF2S065LtZtbzClN9S9N+t5T+14zf77FmFTNWOMi07jPaPAcX9dt1Dw30WJMqkVfz0rM2Ss+PiuufaVZ1Nwb9jEnoqa92bU9n3NNzmkLdp1T2sdc4pr2Vs1zkbeU4zH5vU9hfK6mX7a4HdLDfJFxOX3+WXWy499o8UxTcr5Si2dAa9oQqTwq9rN4bltFWzF1vaxpQ5T2Ufcyhpox36b4eeMe7m0iorzt3eBYatFH0HXuQc19cotr8rgW65r0MfeteF56xbOBVc+IdpwTmTO3sI/zqGubIec7azGHvOa+uPW9YYt5fy2eFWoxLpNqc9XM7Si9Xra41tWc732s/dFkbGJMav5XzrhM6VhZTT9E5zlok475FTg3W/Sp5vQr9tFf1/fcjJwxlibPChV+9j7usWryTV3Xq56DbrA+Q+p/51xTStubLeYRlK7HUHOPUDXnZAXmdvTRnq/pHykd76iZF9iin7b0e20yBtWgjTaVdbga3MuMy+oLTnzPXefCRqSfxci5xpRe61rME5zGOlEt+hVLz5usa0hFv03pXJia8yi5ns0U1o5tUn9arAvZwzarxqCnscZg6Wdv8Bx0H2NUk3QdP0rJOd9bPIPTpH95TNd1eGruDWvWhVqJORI1/eCpNmzV89s9zDlL1bCq9T8r+n5Lx99Ln2XNUdXeLOynybrPa9CXXjXXepnP0cd51GLdwqznIjueN5N0nVPeYt2YmnkCpc8C1txv1txnlPZN9tEnUPN8cXH7aUrjMl3PgUnfUemaGTX9ijVjZ13nbrVYV7PFOxdS52If44Y131Efn73rc6U5z2Ikj4OKue414x1d1yioqZt9rLM3jXe/1DyHX5znFObPtxqHaXFdT+qYRS/rKzboY2nR9h5Xs07UuKq2TWGbocVnbfG9F8+va7AGex99LC36uWtqc9c5Ui3mL9X0y9bMq1qoyRpPEz5r67kHOf2KpfcILd531qSPOlE3c8agu86ZmnjNKHzWqln/Utdxg4p+7xZryybz7OG9GFm1pnWeLfazwbnZ4t0bNX1pne+LWxyfDfqoWsx57Hr/3mSNggbrM7QY1+5jTmlxP1iD8eOafoYm67oXfgdV9+al92BTeha49PmZmv2s2WYf52Ifc0q7jhv00V5KfUct1tCoWuujcF7bpP2qWb8q1Z6vmovQeC2FFn0ETda0b9BPW3N/1Pf4UdUcs4pn2brew2blmfG9d26DVYxBV82z6HqMV7STm6yl1MN85dX43H7OZ62Za1Rab0r7l2q/g0VWwzN3LcYZarLoOOexRXu+j3ZIi3V0i5+LbvCc/kQdx49W63jHuJqx3dQ2at4bWLrWR02fVR/PMFa9h6Bje35cs3dndrzfTKl5NrBmPL7FmqK9PL/Vx3zFxuNHNd/JSj37X/p8YYt5qlXvaRuzEnNye3nutIdnW1pcP7OO+Y61eBprHbeYG1MzD7C433uF+n6L11aoaNuM6+Ndg1XvWR/Tx1z3FTnfK9rzpfOVata3maTrnOeadl2Tc7Px8wc16/3WPI9Q897A0rmvLa7JNf0GXccJWtxr99Hv3eI9oaXr7NWso9tHG7Z0HfJW16W+nwHP0aK9VDOPpfN4W07/UmF9b/FsYJYVaHvXjJV3nuua+N5rrnV99KnWzGOp6fvtOgZdM3+pl3fWVBzznddsq/jspe9hncb7JmrGUHLG45qsPTOuwf37Spybqf7QmjyLn2WtmFfZYq5Rqo8655nQ0rHIFnPyW6z7OK7F/M/Sfu6ce8Emz9gWXpdq3plY+m7xFs8X18ynbXGP0GKd7EVa3H82biv2Mp+2hxqXc29ZuhZNH+PaLdbRzrrX7thH3cf7JFvcczV5trrB9TPVV9lirY9pHJ81x1KTsbQW47Adx7VbjCOWvpusZlymZuy8dB71apnHUrP2TIv+uq7rSPTyjEhF/S/tY+nj/TJZ835T+51xXPRx3hTPU6u47yj9rE3mTVc8fziNz97i+eIWzw6U3tvU9HH18R711Lyfmn7bmvUTS58ZaXFc9LEmc4v79aq1DgtVvRui4+eYqPC5npo596XHZ82c8qzaUJhfi/chjaua79Xx2f8Wzw70sRZNTTuj5pm6cV2vyTVzjZLnd4PanKXrGHPOuFjH5xF6WVO0j/7livvNcTXvUOpjDbKqdTi6jjk36FtrMT5fuq5Rzfde9T7OwvOopu1TNY8lsc2ua4bn/p2FWjxTP405PDX98dNYo7V0rc6auYU1z5VP5fniwlqRc3/Ux7rZfbxjtmsWNe2nmu+1l3eL16wn0mBuTGkWfYwX91KLG6xB1OQ9Aw2etey6Xy3G25LvsKlYv67F+7W7PtM0KYvU9XIaz7bkXJP7mEPe9TmKnDZY1bNXXccNe3iPQ83cjtQ2a+bG1Oxn6r/po7+uNJuaZ4FT21ipd/61eJ6z6hmwnufxN3teu3Bcu+u7h3M+S4t3O3bNomaeQE1dGFczZ7fFu55yNGjlrnLb3zniumsiNt0Usfnm2HpmS9z4m4ibN0fctClim7WLfydu/1vL/jdrdhj9/c9+46bYvDniDf/3N3HxFZvjJ7/YEjO//dux+Qc/iK0f+MBYu/feMRisXXab224dy+5Xzt+Y2X77Zf/mpG2kPvv4f/OzwY6jv7/55uR+pr6D8c/xn5fdvng/U9uMm25K5jX+d8c/a853sPXmm4o+29lf2jjyv2/ePEgenzN337nTfo//zZ/8YktsvTaW3e/xY2n8e5+0X+NZjJ83pedAzvc8/jdztjG+X6nPsc3axX93/FhK/e+cc3P8Oxnfz9Q5UHNtK71u5WSR81lLt5Hz2UvPzZrPWrpfOdel1Hkz/jdaZJE6N3OOpRafPfUd5NSK0uv8+DV3Ud4TrnWpzz5+jF//W/+tcz0a30bqc+QcrznfUevPnqqBk77DFrU5ldf/+96mZdsdLa7RNe2lVL4533vpMV9zjc45Hrt+9lQWk46l0hqY0w5J/d2aNljpd9ZHnjnXutLPPmmbqetOqv7n1LjU9XO8BuZ87+Ntw5p2SeqakVM7So/p8Wtb6bVv0t9IZpFxrVuJ9mZO7Sg9B1LHVk77qfRaVpNni8+aumeddCyljvkW94Lj/00fbdpUNi36BFLne007ZPxcnXQNSV0zatpcpdfH8e9w0nFQeozn9LGUXoemcb/Uop+hpi8tlV/OvUyL+7iu/Yo5bdrSvrXUsfPFex4Vm7eM/o3PXrip+Hjs2m+TcxyUHls5fYCl33NOFuPXttR1vo97ghbHUul3MulYKs0ip5+2tN+h5nwvvb/M+eype+2cOll6fNb0Vda0bUrbm6nvMOccSH1nOed76jtJ9a3X3IfU9IN3vYdt0fap6bPqow1Wc79Z+r23uPdrMU6YHM+c0B/S9X6zpv05jfGPmtpRel3POZZSba7UtS6nHpW2n2rqQOr4nXRtK71XzGlDjNe48b+bas9P6rtItTtq7ttK2x01n730HEhlldOezzlWUvuR+v2aOpm69tXUzRZjO137CHL2M+ezlo6R1txnlPZZTeq3Kb1vq2lDlPb9thjTy7nOt+67yNnv8fO9tA1Wcy9Tc2yV9l2krn05f6NmbkzpuHbNPdj4/665fpa2dVrsZ+qz5xyfqe+5Zm5MzVhvqs2Q+hs5Yyhd+yonnZst5m62vo/L2c8W1/nSca1Jn720rZgz56S0/71mnlUffZWlYyg5fcEtxr1S31GqHdyiXyx17tbMU03dG9acR6n61KKPKuc8anGP2qJN28c86q7tzZrr/HjdzPmeu372PvqCJ/VV9tGv0LqPOqftmMozp5+x9NhK7VfNsVTz2Uvvi3PmXdTMMev62SeNG5b2h+Tcq3StPzn9sl33M+c+LrVfqX7avuYFlT57kfPZS2tazdhE8fz6Bnm2mMuRumbXfPbS/czpuyj9DnPadal2c83cg1TeOfOsUvfaNfPnW4xJlY7xTeM+o6Zt0+J5hNKxnZr58zVjUi36elZizl7p8Vlz7SvNoubesI85EX08w5Bqz+dck1PzvXOeHSjto+5jbmHOWGTNc5G3lOMx9b1PY3yupl+2tB3Sx3yRmvGNVJ2suS/p45mm1JhUi/HNmjZEaTuuj3Ozpq2Yul7mzD0qfcahRX9e6bMDOf0dfVxPS68hOW3vFsdSiz6CrnMPau6TW1yTS+dR17QVW8x9K/3Oap4NrGnTdp0TOek+rvSz15xHXdsMNf3gNXPIa+6LW98btpj31+JZoRbjMqk2V83cjtLrZYtrXc353sfaHy3GJkqf28sZlykdK6vph+g6r6XFulAtzs0Wfao5/Yp99Nd1HZtoMcbS4jpf+tn7uMeqyTd1Xa95DrrF+gyp/51zTSltb7aYR1C6HkPNPULNnJMWzx+VzoXroz1f0z9SOt6Rc1+c+uw1/bSl32uTMagGbbRprMPV4l6m5l6xdK5WzTOMqWcxcvqTS691NcfrSqwT1aJfMXXe5NTmFv02pXNhas6j1Ho201g7tkX9abEuZB/brOlLm8Yag6WfPac/uY81G2vm4HcdP6p5ZqT0mjyN5z1q5tO2mGtU00/b4phvve5rTX0vrZvTmnOWatPWrP9Z0/dbOv5ec79Uul5ITduw9DxqsUZzzTW5xXMo01g/ucVzkV3Pm5r+kNJnMWr6zmvmCZQ+C1hzv1lzn1HaN9lHn0DN88XF7acpjct0PQdarJlR069YM3bWde5Wi3U1W7xzIXUu9jFuWPMd9fHZuz5XmvMsRh/PDtSMd3Rdo6Cmbvaxzt403v1S8xx+aZ457aWu8+dbjcN0va7nrE3TNYs+1lds0cfSou3dYp2oFuMdpW2GFp+1xfde/D6PjPZSH+ucdJ13WTNnvOb5hBZr4Lb4rF3Pxa7vo6i9H2o99yCnX7H0HqHFO4JaXD9r1oEsfR62Rfu9Zqy8tH+pxbhBTb93i7VlpzHvosX6al3zbLGfLc7Nmj7A0nfQpfJscRy0GJtsUdP6eC9Gi/6R0r6gJu+omcLc2Bbfe4sx0xb9DC3WdS/9Dmran6X3YNN6Frj0+Zma/azZZh/nYus5pS3GDfpoL6W+oz6efckZR+jjvWGlz3PkrJFVOr+7xVoKLfoIWqxx3aKftub+qO/xo5raXPMsW9d72Jw8c773rm2wmjHomnkWXY/xmnZyi2tyH+sWdu236eO5/RbPsrQY56p5P2wf72VbiWfuato6fcyNKe1La9Ge76Md0mId3dK2Ts71MzVPpY93yK/W8Y7ScZkWzx+2WHOsRZ9VH88w1rxjpWt7vo93Z+YcS6n7zT6eDawZj2+xpmjX+bU115Q+xvhavP+9dH3FaT37X/p8YYt5qjXzb7rOyc3ZZuqz9vHcaR/PtrS4ftasvV1ai6ex1nGLuTE18wCL53KuUN9v6jupGYctXeO+xbO+Ne9Z7/psYKt7676fccjJq3S+Us36Nn28f6+mXdfi3Gz9/EHNer81zyPUvDewdO5rH++Tqnm3Tun9eYt77T76vUvrUc31saY/pOt4R00/Yk0trrku9f0MeIv1GVqM4deMd6TOo5z+pdL63uLZwJr5yNNoe5f20zaZ65r43muudX30qdbMY6np++06Bl0zf6mPd9bUHPOl8wSm8U7knLkcffQn9/G8dul+1Xzvfawl2ce5meoPrcmz+FnWjHvY0nOixfsmcp4JLR2LbDEnv8W6j8kxkwbXz5o1crqOb9bcG9bMMSt9drqm77eP+bQt7hG6rpPd4l1vfTzDOI35tH3UuJx+79K1aPoY124xlzDnXrtrH3Uf75Nscc/V4tnqFtfPVF9li7U+pnF81hxLLfoZux4HLca1W4wjlr6brEW9avEu3Jr+5GnMY6lZe6ZFf13pNbiPdQtb9NeV9rH08X6ZnHm/pWPQNWMmfcw56ePdOtOYN13z/OE0PnuL54tbPDtQem+Tk0VqDeEWa+un5v3U9Num5t/UPOPQ9biY1prMqWMp5zpUutZhzjhi13dDtPgcLdZgL11DeFprMqe+95o5jy3eh9SiTnZ99r/FswN9rEWTup7W1PcWx2Mfa3mkzu8WtbnFWGRpO6Rm7KxmXfeuc3T76F+uud+sead817muOcdrzTocXcecW/St1fTTdl3XqOZ7r1n7tPQ8qhmrKB3nnsaa4TltxZq5Rq3fq14zNlnTHz+NNVpL1+qsmVtY81z5NJ4vLq0VOfdHfayb3eJZyxbvyuzafqoZK+vj3eI1c6S6zo1p0SZrMV7cRy1usQZRi/cMtHjWsut+tRhvS81jyRmbSNXimud2uz7TVLNO/jSebcm5Jvcxh7zrcxQ5bbCaZ6+6jhvWjEnVzFduMQ+wtP+4xbvzptFflzp2WjwLXPNe4K73NjVjqDXvWKl5BqzrdalF32XpGm0t1oGsGeeuGVNJbaOP9epSYxU1z2LU9C+Vrqs58Z6qwprBYDCo+i9vSb7xHxGXfTNiMIhrDz4uPvmdO8Svb4rYZaeI39svYs2a0d+Jhz494rtfXP6/+eb8759w6VPj8PvdLh794G3jhFNujMP3WRv//ZCt49dve1ts/ta3Itasie2e85xYu/nHS27zfrtFfOobseR+nfDVo+PwQ+8Zjz7ijkv+jdsdf3xs+vSnl/ybk/bz0Q/edtnPPvG/ueN5o7+/3Q7L7uf43xj/7EfusyV+c/L85/jL3V8dRzzwTkX7mdrmds95Tmy1117L5jXx7y74rDnf86+Oe3Gce+Pu2Z/t6p9viccfus3c/37w+q0jZrZZ9vgcHPnU+PW7/756v8f/5uH7rI3D9t02Prng+JuU38Jjafx7n7Rf41mMnzel50DO9zzxbya2Mb5fOZ8jBluWPZZS/zvn3Bz/Tsb3M+s6VXhtS33vk/5GKoucz1p6Lcv57OP7lTo3az5r6X7lXJdS583432iRRerczDmWWnz21HeQUytSx9L4fzPxmjue99i1LvXZFx3je8/Ewy94Z7d6NLaNnM+ROl6zzv/165t+9lQNnPQdtqjNqbz++fwb4/B91y7Z7pjUtim9Rte0l1L55nzvpcd8zTU6628k2rSpz57KYtKxVNp2zGmHZP3dwjZY6fHaR54517rSzz5pm6nrTqr+59S41PVzvAbmfO/jbcOadsnPro9lrxk5tSN1TR7/u+PXttJr36S/kZVF4lpXep/cqr2Zqh3F98WJYyun/VR6LavJs8VnTd2zTjqWUnWyxb3gxP+mcZs2p3+ka59A6nyvaYdMPFfHriGpa0ZNm6v0+jj+HU76TlNt2tLjtaadPI37pRb9DDV9acnrUMa9TIv7uNS1LtWvmNOmHd9Gqm8tdey8ZJunxRGH3jUe+9h7z/2Nz3zz5rm/VdvnV3pvnXMcpNrzWX0whe26mv7P8fZS6jrfxz1Bi2Op9DuZdCyNX+dTWeT005b2O9Sc76X3lzmfPXWvndOnWnp81vRV1rRtStubqe8w5xxIfWc553vqO0n1rdfch9T0g3e9h61pe9e0HVv0exXvR8b9Zun33mKMtMU4YdZ45tixNdj2Dp3uN2van9MY/6ipHcXX9YxjKTmembjW5dSj0vZTTR1IHb+Trm2l94o5bYjxGjf+d1Pt+Ul9F6k2WM19W2m7o+azp8aHx/czmVVGez7nWEntR2q/a+pk6tpXUzdbjO107SPI2s+Mz1o6Rlpzn1HaZzWp3yY1bthiXktp32+TMb2M63yq37u07yJnv8fP99I2WM29TM2xVdp3kbr25fyNmrkxpePaNfdg4/+75vpZ2tZpsZ+pz55zfKa+55q5MTVjvak2Q+pv5IyhdO2rnHRutpi7mRyLbHANaXGvOH7Ml45rTfrspW3FnDknpf3vNfOs+uirLB1DyekLbjHulfqOUu3gFv1iqXO3Zp5q6t6w6jxK1KcWfVQ551GLe9QWbdrSeUDTaG/WXOcXtesyvueun72XvuAJfVjjfVYt+hWK7y8T7fmctmMqz6xsCo+t1H7VHEs1n730vjhn3kXqGtJifHNin9/Y50gdn6k2Q05fWmn9yemX7bqfOfdxWW2CZfpp+5oXVPrsRc5nT9XenPZ8qq2Y9YxI4zxbzOXIel6h8LOX7mdO30VpuySnXZdqN7eYU5rqX5r0vafutWvmz7cYkyod45vGfUZN26bF8wilYzs18+drxqRa9PWkrsl9zNkrPT5rrn2lWdTcG/YxJ6Kmvdm1PZ9zTU7N9855dqC0j7qPuYU5Y5E1z0XeUo7HVB9gaf9di771mmdss54nbjxfpGZ8I1Una+5Lul4PasakWoxv1rQhSvs/+zg3a9qKqetlztyj0mccWvTnlT47kNvf0fp6WnoNyWl7tziWWvQRpMakUvtZc5/c4ppcOo+6pq3YYu5b6XdW9WxgRZs2lW/Os8CpuYV9nEdd2wxV/eAVc8hr7otTz66uxLy/Fs8KtRiXSbW5auZ2lF4vW1zras73Ptb+aDE2UfrcXs64TOlYWU0/RNd5LTX9y32cmy36VHPqU+n85GmMTbQYY2lxnS/97H3cY9Xkm7qu1zwH3WJ9htT/zrmmVLU3O84jKF2PoeYeoWbOSemxVTPfpsU6Zl375ycdr6XjHTn3xanPXtNPW/q9NhuD6thGm8Y6XC3uZWruFUvnatX086SexcjpT07df3adJ1h93e+4TlSLfsXUeZNTm1v029Q8/1Z6HqXWs5nG2rEt6k+LdSH72GZNX9o01hgs/ewtnoPuY4yqZu5W1/GPnPO9xTM4XZ/3qJlP22KuUU0/bYtjvusciZr1aVNt2Jrnt/uYc5Zq0+Y8B116na+p712fZa1ZO6WmbVh6HrVYo7nmmtziOZSu51GLdQtznovset7U9IeUPotR03deM0+g9FnAmvvNmvuM0r7JPvoEap4vrmo/TWFcpmvtaLFmRk2/Ys3YWdf1AFusq9ninQvJ53J7GDes+Y76+OxdnyvNeRajj2cHasY7uq5RUFM3+1hnr8UciRbz1GrWlpv2/PlW4zCl7c/ScbCavzGN9RVb9LG0aHu3WCeqxXhHaZuhxWdt8b1Xvc+j6xrsPfSxtOjnrlmLu/Q8anFu1rQ3S8/Fru+jqL0faj33IKdfsfQeocX7zppcPxN1M2sMOvE8bM28oNJnrVr0L7UYN6jp926xtuw05l20WF+ta54t9rPFuVnTB5jqny89v1u8r6fF2GSLmtbHezFa9I+U9gW1eF5uGnNjW3zvLcZMW/QztFjXvfh7rWh/lvbfTetZ4PHvKDVPumY/a7bZdVy75t0lLdZ9rRk3aN1eSn1HLdbQqBpHKJyvWFN7a947ULXu0YLP2mIthRZ9BDV9FzVt7z7uj1JrPnTtD6mZY1bzLFvXe9icPHO+99Jnr7Kur4V9qi3ez9V17tu03i3eYt3C1fjcfotnWZqMc1W8Hza1lsI01nTpY83wmmev+nh/XKovrUV7vo92SIt1dEvbOjnXz9Q8ld7eIb8KxztKx2Wm9d7A0rU+qvqseniGsWqOeeGz/zXPttXM5UgdS6n7zT6eDawZj2+xpmhpH2tNzWsxX7H1+FHNHImVeva/9PnCFvNUa+bfpPqoa+YelLbB+njutI9nW5pcPyvW3i6txdNY67jF3JiaeYBVa0KsQN9v6bsMato2fazVVdo+bfZMeGH7cyXO95r2fOl8pZr1bfp4/15Nu67Fudn6+YOa9X5rnkdIjtn3sJ5/i/dJ1bxbp/j+vMG9dh/93i3eE1q6zl7NOrp9tGFL1yFvdV3q2j/foh+sRXup5n+nxju6vsO3pr63eDawZj7yNNrexX3Wrea6LvO911zr+uhTrZnHUtP323UMumb+Uh/vrKk55ruu2Vbz2UvfwzqN903UjKHkjMd1XXum6r2rDWpxH+dmqj+0Js+qZ1kL51W2mGuU6qPOeSa0dCyyxZz8Fus+Zo2ZdLx+1qyR03V8s+besGaOWfGz0xV9v33Mp21xj9B1newW73rr4xn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      "text/plain": [
       "<Figure size 12905.2x602 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 541.935x176.159 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot = rnav.plot_profile(\n",
    "    [test],\n",
    "    sequence=\"SERPINA1\",\n",
    "    profile=\"eCLIP\",\n",
    "    annotations=[\"cds\", \"ddx3x\"],\n",
    ")\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "RNAvigate",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.2"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}