diff --git a/hw_pandas/pandas_plots_homework.ipynb b/hw_pandas/pandas_plots_homework.ipynb new file mode 100644 index 0000000..42ae030 --- /dev/null +++ b/hw_pandas/pandas_plots_homework.ipynb @@ -0,0 +1,9529 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 23, + "id": "0ac4a570", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import re\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import matplotlib.ticker as tck" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "9b9fefda", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "# gff3-format\r\n", + "Reference_10\tbarrnap:0.9\trRNA\t67122\t68652\t0.0\t+\t.\tName=16S_rRNA;product=16S ribosomal RNA\r\n", + "Reference_10\tbarrnap:0.9\trRNA\t68981\t72061\t0.0\t+\t.\tName=23S_rRNA;product=23S ribosomal RNA\r\n", + "Reference_10\tbarrnap:0.9\trRNA\t469298\t470826\t0.0\t+\t.\tName=16S_rRNA;product=16S 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2Reference_10barrnap:0.9rRNA4692984708260.000000e+00+.Name=16S_rRNA;product=16S ribosomal RNA
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4Reference_10barrnap:0.9rRNA362959936326800.000000e+00-.Name=23S_rRNA;product=23S ribosomal RNA
..............................
343Reference_25barrnap:0.9rRNA181987818227850.000000e+00+.Name=23S_rRNA;product=23S ribosomal RNA
344Reference_25barrnap:0.9rRNA182291318230162.900000e-10+.Name=5S_rRNA;product=5S ribosomal RNA
345Reference_25barrnap:0.9rRNA374405637441592.900000e-10-.Name=5S_rRNA;product=5S ribosomal RNA
346Reference_25barrnap:0.9rRNA374428737471940.000000e+00-.Name=23S_rRNA;product=23S ribosomal RNA
347Reference_25barrnap:0.9rRNA374765637491260.000000e+00-.Name=16S_rRNA;product=16S ribosomal RNA
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348 rows × 9 columns

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" + ], + "text/plain": [ + " chromosome source type start end score strand \\\n", + "0 Reference_10 barrnap:0.9 rRNA 67122 68652 0.000000e+00 + \n", + "1 Reference_10 barrnap:0.9 rRNA 68981 72061 0.000000e+00 + \n", + "2 Reference_10 barrnap:0.9 rRNA 469298 470826 0.000000e+00 + \n", + "3 Reference_10 barrnap:0.9 rRNA 471166 474246 0.000000e+00 + \n", + "4 Reference_10 barrnap:0.9 rRNA 3629599 3632680 0.000000e+00 - \n", + ".. ... ... ... ... ... ... ... \n", + "343 Reference_25 barrnap:0.9 rRNA 1819878 1822785 0.000000e+00 + \n", + "344 Reference_25 barrnap:0.9 rRNA 1822913 1823016 2.900000e-10 + \n", + "345 Reference_25 barrnap:0.9 rRNA 3744056 3744159 2.900000e-10 - \n", + "346 Reference_25 barrnap:0.9 rRNA 3744287 3747194 0.000000e+00 - \n", + "347 Reference_25 barrnap:0.9 rRNA 3747656 3749126 0.000000e+00 - \n", + "\n", + " phase attributes \n", + "0 . Name=16S_rRNA;product=16S ribosomal RNA \n", + "1 . Name=23S_rRNA;product=23S ribosomal RNA \n", + "2 . Name=16S_rRNA;product=16S ribosomal RNA \n", + "3 . Name=23S_rRNA;product=23S ribosomal RNA \n", + "4 . Name=23S_rRNA;product=23S ribosomal RNA \n", + ".. ... ... \n", + "343 . Name=23S_rRNA;product=23S ribosomal RNA \n", + "344 . Name=5S_rRNA;product=5S ribosomal RNA \n", + "345 . Name=5S_rRNA;product=5S ribosomal RNA \n", + "346 . Name=23S_rRNA;product=23S ribosomal RNA \n", + "347 . Name=16S_rRNA;product=16S ribosomal RNA \n", + "\n", + "[348 rows x 9 columns]" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Function for reading gff files\n", + "def read_gff(path_to_gff):\n", + " gff_header = ['chromosome', 'source', 'type', 'start', 'end', 'score', 'strand', 'phase', 'attributes']\n", + " return pd.read_csv(path_to_gff, sep='\\t', names=gff_header, comment = '#')\n", + "\n", + "rrna_gff_df = read_gff('data/rrna_annotation.gff')\n", + "rrna_gff_df" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "add9e68e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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chromosomestartendnamescorestrand
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3Reference_1203322785222NODE_25_length_581906_cov_146.02422860+
4Reference_17851691763981NODE_11_length_978812_cov_132.51751560-
.....................
7705Reference_2655324325535601NODE_1329_length_3169_cov_103.9351325+
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7709Reference_2655372885608040NODE_237_length_95089_cov_34.83678560+
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7710 rows × 6 columns

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" + ], + "text/plain": [ + " chromosome start end name \\\n", + "0 Reference_1 197681 200286 NODE_1445_length_2603_cov_1135.593799 \n", + "1 Reference_1 202886 202996 NODE_19543_length_110_cov_1549.290909 \n", + "2 Reference_1 202941 203377 NODE_5666_length_436_cov_418.653543 \n", + "3 Reference_1 203322 785222 NODE_25_length_581906_cov_146.024228 \n", + "4 Reference_1 785169 1763981 NODE_11_length_978812_cov_132.517515 \n", + "... ... ... ... ... \n", + "7705 Reference_26 5532432 5535601 NODE_1329_length_3169_cov_103.935132 \n", + "7706 Reference_26 5535546 5535722 NODE_19238_length_176_cov_67.305785 \n", + "7707 Reference_26 5535667 5537226 NODE_2001_length_1559_cov_98.029920 \n", + "7708 Reference_26 5537171 5537343 NODE_19245_length_172_cov_84.367521 \n", + "7709 Reference_26 5537288 5608040 NODE_237_length_95089_cov_34.836785 \n", + "\n", + " score strand \n", + "0 41 - \n", + "1 0 - \n", + "2 16 + \n", + "3 60 + \n", + "4 60 - \n", + "... ... ... \n", + "7705 5 + \n", + "7706 0 + \n", + "7707 0 + \n", + "7708 0 + \n", + "7709 60 + \n", + "\n", + "[7710 rows x 6 columns]" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Function for reading bed files\n", + "def read_bed(path_to_bed):\n", + " bed_header = ['chromosome', 'start', 'end', 'name', 'score', 'strand']\n", + " return pd.read_csv(path_to_bed, sep='\\t', names=bed_header)\n", + "\n", + "alignment_bed_df = read_bed('data/alignment.bed')\n", + "alignment_bed_df" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "9ebfc18c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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chromosomesourcetypestartendscorestrandphaseattributes
0Reference_10barrnap:0.9rRNA67122686520.000000e+00+.16S
1Reference_10barrnap:0.9rRNA68981720610.000000e+00+.23S
2Reference_10barrnap:0.9rRNA4692984708260.000000e+00+.16S
3Reference_10barrnap:0.9rRNA4711664742460.000000e+00+.23S
4Reference_10barrnap:0.9rRNA362959936326800.000000e+00-.23S
..............................
343Reference_25barrnap:0.9rRNA181987818227850.000000e+00+.23S
344Reference_25barrnap:0.9rRNA182291318230162.900000e-10+.5S
345Reference_25barrnap:0.9rRNA374405637441592.900000e-10-.5S
346Reference_25barrnap:0.9rRNA374428737471940.000000e+00-.23S
347Reference_25barrnap:0.9rRNA374765637491260.000000e+00-.16S
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348 rows × 9 columns

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" + ], + "text/plain": [ + " chromosome source type start end score strand \\\n", + "0 Reference_10 barrnap:0.9 rRNA 67122 68652 0.000000e+00 + \n", + "1 Reference_10 barrnap:0.9 rRNA 68981 72061 0.000000e+00 + \n", + "2 Reference_10 barrnap:0.9 rRNA 469298 470826 0.000000e+00 + \n", + "3 Reference_10 barrnap:0.9 rRNA 471166 474246 0.000000e+00 + \n", + "4 Reference_10 barrnap:0.9 rRNA 3629599 3632680 0.000000e+00 - \n", + ".. ... ... ... ... ... ... ... \n", + "343 Reference_25 barrnap:0.9 rRNA 1819878 1822785 0.000000e+00 + \n", + "344 Reference_25 barrnap:0.9 rRNA 1822913 1823016 2.900000e-10 + \n", + "345 Reference_25 barrnap:0.9 rRNA 3744056 3744159 2.900000e-10 - \n", + "346 Reference_25 barrnap:0.9 rRNA 3744287 3747194 0.000000e+00 - \n", + "347 Reference_25 barrnap:0.9 rRNA 3747656 3749126 0.000000e+00 - \n", + "\n", + " phase attributes \n", + "0 . 16S \n", + "1 . 23S \n", + "2 . 16S \n", + "3 . 23S \n", + "4 . 23S \n", + ".. ... ... \n", + "343 . 23S \n", + "344 . 5S \n", + "345 . 5S \n", + "346 . 23S \n", + "347 . 16S \n", + "\n", + "[348 rows x 9 columns]" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Extracting rRNA type from attributes column\n", + "rrna_gff_df['attributes'] = rrna_gff_df['attributes'].apply(lambda attribute: re.split('=|_', attribute)[1])\n", + "rrna_gff_df" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "356f76d2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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chromosomeattributescount
0Reference_116S2
1Reference_123S2
2Reference_15S2
3Reference_1016S5
4Reference_1023S5
............
71Reference_816S2
72Reference_823S2
73Reference_916S6
74Reference_923S6
75Reference_95S6
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76 rows × 3 columns

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" + ], + "text/plain": [ + " chromosome attributes count\n", + "0 Reference_1 16S 2\n", + "1 Reference_1 23S 2\n", + "2 Reference_1 5S 2\n", + "3 Reference_10 16S 5\n", + "4 Reference_10 23S 5\n", + ".. ... ... ...\n", + "71 Reference_8 16S 2\n", + "72 Reference_8 23S 2\n", + "73 Reference_9 16S 6\n", + "74 Reference_9 23S 6\n", + "75 Reference_9 5S 6\n", + "\n", + "[76 rows x 3 columns]" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Counting rRNAs for every chromosome\n", + "rrnas_by_types = pd.DataFrame({'count' : rrna_gff_df.groupby(['chromosome','attributes']).size()}).reset_index()\n", + "rrnas_by_types" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "8240f47a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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chromosomesourcetypestart_xend_xscore_xstrand_xphaseattributesstart_yend_ynamescore_ystrand_y
0Reference_10barrnap:0.9rRNA67122686520.0+.16S6796468037NODE_19846_length_73_cov_334.7777780+
1Reference_10barrnap:0.9rRNA67122686520.0+.16S512498512739NODE_13330_length_244_cov_1.1957674-
2Reference_10barrnap:0.9rRNA67122686520.0+.16S19401231940316NODE_15898_length_227_cov_0.9593020-
3Reference_10barrnap:0.9rRNA67122686520.0+.16S29599102960140NODE_14270_length_237_cov_1.56593460+
4Reference_10barrnap:0.9rRNA67122686520.0+.16S34220643422561NODE_4494_length_558_cov_1.44731660-
.............................................
126743Reference_25barrnap:0.9rRNA374765637491260.0-.16S37490693749300NODE_15186_length_231_cov_783.7272730-
126744Reference_25barrnap:0.9rRNA374765637491260.0-.16S37492453749677NODE_5718_length_432_cov_495.00000031-
126745Reference_25barrnap:0.9rRNA374765637491260.0-.16S37496223759983NODE_952_length_10361_cov_185.04870960+
126746Reference_25barrnap:0.9rRNA374765637491260.0-.16S37599283761392NODE_2106_length_1464_cov_409.7445000-
126747Reference_25barrnap:0.9rRNA374765637491260.0-.16S37613373788356NODE_398_length_52942_cov_188.77590060+
\n", + "

126748 rows × 14 columns

\n", + "
" + ], + "text/plain": [ + " chromosome source type start_x end_x score_x strand_x \\\n", + "0 Reference_10 barrnap:0.9 rRNA 67122 68652 0.0 + \n", + "1 Reference_10 barrnap:0.9 rRNA 67122 68652 0.0 + \n", + "2 Reference_10 barrnap:0.9 rRNA 67122 68652 0.0 + \n", + "3 Reference_10 barrnap:0.9 rRNA 67122 68652 0.0 + \n", + "4 Reference_10 barrnap:0.9 rRNA 67122 68652 0.0 + \n", + "... ... ... ... ... ... ... ... \n", + "126743 Reference_25 barrnap:0.9 rRNA 3747656 3749126 0.0 - \n", + "126744 Reference_25 barrnap:0.9 rRNA 3747656 3749126 0.0 - \n", + "126745 Reference_25 barrnap:0.9 rRNA 3747656 3749126 0.0 - \n", + "126746 Reference_25 barrnap:0.9 rRNA 3747656 3749126 0.0 - \n", + "126747 Reference_25 barrnap:0.9 rRNA 3747656 3749126 0.0 - \n", + "\n", + " phase attributes start_y end_y \\\n", + "0 . 16S 67964 68037 \n", + "1 . 16S 512498 512739 \n", + "2 . 16S 1940123 1940316 \n", + "3 . 16S 2959910 2960140 \n", + "4 . 16S 3422064 3422561 \n", + "... ... ... ... ... \n", + "126743 . 16S 3749069 3749300 \n", + "126744 . 16S 3749245 3749677 \n", + "126745 . 16S 3749622 3759983 \n", + "126746 . 16S 3759928 3761392 \n", + "126747 . 16S 3761337 3788356 \n", + "\n", + " name score_y strand_y \n", + "0 NODE_19846_length_73_cov_334.777778 0 + \n", + "1 NODE_13330_length_244_cov_1.195767 4 - \n", + "2 NODE_15898_length_227_cov_0.959302 0 - \n", + "3 NODE_14270_length_237_cov_1.565934 60 + \n", + "4 NODE_4494_length_558_cov_1.447316 60 - \n", + "... ... ... ... \n", + "126743 NODE_15186_length_231_cov_783.727273 0 - \n", + "126744 NODE_5718_length_432_cov_495.000000 31 - \n", + "126745 NODE_952_length_10361_cov_185.048709 60 + \n", + "126746 NODE_2106_length_1464_cov_409.744500 0 - \n", + "126747 NODE_398_length_52942_cov_188.775900 60 + \n", + "\n", + "[126748 rows x 14 columns]" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Merging gff and bed files\n", + "merged_df = pd.merge(rrna_gff_df, alignment_bed_df, how='outer', left_on=['chromosome'], right_on=['chromosome'])\n", + "merged_df" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "2df43b82", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Creating barplot\n", + "fig = plt.figure()\n", + "sns.barplot(x='chromosome', y='count', hue='attributes', data=rrnas_by_types)\n", + "plt.legend(title='rRNA type', loc='upper left')\n", + "plt.xlabel('chromosome', size=10)\n", + "plt.ylabel('count', size=10)\n", + "plt.xticks(rotation=90, size=10);" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "a31baaad", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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chromosomesourcetypestart_xend_xscore_xstrand_xphaseattributesstart_yend_ynamescore_ystrand_y
129Reference_11barrnap:0.9rRNA9243589258550.000000e+00+.16S923529930070NODE_1081_length_6540_cov_836.7733230-
156Reference_11barrnap:0.9rRNA9266979296420.000000e+00+.23S923529930070NODE_1081_length_6540_cov_836.7733230-
183Reference_11barrnap:0.9rRNA9298279299361.400000e-12+.5S923529930070NODE_1081_length_6540_cov_836.7733230-
215Reference_11barrnap:0.9rRNA207446020759570.000000e+00+.16S20736312080172NODE_1081_length_6540_cov_836.7733230-
242Reference_11barrnap:0.9rRNA207679920797440.000000e+00+.23S20736312080172NODE_1081_length_6540_cov_836.7733230-
.............................................
121855Reference_14barrnap:0.9rRNA243304024331522.000000e-12-.5S24330272433434NODE_6108_length_407_cov_1153.2329550+
122182Reference_14barrnap:0.9rRNA376695237670642.000000e-12-.5S37669393767346NODE_6108_length_407_cov_1153.2329550+
122583Reference_25barrnap:0.9rRNA132937013294732.900000e-10-.5S13293451329810NODE_5333_length_465_cov_547.6097560-
125060Reference_25barrnap:0.9rRNA182291318230162.900000e-10+.5S18225751823040NODE_5333_length_465_cov_547.6097560+
125776Reference_25barrnap:0.9rRNA374405637441592.900000e-10-.5S37440313744496NODE_5333_length_465_cov_547.6097560-
\n", + "

120 rows × 14 columns

\n", + "
" + ], + "text/plain": [ + " chromosome source type start_x end_x score_x \\\n", + "129 Reference_11 barrnap:0.9 rRNA 924358 925855 0.000000e+00 \n", + "156 Reference_11 barrnap:0.9 rRNA 926697 929642 0.000000e+00 \n", + "183 Reference_11 barrnap:0.9 rRNA 929827 929936 1.400000e-12 \n", + "215 Reference_11 barrnap:0.9 rRNA 2074460 2075957 0.000000e+00 \n", + "242 Reference_11 barrnap:0.9 rRNA 2076799 2079744 0.000000e+00 \n", + "... ... ... ... ... ... ... \n", + "121855 Reference_14 barrnap:0.9 rRNA 2433040 2433152 2.000000e-12 \n", + "122182 Reference_14 barrnap:0.9 rRNA 3766952 3767064 2.000000e-12 \n", + "122583 Reference_25 barrnap:0.9 rRNA 1329370 1329473 2.900000e-10 \n", + "125060 Reference_25 barrnap:0.9 rRNA 1822913 1823016 2.900000e-10 \n", + "125776 Reference_25 barrnap:0.9 rRNA 3744056 3744159 2.900000e-10 \n", + "\n", + " strand_x phase attributes start_y end_y \\\n", + "129 + . 16S 923529 930070 \n", + "156 + . 23S 923529 930070 \n", + "183 + . 5S 923529 930070 \n", + "215 + . 16S 2073631 2080172 \n", + "242 + . 23S 2073631 2080172 \n", + "... ... ... ... ... ... \n", + "121855 - . 5S 2433027 2433434 \n", + "122182 - . 5S 3766939 3767346 \n", + "122583 - . 5S 1329345 1329810 \n", + "125060 + . 5S 1822575 1823040 \n", + "125776 - . 5S 3744031 3744496 \n", + "\n", + " name score_y strand_y \n", + "129 NODE_1081_length_6540_cov_836.773323 0 - \n", + "156 NODE_1081_length_6540_cov_836.773323 0 - \n", + "183 NODE_1081_length_6540_cov_836.773323 0 - \n", + "215 NODE_1081_length_6540_cov_836.773323 0 - \n", + "242 NODE_1081_length_6540_cov_836.773323 0 - \n", + "... ... ... ... \n", + "121855 NODE_6108_length_407_cov_1153.232955 0 + \n", + "122182 NODE_6108_length_407_cov_1153.232955 0 + \n", + "122583 NODE_5333_length_465_cov_547.609756 0 - \n", + "125060 NODE_5333_length_465_cov_547.609756 0 + \n", + "125776 NODE_5333_length_465_cov_547.609756 0 - \n", + "\n", + "[120 rows x 14 columns]" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Extracting rRNAs which intersect with alignment\n", + "rrnas_align_intersect = merged_df[(merged_df['start_x'] >= merged_df['start_y']) & (merged_df['end_x'] <= merged_df['end_y'])]\n", + "rrnas_align_intersect.drop_duplicates()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8b1c6b27", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "006dbb35", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [conda env:bi]", + "language": "python", + "name": "conda-env-bi-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/hw_pandas/pandas_plots_homework.py b/hw_pandas/pandas_plots_homework.py new file mode 100644 index 0000000..0697816 --- /dev/null +++ b/hw_pandas/pandas_plots_homework.py @@ -0,0 +1,38 @@ +import pandas as pd +import re +import matplotlib as plt + +# Function for reading gff files +def read_gff(path_to_gff): + gff_header = ['chromosome', 'source', 'type', 'start', 'end', 'score', 'strand', 'phase', 'attributes'] + return pd.read_csv(path_to_gff, sep='\t', names=gff_header, comment = '#') + +# Function for reading bed files +def read_bed(path_to_bed): + bed_header = ['chromosome', 'start', 'end', 'name', 'score', 'strand'] + return pd.read_csv(path_to_bed, sep='\t', names=bed_header) + +# Creating dataframes from our input files +rrna_gff_df = read_gff('data/rrna_annotation.gff') +alignment_bed_df = read_bed('data/alignment.bed') + +# Extracting rRNA type from attributes column +rrna_gff_df['attributes'] = rrna_gff_df['attributes'].apply(lambda attribute: re.split('=|_', attribute)[1]) + +# Counting rRNAs for every chromosome +rrnas_by_types = pd.DataFrame({'count' : rrna_gff_df.groupby(['chromosome','attributes']).size()}).reset_index() + +# Merging gff and bed files +merged_df = pd.merge(rrna_gff_df, alignment_bed_df, how='outer', left_on=['chromosome'], right_on=['chromosome']) + +# Creating barplot +fig = plt.figure() +sns.barplot(x='chromosome', y='count', hue='attributes', data=rrnas_by_types) +plt.legend(title='rRNA type', loc='upper left') +plt.xlabel('chromosome', size=10) +plt.ylabel('count', size=10) +plt.xticks(rotation=90, size=10); + +# Extracting rRNAs which intersect with alignment +rrnas_align_intersect = merged_df[(merged_df['start_x'] >= merged_df['start_y']) & (merged_df['end_x'] <= merged_df['end_y'])] +rrnas_align_intersect.drop_duplicates() \ No newline at end of file