diff --git a/analysis/Untitled.ipynb b/analysis/Untitled.ipynb
index 46e76239e90df7e75f66aa33c204d20b8c3b4342..f51fa7d11556f4f8f22f41ea11c8d7e3d4ac7a35 100644
--- a/analysis/Untitled.ipynb
+++ b/analysis/Untitled.ipynb
@@ -10,7 +10,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "2022-07-29 14:01\n"
+ "2022-07-29 15:06\n"
]
}
],
@@ -128,6 +128,17 @@
"del hhblits"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "id": "da58c74f-5cd2-435a-8281-6195ed7f15af",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "mmseqs_filtered.drop(columns=['index'], inplace=True)\n",
+ "hhblits_filtered.drop(columns=['index'], inplace=True)"
+ ]
+ },
{
"cell_type": "code",
"execution_count": 9,
@@ -171,7 +182,7 @@
{
"data": {
"text/plain": [
- "<matplotlib.legend.Legend at 0x7f58ab679250>"
+ "<matplotlib.legend.Legend at 0x7f7975465070>"
]
},
"execution_count": 11,
@@ -180,7 +191,7 @@
},
{
"data": {
- "image/png": "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\n",
+ "image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
@@ -213,7 +224,7 @@
},
{
"cell_type": "code",
- "execution_count": 22,
+ "execution_count": 13,
"id": "e8d0cfb7-c86d-41a6-99e7-d514bc1f3873",
"metadata": {},
"outputs": [
@@ -223,7 +234,7 @@
"148"
]
},
- "execution_count": 22,
+ "execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
@@ -236,7 +247,7 @@
},
{
"cell_type": "code",
- "execution_count": 25,
+ "execution_count": 14,
"id": "85231f72-12ad-4cc7-897a-544e0be27228",
"metadata": {
"tags": []
@@ -246,7 +257,7 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 148/148 [01:19<00:00, 1.86it/s]\n"
+ "100%|██████████| 148/148 [01:45<00:00, 1.41it/s]\n"
]
}
],
@@ -269,35 +280,194 @@
]
},
{
- "cell_type": "code",
- "execution_count": null,
- "id": "da58c74f-5cd2-435a-8281-6195ed7f15af",
+ "cell_type": "markdown",
+ "id": "c3d17ee4-6176-443a-b320-a54aaa7ff8cd",
"metadata": {},
- "outputs": [],
"source": [
- "mmseqs_filtered.drop(columns=['index'], inplace=True)\n",
- "hhblits_filtered.drop(columns=['index'], inplace=True)"
+ "(wait for EggNOG-mapper to annotate the sequences)"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 36,
"id": "eb0c48d8-ef71-440f-8ea0-6e7f3118f957",
"metadata": {},
"outputs": [],
"source": [
- "file = '/g/arendt/npapadop/data/spongfold_publish/'\n",
+ "file = '/g/arendt/npapadop/data/spongfold_publish/MM_ffr33uy6.emapper.annotations.tsv'\n",
"sensitive = pd.read_csv(file, sep='\\t', skiprows=4, skipfooter=3, engine='python')"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": 43,
+ "id": "0a97dfb7-4783-44fa-a330-f07b0585fcf8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "test = sensitive['#query'].str.split('_').str[1]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 45,
+ "id": "942d4a2d-2a8b-44ff-89b4-8525f3ad926e",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "A0A6A6LEY7 3\n",
+ "A0A1X7U3S9 3\n",
+ "A0A3N5U8U0 3\n",
+ "A0A6J0B743 3\n",
+ "A0A482W475 3\n",
+ " ..\n",
+ "A0A6I8SCD7 1\n",
+ "A0A1A8MN43 1\n",
+ "A0A0C2FDV2 1\n",
+ "A0A2J8INC7 1\n",
+ "A0A0K0D6S2 1\n",
+ "Name: #query, Length: 20360, dtype: int64"
+ ]
+ },
+ "execution_count": 45,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "test.value_counts()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 39,
+ "id": "909804dd-4006-4dcc-a4ba-308a5b3975d0",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>#query</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>seed_ortholog</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>evalue</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>score</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>eggNOG_OGs</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>max_annot_lvl</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>COG_category</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>Description</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>Preferred_name</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>GOs</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>EC</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>KEGG_ko</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>KEGG_Pathway</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>KEGG_Module</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>KEGG_Reaction</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>KEGG_rclass</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>BRITE</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>KEGG_TC</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>CAZy</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>BiGG_Reaction</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>PFAMs</th>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ "Empty DataFrame\n",
+ "Columns: []\n",
+ "Index: [#query, seed_ortholog, evalue, score, eggNOG_OGs, max_annot_lvl, COG_category, Description, Preferred_name, GOs, EC, KEGG_ko, KEGG_Pathway, KEGG_Module, KEGG_Reaction, KEGG_rclass, BRITE, KEGG_TC, CAZy, BiGG_Reaction, PFAMs]"
+ ]
+ },
+ "execution_count": 39,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "sensitive[sensitive['#query'].str.contains('UPI00005B2EF3')].T"
+ ]
+ },
{
"cell_type": "code",
"execution_count": null,
+ "id": "e6d1eb54-d942-465e-a868-517d442fe1dd",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
"id": "1c156c27-d2ec-47d6-b764-2b54816922a0",
"metadata": {},
"outputs": [],
"source": [
- "sensitive['uniprot'] = sensitive['#query'].str.split('|').str[1]\n",
+ "sensitive['uniprot'] = sensitive['#query'].str.split('_').str[1]\n",
"sensitive.reset_index(drop=True, inplace=True)\n",
"# remove unnecessary columns\n",
"dead_weight = ['#query', 'seed_ortholog', 'EC', 'KEGG_ko', 'KEGG_Pathway',\n",
@@ -309,27 +479,349 @@
" 'Description', 'Preferred_name', 'GOs', 'PFAMs']\n",
"sensitive[to_categorical] = sensitive[to_categorical].astype(\"category\")\n",
"# finally save in parquet format\n",
+ "sensitive.drop_duplicates(inplace=True)\n",
"sensitive.to_parquet('../data/uniprot_profiles.parquet')"
]
},
{
"cell_type": "code",
- "execution_count": null,
- "id": "821c79ed-2090-45c8-bc5c-736ba1096989",
+ "execution_count": 30,
+ "id": "d68690ea-060a-480f-802c-aac6baae570d",
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(20360, 10)"
+ ]
+ },
+ "execution_count": 30,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
- "response"
+ "sensitive.shape"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 33,
+ "id": "f60ac7bf-f031-4bd6-99f3-eb3117b50cf1",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "7417224 A0A409VGI9\n",
+ "7453017 A0A6A6LEY7\n",
+ "3711344 A0A1A8QDB6\n",
+ "5432158 A0A1X7UJF6\n",
+ "1658532 S9X2M4\n",
+ " ... \n",
+ "144364 A0A2F0BPH8\n",
+ "144949 L1J317\n",
+ "4535244 A0A0K0D6S2\n",
+ "2883206 UPI0003F0A19A\n",
+ "2883636 A0A7S2RZY7\n",
+ "Name: target, Length: 24540, dtype: object"
+ ]
+ },
+ "execution_count": 33,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "unique_up_id"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 35,
"id": "fd08dceb-c307-4ad6-bd23-91b70aaf7ef4",
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>query</th>\n",
+ " <th>target</th>\n",
+ " <th>seq. id.</th>\n",
+ " <th>alignment length</th>\n",
+ " <th>no. mismatches</th>\n",
+ " <th>no. gap open</th>\n",
+ " <th>query start</th>\n",
+ " <th>query end</th>\n",
+ " <th>target start</th>\n",
+ " <th>target end</th>\n",
+ " <th>e value</th>\n",
+ " <th>bit score</th>\n",
+ " <th>gene_id</th>\n",
+ " <th>normalized bit score</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>3537669</th>\n",
+ " <td>c100005_g1_i4_m.41851</td>\n",
+ " <td>UPI000C6D6B72</td>\n",
+ " <td>0.262</td>\n",
+ " <td>138</td>\n",
+ " <td>100</td>\n",
+ " <td>0</td>\n",
+ " <td>13</td>\n",
+ " <td>150</td>\n",
+ " <td>7</td>\n",
+ " <td>142</td>\n",
+ " <td>1.464000e-04</td>\n",
+ " <td>53</td>\n",
+ " <td>c100005_g1</td>\n",
+ " <td>0.384058</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3711020</th>\n",
+ " <td>c100012_g4_i1_m.41921</td>\n",
+ " <td>A0A4V1IVK5</td>\n",
+ " <td>0.681</td>\n",
+ " <td>133</td>\n",
+ " <td>41</td>\n",
+ " <td>0</td>\n",
+ " <td>32</td>\n",
+ " <td>164</td>\n",
+ " <td>73</td>\n",
+ " <td>203</td>\n",
+ " <td>3.106000e-49</td>\n",
+ " <td>183</td>\n",
+ " <td>c100012_g4</td>\n",
+ " <td>1.375940</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3108519</th>\n",
+ " <td>c100014_g2_i1_m.41927</td>\n",
+ " <td>A0A1D1W000</td>\n",
+ " <td>0.456</td>\n",
+ " <td>59</td>\n",
+ " <td>32</td>\n",
+ " <td>0</td>\n",
+ " <td>4</td>\n",
+ " <td>62</td>\n",
+ " <td>118</td>\n",
+ " <td>176</td>\n",
+ " <td>4.196000e-04</td>\n",
+ " <td>52</td>\n",
+ " <td>c100014_g2</td>\n",
+ " <td>0.881356</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>7530038</th>\n",
+ " <td>c100023_g1_i1_m.41965</td>\n",
+ " <td>UPI0009E61F51</td>\n",
+ " <td>0.342</td>\n",
+ " <td>90</td>\n",
+ " <td>57</td>\n",
+ " <td>0</td>\n",
+ " <td>3</td>\n",
+ " <td>92</td>\n",
+ " <td>2743</td>\n",
+ " <td>2829</td>\n",
+ " <td>4.556000e-04</td>\n",
+ " <td>53</td>\n",
+ " <td>c100023_g1</td>\n",
+ " <td>0.588889</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>5277692</th>\n",
+ " <td>c100036_g1_i4_m.42040</td>\n",
+ " <td>A0A7S3BMQ4</td>\n",
+ " <td>0.523</td>\n",
+ " <td>99</td>\n",
+ " <td>46</td>\n",
+ " <td>0</td>\n",
+ " <td>71</td>\n",
+ " <td>169</td>\n",
+ " <td>1</td>\n",
+ " <td>98</td>\n",
+ " <td>2.166000e-19</td>\n",
+ " <td>100</td>\n",
+ " <td>c100036_g1</td>\n",
+ " <td>1.010101</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>...</th>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>8219227</th>\n",
+ " <td>c99972_g1_i2_m.41694</td>\n",
+ " <td>UPI0009E28D88</td>\n",
+ " <td>0.466</td>\n",
+ " <td>63</td>\n",
+ " <td>33</td>\n",
+ " <td>0</td>\n",
+ " <td>8</td>\n",
+ " <td>70</td>\n",
+ " <td>392</td>\n",
+ " <td>453</td>\n",
+ " <td>1.130000e-05</td>\n",
+ " <td>57</td>\n",
+ " <td>c99972_g1</td>\n",
+ " <td>0.904762</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>6087296</th>\n",
+ " <td>c99980_g3_i1_m.41746</td>\n",
+ " <td>UPI00005B2EF3</td>\n",
+ " <td>0.434</td>\n",
+ " <td>62</td>\n",
+ " <td>35</td>\n",
+ " <td>0</td>\n",
+ " <td>2</td>\n",
+ " <td>63</td>\n",
+ " <td>7</td>\n",
+ " <td>68</td>\n",
+ " <td>4.330000e-05</td>\n",
+ " <td>51</td>\n",
+ " <td>c99980_g3</td>\n",
+ " <td>0.822581</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3238079</th>\n",
+ " <td>c99987_g1_i2_m.41772</td>\n",
+ " <td>A0A1X7URN4</td>\n",
+ " <td>0.529</td>\n",
+ " <td>220</td>\n",
+ " <td>97</td>\n",
+ " <td>0</td>\n",
+ " <td>2</td>\n",
+ " <td>221</td>\n",
+ " <td>37</td>\n",
+ " <td>243</td>\n",
+ " <td>1.868000e-61</td>\n",
+ " <td>221</td>\n",
+ " <td>c99987_g1</td>\n",
+ " <td>1.004545</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>574005</th>\n",
+ " <td>c99993_g2_i1_m.41786</td>\n",
+ " <td>UPI00177B4952</td>\n",
+ " <td>0.663</td>\n",
+ " <td>137</td>\n",
+ " <td>41</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>137</td>\n",
+ " <td>23</td>\n",
+ " <td>145</td>\n",
+ " <td>1.384000e-49</td>\n",
+ " <td>183</td>\n",
+ " <td>c99993_g2</td>\n",
+ " <td>1.335766</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>574028</th>\n",
+ " <td>c99993_g3_i1_m.41790</td>\n",
+ " <td>UPI00106CF215</td>\n",
+ " <td>0.265</td>\n",
+ " <td>133</td>\n",
+ " <td>95</td>\n",
+ " <td>0</td>\n",
+ " <td>3</td>\n",
+ " <td>135</td>\n",
+ " <td>93</td>\n",
+ " <td>222</td>\n",
+ " <td>5.938000e-04</td>\n",
+ " <td>52</td>\n",
+ " <td>c99993_g3</td>\n",
+ " <td>0.390977</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>3347 rows × 14 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " query target seq. id. alignment length \\\n",
+ "3537669 c100005_g1_i4_m.41851 UPI000C6D6B72 0.262 138 \n",
+ "3711020 c100012_g4_i1_m.41921 A0A4V1IVK5 0.681 133 \n",
+ "3108519 c100014_g2_i1_m.41927 A0A1D1W000 0.456 59 \n",
+ "7530038 c100023_g1_i1_m.41965 UPI0009E61F51 0.342 90 \n",
+ "5277692 c100036_g1_i4_m.42040 A0A7S3BMQ4 0.523 99 \n",
+ "... ... ... ... ... \n",
+ "8219227 c99972_g1_i2_m.41694 UPI0009E28D88 0.466 63 \n",
+ "6087296 c99980_g3_i1_m.41746 UPI00005B2EF3 0.434 62 \n",
+ "3238079 c99987_g1_i2_m.41772 A0A1X7URN4 0.529 220 \n",
+ "574005 c99993_g2_i1_m.41786 UPI00177B4952 0.663 137 \n",
+ "574028 c99993_g3_i1_m.41790 UPI00106CF215 0.265 133 \n",
+ "\n",
+ " no. mismatches no. gap open query start query end target start \\\n",
+ "3537669 100 0 13 150 7 \n",
+ "3711020 41 0 32 164 73 \n",
+ "3108519 32 0 4 62 118 \n",
+ "7530038 57 0 3 92 2743 \n",
+ "5277692 46 0 71 169 1 \n",
+ "... ... ... ... ... ... \n",
+ "8219227 33 0 8 70 392 \n",
+ "6087296 35 0 2 63 7 \n",
+ "3238079 97 0 2 221 37 \n",
+ "574005 41 0 1 137 23 \n",
+ "574028 95 0 3 135 93 \n",
+ "\n",
+ " target end e value bit score gene_id normalized bit score \n",
+ "3537669 142 1.464000e-04 53 c100005_g1 0.384058 \n",
+ "3711020 203 3.106000e-49 183 c100012_g4 1.375940 \n",
+ "3108519 176 4.196000e-04 52 c100014_g2 0.881356 \n",
+ "7530038 2829 4.556000e-04 53 c100023_g1 0.588889 \n",
+ "5277692 98 2.166000e-19 100 c100036_g1 1.010101 \n",
+ "... ... ... ... ... ... \n",
+ "8219227 453 1.130000e-05 57 c99972_g1 0.904762 \n",
+ "6087296 68 4.330000e-05 51 c99980_g3 0.822581 \n",
+ "3238079 243 1.868000e-61 221 c99987_g1 1.004545 \n",
+ "574005 145 1.384000e-49 183 c99993_g2 1.335766 \n",
+ "574028 222 5.938000e-04 52 c99993_g3 0.390977 \n",
+ "\n",
+ "[3347 rows x 14 columns]"
+ ]
+ },
+ "execution_count": 35,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
- "mmseqs_filtered[~mmseqs_filtered['target'].isin(sensitive['uniprot'])]"
+ "hhblits_filtered[~hhblits_filtered['target'].isin(sensitive['uniprot'])]"
]
},
{
diff --git a/analysis/analysis.ipynb b/analysis/analysis.ipynb
index 1552153f502a948c3e32aae0bf01ebf90887730a..5886b4552c49b6a5decf2afa2e2ad29e18e51cfd 100644
--- a/analysis/analysis.ipynb
+++ b/analysis/analysis.ipynb
@@ -10,7 +10,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "2022-07-05 10:59\n"
+ "2022-08-01 14:42\n"
]
}
],
@@ -206,7 +206,7 @@
"metadata": {},
"outputs": [],
"source": [
- "max_annot_lvl = pd.read_csv('/g/arendt/npapadop/data/spongfold_publish/max_annot_lvl_count.tsv', sep='\\t', header=None)\n",
+ "max_annot_lvl = pd.read_csv('../data/max_annot_lvl_count.tsv', sep='\\t', header=None)\n",
"max_annot_lvl.columns = ['max_annot_lvl', 'general']"
]
},
@@ -266,7 +266,7 @@
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": 11,
"id": "5879eb34",
"metadata": {},
"outputs": [
@@ -290,7 +290,7 @@
"\n",
"# read the AlphaFold average pLDDT scores\n",
"for animal in predicted_proteomes:\n",
- " annotation[animal] = pd.read_csv('/g/arendt/npapadop/data/spongfold_publish/alphafold_performance/' + animal + '.tsv', sep='\\t')\n",
+ " annotation[animal] = pd.read_csv('../data/alphafold_performance/' + animal + '.tsv', sep='\\t')\n",
"\n",
"# convert to a list of arrays so we can make a boxplot\n",
"animal_plddt = [annotation[f]['plddt'].values for f in annotation]\n",
@@ -457,59 +457,20 @@
"structural_annotation = pd.read_parquet('../data/structure_annotation.parquet')"
]
},
- {
- "cell_type": "code",
- "execution_count": 18,
- "id": "a84dc48c",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYsAAAEGCAYAAACUzrmNAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8rg+JYAAAACXBIWXMAAAsTAAALEwEAmpwYAAAwYUlEQVR4nO2de5RcVZ3vP79uithBpcmAXGgIiS6EISKJyWA03rmCM4aRUaOA4OiVO7JEHXzAMLnTqHcIDi4zZhSX80Dj6IgjQnjZBhGDI3EeCGpiEkKEDCgY0smSjKQRSRs6nd/9o041p6vPOXVO9anHqfp+1urVp3edx95VXfu39+9p7o4QQgiRRE+rOyCEEKL9kbAQQghREwkLIYQQNZGwEEIIURMJCyGEEDU5pNUdaBRHHnmkz5kzp9XdEEKIQrFx48b/dvejqts7VljMmTOHDRs2tLobQghRKMzsF1HtUkMJIYSoiYSFEEKImkhYCCGEqImEhRBCiJpIWAghhKhJx3pDCSFENUObhlm1bju7RkY5tr+P5UtPYtmCgVZ3qxBIWAghuoKhTcNccdtWRsfGARgeGeWK27YCSGCkQGooIURXsGrd9glBUWF0bJxV67a3qEfFQsJCCNEV7BoZzdQuJiM1lBCiKzi2v4/hCMFwbH/ftO/dDbYQ7SyEEF3B8qUn0VfqndTWV+pl+dKTpnXfii1keGQU5zlbyNCm4Wndt92QsBBCdAXLFgzwybeeykB/HwYM9PfxybeeOu0dQLfYQqSGEkIUnrRqoGULBnJXD3WLLUQ7CyFEoWm1GijO5pGHLaSdkLAQQhSaVquB8raFDG0aZsnKu5k7eAdLVt7dNrYPqaGEEIWm1WqgilorD2+odg4clLAQQhSaRrrEpiUvW0jSLqnVwkJqKCFEoWmUS2wraPUuKQkJCyFEoWmUS2wraGdjudRQQojC0wiX2HqYbiT38qUnTbJZQPvskiQshBAiB/IwTudpLM8bCQshhMiBvIzT7bJLqqZhNgszO97M1pvZg2a2zcw+HLSvMLNhM9sc/LwhdM0VZvaImW03s6Wh9oVmtjV47XNmZo3qtxBC1EM7G6fzoJEG7gPA5e7+u8Bi4BIzOyV47Rp3nx/8fBsgeO0CYB5wFvCPZlZxcbgWuBg4Mfg5q4H9FkKIzLSzcToPGiYs3H23u/8kOH4aeBBI2lu9GbjR3fe7+6PAI8DpZnYM8EJ3v9fdHfgqsKxR/RZCiHroJBfeKJriOmtmc4AFwA+Dpg+Y2f1m9mUzOyJoGwAeD122M2gbCI6r26Oec7GZbTCzDXv27MlzCEIIkUgnufBG0XADt5k9H7gVuNTdf21m1wJ/DXjw+9PAu4EoO4QntE9tdF8NrAZYtGhR5DlCCNEo2tU4nQcN3VmYWYmyoLje3W8DcPdfuvu4ux8EvgicHpy+Ezg+dPlxwK6g/biIdiGEEE2ikd5QBnwJeNDdPxNqPyZ02luAB4LjtcAFZjbDzOZSNmT/yN13A0+b2eLgnu8CvtmofgshhJhKI9VQS4D/DWw1s81B20eAt5vZfMqqpMeA9wK4+zYzuwn4KWVPqkvcveK0/H7gK0AfcGfwI4QQoklY2cGo81i0aJFv2LCh1d0QQjSJ6abaEGXMbKO7L6puVwS3EKLwtHMdiE5BWWeFEIWn1dXyugEJCyFE4en0VBvtgNRQQoiG0EwbQjtUy+t0JCyEELnTLBtCRSBFCYpOSrXRDkhYCCFyp5G1pMMCwohO52DAOQs7N5q6FUhYCCFyp1E2hOodS5zjvwPrH1J+uDyRsBBCTIso20SjbAhRO5Y4ZNzOFwkLIUTdxNkmzlk4wK0bh+uqJZ1kGM8iAGTczhe5zgoh6ibONrH+oT11peuuCJ/hkVGc54TP0KZhIJsAOOPko7IORySgnYUQom6SbBP1pOuuZRhfvvSkSTuZJGSzyBftLIQQdZN3KdFahvFKgaH+vlLd9xL1IWEhhKibvEuJphE+yxYMsPnK1/PZ8+czkCCUZLPIFwkLIQrO0KZhlqy8m7mDd7Bk5d0T+v1mUFnpHzHzuZX+jEPqn1ayCJ9lCwa4Z/BMPnv+/I6ufd0uyGYhRIFpdrbVKE8lgN+OHZw4Z2R0rO4+VM7PkiaknmtEdlTPQogCs2Tl3ZHxDAP9fdwzeGauz6oWTFCOlH5eqYfRkLBoZB9E41E9CyE6kGZmW43yVHKIFBTT6UN499I/s4Q7PDU6ph1Di5GwEKLA5B0pnVdAXJY+hJ95eF+Jp/cfYPxgWeOxd9/YxHkqaNRaZOAWosDk6Y2UZ0Bclmjt8DNHRscmBEUUKmjUOiQshCgwFW+krJHSUdSqNrd86UlYzLVHzCzV1YcsuZ4qKH6iNUgNJUTBqSdSOoo0AXEbfvEk19+3Y1K2175SL1e+cV5dfahn4lf8RGvQzkIIAaQLiLt62alcEwTDTXcnA3B4ikjsMIqfaB3aWQjRBPIqMdrIUqVReZeiJue8djIAFqfXimBA3lAtRcJCiAaTV+BcowPwsga35SG4RkLeTtUYyF22jZCwEKLB5FVitJGlSiuk3TXkJbjiXH+jAvoauasStZGwEKLB5BU418wAvFpkFVxxE30t1VdcvW3FXDQfCQshGkxegXONKlVaD1kEV5pdSJQgqVVvO+9dlUimYd5QZna8ma03swfNbJuZfThon2Vm3zWzh4PfR4SuucLMHjGz7Wa2NNS+0My2Bq99ziyLWUyIfMma5TWvwLm804FPhyx1LGrFb1Syxz668mzuGTxzkgCpFYOhmIvm0UjX2QPA5e7+u8Bi4BIzOwUYBL7n7icC3wv+JnjtAmAecBbwj2ZW+WZcC1wMnBj8nNXAfgsRS60o5yjyCpwL3weg12xi0m1mWnKIFlxGdCnTetVnaQSBYi6aR8PUUO6+G9gdHD9tZg8CA8CbgdcGp10HfB/4y6D9RnffDzxqZo8Ap5vZY8AL3f1eADP7KrAMuLNRfRcijnqNzHm5m1bu0cy05HH92PCLJ/nafTsm2hxY8+PHWXTCrEn9qFd9FnddBcVcNJem2CzMbA6wAPghcHQgSHD33Wb2ouC0AeC+0GU7g7ax4Li6Peo5F1PegTB79uwcRyBEmXYwMtdS6zTL9fWO+3dPaRsbd/78ps1ctmbzxD3POPmoyKjvWhN9lPG7YuRWzEXzabiwMLPnA7cCl7r7rxPMDVEveEL71Eb31cBqKNezyN5bIZJpByNznGCq7DDS7DjycH3dGxMjUckDODwyyvJbtoBP/sIacM7C2jstFTVqLxoqLMysRFlQXO/utwXNvzSzY4JdxTHAE0H7TuD40OXHAbuC9uMi2oVoOmmjnBtJnMCq2DDCxKnIprs7SWsjGRufumZzYP1De1JdP131nWIz8qOR3lAGfAl40N0/E3ppLXBhcHwh8M1Q+wVmNsPM5lI2ZP8oUFk9bWaLg3u+K3SNEE0lzyyv9RLnFTUeU/VyeGR0iudWrd1JkgG/siuZDs1Q29XjjCDiaVhZVTN7DfAfwFagUkrrI5TtFjcBs4EdwHnu/mRwzUeBd1P2pLrU3e8M2hcBXwH6KBu2P+g1Oq6yqqKTiVoxV4LXkugr9fLJt54ae26vWaTQ6TXjoDvH9vex79kDsSqotDSj5GozS852Ek0vq+ru/0m0vQHgdTHXfAL4RET7BuBl+fVOiOKRRqVSrSKrpqJqilOnxV1bESC1hFE1pV4Dh7FQQaNmqe3awRmhk1AEtxAFII1ButogHLf1Hh4Z5bI1mzm8r8TzSj2M7BubqHWdtRBRFOFdSEUotMJu0A7OCJ1Ew9RQrUZqKNFJ1KNSibsmTF+pl3MWDnDrxuFcBEVFzdUORuRqAQvt1b92JU4NpeJHQrSArClD6lGpRBnCqxkdG+dr9+2IFRRxeuT+vtJEJHn1/VoRUR5FOzgjdBJSQwnRZOqJcahHpRJWS2W1NVToK/Xg2JTV+Yo3zQOibSTtlBE2z0JN3Y52FkI0mVoxDlHUm0Swkga81g4jjn1jBxkdG6c3CKYNr86TEv3VGo8oHtpZCNFk6lEppYlmjvOWSpO9tRbj7hPCqfLMPBIBiuIgYSFEk6nXSydJpfKxoa2TkvoNj4yy/OYtQH6TdnU0eK1Ef/I66iykhhKiyeRdl2Jo0/AkQVFh7KCzYu22XCftsOBJUm8pI2znoZ2FEE0m7wR5SbaBkdExRkanF20dJix4qg3olehvZYTtTBRnIUTBCNsm+meWpp16o4IBr37JLB771eiUmtegGIVuoenpPoQQ8WTNhlo5v3oSz0tQENzzsV+NTgT5KWOrCCNhIUSTyRpnUX1+I3UBYZuEYhREGBm4hWgyWeMs8nB9TYs8mEQcEhZCNJmscRZZXV8H+vvo7yvVPK/6yy8PJpGE1FBCNJlacRbVtoLD+0qZPJrSpPY4YmaJK984TzYJkRoJCyGaTFJp1ih7RqnXKPXYpJoQ02Vk35hsEiITUkMJ0WCqM8wCsdlQo+wTY+PO8593yKTzl7xk1rT6JNuEyIp2FkI0kDjPp0++9dTIOhRx9omRfWNs+qvXT9zz8pu21N2nUq/JNiEyo52FEA0kq+dT3Io/bM+44ratkXWy02DAqnNPk/pJZEbCQogGktXDqVbeqOm40ZZ6jGvOny9B0aFkLaiVlZrCwsyONrMvmdmdwd+nmNlFufZCiAKS5ssZt1PoMYs8v1Z1t6xutOE6FKvO046iU6nsOIeD2usVdWeeAiONzeIrwD8DHw3+/i9gDfCl3HohRJtT7c56xslHTapbHReFfcbJR0VmhB13n3R+2tQatdKCh0mqzy06iyR1Z14LhDTC4kh3v8nMrgBw9wNm1pxwUiHagCgj9fX37ZiSdqP6yzm0aZgbfvh47H3Dtovq+//5TZu57KbNuJd3B29/5fEsOmEW+549MOU+pV4DZ5JrrQLsuou8AjqTSCMsnjGz3yFISWNmi4GncuuBEG1O1Kotzrxc+XKmNUTvGhmNvH84pGLcna/dtyNyh9LfV5qoh60Au+6l3oJaWUgjLC4H1gIvMbN7gKOA83LrgRBtTpbVWeXLmdYQ3WOWWq0UxWEzDpkQChIO3UtSoGde1BQW7r7RzP4XcBJlz7vt7p5fXmQh2py4VVtUvYfKlzOtABh3n3KfLKjOtYD8C2pFUVNYmNnPgFXu/vlQ27fc/Y9z64UQbUzcqu2chQOsf2jPlC/n0KbhTAJgOkk8wmqGcM0LVa3rPhqdviWNGmoMOMPMXgm8192fBfSfJ7qGrKu2Veu2N7TmRJgzTj4KmGqEr9hKatXKECItaYTFPnc/38z+L/AfZvY2UiyGzOzLwB8DT7j7y4K2FcB7gD3BaR9x928Hr10BXASMAx9y93VB+0LK7rt9wLeBD3un1oIVbUuWVdt0VUNmkPY//NaNwyw6YVaijSRvF8rpoOp7xSWNsDAAd/+UmW0E1gFpsph9Bfh74KtV7de4+99OeoDZKcAFwDzgWOBfzeyl7j4OXAtcDNxHWVicBdyZ4vlC5ErURAdTdxxZYiGiqBYUM0vl2Nl9YwennFsRBLUEVBYB1qgJPWuFQNFepBEWf1U5cPfvmdlS4MJaF7n7v5vZnJT9eDNwo7vvBx41s0eA083sMeCF7n4vgJl9FViGhIVoMlET3fKbt4CVs8JW2i5bsxlnqvF7Oswo9TLz0EPYl+BLX0tApXWhbOSE3ozAsbzRTug5YoWFmZ3s7g8Bw2b2iqqXvzWNZ37AzN4FbAAud/e9lG0g94XO2Rm0jQXH1e1xfb6Y8i6E2bNnT6OLopsITwiH95UwK2d5DU8OkanDI+pLeNXvPNi7b4y9++IdECsR5VGBglAWXMMjoyxZeXfNya6RE3ozAsfyRDuhySTtLP6c8sT76YjXHKgnj8C1wF8H1/91cO93E6i6Ip4R1x6Ju68GVgMsWrRIdg1Rk+oJIVyRrrJT2PCLJ9t2Qusr9U6kHokTFJX2NJNdIyf0ZgSO5UkRd0KNJFZYuPvFwe8z8nqYu/+ycmxmX+S5HcpO4PjQqccBu4L24yLahaiLarXC3mf2MxphC6jgwPX37aB/Zilxdd8KKm6xccbtivtsmFqTXdYJPYuaphmBY3kSJyCnY48qMmmyzp5nZi8Ijj9mZreZ2YJ6HmZmx4T+fAvwQHC8FrjAzGaY2VzgROBH7r4beNrMFpuZAe8CvlnPs0X+NDolct5EZeaMMhpX49CWguKewTNZtmAgdlKLSzWStEuolSI9TNZMp7Uy6rYbcQLSoO3/1xtBGgP3/3P3m83sNcBS4G+BzwOvTLrIzG4AXgscaWY7gSuB15rZfMrfv8eA9wK4+zYzuwn4KXAAuCTwhAJ4P8+5zt6JjNttQTP1uVlWr0nnXnX7trprQbSKJS+ZxU92PJW4Go/bDUTtLOC59OhR72FUTMkZJx/FqnXbuWzN5pp2nFo7lyLV/V6+9KQJh4UwDl2pirJaIQtmtsndF5jZJ4Gt7v71SltzulgfixYt8g0bNrS6Gx3LkpV3R05QeafFrhZKUJ4so1akHxvaOsXIW4m0vuP+3W23O0iikmn26mWn1hSWce/ROQsHJqVRDxP3HlaT9P5HTaRQXnk/uvLszGNuR+YM3hHZ3kljrMbMNrr7our2NDuLYTP7AvAHwN+Y2QxUYa/raZZnS9rV69Cm4di04XFeQu3KY1WTUK3VeFKE+aITZnH5TVsy2y4qJL3/cTua/pkllqy8uyPcTQcKZpRvJGkm/bdRDsQ7y91HKAfkLW9kp0T7U6tWdF6kFUpJKTaKJCjqZdmCAe4ZPJNHV549YcuotB+sw3ZR65xdI6OR9o1Sr/Gb3x5oaMW2ZpLFhtPp1BQW7r7P3W9z94eDv3e7+12N75poZ5r1JUorlNrVtTUrR8ws5X7P6Qj2pGujDNaHHXrIlPiTcJGnolE0o3wjSaOGEmIKzUiJDNHulqUeY9+zB5g7eEduKTbagVKvceUb5+V+3+m4rNa6tlpFNjdGx19kYV4ko3wjkbAQddOML1G1UDq8r8Qzzx6YMFZX1BznLByIrCRXFBqZSnw6gj3rtUULvBPpqekNNeUCsyXAn7j7JY3pUj7IG6qxDG0a5qrbt01M2pXyno0WHnFeWDNLPaliJtqRLJ5J7Z6nKIv3mmhP4ryhUnk1mdl8M/tUkNjvauChnPsnCsTQpmGW37JlkivqyOgYy2/e0nBDZpw6o6iCAtLp9LMGwLUK6fg7l6REgi+lnDb87cCvgDWUdyK5pf8QxWTVuu0TmVbDjB30RHfMPFbG7W6bOOzQXp49cDAyyWAStXT6RcpTJB1/Z5K0s3gIeB3wRnd/jbv/HeXCRKLLSZrY4l6b7sq4klpkeGQ0MrtkO2DAto+fxarzTmMgo46+lk6/aBlbReeRZOA+h/LOYr2ZfQe4kegssKLLSFrdx016WYLrogoMhfXg7Ro3URl7ZWUdpb8v9dikGhiQzjNJhmPRapKyzn4D+IaZHUa54NBlwNFmdi3wDcVadC/Ll57E8lu2TFFFlXosdtJLszKOyzc145CeQuR02v3UKHMG75iUqgOmehJFtdVS22R1f52uyq8IxnTRXGq6zrr7M8D1wPVmNgs4FxgEJCy6lHBivrTeUIf3lSbVigi3V4jbfRRBUABUzBTj7hNuvFcvizbuRrUlTdBJLqzV11XqW9Sb5FFFf0QUqV1nzex3gN8Hdrj7xob2KgfkOtteLPj4XZGJ/I6YWeLKN86bJHiKxEB/H7sCO0wUnz1/fl0TNEx2OY0TJFHXxZV0TZvksVlJIkV7kjmRoJl9Cxh09weCOhQ/oVwK9cVm9kV3/2zDeis6jpEYQbB331ikSqsIGHDP4JmxmUmB1CvyJJtO5T5RK/2o6+LeybTGcBnTRRRJ3lBz3b1SnOhPge+6+xuBxZRLoQoxhbiCSHGG2F6zQgoKeG5MvRbv95E2L1LSBJ0kSLJM4A6pilQ1K0mkKBZJwiK8FHwd8G0Ad38aKG4ElGgYSe6xcYkH46q5tTth4/LbX3l84rlpJvSkCTpJkCRVc4sijcuyMq2KKJKExeNm9kEzewvwCuA7AGbWB+SfGlMUlspu4tI1mxPdY6Mie7PGI7QDvWaTopKvXnYq71w8O/b8yoSeVIY2aYJOEiRx171j8ezY97bWbkdR2CKKJG+oi4CPUy56dH5QywLKaqh/bnC/RJOp11UyysBaTWVlHBfZe+mazXX3uxUcdJ8yjquXncqiE2bFurfW8jCqlbAv7r61rps7eEekDaPWbme6UdjNdr2Vq2/jSYqzeAJ4X0T7emB9Izslmst0XCWj9OnVxK2MK1/wonFsf1/i5BTVvmTl3TWDEuMm6FoCIWlizyuYL2sd9Ga63srVtzkkeUOtTbrQ3d+Uf3dEK5hO3qFaK9Q4XffQpmGW37wldQ6lGYf0sP9A601lfaVezjj5qFS7hDDT9TCqd6WfJpgva43vWpNxs/NYFSlvVpFJUkO9CngcuAH4IUr10bHUmsjiUnAklTKFsm4/rB8Pf3FXrN2WKdleT4LHUbOo2CrqmZxala6j1q4kjSDIOt5mu97K1bc5JAmL/wH8IeWss38C3AHc4O7bmtEx0TySJrKoyWT5zVum5DeKouLpNDwyyqVrNnPpms3095V49sB45pTizYzi7rHnorErhAPkLouxsSRNTtOpVjddknYlaQRB1sm42YJRebOaQ6w3lLuPu/t33P1CykbtR4Dvm9kHm9Y70RSSPHGiJpOxgx4rKGaWkkukjIyOtVXtiVKv8c7Ac6ji+fOZt83ns+fPj/UGCqcoCRPXDskeRkleUo0mjSDIGnfRbNdbufo2h8TcUGY2Azib8u5iDvA54LbGd0s0kyRVRdwqOgoD9h8oTtxEj8Gqc0+LXXXHtcdpxGppyqJW+K02zqZZlWfdFTWrPnurntetJBm4rwNeBtwJXBWK5hYdSJyqIkuxoXYvTFRNUuR1VHK+9Q/tScwDFZfSJIlWG2fTCIJ6JuNmF0Cq53lyt81GbCJBMzsIPBP8GT7JAHf3Fza4b9NCiQSzk6aWBETXZCgqUcnx0sSOpL1XLeLiIAx4dOXZme5VL904aapWeDyZEwm6e6r63KIziDNkP/95hzA6Nk6vGePuDEwSIvcz2kb2h3oYHhll7uAdkybJNLEj1dSrI28H42w3lkFt9Y6uiDRMIJjZl83sCTN7INQ2y8y+a2YPB7+PCL12hZk9YmbbzWxpqH2hmW0NXvucWRv4UHYYQ5uGufymLZGG7Era8Ipn0zP7D0y8XnRBUaE6j1UWl8vppsOQcbY1yN02OzWLH02DrwB/D3w11DYIfM/dV5rZYPD3X5rZKZRLuM4DjgX+1cxe6u7jwLXAxcB9lJMZnkXZjiJyoLKjSJvQb2S0nFJ8PEOMRFGorCzT2l7yqO8g42xraIcdXdFo2M7C3f8deLKq+c3AdcHxdZTLtVbab3T3/e7+KGU33dODOhovdPd7vWxc+WroGpED9ahcxsZ9ShxCp7BrZDRytV+NUd6N5OHqumzBAPcMnsmjK8/mnsEzJSiagHZ02Wm2XeJod98NEPx+UdA+QDlavMLOoG0gOK5uj8TMLjazDWa2Yc+ePbl2vFPRtnsyx/b3RcZEvDOUxTVciS5Nym/RfiizbnYaqYbKQpQdwhPaI3H31cBqKHtD5dO1zqZo7q6N5oyTj4psX3TCLK5edmpkyVEZRotJNxr2p0OzhcUvzewYd98dqJieCNp3AuEKMscBu4L24yLaRU5E+dl3M7duHJ74HRUoJ8No+9KNLsDNpNlqqLXAhcHxhcA3Q+0XmNkMM5sLnAj8KFBVPW1miwMvqHeFrhE5UNmOJwWodROjY+Pc8MPHY90qVXK0PUmq0ijyoZGuszcA9wInmdlOM7sIWAn8oZk9TDlJ4UqAIDnhTcBPKVfkuyTwhAJ4P/BPlI3eP0OeULmzbMEAn37baTWNut1CnGdYnPFbhtHWkxQ3IfKhYWood397zEuvizn/E8AnIto3UE47IhpI2IWzW2wYR8wsTcSRhKkEIFZTMX6DXF3bjXrVg1JdpUdR2h1I2iym1edB2YZRxLrY9RAlKPpKvbz9lcfH7h40ubQn9agHpbrKRmxuqKLTrbmh0ua8qTf/USdjwDsWz+bqZadOEgqH95UwKwuXsNssKJ9Qu1BPrqcozzbIJ9iyyGTODSWKSZzudsXabZNWxM/sPyBBUYUD6x8qx+dU3CqrJ6HqpZXcZtuDetSD8mzLhoRFhxFnbxgZHWNkdCzxnKJx9AsO5Ymnn00s7ZqV6okiTYS7Jpf2IGvchFJ+ZEM2iw5iaNNwVxVKf/KZsVwFBUydKNIIAk0uxUSebdmQsOggVq3bnvvk2c6M5ZygKmqiqCUINLkUF6X8yIbUUB2E1CH1MxCj446KcK8YueOuEcVBKT/SI2FREKK8c0b2jU0y5CnPU23MwD15oq92jz1n4cBESdV2dpdtpFuvXIaFXGcLQC0314qLIEwtgSqeo9RrrDr3tMRJrqjlNhvZ76K+J6I+4lxnZbMoALU8csLum2EdbH9fid6ebjJ5JzM27lx1+7bEgMWipo1oZL+L+p6IfJEaqgCksUVUzqnWwQ5tGuYjt93Pvg4pgTpd9u4bm4jcDmeTrbxnRfW9b2S/i/qeiHyRsCgAaWwRYa+dav3yWxcex9fu29HobraMmaWeuoVhdVBdvb73jdDpZ7lnXL/7Z5am1Yeke8tluLuQGqoA1CrzWeox9j17gLmDd7Dg43ex/OYtk/LddLKgAHjrwuN45+LZE2nWe81Y8pJZqbPoDo+MTqij6vG9b0SOoaz3XL70JEq9U1WOv/ntgWnnOlI8ggAZuAtDnDfU4X0lnnn2AGPjnfk5psGAa86fP2XVXb0yf2b/gYko9mpKPcaq806bSPGRZZfQiBxD9dxz/lV3RY4vj1xH8obqHuIM3BIWBSduUuk2jphZwp2JyfKImSWufOO8Kfaby9Zsjg1cjJtUa02UcwfviLynAY+uPLuu8cTds9LPqL40oh+i+5A3VAcQlXpcRsYye/eNTVpV7903xvJbtkxSwSxbMJAY4R71XqZRBzWiel7ctRb0IaovquInGomERUGIm7TyMGC2O32lXvr7so9zbNy5/KbJAiOpVkfUpJrGbbQROv2oe1anR6/ui2wLopFIWBSEuEnLnY5PHnjOwgFWvGleXWVfx90nrb7jDMGlHoucVNO4jTYix1DUPeN2RWG3aeU6Eo1CNouCMGfwjlZ3oWWEI9QrZV/jSp/GEbZHDG0a5qrbt03EW/T3lVjxpnmRk2o7Fchpp76IzkXFjwpOj0HOSVYLQ0XVcs/gmakMy1FU7wTSrrajEgm2SrXTTn0R3YeERZtR8bwJr54H+vu6VlBUiFIHZUmcWK+Rt54KbI2infoiug+podoI1cVOpjpTbNT7Veo18Mm1LsJJ71oZL5Dm2YpnEK1Gaqg2JTw59GTUw3cb1bmc4lbaUW1R9bSjckM1ijTPbmX/hKiFdhYtRDuJ+qjXoJuHgbjelX+aZ8uALdoB7SzakFqpx0U09QYiTjd76nRW/mmereyuop1RnEUL0SRQH/Uaq+Ou6zFLlWyv3roOQ5uG6bHoaJhwnxSBLdoZCYsW0u2TwIkvOoxSHcWZ6nUVjcveWx24F0c9K//KbiTKFlXt9qoIbNHOtERYmNljZrbVzDab2YagbZaZfdfMHg5+HxE6/woze8TMtpvZ0lb0uRF0+ySwc+9vmXPkzMzXrVq3PVPa7UpOrcvWbOZ5pZ7IiPfRsXFWrN2WeJ96Vv5xqsZesynR1YrAFu1MK20WZ7j7f4f+HgS+5+4rzWww+PsvzewU4AJgHnAs8K9m9lJ3L7Syv2Io7WZGx8Z5+IlnYl+vTOrVa/I4W0GU8Rkm1yWvRG1HMTI6xtCm4djJuZ6guLhdx0H3yOdkCRgUopm0kxrqzcB1wfF1wLJQ+43uvt/dHwUeAU5vfvfyY2jTMMtv2aLU4gn0lXq55vz5PLry7Mjkf9W2grhEi1fdvi2TE0GSAK9n5S87hOgUWrWzcOAuM3PgC+6+Gjja3XcDuPtuM3tRcO4AcF/o2p1B2xTM7GLgYoDZs2c3qu+ZGdo0zIq12yZSaEdlDxWTCU/CaWwFccbnrN5mtZwOsq78laJDdAqtEhZL3H1XIBC+a2YPJZwbpWKOnGsDobMaynEW0+9mfVQLh2okKLKRpgZ0Vs+yuFxbx/b31R1LkXSdorJF0WmJsHD3XcHvJ8zsG5TVSr80s2OCXcUxwBPB6TuB40OXHwfsamqHMzC0aZjlN2+ZlG5CZGfVuu0TE2qa1XmcQOnvK7H/wMEp156zcIBbNw5PaT/j5KOmxFIsv3kLV92+jZF9Y4lpOpJiMCQcRNFpus3CzA4zsxdUjoHXAw8Aa4ELg9MuBL4ZHK8FLjCzGWY2FzgR+FFze52eVeu2S1DkQNZ6EXFupyveNC/y2quXnRrZvv6hPVNUV2MHnb37xmIr5UH9MRhCFIVW7CyOBr5h5SClQ4Cvu/t3zOzHwE1mdhGwAzgPwN23mdlNwE+BA8Al7ewJpUC7fKg2ANdanddS96T1PLpszeaafasIgfC1ir4WnU7ThYW7/xw4LaL9V8DrYq75BPCJBnctF7KkzRbx7Hv2QKIbaxR5qHvSfn7VQiDuOqec80l2ClF02sl1tiNYvvSkuqKSxWT27htLFVWdN3FR3tVU73ySrotTXQlRJCQscmbZggFWnXca/X2liTa9ybWJi6pOk3dpycq7mTt4B0tW3j3tCbnaPtLfV5pSszvK9TV8XRSyX4iio6yzORDlMrn5ytcztGmYj9x2P/vGDra6i21Hqdc47NBDeGp0LFH1kybvUt71H6rVWWldaSvXxZV7lf1CFBkJi2kSN2Ft+MWTfP2HO7q+HGoUvWasOve0SRNuXC2HrHmXoozP0yWrLSRNXIgQRUPCok6SAu9Gx8b52n07WtCrYhDOixSuOV4d2V5v3qVWr+AVtS06EQmLOlDg3fTon1m251TvypznUqFU19sOUxEwce/+4SF7UStQ1LboRCQs6kCBd9OjUtohSo3klNVUu0ZGJwzC1faDWqVon6nD7TZvFLXd3dSbMqadkaNORoY2DSuOYpo8Faju4tRF4+4T0dLLb9kyycMpTSnasXGX55FoGXEZkIvuOi1hkYHKP4GYHhU1URqD79i4c9XtzxUlSmuPaLXdQnQvnZr6RcIiA2lWtaI2lXLUaQPgwgWL0noUyfNItIp2dbyYLhIWGSj6h90ujAST/7IFA5yzcCAyIC+ONAJGnkeilXRqwSsJiwy02sumaPRatBgIf2nWP7SnZn2PcDR8VAbady6erbrVom2Iy4Bc9AWMvKFSMrRpmGeePdDqbrQtfaXeKZN0lOdS9Zem1m6t1GOseNO8SW3yNBLtTKe6TktYpGTF2m2MjctdNoq4mIg0X5qkVB9JsRZCtDOduKCRsEjBx4a2xpZI7WaidhPV1PrSxEU7S5UkRHshYVGDoU3DSt0R0N9X4rAZh+S6te7ULbsQnYaERQ0uTVE5rRuolChtxCTeiVt2IToNCYsIPja0tWt2E71mjHuyLWa6toNOTH0gRLchYVFFNwkKoKageGzl2dO6f6NqTgghmoviLKroJkFRi7iqb1no1NQHQnQbEhYh3vHFe1vdhbYhryCiTk19IES3IWERMLRpmHt+9mSru9EW5BkF3ampD4ToNiQsAuT19Bz3DJ6Zmz2hU1MfCNFtyMAtJpGHnSKM4iiE6AwkLMQEjVrxK45CiOIjNZQAyvEWSrEhhIhDwkIAcNBdgkIIEYuERQdiQE+WikLIO0kIkUxhhIWZnWVm283sETMbbHV/2pW+Ui/XnD+fz7xtfqqSpZVr5J0khEiiEAZuM+sF/gH4Q2An8GMzW+vuP21tz5qDGbiXdwsHQ9k5jphZ4uyXH8P6h/bEehpFeSEpV5MQIiuFEBbA6cAj7v5zADO7EXgzUAhhMeOQHnoN9o0dBMqpvucd+wLu+/lext3pNWPxi4/gJzueyrWuQ5wXkryThBBZKYqwGAAeD/29E3hl9UlmdjFwMcDs2bOb0jED3rF4NrduHJ6SA6mv1MMn3/ry1BOzVvxCiHalKMIiylw7JV2qu68GVgMsWrQoUw3UJS+ZlTndx6G9xqfOPY1lCwZYdMKsaU/0WvELIdqVogiLncDxob+PA3bl+YDr3/Mq3vHFe6cIjHcuns3Vy06teb0meiFEJ1MUYfFj4EQzmwsMAxcAf5L3Q65/z6vyvqUQQnQEhRAW7n7AzD4ArAN6gS+7+7YWd0sIIbqGQggLAHf/NvDtVvdDCCG6kcIE5QkhhGgdEhZCCCFqImEhhBCiJuaeKRyhMJjZHuAXMS8fCfx3E7vTbDp5fBpbMenksUFnje8Edz+qurFjhUUSZrbB3Re1uh+NopPHp7EVk04eG3T++EBqKCGEECmQsBBCCFGTbhUWq1vdgQbTyePT2IpJJ48NOn983WmzEEIIkY1u3VkIIYTIgISFEEKImnSdsOiEWt5m9piZbTWzzWa2IWibZWbfNbOHg99HhM6/IhjvdjNb2rqeT8XMvmxmT5jZA6G2zGMxs4XBe/KImX3OzKJqoDSdmPGtMLPh4PPbbGZvCL1WiPGZ2fFmtt7MHjSzbWb24aC9Iz67hPEV/rOrG3fvmh/KGWt/BrwYOBTYApzS6n7VMY7HgCOr2j4FDAbHg8DfBMenBOOcAcwNxt/b6jGE+v37wCuAB6YzFuBHwKsoF8q6E/ijVo8tYXwrgL+IOLcw4wOOAV4RHL8A+K+g/x3x2SWMr/CfXb0/3bazmKjl7e7PApVa3p3Am4HrguPrgGWh9hvdfb+7Pwo8Qvl9aAvc/d+B6hKFmcZiZscAL3T3e7387fxq6JqWEjO+OAozPnff7e4/CY6fBh6kXP64Iz67hPHFUajx1UO3CYuoWt5FLG/nwF1mtjGoOw5wtLvvhvI/OvCioL2IY846loHguLq9nfmAmd0fqKkqqppCjs/M5gALgB/SgZ9d1figgz67LHSbsEhVy7sALHH3VwB/BFxiZr+fcG6njBnix1K0MV4LvASYD+wGPh20F258ZvZ84FbgUnf/ddKpEW1tPTaIHF/HfHZZ6TZh0fBa3s3A3XcFv58AvkFZrfTLYMtL8PuJ4PQijjnrWHYGx9XtbYm7/9Ldx939IPBFnlMLFmp8ZlaiPJFe7+63Bc0d89lFja9TPrt66DZhMVHL28wOpVzLe22L+5QJMzvMzF5QOQZeDzxAeRwXBqddCHwzOF4LXGBmM6xcw/xEyga3dibTWAJ1x9NmtjjwNHlX6Jq2ozKZBryF8ucHBRpf0I8vAQ+6+2dCL3XEZxc3vk747Oqm1Rb2Zv8Ab6Ds2fAz4KOt7k8d/X8xZa+LLcC2yhiA3wG+Bzwc/J4VuuajwXi302aeGMANlLfzY5RXYRfVMxZgEeUv7s+AvyfITtDqn5jx/QuwFbif8iRzTNHGB7yGsjrlfmBz8POGTvnsEsZX+M+u3h+l+xBCCFGTblNDCSGEqAMJCyGEEDWRsBBCCFETCQshhBA1kbAQQghREwkLITJgZq81s1eH/n6fmb0r4rw54UyzKe/9mzz6WHXP+VWZUVeY2V/k/RzR+RzS6g4IUTBeC/wG+AGAu3++pb2pzXzKfv7fbnE/RMHRzkJ0PMEq/yEzuy5IAHeLmc0MXltoZv8WJGVcF0pV8X0z+6yZ/cDMHjCz04OEcu8DLgtqGfzP8Eo9uNcWM7sXuCT0/F4zW2VmPw6e/94UfV4eOv+q0DgeNLMvBjUW7jKzvuC13wvOvTd41gNBloKPA+cH/T0/uP0pwfh+bmYfyu2NFh2NhIXoFk4CVrv7y4FfA38W5P75O+Bcd18IfBn4ROiaw9z91cCfAV9298eAzwPXuPt8d/+Pqmf8M/Ahd39VVftFwFPu/nvA7wHvCVJCRGJmr6ecLuJ0yjuDhaFkkScC/+Du84AR4JzQs98XPHscwMtp+P8KWBP0d01w7snA0uD+VwbvgxCJSA0luoXH3f2e4PhrwIeA7wAvA75bTttDL+XUHBVugHJNCjN7oZn1x93czA4H+t3934Kmf6GcFRjK+btebmbnBn8fTnnSfzTmdq8PfjYFfz8/OH8H8Ki7bw7aNwJzgn69wN1/ELR/HfjjuL4Cd7j7fmC/mT0BHM3kNNpCTEHCQnQL1XltKumjt0XsBJKuicMSXjfgg+6+rmYvnzv/k+7+hUmNZTXY/lDTONBHdBrsJKrvoXlA1ERqKNEtzDazilB4O/CflBO+HVVpN7OSmc0LXXN+0P4aymqkp4CnKZfZnIS7jwBPBecCvCP08jrg/RV1j5m9NMgYHMc64N1WrqWAmQ2Y2YviTnb3vQSZTYOmC0IvR/ZXiKxIWIhu4UHgQjO7H5gFXBvo9M8F/sbMtlDOLPrq0DV7zewHlO0UFwVttwNvqRi4q57xp8A/BAbu0VD7PwE/BX4SuNN+gYTVvLvfRVmVdK+ZbQVuofaEfxGwOni2AU8F7espG7TDBm4hMqOss6LjCdQ333L3l2W45vvAX7j7hkb1K0/M7Pnu/pvgeJBy6uwPt7hbooOQrlKIzuBsM7uC8nf6F8D/aW13RKehnYUQQoiayGYhhBCiJhIWQgghaiJhIYQQoiYSFkIIIWoiYSGEEKIm/x9GKw/NcdjAfQAAAABJRU5ErkJggg==\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "fig, ax = plt.subplots()\n",
- "ax.scatter(structural_annotation['query length'], structural_annotation['alignment length'])\n",
- "ax.set_xlabel('peptide length')\n",
- "ax.set_ylabel('MSA size')\n",
- "plt.savefig('./figures/analysis-spongilla_af_query_length_vs_alignment_length.pdf')"
- ]
- },
{
"cell_type": "markdown",
"id": "f3f687cc",
"metadata": {},
"source": [
- "There are clearly some really bad predictions here - we shouldn't trust a structural analog for a 2500-AA long protein that was found based on an alignment of <300AA. How do we threshold?\n",
- "\n",
"We will have a look at the distribution of (log) bit scores to understand a bit better:"
]
},
{
"cell_type": "code",
- "execution_count": 19,
+ "execution_count": 18,
"id": "49bb1825",
"metadata": {},
"outputs": [
- {
- "data": {
- "text/plain": [
- "Text(0, 0.5, 'frequency')"
- ]
- },
- "execution_count": 19,
- "metadata": {},
- "output_type": "execute_result"
- },
{
"data": {
"image/png": "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\n",
@@ -527,7 +488,7 @@
"fig, ax = plt.subplots()\n",
"ax.hist(np.log(structural_annotation['bit score']), bins=100);\n",
"ax.set_xlabel('log bit score')\n",
- "ax.set_ylabel('frequency')"
+ "ax.set_ylabel('frequency');"
]
},
{
@@ -540,7 +501,7 @@
},
{
"cell_type": "code",
- "execution_count": 20,
+ "execution_count": 19,
"id": "adb3c4ba",
"metadata": {},
"outputs": [],
@@ -550,7 +511,7 @@
},
{
"cell_type": "code",
- "execution_count": 21,
+ "execution_count": 20,
"id": "aa70acef",
"metadata": {},
"outputs": [
@@ -560,13 +521,13 @@
"(25232, 41943)"
]
},
- "execution_count": 21,
+ "execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "np.sum(structural_annotation['bit score'] > bitscore_cut_off), len(structural_annotation)"
+ "np.sum(structural_annotation['bit score'] >= bitscore_cut_off), len(structural_annotation)"
]
},
{
@@ -579,7 +540,7 @@
},
{
"cell_type": "code",
- "execution_count": 22,
+ "execution_count": 21,
"id": "44085a9a",
"metadata": {},
"outputs": [],
@@ -602,12 +563,12 @@
},
{
"cell_type": "code",
- "execution_count": 23,
+ "execution_count": 22,
"id": "c41c810c",
"metadata": {},
"outputs": [],
"source": [
- "taxonomy = pd.read_csv('/g/arendt/npapadop/data/spongfold_publish/max_annot_lvl_count.tsv', sep='\\t', header=None)\n",
+ "taxonomy = pd.read_csv('../data/max_annot_lvl_count.tsv', sep='\\t', header=None)\n",
"taxonomy.columns = ['max_annot_lvl', 'eggnog_max_taxonomy']\n",
"\n",
"structural_annotation = structural_annotation.merge(taxonomy, on='max_annot_lvl', how='left')"
@@ -615,7 +576,7 @@
},
{
"cell_type": "code",
- "execution_count": 24,
+ "execution_count": 23,
"id": "ea08d1ad",
"metadata": {},
"outputs": [
@@ -649,7 +610,7 @@
"id": "1f4a062b",
"metadata": {},
"source": [
- "Most hits come from Metazoa, and they also have the best bit scores by far. The only other group with clearly high scores are amoebozoa, a group with problematic taxonomy. It is very interesting to see how many genes are contributed by Streptophyta and Fungi, as well as the many bacterial groups that show up. What are these proteins?\n",
+ "Most hits come from Metazoa, and they also have the best bit scores by far. The only other group with clearly high scores are amoebozoa, a group with problematic taxonomy. It is very interesting to see how many genes are contributed by plants, fungi, and bacteria. What are these proteins?\n",
"\n",
"* lost in other animal lineages but present in the common ancestor, and therefore only visible in sponges and unicellular organisms?\n",
"* present in animals, just not in the ones currently in the database?\n",
@@ -657,19 +618,17 @@
"* sponge innovations with structural analogs in unicellular organisms?\n",
"* sponge symbiont proteins that were accidentally sequenced? Remember, this is a transcriptome, so we don't have the extra safety of the genomic scaffold.\n",
"\n",
- "How I wish I had a master student\n",
- "\n",
"Next up, we are going to look at the same question but use the UniProt taxonomic lineage instead:"
]
},
{
"cell_type": "code",
- "execution_count": 25,
+ "execution_count": 24,
"id": "9a049b71",
"metadata": {},
"outputs": [],
"source": [
- "taxonomy = pd.read_csv('/g/arendt/npapadop/data/spongfold_publish/tax_count.tsv', sep='\\t', header=None)\n",
+ "taxonomy = pd.read_csv('../data/tax_count.tsv', sep='\\t', header=None)\n",
"taxonomy.columns = ['Taxonomic lineage (PHYLUM)', 'UniProt detailed', 'UniProt coarse']\n",
"\n",
"structural_annotation = structural_annotation.merge(taxonomy, on='Taxonomic lineage (PHYLUM)', how='left')"
@@ -677,7 +636,7 @@
},
{
"cell_type": "code",
- "execution_count": 26,
+ "execution_count": 25,
"id": "bed5dbe5",
"metadata": {},
"outputs": [
@@ -718,7 +677,7 @@
},
{
"cell_type": "code",
- "execution_count": 27,
+ "execution_count": 26,
"id": "60b5a9c0",
"metadata": {},
"outputs": [],
@@ -762,7 +721,7 @@
},
{
"cell_type": "code",
- "execution_count": 28,
+ "execution_count": 27,
"id": "bf51e55e",
"metadata": {},
"outputs": [],
@@ -775,7 +734,7 @@
},
{
"cell_type": "code",
- "execution_count": 29,
+ "execution_count": 28,
"id": "4ac88f71",
"metadata": {},
"outputs": [],
@@ -786,7 +745,7 @@
},
{
"cell_type": "code",
- "execution_count": 30,
+ "execution_count": 29,
"id": "7a4c002b",
"metadata": {},
"outputs": [
@@ -822,12 +781,12 @@
"source": [
"## Comparing to sequence-based annotation:\n",
"\n",
- "We will now compare the (novel) structure-based annotation to the (classical) sequence-based annotation. For simplicity's sake we'll start by comparing the root EggNOG orthogroup:"
+ "Refer to `suppl-struct_seq_agreement.ipynb`"
]
},
{
"cell_type": "code",
- "execution_count": 31,
+ "execution_count": 30,
"id": "57691b18",
"metadata": {},
"outputs": [],
@@ -846,7 +805,7 @@
},
{
"cell_type": "code",
- "execution_count": 45,
+ "execution_count": 31,
"id": "119ede73",
"metadata": {},
"outputs": [],
@@ -868,7 +827,7 @@
},
{
"cell_type": "code",
- "execution_count": 46,
+ "execution_count": 32,
"id": "dbea544a",
"metadata": {},
"outputs": [
@@ -916,7 +875,7 @@
},
{
"cell_type": "code",
- "execution_count": 47,
+ "execution_count": 33,
"id": "ecad5b57",
"metadata": {},
"outputs": [],
@@ -927,7 +886,7 @@
},
{
"cell_type": "code",
- "execution_count": 52,
+ "execution_count": 34,
"id": "2de08d9c",
"metadata": {},
"outputs": [
@@ -942,15 +901,6 @@
"needs_background": "light"
},
"output_type": "display_data"
- },
- {
- "data": {
- "text/plain": [
- "<Figure size 432x288 with 0 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
}
],
"source": [
@@ -977,13 +927,13 @@
},
{
"cell_type": "code",
- "execution_count": 36,
+ "execution_count": 35,
"id": "930ecb97",
"metadata": {},
"outputs": [
{
"data": {
- "image/png": "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\n",
+ "image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
@@ -1018,7 +968,7 @@
},
{
"cell_type": "code",
- "execution_count": 37,
+ "execution_count": 36,
"id": "0e4a2bf1",
"metadata": {},
"outputs": [],
@@ -1028,13 +978,13 @@
},
{
"cell_type": "code",
- "execution_count": 38,
+ "execution_count": 37,
"id": "176505e7",
"metadata": {},
"outputs": [
{
"data": {
- "image/png": "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\n",
+ "image/png": "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\n",
"text/plain": [
"<Figure size 864x576 with 1 Axes>"
]
@@ -1064,13 +1014,13 @@
},
{
"cell_type": "code",
- "execution_count": 39,
+ "execution_count": 38,
"id": "0450e661",
"metadata": {},
"outputs": [
{
"data": {
- "image/png": "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\n",
+ "image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
@@ -1095,13 +1045,13 @@
},
{
"cell_type": "code",
- "execution_count": 40,
+ "execution_count": 39,
"id": "e8b34765",
"metadata": {},
"outputs": [
{
"data": {
- "image/png": "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\n",
+ "image/png": "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\n",
"text/plain": [
"<Figure size 864x576 with 1 Axes>"
]
@@ -1120,1205 +1070,6 @@
"# ax.set_ylim(0, 4000);\n",
"plt.savefig('./figures/analysis-sequence_structure_agreement_pLDDT_vs_seq_id.pdf')"
]
- },
- {
- "cell_type": "code",
- "execution_count": 41,
- "id": "1333128a",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "no seq. annot. 8643\n",
- "Specific OG\\nsame name 7059\n",
- "Root OG\\nsame name 2958\n",
- "Root OG 2665\n",
- "Specific OG 2343\n",
- "no agreement 719\n",
- "50% PFAM 555\n",
- "50% PFAM\\nsame name 290\n",
- "Name: annotation status, dtype: int64"
- ]
- },
- "execution_count": 41,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "structural_annotation['annotation status'].value_counts()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 42,
- "id": "1dbc709c",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": "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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "fig, ax = plt.subplots()\n",
- "no_agreement = structural_annotation['annotation status'] == 'Specific OG'\n",
- "ax.hist(structural_annotation[no_agreement]['seq. id.'] * 100, bins=100);\n",
- "ax.set_title('Sequence identity to best structural hit (no agreement)')\n",
- "ax.set_ylabel('freq')\n",
- "ax.set_xlabel('%seq. id.');"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "eeec79fa",
- "metadata": {},
- "source": [
- "The peak of this alignment is well within the twilight zone - where we mostly wouldn't trust the sequence alignment! The categories of agreement between structure and sequence correlate heavily with percent sequence identity; proteins in the Specific OG category are the ones where finding homologs is easier. This is why AlphaFold has an easier time predicting their structure - we could reasonably solve them by homology modelling - and why their FoldSeek bit scores are higher - their orthologs are close enough to have ~40% sequence identity, so of course we can align them over their entire length and get better average bit scores.\n",
- "\n",
- "same for no seq agreement/no agreement --> no good way of deciding who to trust more!\n",
- "\n",
- "visualize FoldSeek alignment"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 43,
- "id": "86612474",
- "metadata": {},
- "outputs": [],
- "source": [
- "test = structural_annotation.merge(sequence_annotation, on='protein_id', suffixes=['_struct', '_seq'])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 44,
- "id": "7ca036a2",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": "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\n",
- "text/plain": [
- "<Figure size 864x576 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "fig, ax = plt.subplots(figsize=(12, 8))\n",
- "sns.boxplot(data=test, x='plddt_buckets', y='score_seq', palette='plasma', whis=[5, 95])\n",
- "ax.set_ylim(0, 1000);"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 45,
- "id": "fb95029e",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": "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\n",
- "text/plain": [
- "<Figure size 864x576 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "fig, ax = plt.subplots(figsize=(12, 8))\n",
- "sns.boxplot(data=test, x='plddt_buckets', y='score_seq', hue='annotation status',\n",
- " palette=color_reference, ax=ax, fliersize=0, hue_order=order, whis=[5, 95])\n",
- "ax.set_ylim(0, 1000);"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 46,
- "id": "d15757f7",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Index(['isoform', 'MSA size', 'query length', 'gene name', 'protein_id',\n",
- " 'gene_id_struct', 'has_start', 'has_end', 'target', 'seq. id.',\n",
- " 'alignment length', 'e value', 'bit score', 'uniprot', 'evalue_struct',\n",
- " 'score_struct', 'eggNOG_OGs_struct', 'max_annot_lvl_struct',\n",
- " 'COG_category_struct', 'Description_struct', 'Preferred_name_struct',\n",
- " 'GOs_struct', 'PFAMs_struct', 'Entry name', 'Gene names',\n",
- " 'Function [CC]', 'Taxonomic lineage (PHYLUM)', 'origin', 'plddt',\n",
- " 'complete_protein', 'eggnog_max_taxonomy', 'UniProt detailed',\n",
- " 'UniProt coarse', 'annotation status', 'plddt_buckets', 'evalue_seq',\n",
- " 'score_seq', 'eggNOG_OGs_seq', 'max_annot_lvl_seq', 'COG_category_seq',\n",
- " 'Description_seq', 'Preferred_name_seq', 'GOs_seq', 'PFAMs_seq',\n",
- " 'gene_id_seq', 'general'],\n",
- " dtype='object')"
- ]
- },
- "execution_count": 46,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "test.columns"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 47,
- "id": "f0019b0b",
- "metadata": {},
- "outputs": [],
- "source": [
- "correlatable = test[['MSA size', 'query length', 'seq. id.', 'alignment length', 'bit score', 'score_struct', 'plddt', 'score_seq']].copy()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 48,
- "id": "a921c607",
- "metadata": {},
- "outputs": [],
- "source": [
- "correlatable.columns = [\n",
- " 'Spongilla MSA size (AF)',\n",
- " 'Spongilla query length (AF)',\n",
- " 'best match str. state id. (FS)',\n",
- " 'rel. ali. length (FS)',\n",
- " 'bit score (FS)',\n",
- " 'bit score (EggNOG-FS)',\n",
- " 'pLDDT (AF)',\n",
- " 'bit score (EggNOG-seq)'\n",
- "]\n",
- "# correlatable['e-value (EggNOG-seq)'] = -np.log(correlatable['e-value (EggNOG-seq)']+1)\n",
- "correlatable['corrected bit score (FS)'] = correlatable['bit score (FS)'] / correlatable['rel. ali. length (FS)']\n",
- "order = ['Spongilla MSA size (AF)', 'pLDDT (AF)', 'best match str. state id. (FS)', 'corrected bit score (FS)', 'bit score (EggNOG-FS)', 'Spongilla query length (AF)', 'ali. length (FS)', 'bit score (FS)', 'bit score (EggNOG-seq)']\n",
- "correlatable['rel. ali. length (FS)'] = correlatable['rel. ali. length (FS)'] / correlatable['Spongilla query length (AF)']"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 49,
- "id": "182405c9",
- "metadata": {},
- "outputs": [],
- "source": [
- "cor = np.corrcoef(correlatable.T)\n",
- "cor = pd.DataFrame(cor, columns=correlatable.columns, index=correlatable.columns)\n",
- "mask = np.triu(np.ones_like(cor))\n",
- "# cor = cor[order].loc[order]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 50,
- "id": "bfe03409",
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/opt/conda/lib/python3.9/site-packages/seaborn/matrix.py:1216: UserWarning: ``square=True`` ignored in clustermap\n",
- " warnings.warn(msg)\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "<seaborn.matrix.ClusterGrid at 0x7f64704cf880>"
- ]
- },
- "execution_count": 50,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": "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\n",
- "text/plain": [
- "<Figure size 720x720 with 4 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "# fig, ax = plt.subplots(figsize=(10, 10))\n",
- "sns.clustermap(cor, cmap='RdYlBu_r', vmin=-1, vmax=1, square=True)\n",
- "# plt.savefig('./figures/analysis-sequence_structure_correlation.pdf')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 51,
- "id": "532ec330",
- "metadata": {},
- "outputs": [],
- "source": [
- "good_struct = test['plddt'] >= 90\n",
- "bad_fs = test['seq. id.'] <= 0.2\n",
- "green = test['annotation status'] == 'no agreement'"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 52,
- "id": "6bb3ecd2",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Index(['isoform', 'MSA size', 'query length', 'gene name', 'protein_id',\n",
- " 'gene_id_struct', 'has_start', 'has_end', 'target', 'seq. id.',\n",
- " 'alignment length', 'e value', 'bit score', 'uniprot', 'evalue_struct',\n",
- " 'score_struct', 'eggNOG_OGs_struct', 'max_annot_lvl_struct',\n",
- " 'COG_category_struct', 'Description_struct', 'Preferred_name_struct',\n",
- " 'GOs_struct', 'PFAMs_struct', 'Entry name', 'Gene names',\n",
- " 'Function [CC]', 'Taxonomic lineage (PHYLUM)', 'origin', 'plddt',\n",
- " 'complete_protein', 'eggnog_max_taxonomy', 'UniProt detailed',\n",
- " 'UniProt coarse', 'annotation status', 'plddt_buckets', 'evalue_seq',\n",
- " 'score_seq', 'eggNOG_OGs_seq', 'max_annot_lvl_seq', 'COG_category_seq',\n",
- " 'Description_seq', 'Preferred_name_seq', 'GOs_seq', 'PFAMs_seq',\n",
- " 'gene_id_seq', 'general'],\n",
- " dtype='object')"
- ]
- },
- "execution_count": 52,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "test.columns"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 53,
- "id": "eaa00c9d",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>isoform</th>\n",
- " <th>query length</th>\n",
- " <th>PFAMs_seq</th>\n",
- " <th>PFAMs_struct</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>179</th>\n",
- " <td>c81555_g1_i1</td>\n",
- " <td>178</td>\n",
- " <td>-</td>\n",
- " <td>DUF4371,Dimer_Tnp_hAT</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>209</th>\n",
- " <td>c88669_g1_i1</td>\n",
- " <td>240</td>\n",
- " <td>-</td>\n",
- " <td>-</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>210</th>\n",
- " <td>c88669_g1_i2</td>\n",
- " <td>263</td>\n",
- " <td>-</td>\n",
- " <td>Cupin_2</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>685</th>\n",
- " <td>c60449_g1_i1</td>\n",
- " <td>436</td>\n",
- " <td>DUF5054</td>\n",
- " <td>Ubiquitin_2,ubiquitin</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1486</th>\n",
- " <td>c99734_g1_i1</td>\n",
- " <td>416</td>\n",
- " <td>FG-GAP</td>\n",
- " <td>zf-RING_2</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1553</th>\n",
- " <td>c102743_g2_i2</td>\n",
- " <td>121</td>\n",
- " <td>Peptidase_C39,Peptidase_C39_2,Peptidase_C70</td>\n",
- " <td>-</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2630</th>\n",
- " <td>c102480_g2_i1</td>\n",
- " <td>288</td>\n",
- " <td>-</td>\n",
- " <td>Peptidase_C48,RVT_1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2673</th>\n",
- " <td>c103207_g1_i2</td>\n",
- " <td>677</td>\n",
- " <td>-</td>\n",
- " <td>Glyco_hydro_28</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2947</th>\n",
- " <td>c91203_g1_i1</td>\n",
- " <td>132</td>\n",
- " <td>RVT_1</td>\n",
- " <td>DEAD,HA2,Helicase_C,OB_NTP_bind</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3084</th>\n",
- " <td>c98674_g1_i2</td>\n",
- " <td>209</td>\n",
- " <td>-</td>\n",
- " <td>EGF_2,Reeler</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3181</th>\n",
- " <td>c102570_g1_i2</td>\n",
- " <td>261</td>\n",
- " <td>-</td>\n",
- " <td>AAT</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4147</th>\n",
- " <td>c101698_g3_i1</td>\n",
- " <td>365</td>\n",
- " <td>-</td>\n",
- " <td>DDE_Tnp_1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4176</th>\n",
- " <td>c101923_g1_i1</td>\n",
- " <td>442</td>\n",
- " <td>DUF5054</td>\n",
- " <td>Bgal_small_N,DUF4981,Glyco_hydro_2,Glyco_hydro...</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4216</th>\n",
- " <td>c102743_g1_i2</td>\n",
- " <td>132</td>\n",
- " <td>-</td>\n",
- " <td>FGF</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4925</th>\n",
- " <td>c104799_g1_i3</td>\n",
- " <td>200</td>\n",
- " <td>-</td>\n",
- " <td>Melibiase_2,Ricin_B_lectin</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>5324</th>\n",
- " <td>c103207_g1_i1</td>\n",
- " <td>600</td>\n",
- " <td>-</td>\n",
- " <td>Beta_helix,G8,PA14,TIG</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>5499</th>\n",
- " <td>c30300_g1_i1</td>\n",
- " <td>87</td>\n",
- " <td>-</td>\n",
- " <td>DUF4371,Dimer_Tnp_hAT</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>5651</th>\n",
- " <td>c95887_g1_i1</td>\n",
- " <td>153</td>\n",
- " <td>-</td>\n",
- " <td>Acetyltransf_1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>6059</th>\n",
- " <td>c79379_g1_i1</td>\n",
- " <td>161</td>\n",
- " <td>-</td>\n",
- " <td>-</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>6483</th>\n",
- " <td>c104487_g2_i2</td>\n",
- " <td>160</td>\n",
- " <td>Pro_isomerase</td>\n",
- " <td>GIIM,RVT_1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>6748</th>\n",
- " <td>c96911_g1_i2</td>\n",
- " <td>242</td>\n",
- " <td>-</td>\n",
- " <td>Lipase_bact_N</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>6749</th>\n",
- " <td>c96911_g1_i3</td>\n",
- " <td>179</td>\n",
- " <td>-</td>\n",
- " <td>Lipase_bact_N</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>6825</th>\n",
- " <td>c99532_g2_i1</td>\n",
- " <td>185</td>\n",
- " <td>-</td>\n",
- " <td>-</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>7386</th>\n",
- " <td>c99475_g1_i2</td>\n",
- " <td>135</td>\n",
- " <td>-</td>\n",
- " <td>Clr5</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>7459</th>\n",
- " <td>c102743_g1_i1</td>\n",
- " <td>105</td>\n",
- " <td>-</td>\n",
- " <td>Astacin,RicinB_lectin_2</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>8430</th>\n",
- " <td>c101727_g1_i1</td>\n",
- " <td>523</td>\n",
- " <td>Abhydrolase_5</td>\n",
- " <td>Abhydrolase_4,Hydrolase_4</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>8608</th>\n",
- " <td>c104435_g1_i2</td>\n",
- " <td>560</td>\n",
- " <td>-</td>\n",
- " <td>HMGL-like</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>8635</th>\n",
- " <td>c104799_g1_i2</td>\n",
- " <td>747</td>\n",
- " <td>-</td>\n",
- " <td>Glyco_hydro_36C,Glyco_hydro_36N,Melibiase</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>9559</th>\n",
- " <td>c102743_g2_i1</td>\n",
- " <td>107</td>\n",
- " <td>-</td>\n",
- " <td>-</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>9805</th>\n",
- " <td>c88232_g2_i1</td>\n",
- " <td>273</td>\n",
- " <td>-</td>\n",
- " <td>DUF4216,DUF4218,Transpos_assoc,Transposase_21</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>9989</th>\n",
- " <td>c98287_g1_i2</td>\n",
- " <td>173</td>\n",
- " <td>-</td>\n",
- " <td>Peptidase_C48,RVT_1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>10046</th>\n",
- " <td>c101727_g1_i2</td>\n",
- " <td>522</td>\n",
- " <td>Abhydrolase_5</td>\n",
- " <td>Abhydrolase_4,Hydrolase_4</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>11087</th>\n",
- " <td>c99375_g2_i1</td>\n",
- " <td>147</td>\n",
- " <td>-</td>\n",
- " <td>Alpha-amylase,CBM_48</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>11362</th>\n",
- " <td>c53973_g1_i1</td>\n",
- " <td>75</td>\n",
- " <td>-</td>\n",
- " <td>Exo_endo_phos_2,RVT_1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>11538</th>\n",
- " <td>c97085_g1_i1</td>\n",
- " <td>232</td>\n",
- " <td>-</td>\n",
- " <td>MannoseP_isomer,NTP_transferase</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>11610</th>\n",
- " <td>c99532_g2_i2</td>\n",
- " <td>162</td>\n",
- " <td>-</td>\n",
- " <td>-</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>13371</th>\n",
- " <td>c94949_g2_i1</td>\n",
- " <td>235</td>\n",
- " <td>APH,HMG-CoA_red</td>\n",
- " <td>EcKinase</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>13589</th>\n",
- " <td>c100095_g1_i2</td>\n",
- " <td>275</td>\n",
- " <td>-</td>\n",
- " <td>Glyco_transf_92</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>13939</th>\n",
- " <td>c103838_g3_i1</td>\n",
- " <td>446</td>\n",
- " <td>-</td>\n",
- " <td>AAT</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>14571</th>\n",
- " <td>c104799_g1_i1</td>\n",
- " <td>200</td>\n",
- " <td>-</td>\n",
- " <td>Alpha-amylase,Alpha-amylase_N,Malt_amylase_C</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>14893</th>\n",
- " <td>c101923_g2_i1</td>\n",
- " <td>253</td>\n",
- " <td>DUF5054</td>\n",
- " <td>Alpha-mann_mid,Glyco_hydro_38,Glyco_hydro_38C</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>16174</th>\n",
- " <td>c112781_g1_i1</td>\n",
- " <td>106</td>\n",
- " <td>-</td>\n",
- " <td>Cellulase</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>16419</th>\n",
- " <td>c98674_g1_i1</td>\n",
- " <td>209</td>\n",
- " <td>-</td>\n",
- " <td>EGF_CA,MAM</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " isoform query length \\\n",
- "179 c81555_g1_i1 178 \n",
- "209 c88669_g1_i1 240 \n",
- "210 c88669_g1_i2 263 \n",
- "685 c60449_g1_i1 436 \n",
- "1486 c99734_g1_i1 416 \n",
- "1553 c102743_g2_i2 121 \n",
- "2630 c102480_g2_i1 288 \n",
- "2673 c103207_g1_i2 677 \n",
- "2947 c91203_g1_i1 132 \n",
- "3084 c98674_g1_i2 209 \n",
- "3181 c102570_g1_i2 261 \n",
- "4147 c101698_g3_i1 365 \n",
- "4176 c101923_g1_i1 442 \n",
- "4216 c102743_g1_i2 132 \n",
- "4925 c104799_g1_i3 200 \n",
- "5324 c103207_g1_i1 600 \n",
- "5499 c30300_g1_i1 87 \n",
- "5651 c95887_g1_i1 153 \n",
- "6059 c79379_g1_i1 161 \n",
- "6483 c104487_g2_i2 160 \n",
- "6748 c96911_g1_i2 242 \n",
- "6749 c96911_g1_i3 179 \n",
- "6825 c99532_g2_i1 185 \n",
- "7386 c99475_g1_i2 135 \n",
- "7459 c102743_g1_i1 105 \n",
- "8430 c101727_g1_i1 523 \n",
- "8608 c104435_g1_i2 560 \n",
- "8635 c104799_g1_i2 747 \n",
- "9559 c102743_g2_i1 107 \n",
- "9805 c88232_g2_i1 273 \n",
- "9989 c98287_g1_i2 173 \n",
- "10046 c101727_g1_i2 522 \n",
- "11087 c99375_g2_i1 147 \n",
- "11362 c53973_g1_i1 75 \n",
- "11538 c97085_g1_i1 232 \n",
- "11610 c99532_g2_i2 162 \n",
- "13371 c94949_g2_i1 235 \n",
- "13589 c100095_g1_i2 275 \n",
- "13939 c103838_g3_i1 446 \n",
- "14571 c104799_g1_i1 200 \n",
- "14893 c101923_g2_i1 253 \n",
- "16174 c112781_g1_i1 106 \n",
- "16419 c98674_g1_i1 209 \n",
- "\n",
- " PFAMs_seq \\\n",
- "179 - \n",
- "209 - \n",
- "210 - \n",
- "685 DUF5054 \n",
- "1486 FG-GAP \n",
- "1553 Peptidase_C39,Peptidase_C39_2,Peptidase_C70 \n",
- "2630 - \n",
- "2673 - \n",
- "2947 RVT_1 \n",
- "3084 - \n",
- "3181 - \n",
- "4147 - \n",
- "4176 DUF5054 \n",
- "4216 - \n",
- "4925 - \n",
- "5324 - \n",
- "5499 - \n",
- "5651 - \n",
- "6059 - \n",
- "6483 Pro_isomerase \n",
- "6748 - \n",
- "6749 - \n",
- "6825 - \n",
- "7386 - \n",
- "7459 - \n",
- "8430 Abhydrolase_5 \n",
- "8608 - \n",
- "8635 - \n",
- "9559 - \n",
- "9805 - \n",
- "9989 - \n",
- "10046 Abhydrolase_5 \n",
- "11087 - \n",
- "11362 - \n",
- "11538 - \n",
- "11610 - \n",
- "13371 APH,HMG-CoA_red \n",
- "13589 - \n",
- "13939 - \n",
- "14571 - \n",
- "14893 DUF5054 \n",
- "16174 - \n",
- "16419 - \n",
- "\n",
- " PFAMs_struct \n",
- "179 DUF4371,Dimer_Tnp_hAT \n",
- "209 - \n",
- "210 Cupin_2 \n",
- "685 Ubiquitin_2,ubiquitin \n",
- "1486 zf-RING_2 \n",
- "1553 - \n",
- "2630 Peptidase_C48,RVT_1 \n",
- "2673 Glyco_hydro_28 \n",
- "2947 DEAD,HA2,Helicase_C,OB_NTP_bind \n",
- "3084 EGF_2,Reeler \n",
- "3181 AAT \n",
- "4147 DDE_Tnp_1 \n",
- "4176 Bgal_small_N,DUF4981,Glyco_hydro_2,Glyco_hydro... \n",
- "4216 FGF \n",
- "4925 Melibiase_2,Ricin_B_lectin \n",
- "5324 Beta_helix,G8,PA14,TIG \n",
- "5499 DUF4371,Dimer_Tnp_hAT \n",
- "5651 Acetyltransf_1 \n",
- "6059 - \n",
- "6483 GIIM,RVT_1 \n",
- "6748 Lipase_bact_N \n",
- "6749 Lipase_bact_N \n",
- "6825 - \n",
- "7386 Clr5 \n",
- "7459 Astacin,RicinB_lectin_2 \n",
- "8430 Abhydrolase_4,Hydrolase_4 \n",
- "8608 HMGL-like \n",
- "8635 Glyco_hydro_36C,Glyco_hydro_36N,Melibiase \n",
- "9559 - \n",
- "9805 DUF4216,DUF4218,Transpos_assoc,Transposase_21 \n",
- "9989 Peptidase_C48,RVT_1 \n",
- "10046 Abhydrolase_4,Hydrolase_4 \n",
- "11087 Alpha-amylase,CBM_48 \n",
- "11362 Exo_endo_phos_2,RVT_1 \n",
- "11538 MannoseP_isomer,NTP_transferase \n",
- "11610 - \n",
- "13371 EcKinase \n",
- "13589 Glyco_transf_92 \n",
- "13939 AAT \n",
- "14571 Alpha-amylase,Alpha-amylase_N,Malt_amylase_C \n",
- "14893 Alpha-mann_mid,Glyco_hydro_38,Glyco_hydro_38C \n",
- "16174 Cellulase \n",
- "16419 EGF_CA,MAM "
- ]
- },
- "execution_count": 53,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "test[good_struct & bad_fs & green][['isoform', 'query length', 'PFAMs_seq', 'PFAMs_struct']]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 55,
- "id": "7b65098a",
- "metadata": {},
- "outputs": [],
- "source": [
- "LUT = structural_annotation.merge(sequence_annotation, on='protein_id', suffixes=['_struct', '_seq'], how='outer')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 56,
- "id": "916ab425",
- "metadata": {},
- "outputs": [],
- "source": [
- "def merge_gene_id(row):\n",
- " x = row['gene_id_struct']\n",
- " y = row['gene_id_seq']\n",
- "# print(type(x), type(y))\n",
- "# print(x, y)\n",
- " if type(x) is str:\n",
- " return x\n",
- " else:\n",
- " return y"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 57,
- "id": "aecdb159",
- "metadata": {},
- "outputs": [],
- "source": [
- "LUT['gene_id'] = LUT[['gene_id_struct', 'gene_id_seq']].apply(merge_gene_id, axis=1)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 58,
- "id": "d895f641",
- "metadata": {},
- "outputs": [],
- "source": [
- "LUT.to_csv('../data/spongilla_lut.tsv', sep='\\t', index=False)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 59,
- "id": "b231ed89",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Index(['isoform', 'MSA size', 'query length', 'gene name', 'protein_id',\n",
- " 'gene_id', 'has_start', 'has_end', 'target', 'seq. id.',\n",
- " 'alignment length', 'e value', 'bit score', 'uniprot', 'evalue',\n",
- " 'score', 'eggNOG_OGs', 'max_annot_lvl', 'COG_category', 'Description',\n",
- " 'Preferred_name', 'GOs', 'PFAMs', 'Entry name', 'Gene names',\n",
- " 'Function [CC]', 'Taxonomic lineage (PHYLUM)', 'origin', 'plddt',\n",
- " 'complete_protein', 'eggnog_max_taxonomy', 'UniProt detailed',\n",
- " 'UniProt coarse', 'annotation status', 'plddt_buckets'],\n",
- " dtype='object')"
- ]
- },
- "execution_count": 59,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "structural_annotation.columns"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 60,
- "id": "12c54345",
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/opt/conda/lib/python3.9/site-packages/pandas/core/arraylike.py:364: RuntimeWarning: divide by zero encountered in log10\n",
- " result = getattr(ufunc, method)(*inputs, **kwargs)\n"
- ]
- },
- {
- "data": {
- "image/png": "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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "fig, ax = plt.subplots()\n",
- "x = np.log10(structural_annotation['evalue'])\n",
- "non_inf = x > -np.Inf\n",
- "ax.hist(x[non_inf], bins=100);"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 61,
- "id": "4547cbf3",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "5.27e-183"
- ]
- },
- "execution_count": 61,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "np.median(structural_annotation['evalue'][non_inf])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 64,
- "id": "0390ad70",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>Spongilla MSA size (AF)</th>\n",
- " <th>Spongilla query length (AF)</th>\n",
- " <th>best match str. state id. (FS)</th>\n",
- " <th>rel. ali. length (FS)</th>\n",
- " <th>bit score (FS)</th>\n",
- " <th>bit score (EggNOG-FS)</th>\n",
- " <th>pLDDT (AF)</th>\n",
- " <th>bit score (EggNOG-seq)</th>\n",
- " <th>corrected bit score (FS)</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>7516.0</td>\n",
- " <td>839</td>\n",
- " <td>0.277</td>\n",
- " <td>0.617402</td>\n",
- " <td>1124.0</td>\n",
- " <td>1704.0</td>\n",
- " <td>66.247545</td>\n",
- " <td>135.0</td>\n",
- " <td>2.169884</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>7758.0</td>\n",
- " <td>142</td>\n",
- " <td>0.340</td>\n",
- " <td>1.035211</td>\n",
- " <td>578.0</td>\n",
- " <td>1258.0</td>\n",
- " <td>91.322817</td>\n",
- " <td>155.0</td>\n",
- " <td>3.931973</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>14083.0</td>\n",
- " <td>101</td>\n",
- " <td>0.366</td>\n",
- " <td>1.000000</td>\n",
- " <td>354.0</td>\n",
- " <td>842.0</td>\n",
- " <td>83.675347</td>\n",
- " <td>110.0</td>\n",
- " <td>3.504950</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>12127.0</td>\n",
- " <td>401</td>\n",
- " <td>0.695</td>\n",
- " <td>1.000000</td>\n",
- " <td>2533.0</td>\n",
- " <td>8777.0</td>\n",
- " <td>93.212818</td>\n",
- " <td>595.0</td>\n",
- " <td>6.316708</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>15776.0</td>\n",
- " <td>70</td>\n",
- " <td>0.676</td>\n",
- " <td>1.014286</td>\n",
- " <td>346.0</td>\n",
- " <td>174.0</td>\n",
- " <td>88.737286</td>\n",
- " <td>99.8</td>\n",
- " <td>4.873239</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>...</th>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>16584</th>\n",
- " <td>3909.0</td>\n",
- " <td>1229</td>\n",
- " <td>0.276</td>\n",
- " <td>1.025224</td>\n",
- " <td>4063.0</td>\n",
- " <td>2744.0</td>\n",
- " <td>71.182449</td>\n",
- " <td>1084.0</td>\n",
- " <td>3.224603</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>16585</th>\n",
- " <td>6938.0</td>\n",
- " <td>203</td>\n",
- " <td>0.572</td>\n",
- " <td>1.014778</td>\n",
- " <td>1138.0</td>\n",
- " <td>712.0</td>\n",
- " <td>89.553202</td>\n",
- " <td>184.0</td>\n",
- " <td>5.524272</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>16586</th>\n",
- " <td>5812.0</td>\n",
- " <td>311</td>\n",
- " <td>0.485</td>\n",
- " <td>1.000000</td>\n",
- " <td>1407.0</td>\n",
- " <td>1833.0</td>\n",
- " <td>90.416688</td>\n",
- " <td>395.0</td>\n",
- " <td>4.524116</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>16587</th>\n",
- " <td>6110.0</td>\n",
- " <td>150</td>\n",
- " <td>0.646</td>\n",
- " <td>0.980000</td>\n",
- " <td>638.0</td>\n",
- " <td>296.0</td>\n",
- " <td>71.247200</td>\n",
- " <td>212.0</td>\n",
- " <td>4.340136</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>16588</th>\n",
- " <td>9311.0</td>\n",
- " <td>993</td>\n",
- " <td>0.142</td>\n",
- " <td>0.769386</td>\n",
- " <td>569.0</td>\n",
- " <td>1642.0</td>\n",
- " <td>67.393122</td>\n",
- " <td>507.0</td>\n",
- " <td>0.744764</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "<p>16589 rows × 9 columns</p>\n",
- "</div>"
- ],
- "text/plain": [
- " Spongilla MSA size (AF) Spongilla query length (AF) \\\n",
- "0 7516.0 839 \n",
- "1 7758.0 142 \n",
- "2 14083.0 101 \n",
- "3 12127.0 401 \n",
- "4 15776.0 70 \n",
- "... ... ... \n",
- "16584 3909.0 1229 \n",
- "16585 6938.0 203 \n",
- "16586 5812.0 311 \n",
- "16587 6110.0 150 \n",
- "16588 9311.0 993 \n",
- "\n",
- " best match str. state id. (FS) rel. ali. length (FS) bit score (FS) \\\n",
- "0 0.277 0.617402 1124.0 \n",
- "1 0.340 1.035211 578.0 \n",
- "2 0.366 1.000000 354.0 \n",
- "3 0.695 1.000000 2533.0 \n",
- "4 0.676 1.014286 346.0 \n",
- "... ... ... ... \n",
- "16584 0.276 1.025224 4063.0 \n",
- "16585 0.572 1.014778 1138.0 \n",
- "16586 0.485 1.000000 1407.0 \n",
- "16587 0.646 0.980000 638.0 \n",
- "16588 0.142 0.769386 569.0 \n",
- "\n",
- " bit score (EggNOG-FS) pLDDT (AF) bit score (EggNOG-seq) \\\n",
- "0 1704.0 66.247545 135.0 \n",
- "1 1258.0 91.322817 155.0 \n",
- "2 842.0 83.675347 110.0 \n",
- "3 8777.0 93.212818 595.0 \n",
- "4 174.0 88.737286 99.8 \n",
- "... ... ... ... \n",
- "16584 2744.0 71.182449 1084.0 \n",
- "16585 712.0 89.553202 184.0 \n",
- "16586 1833.0 90.416688 395.0 \n",
- "16587 296.0 71.247200 212.0 \n",
- "16588 1642.0 67.393122 507.0 \n",
- "\n",
- " corrected bit score (FS) \n",
- "0 2.169884 \n",
- "1 3.931973 \n",
- "2 3.504950 \n",
- "3 6.316708 \n",
- "4 4.873239 \n",
- "... ... \n",
- "16584 3.224603 \n",
- "16585 5.524272 \n",
- "16586 4.524116 \n",
- "16587 4.340136 \n",
- "16588 0.744764 \n",
- "\n",
- "[16589 rows x 9 columns]"
- ]
- },
- "execution_count": 64,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "correlatable"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 65,
- "id": "a3e610ea",
- "metadata": {},
- "outputs": [
- {
- "ename": "KeyError",
- "evalue": "'Spongilla MSA size (AF)'",
- "output_type": "error",
- "traceback": [
- "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)",
- "\u001b[0;32m/tmp/ipykernel_1085/2741948371.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m12\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m8\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;31m# order = ['no seq. annot.', 'no agreement', 'COG category', '50% PFAM', 'Root OG', 'Specific OG']\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m sns.boxplot(data=structural_annotation, x='plddt_buckets', y='evalue', hue='annotation status',\n\u001b[0m\u001b[1;32m 4\u001b[0m palette=color_reference, ax=ax, fliersize=0, hue_order=order, whis=[5, 95])\n\u001b[1;32m 5\u001b[0m \u001b[0;31m# ax.set_ylim(0, 10000);\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;32m/opt/conda/lib/python3.9/site-packages/seaborn/_decorators.py\u001b[0m in \u001b[0;36minner_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 44\u001b[0m )\n\u001b[1;32m 45\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 46\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 47\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 48\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;32m/opt/conda/lib/python3.9/site-packages/seaborn/categorical.py\u001b[0m in \u001b[0;36mboxplot\u001b[0;34m(x, y, hue, data, order, hue_order, orient, color, palette, saturation, width, dodge, fliersize, linewidth, whis, ax, **kwargs)\u001b[0m\n\u001b[1;32m 2238\u001b[0m ):\n\u001b[1;32m 2239\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2240\u001b[0;31m plotter = _BoxPlotter(x, y, hue, data, order, hue_order,\n\u001b[0m\u001b[1;32m 2241\u001b[0m \u001b[0morient\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpalette\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msaturation\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2242\u001b[0m width, dodge, fliersize, linewidth)\n",
- "\u001b[0;32m/opt/conda/lib/python3.9/site-packages/seaborn/categorical.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, x, y, hue, data, order, hue_order, orient, color, palette, saturation, width, dodge, fliersize, linewidth)\u001b[0m\n\u001b[1;32m 405\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 406\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mestablish_variables\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morient\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhue_order\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 407\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mestablish_colors\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpalette\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msaturation\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 408\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 409\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdodge\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdodge\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;32m/opt/conda/lib/python3.9/site-packages/seaborn/categorical.py\u001b[0m in \u001b[0;36mestablish_colors\u001b[0;34m(self, color, palette, saturation)\u001b[0m\n\u001b[1;32m 304\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 305\u001b[0m \u001b[0mlevels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhue_names\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 306\u001b[0;31m \u001b[0mpalette\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mpalette\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0ml\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0ml\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mlevels\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 307\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 308\u001b[0m \u001b[0mcolors\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcolor_palette\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpalette\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_colors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;32m/opt/conda/lib/python3.9/site-packages/seaborn/categorical.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 304\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 305\u001b[0m \u001b[0mlevels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhue_names\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 306\u001b[0;31m \u001b[0mpalette\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mpalette\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0ml\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0ml\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mlevels\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 307\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 308\u001b[0m \u001b[0mcolors\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcolor_palette\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpalette\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_colors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;31mKeyError\u001b[0m: 'Spongilla MSA size (AF)'"
- ]
- },
- {
- "data": {
- "image/png": "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\n",
- "text/plain": [
- "<Figure size 864x576 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "fig, ax = plt.subplots(figsize=(12, 8))\n",
- "# order = ['no seq. annot.', 'no agreement', 'COG category', '50% PFAM', 'Root OG', 'Specific OG']\n",
- "sns.boxplot(data=structural_annotation, x='plddt_buckets', y='evalue', hue='annotation status',\n",
- " palette=color_reference, ax=ax, fliersize=0, hue_order=order, whis=[5, 95])\n",
- "# ax.set_ylim(0, 10000);\n",
- "# plt.savefig('./figures/analysis-sequence_structure_agreement_pLDDT_vs_bit_score.pdf')"
- ]
}
],
"metadata": {
diff --git a/analysis/io-get_uniprot_sequences.ipynb b/analysis/io-get_uniprot_sequences.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..234856f4f3d6d6a8c9a79bbf4fffb9e69ea76c29
--- /dev/null
+++ b/analysis/io-get_uniprot_sequences.ipynb
@@ -0,0 +1,172 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "a9b30dc7-46a5-47f9-9a33-d74c135683eb",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "2022-08-01 11:33\n"
+ ]
+ }
+ ],
+ "source": [
+ "from datetime import datetime, timezone\n",
+ "import pytz\n",
+ "\n",
+ "utc_dt = datetime.now(timezone.utc) # UTC time\n",
+ "dt = utc_dt.astimezone()\n",
+ "tz = pytz.timezone('Europe/Berlin')\n",
+ "berlin_now = datetime.now(tz)\n",
+ "print(f'{berlin_now:%Y-%m-%d %H:%M}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "715dbc9f-7bdb-4dfd-a345-2b93d78ce54a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import glob\n",
+ "from os.path import exists\n",
+ "from tqdm import tqdm\n",
+ "import requests\n",
+ "\n",
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "\n",
+ "import matplotlib.pyplot as plt\n",
+ "import seaborn as sns"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "5757388f-4df8-4f11-81de-eeb41a0741a3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "pdb = pd.read_parquet('../data/pdb_tmp.parquet')\n",
+ "swp = pd.read_parquet('../data/swp_tmp.parquet')\n",
+ "afdb = pd.read_parquet('../data/afdb_tmp.parquet')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "5a74880c-91e0-4415-8326-44bf37eabd9d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "unique_up_id = pd.concat([pdb['uniprot'].drop_duplicates(),\n",
+ " swp['uniprot'].drop_duplicates(),\n",
+ " afdb['uniprot'].drop_duplicates()])\n",
+ "unique_up_id.drop_duplicates(inplace=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "101dbd46-ca76-4143-a877-0a9418cabcd4",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "request_size = 500"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "b7c254f6-6b23-4f1c-8cb7-c945aafb9f7c",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ " 90%|█████████ | 8429/9330 [52:48<04:45, 3.16it/s] IOPub message rate exceeded.\n",
+ "The notebook server will temporarily stop sending output\n",
+ "to the client in order to avoid crashing it.\n",
+ "To change this limit, set the config variable\n",
+ "`--NotebookApp.iopub_msg_rate_limit`.\n",
+ "\n",
+ "Current values:\n",
+ "NotebookApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n",
+ "NotebookApp.rate_limit_window=3.0 (secs)\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "request_size = 100\n",
+ "no_chunks = np.ceil(len(unique_up_id) / request_size).astype(int)\n",
+ "\n",
+ "with open('../data/uniprotinfo.fasta', 'w') as result:\n",
+ " for i in tqdm(range(no_chunks)):\n",
+ " a = i * request_size\n",
+ " b = (i+1) * request_size\n",
+ " chunk = [str(c) for c in unique_up_id[a:b]]\n",
+ " url = f\"https://www.ebi.ac.uk/proteins/api/proteins?offset=0&size=100&accession={','.join(chunk)}&format=fasta\"\n",
+ " response = requests.get(url)\n",
+ " result.write(response.text)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "0a4ca4a1-f008-4c41-95e5-7226f8f11389",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "../data/uniprot_sequences.fasta: 928687 sequences, 368383841 bp => dividing into 10 parts of <= 100000 sequences\n",
+ "All done, 20 seconds elapsed\n"
+ ]
+ }
+ ],
+ "source": [
+ "%%bash\n",
+ "mkdir ../data/uniprot_fastas/ -p\n",
+ "perl ../scripts/fasta-splitter.pl --part-size 100000 ../data/uniprotinfo.fasta --nopad --measure count --out-dir ../data/uniprot_fastas/"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "cd368a7f-a2b5-489e-9f55-0a9854e457c0",
+ "metadata": {},
+ "source": [
+ "(submit to EggNOG-mapper and wait for it to annotate the sequences, then download files and rename)"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "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": "3.9.6"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/analysis/process_pdb.ipynb b/analysis/io-process_pdb.ipynb
similarity index 99%
rename from analysis/process_pdb.ipynb
rename to analysis/io-process_pdb.ipynb
index c8b8d0f4d1b7d4e46bb502799969fc10eb75c1b9..97fd247f2b992918342ea3e18d00fa2085fb239c 100644
--- a/analysis/process_pdb.ipynb
+++ b/analysis/io-process_pdb.ipynb
@@ -631,7 +631,7 @@
"metadata": {},
"outputs": [],
"source": [
- "pdb.to_parquet('/g/arendt/npapadop/repos/coffe/data/pdb_tmp.parquet')"
+ "pdb.to_parquet('../data/pdb_tmp.parquet')"
]
}
],
diff --git a/analysis/read-write.ipynb b/analysis/read-write.ipynb
index 1a74c5b7d6d914b9fb487052445f6bf0618f3d27..7da1e4cc59a61889f2517a980f998f7dbe292107 100644
--- a/analysis/read-write.ipynb
+++ b/analysis/read-write.ipynb
@@ -10,7 +10,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "2022-07-15 10:36\n"
+ "2022-08-01 13:18\n"
]
}
],
@@ -140,7 +140,7 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "223492it [00:00, 1285673.28it/s]\n"
+ "223492it [00:00, 688176.47it/s]\n"
]
}
],
@@ -200,7 +200,7 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 41945/41945 [01:22<00:00, 509.76it/s] \n"
+ "100%|██████████| 41945/41945 [14:03<00:00, 49.70it/s] \n"
]
}
],
@@ -249,196 +249,6 @@
{
"cell_type": "code",
"execution_count": 8,
- "id": "d57aa70c-9646-4dfb-80b8-2eb3791c32ba",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>query</th>\n",
- " <th>MSA size</th>\n",
- " <th>query length</th>\n",
- " <th>gene name</th>\n",
- " <th>protein_id</th>\n",
- " <th>gene_id</th>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>isoform</th>\n",
- " <th></th>\n",
- " <th></th>\n",
- " <th></th>\n",
- " <th></th>\n",
- " <th></th>\n",
- " <th></th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>c100000_g1_i1</th>\n",
- " <td>0</td>\n",
- " <td>2765</td>\n",
- " <td>433</td>\n",
- " <td>c100000_g1_i1_m.41809</td>\n",
- " <td>41809</td>\n",
- " <td>c100000_g1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>c103531_g2_i2</th>\n",
- " <td>10000</td>\n",
- " <td>2082</td>\n",
- " <td>152</td>\n",
- " <td>c103531_g2_i2_m.66483</td>\n",
- " <td>66483</td>\n",
- " <td>c103531_g2</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>c103531_g3_i1</th>\n",
- " <td>10001</td>\n",
- " <td>1924</td>\n",
- " <td>201</td>\n",
- " <td>c103531_g3_i1_m.66482</td>\n",
- " <td>66482</td>\n",
- " <td>c103531_g3</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>c103531_g4_i1</th>\n",
- " <td>10002</td>\n",
- " <td>1959</td>\n",
- " <td>215</td>\n",
- " <td>c103531_g4_i1_m.66484</td>\n",
- " <td>66484</td>\n",
- " <td>c103531_g4</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>c103532_g1_i1</th>\n",
- " <td>10003</td>\n",
- " <td>203</td>\n",
- " <td>288</td>\n",
- " <td>c103532_g1_i1_m.66485</td>\n",
- " <td>66485</td>\n",
- " <td>c103532_g1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>...</th>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>c103530_g1_i3</th>\n",
- " <td>9998</td>\n",
- " <td>9311</td>\n",
- " <td>992</td>\n",
- " <td>c103530_g1_i3_m.66470</td>\n",
- " <td>66470</td>\n",
- " <td>c103530_g1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>c103531_g2_i1</th>\n",
- " <td>9999</td>\n",
- " <td>2103</td>\n",
- " <td>221</td>\n",
- " <td>c103531_g2_i1_m.66481</td>\n",
- " <td>66481</td>\n",
- " <td>c103531_g2</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>c100434_g1_i2</th>\n",
- " <td>999</td>\n",
- " <td>15780</td>\n",
- " <td>124</td>\n",
- " <td>c100434_g1_i2_m.44014</td>\n",
- " <td>44014</td>\n",
- " <td>c100434_g1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>c100036_g1_i3</th>\n",
- " <td>99</td>\n",
- " <td>3</td>\n",
- " <td>290</td>\n",
- " <td>c100036_g1_i3_m.42037</td>\n",
- " <td>42037</td>\n",
- " <td>c100036_g1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>c100002_g1_i1</th>\n",
- " <td>9</td>\n",
- " <td>1</td>\n",
- " <td>112</td>\n",
- " <td>c100002_g1_i1_m.41844</td>\n",
- " <td>41844</td>\n",
- " <td>c100002_g1</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "<p>41945 rows × 6 columns</p>\n",
- "</div>"
- ],
- "text/plain": [
- " query MSA size query length gene name \\\n",
- "isoform \n",
- "c100000_g1_i1 0 2765 433 c100000_g1_i1_m.41809 \n",
- "c103531_g2_i2 10000 2082 152 c103531_g2_i2_m.66483 \n",
- "c103531_g3_i1 10001 1924 201 c103531_g3_i1_m.66482 \n",
- "c103531_g4_i1 10002 1959 215 c103531_g4_i1_m.66484 \n",
- "c103532_g1_i1 10003 203 288 c103532_g1_i1_m.66485 \n",
- "... ... ... ... ... \n",
- "c103530_g1_i3 9998 9311 992 c103530_g1_i3_m.66470 \n",
- "c103531_g2_i1 9999 2103 221 c103531_g2_i1_m.66481 \n",
- "c100434_g1_i2 999 15780 124 c100434_g1_i2_m.44014 \n",
- "c100036_g1_i3 99 3 290 c100036_g1_i3_m.42037 \n",
- "c100002_g1_i1 9 1 112 c100002_g1_i1_m.41844 \n",
- "\n",
- " protein_id gene_id \n",
- "isoform \n",
- "c100000_g1_i1 41809 c100000_g1 \n",
- "c103531_g2_i2 66483 c103531_g2 \n",
- "c103531_g3_i1 66482 c103531_g3 \n",
- "c103531_g4_i1 66484 c103531_g4 \n",
- "c103532_g1_i1 66485 c103532_g1 \n",
- "... ... ... \n",
- "c103530_g1_i3 66470 c103530_g1 \n",
- "c103531_g2_i1 66481 c103531_g2 \n",
- "c100434_g1_i2 44014 c100434_g1 \n",
- "c100036_g1_i3 42037 c100036_g1 \n",
- "c100002_g1_i1 41844 c100002_g1 \n",
- "\n",
- "[41945 rows x 6 columns]"
- ]
- },
- "execution_count": 8,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "sequence_info"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
"id": "5ae5dfb2-84a6-420f-8ac2-2c79d4f95e16",
"metadata": {},
"outputs": [],
@@ -450,7 +260,7 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 9,
"id": "c61347d2-f602-4f76-a656-70528242a4be",
"metadata": {
"tags": []
@@ -470,17 +280,18 @@
"source": [
"## 2. AlphaFold-predicted structures\n",
"\n",
- "We used the MSAs to predict protein structures using ColabFold. Here, we read the per-residue pLDDT score from the best predicted structure per peptide and average it over all residues; then we merge _Spongilla_ isoforms by keeping the best score per gene ID."
+ "We used the MSAs to predict protein structures using ColabFold. Here, we read the per-residue pLDDT score from the best predicted structure per peptide and average it over all residues; then we merge _Spongilla_ isoforms by keeping the best score per gene ID. We will write this in `../data/structure_predictions.csv`."
]
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": null,
"id": "3831f7ff-07b6-42ae-8276-ab899ab6ffa0",
"metadata": {},
"outputs": [],
"source": [
- "alphafold.to_csv(\"../data/structure_predictions.csv\")"
+ "# not executed\n",
+ "# !sbatch ../scripts/io_parse_structures.sh"
]
},
{
@@ -498,11 +309,13 @@
"source": [
"## 3. FoldSeek predictions\n",
"\n",
- "We searched against AlphaFoldDB (predicted), PDB (crystal), and SwissProt (predicted) protein structures to detect structural similarity to our predicted _Spongilla_ peptide structures using FoldSeek. Here we read the FoldSeek output. In order to obtain useful phylogenetic information as well as functional annotation, we will translate all structural hits to their UniProt IDs and then query the EggNOG database and UniProt for annotation.\n",
+ "We searched against AlphaFoldDB (Release 3, January 2021, containing predictions for SwissProt), and PDB (crystal) protein structures to detect structural similarity to our predicted _Spongilla_ peptide structures using FoldSeek. Here we read the FoldSeek output. In order to obtain useful phylogenetic information as well as functional annotation, we will translate all structural hits to their UniProt IDs and then query the EggNOG database and UniProt for annotation.\n",
"\n",
"### 3.1 Reading results\n",
"\n",
- "The FoldSeek results come in BLAST format. We will read them and convert the query (FoldSeek's internal unique ID for each input structure) from string to integer."
+ "The FoldSeek results come in BLAST format. We will read them and convert the query (FoldSeek's internal unique ID for each input structure) from string to integer.\n",
+ "\n",
+ "We'll save those in `parquet` format to make reading them much faster."
]
},
{
@@ -512,9 +325,10 @@
"metadata": {},
"outputs": [],
"source": [
- "!sbatch ../scripts/io_read_pdb.sh\n",
- "!sbatch ../scripts/io_read_afdb.sh\n",
- "!sbatch ../scripts/io_read_swp.sh"
+ "# NOT EXECUTED\n",
+ "# !sbatch ../scripts/io_read_pdb.sh\n",
+ "# !sbatch ../scripts/io_read_afdb.sh\n",
+ "# !sbatch ../scripts/io_read_swp.sh"
]
},
{
@@ -522,7 +336,7 @@
"id": "1c3f8ded-6be0-4749-ad85-da5d65be9be6",
"metadata": {},
"source": [
- "should take about 3-5h to complete."
+ "this step should take about 3-5h to complete on a single core."
]
},
{
@@ -530,43 +344,19 @@
"id": "c2343e57-f527-4634-87a9-cbd2bc8aee5f",
"metadata": {},
"source": [
- "For PDB the situation is more complicated. We will take the PDB IDs and translate them to UniProt accession numbers using the UniProt API. This will return an inflated list of IDs since some PDB entries contain complexes."
+ "For PDB the situation is more complicated. We will take the PDB IDs and translate them to UniProt accession numbers using the UniProt API. This will return an inflated list of IDs since some PDB entries contain complexes. Unfortunately, there is no easy way around this. Refer to `io-process_pdb.ipynb`."
]
},
{
"cell_type": "code",
- "execution_count": 1,
- "id": "5b403921-8514-431a-a34d-f92fbf034ab8",
+ "execution_count": 10,
+ "id": "09f34772-e59f-431f-8bdd-24923f7132d5",
"metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "perl: warning: Setting locale failed.\n",
- "perl: warning: Please check that your locale settings:\n",
- "\tLANGUAGE = (unset),\n",
- "\tLC_ALL = (unset),\n",
- "\tLC_CTYPE = \"UTF-8\",\n",
- "\tLC_TERMINAL = \"iTerm2\",\n",
- "\tLANG = \"en_US.UTF-8\"\n",
- " are supported and installed on your system.\n",
- "perl: warning: Falling back to a fallback locale (\"en_US.UTF-8\").\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "../data/uniprotinfo.fasta: 7206 sequences, 4638410 bp => dividing into 1 part of <= 100000 sequences\n",
- "All done, 0 seconds elapsed\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
- "%%bash\n",
- "mkdir ../data/uniprot_fastas/ -p\n",
- "perl ../scripts/fasta-splitter.pl --part-size 100000 ../data/uniprotinfo.fasta --nopad --measure count --out-dir ../data/uniprot_fastas/"
+ "pdb_tmp = pd.read_parquet('../data/pdb_tmp.parquet')\n",
+ "swp_tmp = pd.read_parquet('../data/swp_tmp.parquet')\n",
+ "afdb_tmp = pd.read_parquet('../data/afdb_tmp.parquet')"
]
},
{
@@ -574,14 +364,14 @@
"id": "cc1e0086-8c3f-4881-938e-0270a9f9e53e",
"metadata": {},
"source": [
- "These were manually submitted to emapper, and the results (the `out.emapper.annotations` tsv file of each run) downloaded and saved with an `eggnog` ending.\n",
+ "We extract the UniProt IDs from the results of all databases and retrieve their sequences from UniProt, using the [EBI 'proteins' API](https://www.ebi.ac.uk/proteins/api/doc/). Refer to `io-get_uniprot_sequences.ipynb`.\n",
"\n",
- "Let's process the retrieved emapper results:"
+ "The resulting fasta files are (manually) submitted to EggNOG-mapper and downloaded, then renamed according to their respective input file. We will read and concatenate them here:"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 11,
"id": "22630619-3006-425f-bace-980499b61afd",
"metadata": {},
"outputs": [],
@@ -602,7 +392,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 12,
"id": "65cd9d5e-fdac-4a14-94f7-787fdba690fa",
"metadata": {},
"outputs": [],
@@ -621,17 +411,8 @@
" 'Description', 'Preferred_name', 'GOs', 'PFAMs']\n",
"uniprot_annotation[to_categorical] = uniprot_annotation[to_categorical].astype(\"category\")\n",
"# finally save in parquet format\n",
- "uniprot_annotation.to_parquet('../data/uniprot_annotation.parquet')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "500fd12f-5e1e-48ef-99c1-3db0424d7cb6",
- "metadata": {},
- "outputs": [],
- "source": [
- "uniprot_annotation = pd.read_parquet('../data/uniprot_annotation.parquet')"
+ "uniprot_annotation.to_parquet('../data/uniprot_annotation.parquet')\n",
+ "# uniprot_annotation = pd.read_parquet('../data/uniprot_annotation.parquet')"
]
},
{
@@ -651,7 +432,8 @@
"metadata": {},
"outputs": [],
"source": [
- "!python /g/arendt/npapadop/repos/UPIMAPI/upimapi.py -i ../data/foldseek_unique_ids.txt -o ../data/ --no-annotation"
+ "# not executed\n",
+ "# !python /g/arendt/npapadop/repos/UPIMAPI/upimapi.py -i ../data/foldseek_unique_ids.txt -o ../data/ --no-annotation"
]
},
{
@@ -664,7 +446,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 13,
"id": "87957304-19a6-451d-aa82-9ece85cc90de",
"metadata": {},
"outputs": [],
@@ -677,7 +459,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 14,
"id": "09465de2-c112-4de3-b487-6a27f8cf0504",
"metadata": {},
"outputs": [],
@@ -695,14 +477,14 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 15,
"id": "019667f8-47b2-4959-abe8-38e5529b4e86",
"metadata": {},
"outputs": [],
"source": [
- "pdb = pdb.merge(foldseek_full, on='uniprot', how='left').merge(uniprot_annotation, on='uniprot', how='left')\n",
- "afdb = afdb.merge(foldseek_full, on='uniprot', how='left').merge(uniprot_annotation, on='uniprot', how='left')\n",
- "swp = swp.merge(foldseek_full, on='uniprot', how='left').merge(uniprot_annotation, on='uniprot', how='left')"
+ "pdb = pdb_tmp.merge(foldseek_full, on='uniprot', how='left').merge(uniprot_annotation, on='uniprot', how='left')\n",
+ "afdb = afdb_tmp.merge(foldseek_full, on='uniprot', how='left').merge(uniprot_annotation, on='uniprot', how='left')\n",
+ "swp = swp_tmp.merge(foldseek_full, on='uniprot', how='left').merge(uniprot_annotation, on='uniprot', how='left')"
]
},
{
@@ -715,7 +497,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 16,
"id": "85f48253-2176-4b3b-ae78-e18dc8156d6f",
"metadata": {},
"outputs": [],
@@ -740,7 +522,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 17,
"id": "fe4b9101-4312-440b-a437-1dd701e02129",
"metadata": {},
"outputs": [],
@@ -769,7 +551,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 18,
"id": "9bbd5e11-77c9-4657-b0e8-d6d3e7055d3a",
"metadata": {},
"outputs": [],
@@ -792,7 +574,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 19,
"id": "542ca93a-9f73-45ca-8c76-48152dae9a58",
"metadata": {
"tags": []
@@ -808,7 +590,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 20,
"id": "7bb9638e-06d3-42a4-bb74-9220c0cfe886",
"metadata": {},
"outputs": [],
@@ -820,7 +602,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 21,
"id": "b52b7268-3247-4884-84cb-5fa3d452878a",
"metadata": {},
"outputs": [],
@@ -832,7 +614,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 22,
"id": "d85514be-1d56-4837-801d-a3bc24a8e889",
"metadata": {},
"outputs": [],
@@ -851,17 +633,19 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 75,
"id": "c9e7e73d-1670-4cf4-b6b6-f4f9cebc928a",
"metadata": {},
"outputs": [],
"source": [
- "structural_annotation = sequence_info.join(best).join(alphafold)"
+ "structural_annotation = sequence_info.join(best)\n",
+ "structural_annotation = structural_annotation.set_index('isoform').join(alphafold.set_index('isoform'))\n",
+ "structural_annotation = structural_annotation.reset_index().set_index('query')"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 76,
"id": "21d1d18c-5de1-43e8-83f3-04d4bf07eaa2",
"metadata": {},
"outputs": [],
@@ -881,7 +665,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 61,
"id": "c1bd80a1-f155-4330-8894-fcca687f48a5",
"metadata": {},
"outputs": [],
@@ -896,21 +680,13 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 62,
"id": "6c7e43fe-2951-4104-b15b-129286601a4d",
"metadata": {},
"outputs": [],
"source": [
"eggnog.to_csv('../data/Slacustris_eggnog.tsv', sep='\\t', index=False)"
]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "f46b7ad3-99c8-487b-a3c1-29aee10b9775",
- "metadata": {},
- "outputs": [],
- "source": []
}
],
"metadata": {
diff --git a/analysis/suppl-annotation_categories.ipynb b/analysis/suppl-annotation_categories.ipynb
index c2720cfa9fc0fdb9e8e78b36c7be8a5ba8e87de5..fb38f74496160f60bf0d3946f6946cd398efba29 100755
--- a/analysis/suppl-annotation_categories.ipynb
+++ b/analysis/suppl-annotation_categories.ipynb
@@ -1426,7 +1426,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.8.6"
+ "version": "3.9.6"
}
},
"nbformat": 4,
diff --git a/analysis/suppl-struct_seq_agreement.ipynb b/analysis/suppl-struct_seq_agreement.ipynb
index 5a7024b2a01adf9d90b5297ec00e4ccda84f2b46..dbddab5cd11cef99492f7d01807e3f2c0632b1c1 100644
--- a/analysis/suppl-struct_seq_agreement.ipynb
+++ b/analysis/suppl-struct_seq_agreement.ipynb
@@ -10,7 +10,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "2022-06-10 13:42\n"
+ "2022-08-01 14:41\n"
]
}
],