diff --git a/config.yaml b/config.yaml
index 26c49f8..4b7f516 100755
--- a/config.yaml
+++ b/config.yaml
@@ -25,6 +25,14 @@ read_length: [150,300]
 # Number of random prunings to compute
 pruning_count: 50
 
+# simulate read with ART instead: 1=on, 0=off
+simulate_ART: 1
+
+# ART simulation platform, one of: illumina, 454
+ART_platform: '454'
+
+# number of reads per pruned sequence to generate with ART
+num_reads: 50
 
 ## IF "ACCURACY" IS EVALUATED
 #############################
diff --git a/examples/6_placement_likelihood/ll_difference.ipynb b/examples/6_placement_likelihood/ll_difference.ipynb
deleted file mode 100644
index 42a4f9b..0000000
--- a/examples/6_placement_likelihood/ll_difference.ipynb
+++ /dev/null
@@ -1,1106 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# LogLikelihood difference\n",
-    "\n",
-    "This examples demonstrates how visualize the difference in LogLikelihood (LL) values obtained by LAC procedure for different software and parameter combinations."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Get the data\n",
-    "\n",
-    "First, run the example as described in the README. Then load the produced .csv file:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 351,
-   "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>pruning</th>\n",
-       "      <th>g</th>\n",
-       "      <th>query</th>\n",
-       "      <th>length</th>\n",
-       "      <th>LL</th>\n",
-       "      <th>software</th>\n",
-       "      <th>max-strikes</th>\n",
-       "      <th>strike-box</th>\n",
-       "      <th>max-pitches</th>\n",
-       "      <th>red</th>\n",
-       "      <th>ar</th>\n",
-       "      <th>k</th>\n",
-       "      <th>omega</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0.01</td>\n",
-       "      <td>8f43808640b0e835dfd588c7c30a955c78cf2252-17084050</td>\n",
-       "      <td>0</td>\n",
-       "      <td>-39708.145904</td>\n",
-       "      <td>epa</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0.10</td>\n",
-       "      <td>8f43808640b0e835dfd588c7c30a955c78cf2252-17084050</td>\n",
-       "      <td>0</td>\n",
-       "      <td>-39705.898393</td>\n",
-       "      <td>epa</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0.01</td>\n",
-       "      <td>ea4a29b8b59fb031b08cf1a7575658013dd45baa-9896769</td>\n",
-       "      <td>0</td>\n",
-       "      <td>-39779.279980</td>\n",
-       "      <td>epa</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0.10</td>\n",
-       "      <td>ea4a29b8b59fb031b08cf1a7575658013dd45baa-9896769</td>\n",
-       "      <td>0</td>\n",
-       "      <td>-39778.547272</td>\n",
-       "      <td>epa</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0.01</td>\n",
-       "      <td>64463bb954837427bbaa8bff6de57ba4bbaccb8b-9717282</td>\n",
-       "      <td>0</td>\n",
-       "      <td>-39791.397634</td>\n",
-       "      <td>epa</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   pruning     g                                              query  length  \\\n",
-       "0        0  0.01  8f43808640b0e835dfd588c7c30a955c78cf2252-17084050       0   \n",
-       "1        0  0.10  8f43808640b0e835dfd588c7c30a955c78cf2252-17084050       0   \n",
-       "2        0  0.01   ea4a29b8b59fb031b08cf1a7575658013dd45baa-9896769       0   \n",
-       "3        0  0.10   ea4a29b8b59fb031b08cf1a7575658013dd45baa-9896769       0   \n",
-       "4        0  0.01   64463bb954837427bbaa8bff6de57ba4bbaccb8b-9717282       0   \n",
-       "\n",
-       "             LL software  max-strikes  strike-box  max-pitches  red   ar   k  \\\n",
-       "0 -39708.145904      epa          NaN         NaN          NaN  NaN  NaN NaN   \n",
-       "1 -39705.898393      epa          NaN         NaN          NaN  NaN  NaN NaN   \n",
-       "2 -39779.279980      epa          NaN         NaN          NaN  NaN  NaN NaN   \n",
-       "3 -39778.547272      epa          NaN         NaN          NaN  NaN  NaN NaN   \n",
-       "4 -39791.397634      epa          NaN         NaN          NaN  NaN  NaN NaN   \n",
-       "\n",
-       "   omega  \n",
-       "0    NaN  \n",
-       "1    NaN  \n",
-       "2    NaN  \n",
-       "3    NaN  \n",
-       "4    NaN  "
-      ]
-     },
-     "execution_count": 351,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import pandas as pd\n",
-    "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns\n",
-    "sns.set_style(\"whitegrid\")\n",
-    "\n",
-    "df = pd.read_csv(\"run/likelihood.csv\", sep=\";\")\n",
-    "df.rename(columns={'likelihood': 'LL', 'ms' : 'max-strikes', 'sb' : 'strike-box', 'mp': 'max-pitches', 'o': 'omega'}, inplace=True)\n",
-    "df.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "What software was tested:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 349,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['epa' 'epang' 'pplacer' 'rappas']\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(df[\"software\"].unique())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Calculate LL-difference\n",
-    "\n",
-    "Let's take a look at the LL values, produced by EPA: "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 361,
-   "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>software</th>\n",
-       "      <th>G</th>\n",
-       "      <th>query</th>\n",
-       "      <th>LL</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>epa</td>\n",
-       "      <td>0.01</td>\n",
-       "      <td>8f43808640b0e835dfd588c7c30a955c78cf2252-17084050</td>\n",
-       "      <td>-39708.145904</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>epa</td>\n",
-       "      <td>0.10</td>\n",
-       "      <td>8f43808640b0e835dfd588c7c30a955c78cf2252-17084050</td>\n",
-       "      <td>-39705.898393</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>epa</td>\n",
-       "      <td>0.01</td>\n",
-       "      <td>ea4a29b8b59fb031b08cf1a7575658013dd45baa-9896769</td>\n",
-       "      <td>-39779.279980</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>epa</td>\n",
-       "      <td>0.10</td>\n",
-       "      <td>ea4a29b8b59fb031b08cf1a7575658013dd45baa-9896769</td>\n",
-       "      <td>-39778.547272</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>epa</td>\n",
-       "      <td>0.01</td>\n",
-       "      <td>64463bb954837427bbaa8bff6de57ba4bbaccb8b-9717282</td>\n",
-       "      <td>-39791.397634</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  software     G                                              query  \\\n",
-       "0      epa  0.01  8f43808640b0e835dfd588c7c30a955c78cf2252-17084050   \n",
-       "1      epa  0.10  8f43808640b0e835dfd588c7c30a955c78cf2252-17084050   \n",
-       "2      epa  0.01   ea4a29b8b59fb031b08cf1a7575658013dd45baa-9896769   \n",
-       "3      epa  0.10   ea4a29b8b59fb031b08cf1a7575658013dd45baa-9896769   \n",
-       "4      epa  0.01   64463bb954837427bbaa8bff6de57ba4bbaccb8b-9717282   \n",
-       "\n",
-       "             LL  \n",
-       "0 -39708.145904  \n",
-       "1 -39705.898393  \n",
-       "2 -39779.279980  \n",
-       "3 -39778.547272  \n",
-       "4 -39791.397634  "
-      ]
-     },
-     "execution_count": 361,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "epa_columns = [\"software\", \"g\", \"query\", \"LL\"]\n",
-    "df_epa = df[df[\"software\"] == \"epa\"][epa_columns].copy()\n",
-    "df_epa.rename(columns={'g': 'G'}, inplace=True)\n",
-    "df_epa.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 355,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0.5, 0.98, 'Log Likelihood of extended trees')"
-      ]
-     },
-     "execution_count": 355,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 360x360 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ax = sns.catplot(data=df_epa, x=\"G\", y=\"LL\", palette=\"muted\")\n",
-    "ax.set_axis_labels(\"G\", \"LogLikelihood\")\n",
-    "plt.subplots_adjust(left=0.1, top=0.9)\n",
-    "ax.fig.suptitle(\"Log Likelihood of extended trees\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's compare LL values query-by-query, taking the [EPA, g=0.1] run as baseline."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 356,
-   "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>LL</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>73</th>\n",
-       "      <td>0425c2f49866ff47d2d6678dfad027644b20dc0d-1708795</td>\n",
-       "      <td>-39792.713826</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>121</th>\n",
-       "      <td>07a16500d8ec9d9ff772b852d5b12fcc67185e0c-1296378</td>\n",
-       "      <td>-39706.944825</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>07c4ff61b0b704a6e3e2aee860c9d10447abe20f-5648814</td>\n",
-       "      <td>-39715.289683</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>81</th>\n",
-       "      <td>07db0d6f4e199b4015f4c81eace27063926865ad-1635504</td>\n",
-       "      <td>-39707.965122</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>0dbcba127f79ff6ec9aa56486685d702f1da0ca7-4235956</td>\n",
-       "      <td>-39793.188953</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                                query            LL\n",
-       "73   0425c2f49866ff47d2d6678dfad027644b20dc0d-1708795 -39792.713826\n",
-       "121  07a16500d8ec9d9ff772b852d5b12fcc67185e0c-1296378 -39706.944825\n",
-       "9    07c4ff61b0b704a6e3e2aee860c9d10447abe20f-5648814 -39715.289683\n",
-       "81   07db0d6f4e199b4015f4c81eace27063926865ad-1635504 -39707.965122\n",
-       "13   0dbcba127f79ff6ec9aa56486685d702f1da0ca7-4235956 -39793.188953"
-      ]
-     },
-     "execution_count": 356,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "baseline = df_epa[df_epa[\"G\"] == 0.1][[\"query\", \"LL\"]].copy()\n",
-    "baseline.sort_values(\"query\", inplace=True)\n",
-    "baseline.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 357,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>software</th>\n",
-       "      <th>G</th>\n",
-       "      <th>query</th>\n",
-       "      <th>LL</th>\n",
-       "      <th>LL_baseline</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>epa</td>\n",
-       "      <td>0.01</td>\n",
-       "      <td>8f43808640b0e835dfd588c7c30a955c78cf2252-17084050</td>\n",
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-       "      <th>1</th>\n",
-       "      <td>epa</td>\n",
-       "      <td>0.10</td>\n",
-       "      <td>8f43808640b0e835dfd588c7c30a955c78cf2252-17084050</td>\n",
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-       "      <td>-39705.898393</td>\n",
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-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>epa</td>\n",
-       "      <td>0.01</td>\n",
-       "      <td>ea4a29b8b59fb031b08cf1a7575658013dd45baa-9896769</td>\n",
-       "      <td>-39779.279980</td>\n",
-       "      <td>-39778.547272</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>epa</td>\n",
-       "      <td>0.10</td>\n",
-       "      <td>ea4a29b8b59fb031b08cf1a7575658013dd45baa-9896769</td>\n",
-       "      <td>-39778.547272</td>\n",
-       "      <td>-39778.547272</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>epa</td>\n",
-       "      <td>0.01</td>\n",
-       "      <td>64463bb954837427bbaa8bff6de57ba4bbaccb8b-9717282</td>\n",
-       "      <td>-39791.397634</td>\n",
-       "      <td>-39791.397634</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  software     G                                              query  \\\n",
-       "0      epa  0.01  8f43808640b0e835dfd588c7c30a955c78cf2252-17084050   \n",
-       "1      epa  0.10  8f43808640b0e835dfd588c7c30a955c78cf2252-17084050   \n",
-       "2      epa  0.01   ea4a29b8b59fb031b08cf1a7575658013dd45baa-9896769   \n",
-       "3      epa  0.10   ea4a29b8b59fb031b08cf1a7575658013dd45baa-9896769   \n",
-       "4      epa  0.01   64463bb954837427bbaa8bff6de57ba4bbaccb8b-9717282   \n",
-       "\n",
-       "             LL   LL_baseline  \n",
-       "0 -39708.145904 -39705.898393  \n",
-       "1 -39705.898393 -39705.898393  \n",
-       "2 -39779.279980 -39778.547272  \n",
-       "3 -39778.547272 -39778.547272  \n",
-       "4 -39791.397634 -39791.397634  "
-      ]
-     },
-     "execution_count": 357,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_epa = df_epa.merge(baseline, left_on=\"query\", right_on=\"query\", suffixes=('', '_baseline'))\n",
-    "df_epa.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 358,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "\n",
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-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>software</th>\n",
-       "      <th>G</th>\n",
-       "      <th>query</th>\n",
-       "      <th>LL</th>\n",
-       "      <th>LL_baseline</th>\n",
-       "      <th>LL difference</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>epa</td>\n",
-       "      <td>0.01</td>\n",
-       "      <td>8f43808640b0e835dfd588c7c30a955c78cf2252-17084050</td>\n",
-       "      <td>-39708.145904</td>\n",
-       "      <td>-39705.898393</td>\n",
-       "      <td>-2.247511</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>epa</td>\n",
-       "      <td>0.10</td>\n",
-       "      <td>8f43808640b0e835dfd588c7c30a955c78cf2252-17084050</td>\n",
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-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>epa</td>\n",
-       "      <td>0.01</td>\n",
-       "      <td>ea4a29b8b59fb031b08cf1a7575658013dd45baa-9896769</td>\n",
-       "      <td>-39779.279980</td>\n",
-       "      <td>-39778.547272</td>\n",
-       "      <td>-0.732708</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>epa</td>\n",
-       "      <td>0.10</td>\n",
-       "      <td>ea4a29b8b59fb031b08cf1a7575658013dd45baa-9896769</td>\n",
-       "      <td>-39778.547272</td>\n",
-       "      <td>-39778.547272</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>epa</td>\n",
-       "      <td>0.01</td>\n",
-       "      <td>64463bb954837427bbaa8bff6de57ba4bbaccb8b-9717282</td>\n",
-       "      <td>-39791.397634</td>\n",
-       "      <td>-39791.397634</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  software     G                                              query  \\\n",
-       "0      epa  0.01  8f43808640b0e835dfd588c7c30a955c78cf2252-17084050   \n",
-       "1      epa  0.10  8f43808640b0e835dfd588c7c30a955c78cf2252-17084050   \n",
-       "2      epa  0.01   ea4a29b8b59fb031b08cf1a7575658013dd45baa-9896769   \n",
-       "3      epa  0.10   ea4a29b8b59fb031b08cf1a7575658013dd45baa-9896769   \n",
-       "4      epa  0.01   64463bb954837427bbaa8bff6de57ba4bbaccb8b-9717282   \n",
-       "\n",
-       "             LL   LL_baseline  LL difference  \n",
-       "0 -39708.145904 -39705.898393      -2.247511  \n",
-       "1 -39705.898393 -39705.898393       0.000000  \n",
-       "2 -39779.279980 -39778.547272      -0.732708  \n",
-       "3 -39778.547272 -39778.547272       0.000000  \n",
-       "4 -39791.397634 -39791.397634       0.000000  "
-      ]
-     },
-     "execution_count": 358,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_epa[\"LL difference\"] = df_epa[\"LL\"] - df_epa[\"LL_baseline\"]\n",
-    "df_epa.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 360,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0.5, 0.98, 'Log Likelihood difference (vs. EPA, G=0.1)')"
-      ]
-     },
-     "execution_count": 360,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
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\n",
-      "text/plain": [
-       "<Figure size 360x360 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ax = sns.catplot(data=df_epa, x=\"G\", y=\"LL difference\", palette=\"muted\")\n",
-    "ax.set_axis_labels(\"G\", \"LogLikelihood\")\n",
-    "plt.subplots_adjust(left=0.1, top=0.9)\n",
-    "ax.fig.suptitle(\"Log Likelihood difference (vs. EPA, G=0.1)\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Other software\n",
-    "\n",
-    "Let's build this plot it for every tested software."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 365,
-   "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>pruning</th>\n",
-       "      <th>g</th>\n",
-       "      <th>query</th>\n",
-       "      <th>length</th>\n",
-       "      <th>LL</th>\n",
-       "      <th>software</th>\n",
-       "      <th>max-strikes</th>\n",
-       "      <th>strike-box</th>\n",
-       "      <th>max-pitches</th>\n",
-       "      <th>red</th>\n",
-       "      <th>ar</th>\n",
-       "      <th>k</th>\n",
-       "      <th>omega</th>\n",
-       "      <th>LL_baseline</th>\n",
-       "      <th>LL difference</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0.01000</td>\n",
-       "      <td>8f43808640b0e835dfd588c7c30a955c78cf2252-17084050</td>\n",
-       "      <td>0</td>\n",
-       "      <td>-39708.145904</td>\n",
-       "      <td>epa</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-39705.898393</td>\n",
-       "      <td>-2.247511</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0.10000</td>\n",
-       "      <td>8f43808640b0e835dfd588c7c30a955c78cf2252-17084050</td>\n",
-       "      <td>0</td>\n",
-       "      <td>-39705.898393</td>\n",
-       "      <td>epa</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-39705.898393</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0.99900</td>\n",
-       "      <td>8f43808640b0e835dfd588c7c30a955c78cf2252-17084050</td>\n",
-       "      <td>0</td>\n",
-       "      <td>-39709.165734</td>\n",
-       "      <td>epang</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-39705.898393</td>\n",
-       "      <td>-3.267341</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0.99999</td>\n",
-       "      <td>8f43808640b0e835dfd588c7c30a955c78cf2252-17084050</td>\n",
-       "      <td>0</td>\n",
-       "      <td>-39707.929337</td>\n",
-       "      <td>epang</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-39705.898393</td>\n",
-       "      <td>-2.030944</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>8f43808640b0e835dfd588c7c30a955c78cf2252-17084050</td>\n",
-       "      <td>0</td>\n",
-       "      <td>-39707.929337</td>\n",
-       "      <td>pplacer</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>40.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-39705.898393</td>\n",
-       "      <td>-2.030944</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   pruning        g                                              query  \\\n",
-       "0        0  0.01000  8f43808640b0e835dfd588c7c30a955c78cf2252-17084050   \n",
-       "1        0  0.10000  8f43808640b0e835dfd588c7c30a955c78cf2252-17084050   \n",
-       "2        0  0.99900  8f43808640b0e835dfd588c7c30a955c78cf2252-17084050   \n",
-       "3        0  0.99999  8f43808640b0e835dfd588c7c30a955c78cf2252-17084050   \n",
-       "4        0      NaN  8f43808640b0e835dfd588c7c30a955c78cf2252-17084050   \n",
-       "\n",
-       "   length            LL software  max-strikes  strike-box  max-pitches  red  \\\n",
-       "0       0 -39708.145904      epa          NaN         NaN          NaN  NaN   \n",
-       "1       0 -39705.898393      epa          NaN         NaN          NaN  NaN   \n",
-       "2       0 -39709.165734    epang          NaN         NaN          NaN  NaN   \n",
-       "3       0 -39707.929337    epang          NaN         NaN          NaN  NaN   \n",
-       "4       0 -39707.929337  pplacer          6.0         3.0         40.0  NaN   \n",
-       "\n",
-       "    ar   k  omega   LL_baseline  LL difference  \n",
-       "0  NaN NaN    NaN -39705.898393      -2.247511  \n",
-       "1  NaN NaN    NaN -39705.898393       0.000000  \n",
-       "2  NaN NaN    NaN -39705.898393      -3.267341  \n",
-       "3  NaN NaN    NaN -39705.898393      -2.030944  \n",
-       "4  NaN NaN    NaN -39705.898393      -2.030944  "
-      ]
-     },
-     "execution_count": 365,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df2 = df.merge(baseline, left_on=\"query\", right_on=\"query\", suffixes=('', '_baseline'))\n",
-    "df2[\"LL difference\"] = df2[\"LL\"] - df2[\"LL_baseline\"]\n",
-    "df2.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 377,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<seaborn.axisgrid.FacetGrid at 0x7f1b94c85dd0>"
-      ]
-     },
-     "execution_count": 377,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 360x360 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "epa_columns = [\"software\", \"g\", \"query\", \"LL\", \"LL difference\"]\n",
-    "df_epa = df2[df2[\"software\"] == \"epa\"][epa_columns].copy()\n",
-    "df_epa.rename(columns={'g': 'G'}, inplace=True)\n",
-    "\n",
-    "sns.catplot(data=df_epa, x=\"G\", y=\"LL difference\", palette=\"muted\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 378,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<seaborn.axisgrid.FacetGrid at 0x7f1b92671f50>"
-      ]
-     },
-     "execution_count": 378,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 360x360 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "epang_columns = [\"software\", \"g\", \"query\", \"LL\", \"LL difference\"]\n",
-    "df_epang = df2[df2[\"software\"] == \"epang\"][epang_columns]\n",
-    "\n",
-    "sns.catplot(data=df_epang, x=\"g\", y=\"LL difference\", palette=\"muted\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 379,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<seaborn.axisgrid.FacetGrid at 0x7f1b9252c850>"
-      ]
-     },
-     "execution_count": 379,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 417.75x360 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pplacer_columns = [\"software\", \"max-strikes\", \"max-pitches\", \"strike-box\", \"query\", \"LL\", \"LL difference\"]\n",
-    "df_pplacer = df2[df2[\"software\"] == \"pplacer\"][pplacer_columns].copy()\n",
-    "\n",
-    "df_pplacer[\"max-strikes\"] = df_pplacer[\"max-strikes\"].astype(int)\n",
-    "df_pplacer[\"max-pitches\"] = df_pplacer[\"max-pitches\"].astype(int)\n",
-    "df_pplacer[\"strike-box\"] = df_pplacer[\"strike-box\"].astype(int)\n",
-    "\n",
-    "sns.catplot(data=df_pplacer, x=\"max-strikes\", row=\"max-pitches\", hue=\"strike-box\", y=\"LL difference\", dodge=True, palette=\"muted\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 381,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<seaborn.axisgrid.FacetGrid at 0x7f1b92851310>"
-      ]
-     },
-     "execution_count": 381,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 412x360 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "rappas_columns = [\"software\", \"k\", \"omega\", \"red\", \"query\", \"LL\", \"LL difference\"]\n",
-    "df_rappas = df2[df2[\"software\"] == \"rappas\"][rappas_columns].copy()\n",
-    "\n",
-    "df_rappas[\"k\"] = df_rappas[\"k\"].astype(int)\n",
-    "\n",
-    "sns.catplot(data=df_rappas, x=\"k\", hue=\"omega\", y=\"LL difference\", dodge=True, palette=\"muted\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Put it all together\n",
-    "\n",
-    "Let's make a final plot, combining all the plots."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 383,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1008x907.2 with 4 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "num_rows = 2\n",
-    "num_cols = 2\n",
-    "scale = 7\n",
-    "fig, axes = plt.subplots(num_rows, num_cols, sharey=True, figsize=(scale * num_cols, scale * 0.9 * num_rows))\n",
-    "plt.subplots_adjust(left=0.1, top=0.93)\n",
-    "fig.suptitle(\"LogLikelihood difference (vs. EPA, g=0.1)\", fontsize=16)\n",
-    "\n",
-    "palette = sns.color_palette(\"deep\", 8)\n",
-    "\n",
-    "k = 0\n",
-    "order = [\"pplacer\", \"epa\", \"rappas\", \"epang\"]\n",
-    "for i in range(num_rows):\n",
-    "    for j in range(num_cols):\n",
-    "        color = sns.color_palette()[j]\n",
-    "        \n",
-    "        if order[k] == \"pplacer\":\n",
-    "            ax = sns.stripplot(data=df_pplacer, x=\"max-strikes\", hue=\"strike-box\", y=\"LL difference\", ax=axes[i, j],\n",
-    "                               linewidth=0.5, edgecolor='w', dodge=True, palette=palette[0:2])\n",
-    "            ylabel = 'LL Difference'\n",
-    "            title = f'PPLACER | max-pitches=40'\n",
-    "\n",
-    "        elif order[k] == \"epa\":\n",
-    "            ax = sns.stripplot(data=df_epa, x=\"G\", y=\"LL difference\", ax=axes[i, j], \n",
-    "                               linewidth=0.5, edgecolor='w', dodge=True, palette=palette[2:3])\n",
-    "            ylabel = ''\n",
-    "            title = f'EPA'\n",
-    "        elif order[k] == \"rappas\":\n",
-    "            ax = sns.stripplot(data=df_rappas, x=\"k\", hue=\"omega\", y=\"LL difference\", ax=axes[i, j], \n",
-    "                               linewidth=0.5, edgecolor='w', dodge=True, palette=palette[3:5])\n",
-    "            ylabel = 'LL Difference'\n",
-    "            title = f'RAPPAS'\n",
-    "        elif order[k] == \"epang\":\n",
-    "            ax = sns.stripplot(data=df_epang, x=\"g\", y=\"LL difference\", ax=axes[i, j], \n",
-    "                               linewidth=0.5, edgecolor='w', dodge=True, palette=palette[5:6])\n",
-    "            ylabel = ''\n",
-    "            title = f'EPA-NG | default heuristic'\n",
-    "        \n",
-    "        ax.set(ylabel=ylabel, title=title)\n",
-    "        k += 1\n",
-    "        \n",
-    "fig.savefig(\"ll_diff.png\")\n",
-    "fig.savefig(\"ll_diff.svg\")\n",
-    "fig.savefig(\"ll_diff.pdf\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "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": "3.7.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/pewo/alignment/generate_reads.py b/pewo/alignment/generate_reads.py
new file mode 100644
index 0000000..c15eba3
--- /dev/null
+++ b/pewo/alignment/generate_reads.py
@@ -0,0 +1,59 @@
+#!/usr/bin/env python
+"""
+Generate reads with ART.
+"""
+
+__author__ = "Matthias Blanke"
+__license__ = "MIT"
+
+
+import sys
+import os
+import subprocess
+import random
+from Bio import SeqIO
+from typing import Dict
+import pewo.config as cfg
+
+def change_reads_to_art(config: Dict):
+    print("Changing reads to art simulated reads.")
+
+    # Delete all current reads
+    folder = config["workdir"] + "/R"
+    for filename in os.listdir(folder):
+        file_path = os.path.join(folder, filename)
+        try:
+            if os.path.isfile(file_path) or os.path.islink(file_path):
+                os.unlink(file_path)
+            elif os.path.isdir(file_path):
+                shutil.rmtree(file_path)
+        except Exception as e:
+            print('Failed to delete %s. Reason: %s' % (file_path, e))
+
+    # Create new reads with ART
+    for count in range(config["pruning_count"]):
+        for read_length in config["read_length"]:
+            reference_file = config["workdir"] + "/G/" + str(count) + '.fasta'
+            output_file = str(config["workdir"]) + "/R/" + str(count) + "_r" + str(read_length)
+            if config['ART_platform'] == 'illumina':
+                subprocess.run('art_illumina -na -ss HS25 -sam -i ' + reference_file + ' -l ' + str(read_length) + 
+                ' -c ' + str(config['num_reads']) + ' -o ' + str(output_file), shell=True)
+            elif config['ART_platform'] == '454':
+                subprocess.run('art_454 ' + reference_file + ' ' + output_file + ' ' + str(5))
+            else:
+                err('Wrong platform specified for ART: ', config['ART_platform'])
+
+            seqs = list(SeqIO.parse(output_file + '.fq', format='fastq'))
+            SeqIO.write(seqs, output_file + '.fasta', format='fasta')
+
+    # Delete unecessary files created by ART
+    for filename in os.listdir(folder):
+        file_path = os.path.join(folder, filename)
+        if file_path.endswith('fq') or file_path.endswith('sam') or file_path.endswith('stat'):
+            try:
+                if os.path.isfile(file_path) or os.path.islink(file_path):
+                    os.unlink(file_path)
+                elif os.path.isdir(file_path):
+                    shutil.rmtree(file_path)
+            except Exception as e:
+                print('Failed to delete %s. Reason: %s' % (file_path, e))
diff --git a/rules/op/operate_prunings.smk b/rules/op/operate_prunings.smk
index 38152dd..2088638 100755
--- a/rules/op/operate_prunings.smk
+++ b/rules/op/operate_prunings.smk
@@ -9,6 +9,7 @@ __license__ = "MIT"
 
 import os
 import pewo.config as cfg
+import pewo.alignment.generate_reads as gr
 from typing import Dict
 
 
@@ -77,6 +78,8 @@ rule operate_pruning:
                 "{params.count} {params.length} 0 1 {params.states} "
                 "&> {log}"
             )
+            if config["simulate_ART"]:
+                gr.change_reads_to_art(config);
         else:
             shell(
                 "mkdir -p {params.wd}/A {params.wd}/T {params.wd}/G {params.wd}/R;"