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diff --git a/analysis/results/CG_nomod.csv b/analysis/results/CG_nomod.csv
new file mode 100644
index 0000000..9452faa
--- /dev/null
+++ b/analysis/results/CG_nomod.csv
@@ -0,0 +1,1001 @@
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diff --git a/analysis/results/mappy_mod.csv b/analysis/results/mappy_mod.csv
new file mode 100644
index 0000000..86c6945
--- /dev/null
+++ b/analysis/results/mappy_mod.csv
@@ -0,0 +1,1001 @@
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diff --git a/analysis/results/mappy_nomod.csv b/analysis/results/mappy_nomod.csv
new file mode 100644
index 0000000..edcdca5
--- /dev/null
+++ b/analysis/results/mappy_nomod.csv
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diff --git a/analysis/scripts/align_mappy.py b/analysis/scripts/align_mappy.py
new file mode 100644
index 0000000..76afb06
--- /dev/null
+++ b/analysis/scripts/align_mappy.py
@@ -0,0 +1,60 @@
+from pathlib import Path
+import mappy as mp
+from tqdm import tqdm
+import h5py
+from Bio import SeqIO
+import re
+import pandas as pd
+
+from util import get_files, read_fastq
+
+
+def align_mappy(dir_in, file_out, file_fasta):
+    a = mp.Aligner(file_fasta, preset='map-ont')  # Load or build index
+    if not a:
+        raise Exception("ERROR: failed to load/build index")
+
+    reads = get_files(dir_in)
+    files_fastq = {}
+    data = []
+
+    for read in tqdm(reads):
+        with h5py.File(read, 'r', libver='latest') as fd:
+            no_alignment = True
+            fastq = read_fastq(fd)
+            files_fastq[fastq.id] = len(fastq.seq)
+
+            for hit in a.map(fastq.seq):  # Traverse alignments
+                if hit.is_primary:  # Check if the alignment is primary
+                    # Reference
+                    for seq_record in SeqIO.parse(file_fasta, 'fasta'):
+                        ref = seq_record.seq[hit.r_st: hit.r_en]
+                    r_CG_num = len(re.findall(r'(CG)', str(ref)))
+
+                    # Query
+                    query = fastq.seq[hit.q_st: hit.q_en]
+                    if hit.strand == -1:
+                        query = mp.revcomp(query)
+                    q_CG_num = len(re.findall(r'(CG)', str(query)))
+
+                    no_alignment = False
+                    data.append([fastq.id, hit.r_st, hit.r_en, hit.q_st, hit.q_en, r_CG_num, q_CG_num, hit.cigar_str])
+                    break
+
+        if no_alignment:
+            data.append([fastq.id, '', '', '', '', 0, 0, ''])
+
+    data = pd.DataFrame(data, columns=['read_id', 'r_st', 'r_en', 'q_st', 'q_en', 'r_CG_num', 'q_CG_num', 'cigar_str'])
+    data.sort_values('read_id', inplace=True)
+    data.to_csv(file_out, index=False)
+
+    print("Average length of fastq files:", sum(files_fastq.values()) / len(files_fastq.values()))
+
+
+if __name__ == '__main__':
+    modification = 'nomod'  # 'mod' or 'nomod'
+    dir_in = Path(f'/home/sdeur/tmp-guppy/{modification}/workspace')
+    file_out = f'/home/sdeur/tmp-mappy/mappy_{modification}.csv'
+    file_fasta = '/home/sdeur/data/ecoli_k12_mg1655.fasta'
+
+    align_mappy(dir_in, file_out, file_fasta)
diff --git a/analysis/scripts/analyse_CG.py b/analysis/scripts/analyse_CG.py
new file mode 100644
index 0000000..9efa362
--- /dev/null
+++ b/analysis/scripts/analyse_CG.py
@@ -0,0 +1,147 @@
+from pathlib import Path
+import mappy as mp
+from tqdm import tqdm
+import h5py
+from Bio import SeqIO
+import pandas as pd
+
+from util import get_files, read_fastq
+
+
+def count_matches(cs):
+    matches = {'M': 0, 'X': 0, 'D': 0, 'I': 0}
+    mode, value = 'M', 0
+
+    for c in cs:
+        if c == ':':
+            matches[mode] += int(value) if mode == 'M' or mode == 'X' else len(value)
+            mode, value = 'M', ''
+            continue
+        elif c == '*':
+            matches[mode] += int(value) if mode == 'M' or mode == 'X' else len(value)
+            mode, value = 'X', ''
+            continue
+        elif c == '-':
+            matches[mode] += int(value) if mode == 'M' or mode == 'X' else len(value)
+            mode, value = 'D', ''
+            continue
+        elif c == '+':
+            matches[mode] += int(value) if mode == 'M' or mode == 'X' else len(value)
+            mode, value = 'I', ''
+            continue
+
+        if mode == 'X':
+            value = 1
+        else:
+            value += c
+
+    matches[mode] += int(value) if mode == 'M' or mode == 'X' else len(value)
+    return matches
+
+
+def normalize(ref, query, cigar):
+    r = list(ref)
+    q = list(query)
+    i = 0
+    num = ''
+
+    for c in cigar:
+        if c.isnumeric():
+            num += c
+            continue
+
+        elif c == 'M':
+            i += int(num)
+
+        elif c == 'D':
+            for _ in range(int(num)):
+                q.insert(i, '-')
+                i += 1
+
+        elif c == 'I':
+            for _ in range(int(num)):
+                r.insert(i, '-')
+                i += 1
+
+        num = ''
+
+    return ''.join(r), ''.join(q)
+
+
+def count_CG(ref, query):
+    CG_cnt = {'M': 0, 'X': 0, 'D': 0, 'I': 0}
+
+    for i in range(len(ref) - 1):
+        if ref[i] == 'C' and ref[i + 1] == 'G':
+            if query[i] == 'C' and query[i + 1] == 'G':
+                CG_cnt['M'] += 1
+
+            elif query[i] == '-' and query[i + 1] == 'G':
+                CG_cnt['D'] += 1
+
+            elif query[i] in {'A', 'G', 'T'} and query[i + 1] == 'G':
+                CG_cnt['X'] += 1
+
+        elif ref[i] == '-' and ref[i + 1] == 'G':
+            if query[i] == 'C' and query[i + 1] == 'G':
+                CG_cnt['I'] += 1
+
+    return CG_cnt
+
+
+def analyse_CG(dir_in, file_out, file_fasta):
+    a = mp.Aligner(file_fasta, preset='map-ont')  # Load or build index
+    if not a:
+        raise Exception("ERROR: failed to load/build index")
+
+    reads = get_files(dir_in)
+    data = []
+
+    for read in tqdm(reads):
+        with h5py.File(read, 'r', libver='latest') as fd:
+            matches = {'M': 0, 'X': 0, 'D': 0, 'I': 0}
+            CG_cnt = {'M': 0, 'X': 0, 'D': 0, 'I': 0}
+
+            fastq = read_fastq(fd)
+            ref = ''
+            mapq = 0
+
+            for hit in a.map(fastq.seq, cs=True):  # Traverse alignments
+                if hit.is_primary:  # Check if the alignment is primary
+                    # Alignment
+                    matches = count_matches(hit.cs)
+
+                    # Reference
+                    for seq_record in SeqIO.parse(file_fasta, 'fasta'):
+                        ref = seq_record.seq[hit.r_st: hit.r_en]
+
+                    # Query
+                    query = fastq.seq[hit.q_st: hit.q_en]
+                    if hit.strand == -1:
+                        query = mp.revcomp(query)
+
+                    # Normalize
+                    ref, query = normalize(ref, query, hit.cigar_str)
+
+                    # Analyse CG motif
+                    CG_cnt = count_CG(ref, query)
+
+                    mapq = hit.mapq
+                    break
+
+            data.append([fastq.id, len(ref), matches['M'], matches['X'], matches['D'], matches['I'],
+                         CG_cnt['M'], CG_cnt['X'], CG_cnt['D'], CG_cnt['I'], mapq])
+
+    data = pd.DataFrame(data, columns=['read_id', 'alignment_len', 'M', 'X', 'D', 'I',
+                                       'M_CG', 'X_CG', 'D_CG', 'I_CG', 'mapq'])
+    data.sort_values('read_id', inplace=True)
+    data.to_csv(file_out, index=False)
+
+
+if __name__ == '__main__':
+    modification = 'nomod'  # 'mod' or 'nomod'
+    dir_in = Path(f'/home/sdeur/tmp-guppy/{modification}/workspace')
+    file_out = f'/home/sdeur/tmp-mappy/CG_{modification}.csv'
+    file_fasta = '/home/sdeur/data/ecoli_k12_mg1655.fasta'
+
+    analyse_CG(dir_in, file_out, file_fasta)
diff --git a/analysis/scripts/plot_mapq.py b/analysis/scripts/plot_mapq.py
new file mode 100644
index 0000000..3efe635
--- /dev/null
+++ b/analysis/scripts/plot_mapq.py
@@ -0,0 +1,50 @@
+from pathlib import Path
+import mappy as mp
+from tqdm import tqdm
+import h5py
+import matplotlib.pyplot as plt
+import numpy as np
+
+from util import get_files, read_fastq
+
+
+def plot_distribution(data, modification, bins=15):
+    """ Distribution of mapping quality """
+    plt.hist(data, bins=bins, color='green' if modification == 'nomod' else 'red')
+    plt.title(f'Distribution of mapping quality ({modification})')
+    plt.xlabel('mapping quality')
+    plt.ylabel('count')
+    plt.yticks(np.arange(0, 1001, 100))
+    plt.savefig(f'../plots/mapq_{modification}.png')
+
+
+def get_mapqs(dir_in, file_fasta):
+    a = mp.Aligner(file_fasta, preset='map-ont')  # Load or build index
+    if not a:
+        raise Exception("ERROR: failed to load/build index")
+
+    reads = get_files(dir_in)
+    mapqs = []
+
+    for read in tqdm(reads):
+        with h5py.File(read, 'r', libver='latest') as fd:
+            fastq = read_fastq(fd)
+            mapq = 0
+
+            for hit in a.map(fastq.seq):  # Traverse alignments
+                if hit.is_primary:  # Check if the alignment is primary
+                    mapq = hit.mapq
+                    break
+
+            mapqs.append(mapq)
+
+    return mapqs
+
+
+if __name__ == '__main__':
+    modification = 'nomod'  # 'mod' or 'nomod'
+    dir_in = Path(f'/home/sdeur/tmp-guppy/{modification}/workspace')
+    file_fasta = '/home/sdeur/data/ecoli_k12_mg1655.fasta'
+
+    mapqs = get_mapqs(dir_in, file_fasta)
+    plot_distribution(mapqs, modification)
diff --git a/analysis/scripts/plot_signal_length.py b/analysis/scripts/plot_signal_length.py
new file mode 100644
index 0000000..b2eff4f
--- /dev/null
+++ b/analysis/scripts/plot_signal_length.py
@@ -0,0 +1,39 @@
+import os
+from ont_fast5_api.fast5_interface import get_fast5_file
+import numpy as np
+import matplotlib.pyplot as plt
+
+
+def get_signal_length(fast5_filepath):
+    """
+    :param fast5_filepath: can be a single- or multi-read file
+    :return: raw signal length
+    """
+    with get_fast5_file(fast5_filepath, mode="r") as f5:
+        for read in f5.get_reads():
+            raw_data = read.get_raw_data()
+            # print(read.read_id, raw_data, len(raw_data))
+
+    return len(raw_data)
+
+
+def plot_signal_length(dir_in, bins=50):
+    """ Distribution of raw signal length """
+    data = []
+    for file in os.listdir(dir_in):
+        data.append(get_signal_length(os.path.join(dir_in, file)))
+
+    plt.hist(data, bins=bins, color='green' if modification == 'nomod' else 'red')
+    plt.title(f'Distribution of raw signal length ({modification})')
+    plt.xlabel('raw signal length')
+    plt.ylabel('count')
+    plt.xticks(np.arange(0, 600_001, 100_000))  # Change if needed
+    plt.yticks(np.arange(0, 301, 50))  # Change if needed
+    plt.savefig(f'../plots/signal_length_{modification}.png')
+
+
+if __name__ == '__main__':
+    modification = 'nomod'  # 'mod' or 'nomod'
+    dir_in = f'/home/sdeur/tmp-guppy/{modification}/workspace'
+
+    plot_signal_length(dir_in)
diff --git a/analysis/scripts/plot_start_raw_index.py b/analysis/scripts/plot_start_raw_index.py
new file mode 100644
index 0000000..ce3fc32
--- /dev/null
+++ b/analysis/scripts/plot_start_raw_index.py
@@ -0,0 +1,149 @@
+import os
+import numpy as np
+from ont_fast5_api.fast5_interface import get_fast5_file
+import matplotlib.pyplot as plt
+import statistics
+
+
+def get_raw_signal_block(fast5_filename, read_id, start_seq_position, end_seq_position):
+    raw_data = _get_raw_data(fast5_filename, read_id)
+    (move_table, first_sample_template, block_stride) = _get_fast5_data(fast5_filename, read_id)
+    seq_to_move_map = _create_map_from_basecall_to_signal(move_table)
+    _, start_raw_index = _get_raw_signal_block(raw_data, seq_to_move_map, first_sample_template, block_stride,
+                                               start_seq_position, end_seq_position)
+    return list(raw_data), start_raw_index
+
+
+def _get_raw_signal_block(raw_data, seq_to_move_map, first_sample_template, block_stride,
+                          start_seq_position, end_seq_position):
+    start_raw_index = first_sample_template + seq_to_move_map[start_seq_position] * block_stride
+    # Get the raw index of the *next* entry in the seq_to_move map,
+    # so that we include all the raw data for end_seq_position
+    if end_seq_position + 1 == len(seq_to_move_map):
+        end_raw_index = len(raw_data)
+    else:
+        end_raw_index = first_sample_template + seq_to_move_map[end_seq_position + 1] * block_stride
+    return raw_data[start_raw_index:end_raw_index], start_raw_index
+
+
+def _get_raw_data(fast5_filename, read_id):
+    with get_fast5_file(fast5_filename, mode="r") as f5file:
+        read = f5file.get_read(read_id)
+        raw_data = read.get_raw_data()
+    return raw_data
+
+
+def _create_map_from_basecall_to_signal(move_table):
+    cumulative_moves = np.add.accumulate(move_table)
+    sequence_to_move_index = [np.min(np.where(cumulative_moves == x))
+                              for x in
+                              range(1, int(np.max(cumulative_moves) + 1))]
+    return sequence_to_move_index
+
+
+def _get_fast5_data(fast5_filename, read_id):
+    with get_fast5_file(fast5_filename) as f5file:
+        read = f5file.get_read(read_id)
+        basecall_analysis = read.get_latest_analysis('Basecall_1D')
+        segmentation_analysis = read.get_latest_analysis('Segmentation')
+        segmentation_summary = read.get_summary_data(segmentation_analysis)
+        basecall_summary = read.get_summary_data(basecall_analysis)
+        move_table = read.get_analysis_dataset(basecall_analysis, 'BaseCalled_template/Move')
+        first_sample_template = segmentation_summary['segmentation']['first_sample_template']
+        block_stride = basecall_summary['basecall_1d_template']['block_stride']
+    return move_table, first_sample_template, block_stride
+
+
+def plot_distribution(dir_in, files_fastq, bins=50):
+    """ Distribution of start_raw_index """
+    data = []
+
+    for file in os.listdir(dir_in):
+        fast5_filename = os.path.join(dir_in, file)
+        read_id = file[:-6]
+        start_seq_position = 0
+        end_seq_position = files_fastq[read_id] - 1
+        _, start_raw_index = get_raw_signal_block(fast5_filename, read_id, start_seq_position, end_seq_position)
+        data.append(start_raw_index)
+
+    plt.hist(data, bins=bins, color='green' if modification == 'nomod' else 'red')
+    plt.title(f'Distribution of "start_raw_index" ({modification})')
+    plt.xlabel('start_raw_index')
+    plt.ylabel('count')
+    plt.xticks(np.arange(0, 5001, 500))  # Change if needed
+    plt.yticks(np.arange(0, 151, 25))  # Change if needed
+    plt.savefig(f'../plots/start_raw_index_{modification}.png')
+
+
+def plot_raw_signal(dir_in, files_fastq, fast5_file, n=1000):
+    """ raw signal data + start_raw_index line """
+    fast5_filename = os.path.join(dir_in, fast5_file)
+    read_id = fast5_file[:-6]
+    start_seq_position = 0
+    end_seq_position = files_fastq[read_id] - 1
+    raw_data, start_raw_index = get_raw_signal_block(fast5_filename, read_id, start_seq_position, end_seq_position)
+
+    x_first_n = [str(x) for x in range(0, n)]
+    x_last_n = [str(x) for x in range(len(raw_data) - n, len(raw_data))]
+    y_first_n = raw_data[:n]
+    y_last_n = raw_data[-n:]
+
+    x = x_first_n + x_last_n
+    y = y_first_n + y_last_n
+
+    fig, ax = plt.subplots(1, 1)
+    ax.plot(x, y, color='green' if modification == 'nomod' else 'red')
+    plt.axvline(x=str(start_raw_index), color='darkblue', linestyle='--', label=f'start_raw_index = {start_raw_index}')
+
+    ax.set_xticks(x[::100])  # Change if needed
+    ax.set_xticklabels(x[::100], rotation=45)  # Change if needed
+    plt.title(f'read_id: {read_id}')
+    plt.xlabel('time')
+    plt.ylabel('raw signal data')
+    plt.legend()
+    plt.savefig(f'../plots/raw_signal_{read_id}.png')
+
+
+def plot_stdev(dir_in, files_fastq, fast5_file, n=1000, window=50, step=5):
+    """ Standard deviation of the raw signal """
+    fast5_filename = os.path.join(dir_in, fast5_file)
+    read_id = fast5_file[:-6]
+    start_seq_position = 0
+    end_seq_position = files_fastq[read_id] - 1
+    raw_data, start_raw_index = get_raw_signal_block(fast5_filename, read_id, start_seq_position, end_seq_position)
+
+    first_n = raw_data[:n]
+    last_n = raw_data[-n:]
+
+    stdev_first_n = []
+    stdev_last_n = []
+    for i in range(0, n - window, step):
+        points_first_n = np.array(first_n[i: i + window], dtype=np.float64)
+        stdev_first_n.append(statistics.stdev(points_first_n))
+        points_last_n = np.array(last_n[i: i + window], dtype=np.float64)
+        stdev_last_n.append(statistics.stdev(points_last_n))
+
+    fig, axs = plt.subplots(2)
+    fig.suptitle(f'Standard deviation - {read_id}')
+    axs[0].plot(range(0, n - window, step), stdev_first_n, color='green' if modification == 'nomod' else 'red')
+    axs[1].plot(range(0, n - window, step), stdev_last_n, color='green' if modification == 'nomod' else 'red')
+    plt.xlabel('time')
+    axs[0].set_ylabel(f'stdev - first {n}')
+    axs[1].set_ylabel(f'stdev - last {n}')
+    plt.savefig(f'../plots/stdev_{read_id}.png')
+
+
+if __name__ == '__main__':
+    modification = 'nomod'  # 'mod' or 'nomod'
+    dir_in_fastq = f'/home/sdeur/tmp-guppy/{modification}-fastq'
+    dir_in = f'/home/sdeur/tmp-guppy/{modification}/workspace'
+
+    files_fastq = {}  # {read_id : fastq_len}
+    for file_fastq in os.listdir(dir_in_fastq):
+        with open(os.path.join(dir_in_fastq, file_fastq)) as fin:
+            files_fastq[file_fastq[:-6]] = len(fin.readlines()[1].strip())
+
+    # Choose between the following plotting methods
+    plot_distribution(dir_in, files_fastq)
+    plot_raw_signal(dir_in, files_fastq, 'a40f1242-df43-4b9d-a97e-d9af64f3c956.fast5')
+    plot_stdev(dir_in, files_fastq, 'a40f1242-df43-4b9d-a97e-d9af64f3c956.fast5')
diff --git a/analysis/scripts/util.py b/analysis/scripts/util.py
new file mode 100644
index 0000000..9397302
--- /dev/null
+++ b/analysis/scripts/util.py
@@ -0,0 +1,25 @@
+import io
+from Bio import SeqIO
+from Bio.SeqRecord import SeqRecord
+
+
+def get_files(path, recursive=False):
+    if path.is_file():
+        return {path}
+
+    # Finding all input FAST5 files
+    if recursive:
+        files = path.glob('**/*.fast5')
+    else:
+        files = path.glob('*.fast5')
+
+    return set(files)
+
+
+def read_fastq(read) -> SeqRecord:
+    fastq_str = read[f'/Analyses/Basecall_1D_000/BaseCalled_template/Fastq'][()]
+    fastq_str = fastq_str.decode()
+    str_file = io.StringIO(fastq_str)
+
+    record = SeqIO.read(str_file, 'fastq')
+    return record
diff --git a/remove-tombo/basecall.py b/remove-tombo/basecall.py
new file mode 100644
index 0000000..189e151
--- /dev/null
+++ b/remove-tombo/basecall.py
@@ -0,0 +1,27 @@
+from pyguppyclient import GuppyBasecallerClient, yield_reads
+from pyguppyclient.decode import ReadData, CalledReadData
+import numpy as np
+
+from typing import Tuple, List
+
+from util import Interval
+
+
+# config='dna_r9.4.1_450bps_hac' for more precise basecalling
+def basecall(files: List[str], config: str = 'dna_r9.4.1_450bps_fast') -> Tuple[ReadData, CalledReadData]:
+    with GuppyBasecallerClient(config_name=config, trace=True) as client:
+        for file in files:
+            for read in yield_reads(file):
+                called = client.basecall(read)[1]
+                yield read, called
+
+
+def sequence_to_raw(read: ReadData, called: CalledReadData) -> List[Interval]:
+    first_signal_id = len(read.signal) - called.trimmed_samples
+    move_index = np.nonzero(called.move)[0]
+
+    seq_to_raw_start = first_signal_id + move_index * called.model_stride
+    seq_to_raw_len = np.diff(seq_to_raw_start, append=len(read.signal))
+    seq_to_raw_end = seq_to_raw_start + seq_to_raw_len
+
+    return [Interval(st, en) for st, en in zip(seq_to_raw_start, seq_to_raw_end)]
diff --git a/remove-tombo/custom_processor.py b/remove-tombo/custom_processor.py
new file mode 100644
index 0000000..6ce9b67
--- /dev/null
+++ b/remove-tombo/custom_processor.py
@@ -0,0 +1,220 @@
+import argparse
+from pyguppyclient import get_fast5_files
+from pyguppyclient.decode import ReadData, CalledReadData
+import mappy
+from Bio import SeqIO
+import re
+from tqdm import tqdm
+import numpy as np
+
+from typing import Tuple, Set, List, Dict, Optional
+
+from basecall import basecall, sequence_to_raw
+from util import Interval, ResegmentationData
+
+
+def make_aligner(reference_file: str) -> mappy.Aligner:
+    aligner = mappy.Aligner(reference_file, preset='map-ont')  # Load or build index
+    if not aligner:
+        raise Exception("ERROR: failed to load/build index")
+
+    return aligner
+
+
+def get_motif_positions(reference_file: str, motif: str, index: int) -> Dict[str, Tuple[Set[int], Set[int]]]:
+    chromosomes = SeqIO.to_dict(SeqIO.parse(reference_file, 'fasta'))
+    motif_positions = dict()
+
+    for chromosome, record in chromosomes.items():
+        reference = str(record.seq)
+
+        # Forward strand
+        fwd_matches = re.finditer(motif, reference, re.I)
+        fwd_pos = set(m.start() + index for m in fwd_matches)
+
+        # Reverse strand
+        rev_matches = re.finditer(motif, mappy.revcomp(reference), re.I)
+        rev_pos = set(len(reference) - (m.start() + index) - 1 for m in rev_matches)
+
+        motif_positions[chromosome] = fwd_pos, rev_pos
+
+    return motif_positions
+
+
+def get_reference(reference_file: str, contig: str) -> str:
+    chromosomes = SeqIO.to_dict(SeqIO.parse(reference_file, 'fasta'))
+
+    return str(chromosomes[contig].seq)
+
+
+def align(aligner: mappy.Aligner, query: str, mapq: int) -> Optional[mappy.Alignment]:
+    for hit in aligner.map(query):  # Traverse alignments
+        if hit.is_primary:  # Check if the alignment is primary
+            if hit.mapq < mapq:  # Check if the mapping quality is below set threshold
+                return None
+            return hit
+    return None
+
+
+def get_relevant_motif_positions(motif_positions: Dict[str, Tuple[Set[int], Set[int]]], alignment: mappy.Alignment) -> Set[int]:
+    contig_positions = motif_positions[alignment.ctg]
+    strand_positions = contig_positions[0] if alignment.strand == 1 else contig_positions[1]
+
+    relevant_positions = strand_positions & set(range(alignment.r_st, alignment.r_en))
+
+    if alignment.strand == 1:
+        return {pos - alignment.r_st for pos in relevant_positions}
+    else:
+        return {alignment.r_en - 1 - pos for pos in relevant_positions}
+
+
+def resolve_insertions(alignment: mappy.Alignment, seq_to_raw: List[Interval]) -> Tuple[List[Interval], List[Interval]]:
+    cigar = alignment.cigar if alignment.strand == 1 else reversed(alignment.cigar)
+
+    r_pos, q_pos = 0, alignment.q_st
+    r_len = alignment.r_en - alignment.r_st
+
+    signal_intervals = [None] * r_len
+    insertion = False
+    deletion_idx = []
+
+    for length, operation in cigar:
+        if operation in {0, 7, 8}:  # Match or mismatch
+            if insertion:
+                signal_intervals[r_pos] = Interval(center, seq_to_raw[q_pos].end)  # Base to the right
+                insertion = False
+                length -= 1
+                r_pos += 1
+                q_pos += 1
+
+            for i in range(length):
+                signal_intervals[r_pos + i] = seq_to_raw[q_pos + i]
+
+            r_pos += length
+            q_pos += length
+
+        elif operation == 1:  # Insertion
+            insertion_interval = Interval(seq_to_raw[q_pos].start, seq_to_raw[q_pos + length].start)
+            center = int(np.mean(insertion_interval))
+
+            signal_intervals[r_pos - 1] = Interval(signal_intervals[r_pos - 1].start, center)  # Base to the left
+            insertion = True
+
+            q_pos += length
+
+        elif operation in {2, 3}:  # Deletion or skip
+            deletion_idx.append(Interval(r_pos, r_pos + length))
+
+            if insertion:
+                signal_intervals[r_pos] = Interval(center, seq_to_raw[q_pos].start)  # Base to the right
+                insertion = False
+                length -= 1
+                r_pos += 1
+
+            for i in range(length):
+                signal_intervals[r_pos + i] = Interval(seq_to_raw[q_pos].start, seq_to_raw[q_pos].start)
+
+            r_pos += length
+
+        else:
+            raise ValueError('Invalid CIGAR operation')
+
+    return signal_intervals, deletion_idx
+
+
+def resolve_deletions(signal_intervals: List[Interval], deletion_idx: List[Interval]) -> List[Interval]:
+    for idx_st, idx_en in deletion_idx:
+        sig_st, sig_en = signal_intervals[idx_st - 1].start, signal_intervals[idx_en].end
+        intervals = np.array_split(range(sig_st, sig_en), idx_en - idx_st + 2)
+
+        if len(intervals[-1]) == 0:  # If there is not enough signal points to divide among bases
+            while len(intervals[-1]) == 0:
+                intervals.pop(-1)
+            interval = intervals.pop(-1)
+            signal_intervals[idx_en] = Interval(interval[0], interval[-1] + 1)  # Base to the right must have > 0 signal points
+
+        for i in range(idx_st - 1, idx_en + 1):
+            if len(intervals) == 0:  # Bases in the middle (deletions) which have 0 signal points
+                if i < idx_en:
+                    signal_intervals[i] = Interval(signal_intervals[idx_en].start, signal_intervals[idx_en].start)
+                continue
+
+            interval = intervals.pop(0)
+            signal_intervals[i] = Interval(interval[0], interval[-1] + 1)
+
+    return signal_intervals
+
+
+def custom_processor(basecall_data: Tuple[ReadData, CalledReadData], aligner: mappy.Aligner, reference_file: str,
+                     motif_positions: Dict[str, Tuple[Set[int], Set[int]]], mapq: int, window: int) -> ResegmentationData:
+    read, called = basecall_data
+
+    alignment = align(aligner, called.seq, mapq)
+    if not alignment:
+        return None
+
+    relevant_motif_positions = get_relevant_motif_positions(motif_positions, alignment)
+    if not relevant_motif_positions:
+        return None
+
+    seq_to_raw = sequence_to_raw(read, called)
+
+    signal_intervals, deletion_idx = resolve_insertions(alignment, seq_to_raw)
+    signal_intervals = resolve_deletions(signal_intervals, deletion_idx)
+
+    resegmentation_data = []
+
+    for motif_position in relevant_motif_positions:
+        r_len = alignment.r_en - alignment.r_st
+        if motif_position - window < 0 or motif_position + window >= r_len:
+            continue
+
+        position = alignment.r_st + motif_position if alignment.strand == 1 else alignment.r_en - 1 - motif_position
+
+        event_intervals = signal_intervals[motif_position - window: motif_position + window + 1]
+        event_lens = np.array([interval.end - interval.start for interval in event_intervals])
+
+        reference = get_reference(reference_file, alignment.ctg)
+        region = reference[position - window: position + window + 1]
+        bases = region if alignment.strand == 1 else mappy.revcomp(region)
+
+        assert len(event_intervals) == len(event_lens) == len(bases)
+
+        resegmentation_data.append(ResegmentationData(position, event_intervals, event_lens, bases))
+
+    return resegmentation_data
+
+
+def process_data(input_path: str, recursive: bool, reference_file: str,
+                 mapq: int = 0, motif: str = 'CG', index: int = 0, window: int = 8):
+    fast5_files = get_fast5_files(input_path, recursive=recursive)
+
+    aligner = make_aligner(reference_file)
+    motif_positions = get_motif_positions(reference_file, motif, index)
+
+    for basecall_data in tqdm(basecall(fast5_files)):
+        resegmentation_data = custom_processor(basecall_data, aligner, reference_file, motif_positions, mapq, window)
+
+        if resegmentation_data:
+            print(resegmentation_data)
+
+
+def parse_arguments():
+    parser = argparse.ArgumentParser(description='Custom processor')
+
+    parser.add_argument('-i', '--input_path', type=str, required=True,
+                        help='Path to the input file or folder containing FAST5 files')
+
+    parser.add_argument('-r', '--recursive', action='store_true',
+                        help='Flag to indicate if folder will be searched recursively (default: False)')
+
+    parser.add_argument('--reference', type=str, required=True,
+                        help='Path to the reference file')
+
+    return parser.parse_args()
+
+
+if __name__ == '__main__':
+    args = parse_arguments()
+
+    process_data(args.input_path, args.recursive, args.reference)
diff --git a/remove-tombo/plots/ratio_D_mod.png b/remove-tombo/plots/ratio_D_mod.png
new file mode 100644
index 0000000..b9c1a37
Binary files /dev/null and b/remove-tombo/plots/ratio_D_mod.png differ
diff --git a/remove-tombo/plots/ratio_D_mod_final.png b/remove-tombo/plots/ratio_D_mod_final.png
new file mode 100644
index 0000000..db9803c
Binary files /dev/null and b/remove-tombo/plots/ratio_D_mod_final.png differ
diff --git a/remove-tombo/plots/ratio_D_nomod.png b/remove-tombo/plots/ratio_D_nomod.png
new file mode 100644
index 0000000..4a6ad27
Binary files /dev/null and b/remove-tombo/plots/ratio_D_nomod.png differ
diff --git a/remove-tombo/plots/ratio_D_nomod_final.png b/remove-tombo/plots/ratio_D_nomod_final.png
new file mode 100644
index 0000000..8c2930f
Binary files /dev/null and b/remove-tombo/plots/ratio_D_nomod_final.png differ
diff --git a/remove-tombo/plots/ratio_I_mod.png b/remove-tombo/plots/ratio_I_mod.png
new file mode 100644
index 0000000..160998b
Binary files /dev/null and b/remove-tombo/plots/ratio_I_mod.png differ
diff --git a/remove-tombo/plots/ratio_I_nomod.png b/remove-tombo/plots/ratio_I_nomod.png
new file mode 100644
index 0000000..52efe40
Binary files /dev/null and b/remove-tombo/plots/ratio_I_nomod.png differ
diff --git a/remove-tombo/plots/signal_points_D.png b/remove-tombo/plots/signal_points_D.png
new file mode 100644
index 0000000..cecb5bd
Binary files /dev/null and b/remove-tombo/plots/signal_points_D.png differ
diff --git a/remove-tombo/plots/signal_points_M.png b/remove-tombo/plots/signal_points_M.png
new file mode 100644
index 0000000..f914578
Binary files /dev/null and b/remove-tombo/plots/signal_points_M.png differ
diff --git a/remove-tombo/scripts/plot_signal_points.py b/remove-tombo/scripts/plot_signal_points.py
new file mode 100644
index 0000000..fbb2371
--- /dev/null
+++ b/remove-tombo/scripts/plot_signal_points.py
@@ -0,0 +1,47 @@
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+
+
+def plot_distribution(bins=15):
+    """ Distribution of signal points """
+    # Data for matches
+    matches_mod = pd.read_pickle('../plots/matches-mod.pkl')
+    matches_nomod = pd.read_pickle('../plots/matches-nomod.pkl')
+    matches_mod_avg = round(sum(matches_mod) / len(matches_mod), 2)
+    matches_nomod_avg = round(sum(matches_nomod) / len(matches_nomod), 2)
+    matches_mod = list(filter(lambda x: x < 100, matches_mod))
+    matches_nomod = list(filter(lambda x: x < 100, matches_nomod))
+
+    # Data for deletions
+    deletions_mod = pd.read_pickle('../plots/deletions-mod.pkl')
+    deletions_nomod = pd.read_pickle('../plots/deletions-nomod.pkl')
+    deletions_mod_avg = round(sum(deletions_mod) / len(deletions_mod), 2)
+    deletions_nomod_avg = round(sum(deletions_nomod) / len(deletions_nomod), 2)
+    deletions_mod = list(filter(lambda x: x < 100, deletions_mod))
+    deletions_nomod = list(filter(lambda x: x < 100, deletions_nomod))
+
+    # Plot distribution of signal points for matches
+    plt.hist([matches_mod, matches_nomod], bins=bins, color=['red', 'green'],
+             label=[f'mod (avg: {matches_mod_avg})', f'nomod (avg: {matches_nomod_avg})'])
+    plt.legend()
+    plt.title(f'Distribution of Signal Points Around Matches')
+    plt.xlabel('Number of Signal Points')
+    plt.ylabel('Count')
+    plt.xticks(np.arange(0, 101, 10))
+    plt.savefig(f'../plots/signal_points_M.png')
+    plt.clf()
+
+    # Plot distribution of signal points for deltions
+    plt.hist([deletions_mod, deletions_nomod], bins=bins, color=['red', 'green'],
+             label=[f'mod (avg: {deletions_mod_avg})', f'nomod (avg: {deletions_nomod_avg})'])
+    plt.legend()
+    plt.title(f'Distribution of Signal Points Around Deletions')
+    plt.xlabel('Number of Signal Points')
+    plt.ylabel('Count')
+    plt.xticks(np.arange(0, 101, 10))
+    plt.savefig(f'../plots/signal_points_D.png')
+
+
+if __name__ == '__main__':
+    plot_distribution()
diff --git a/remove-tombo/scripts/plot_signal_points_ratios.py b/remove-tombo/scripts/plot_signal_points_ratios.py
new file mode 100644
index 0000000..860a530
--- /dev/null
+++ b/remove-tombo/scripts/plot_signal_points_ratios.py
@@ -0,0 +1,38 @@
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+
+
+def plot_distribution(data, modification, bins=15):
+    """ Distribution of signal points ratio """
+    plt.hist(data, bins=bins, color='green' if modification == 'nomod' else 'red')
+    plt.title(f'Distribution of Signal Points Ratio ({modification})')
+    plt.xlabel('Signal Points Ratio')
+    plt.ylabel('Count')
+    plt.xticks(np.linspace(0, 1, 11))
+    plt.yticks(np.arange(0, 14001, 2000))
+    plt.savefig(f'../plots/ratio_D_{modification}.png')
+
+
+def plot_final_distribution(data, modification, bins=10):
+    """ Distribution of signal points ratio, taking the number of deletions into consideration """
+    plt.hist(data.values(), bins, histtype='bar', label=list(data_mod.keys()))
+    plt.legend(title="Number of Deletions")
+    plt.title(f'Distribution of Signal Points Ratio ({modification})')
+    plt.xlabel('Signal Points Ratio')
+    plt.ylabel('Count')
+    plt.xticks(np.linspace(0, 1, 11))
+    plt.yticks(np.arange(0, 12001, 2000))
+    plt.savefig(f'../plots/ratio_D_{modification}_final.png')
+
+
+if __name__ == '__main__':
+    modification = 'mod'  # 'mod' or 'nomod'
+
+    # ratios = []
+    # plot_distribution(ratios, modification)
+
+    data_mod = pd.read_pickle('../plots/mod-D.pkl')
+    data_nomod = pd.read_pickle('../plots/nomod-D.pkl')
+    data = data_mod if modification == 'mod' else data_nomod
+    plot_final_distribution(data, modification)
diff --git a/remove-tombo/util.py b/remove-tombo/util.py
new file mode 100644
index 0000000..637823b
--- /dev/null
+++ b/remove-tombo/util.py
@@ -0,0 +1,16 @@
+from collections import namedtuple
+from dataclasses import dataclass
+import numpy as np
+
+from typing import List
+
+
+Interval = namedtuple('Interval', ['start', 'end'])  # Start - inclusive, end - exclusive
+
+
+@dataclass
+class ResegmentationData:
+    position: int
+    event_intervals: List[Interval]  # Intervals of signal points
+    event_lens: np.ndarray  # Lengths of intervals of signal points
+    bases: str
diff --git a/scripts/ds_extract_hc_pos.py b/scripts/ds_extract_hc_pos.py
index adc6bbd..a4dab46 100644
--- a/scripts/ds_extract_hc_pos.py
+++ b/scripts/ds_extract_hc_pos.py
@@ -39,4 +39,4 @@ def create_arguments():
 if __name__ == '__main__':
     args = create_arguments()
 
-    extract_hc_pos(args.bed, args.nomod, args.mod)
\ No newline at end of file
+    extract_hc_pos(args.bed, args.nomod, args.mod)
diff --git a/scripts/organize_by_chr.py b/scripts/organize_by_chr.py
index a7ce744..de3b984 100644
--- a/scripts/organize_by_chr.py
+++ b/scripts/organize_by_chr.py
@@ -84,13 +84,13 @@ def write_mappings(mappings: Dict[str, List[Path]], out_folder: Path) -> None:
                 mapping_f.write(f'    {str(p)}\n')
 
 
-
 def get_read_id(path):
     with h5py.File(path, 'r') as f:
         read_name = list(f[FAST5_READS_PATH].keys())
         read_group = f[f'{FAST5_READS_PATH}/{read_name}']
         return read_group.attrs['read_id']
 
+
 def organize_fast5(fast5_path: Path, align_path: Path, out_folder: Path, recursive: bool = False) -> None:
     align_data = {}
     counter = Counter()
diff --git a/scripts/shuffle_data.py b/scripts/shuffle_data.py
index 7cc00fe..bc809e4 100644
--- a/scripts/shuffle_data.py
+++ b/scripts/shuffle_data.py
@@ -105,7 +105,7 @@ def create_args():
 
     check_size_parser.add_argument('-i', '--input_path', type=str, required=True)
     
-    #Shuffle command
+    # Shuffle command
     shuffle_parser = subparsers.add_parser('shuffle', help='Shuffle dataset.')
     check_size_parser.set_defaults(command=shuffle)
 
@@ -129,4 +129,4 @@ def create_args():
 if __name__ == '__main__':
     args = create_args()
     
-    args.command(args)
\ No newline at end of file
+    args.command(args)
diff --git a/scripts/subsample_fast5.py b/scripts/subsample_fast5.py
index d3f6018..e08b6f3 100644
--- a/scripts/subsample_fast5.py
+++ b/scripts/subsample_fast5.py
@@ -4,7 +4,7 @@
 import shutil
 
 from tqdm import tqdm
-#import tqdm.contrib
+# import tqdm.contrib
 
 from typing import *
 
@@ -60,7 +60,7 @@ def sample_fast5(args):
 
     tqdm.write(f'{len(files)} files for sampling.')
 
-    # create output dir
+    # Create output dir
     try:
         args.out_folder.mkdir(parents=True, exist_ok=False)
     except FileExistsError:
diff --git a/src/extract_features.py b/src/extract_features.py
index 34b8863..ed8589e 100644
--- a/src/extract_features.py
+++ b/src/extract_features.py
@@ -2,10 +2,12 @@
 
 import sys
 import time
+import numpy as np
 from tqdm import tqdm
 from pathlib import Path
 import traceback
 from concurrent.futures import as_completed
+from pyguppyclient.decode import ReadData, CalledReadData
 
 import argparse
 
@@ -14,6 +16,10 @@
 from utils.parallel_processing import get_executor
 from utils.writer import BinaryWriter
 
+from processor.basecall import basecall
+from processor.custom_processor import CustomProcessor
+from processor.util import Interval
+
 BATCH_SIZE = 4_000
 
 
@@ -41,8 +47,9 @@ def get_info_args(args: argparse.Namespace) -> Dict[str, Any]:
     """
     info_args = {
         'norm_method': args.norm_method,
+        'mapq': args.mapq,
         'motif': args.motif,
-        'sample_size': args.sample_size,
+        'index': args.index,
         'window': args.window,
         'label': args.label
     }
@@ -50,17 +57,40 @@ def get_info_args(args: argparse.Namespace) -> Dict[str, Any]:
     return info_args
 
 
-def generate_data(read: Read,
+def get_raw_signal(read: ReadData, event_interval: Interval, norm_method, continuous: bool=True) -> np.ndarray:
+    """ Returns the raw signal for the read.
+
+    Returns the raw signal for the given read. Optionally, it converts discrete signal to continuous signal.
+
+    :param read: Basecalled read
+    :param event_interval: Interval of signal points
+    :param norm_method: Signal normalization method
+    :param continuous: True if returned signal should be continuous, otherwise False
+    :return: Raw signal for the given read
+    """
+    signal = read.signal[event_interval.start: event_interval.end]
+
+    if continuous:
+        signal = read.daq_scaling * (signal + read.daq_offset)
+
+    norm_func = normalization_factory(norm_method)
+    if norm_func:
+        signal = norm_func(signal)
+
+    return signal
+
+
+def generate_data(read: ReadData,
                   reseg_data: List[ResegmentationData],
-                  sample_size: int=20) -> Optional[List[Example]]:
+                  norm_method: str) -> Optional[List[Example]]:
     """ Function that generates data for the specified read.
 
     This function generates list of examples for the given read. Data is generated from raw signal for the given read,
-    and information extracted from resegmentation group.
+    and information extracted from resegmentation data.
 
     :param read: Read for which examples will be generated
-    :param reseg_data: Resegmentation data extracted from the resegmentation group
-    :param sample_size: Number of sample points per base
+    :param reseg_data: Resegmentation data
+    :param norm_method: Signal normalization method
     :return: List of generated examples for the given read and resegmentation data
     """
     all_examples = []
@@ -69,12 +99,12 @@ def generate_data(read: Read,
         example_points = []
 
         for interval in reseg_example.event_intervals:
-            points, _ = read.signal_for_interval(interval, sample_size)
+            points = get_raw_signal(read, interval, norm_method)
             example_points.append(points)
 
         example_points = np.concatenate(example_points)
 
-        example = Example(reseg_example.position, reseg_example.bases, example_points)
+        example = Example(reseg_example.position, reseg_example.bases, example_points, reseg_example.event_lens)
         all_examples.append(example)
 
     if len(all_examples) == 0:
@@ -84,39 +114,43 @@ def generate_data(read: Read,
 
 
 def process_read(
-        path: Path,
-        reseg_path: Optional[str]=None,
+        basecall_data: Tuple[ReadData, CalledReadData],
+        reference: str,
         norm_method: Optional[str]='standardization',
+        mapq: int = 10,
         motif: str='CG',
-        sample_size: int=20,
+        index: int=0,
         window: int=8,
         bed_pos: Optional[Set[GenomicPos]]=None) -> Optional[FeaturesData]:
-    """ This function process the given read to generate data.
+    """ This function processes the given read to generate data.
 
-    This function process extracts resegmentation information, samples the signal and extracts alignment data.
+    This function extracts resegmentation information, and extracts alignment data.
 
-    :param path: Path to the signle FAST5 read
-    :param reseg_path: Resegmentation path in re-segmented FAST5 file
+    :param basecall_data: Basecalled data: (read, called)
+    :param reference: Path to the reference file
     :param norm_method: Signal normalization method
+    :param mapq: Mapping quality threshold
     :param motif: Motif for which positions will be extracted
-    :param sample_size: Number of sample points per base
+    :param index: Index of the central position in the motif
     :param window: Size of left and right windows around central position
     :param bed_pos: Positions used for filtering motif positions
     :return: FeaturesData object if at least one example is present, otherwise None
     """
-    with h5py.File(str(path), 'r', libver='latest') as fd:
-        read = Read(fd, norm_method)
+    processor = CustomProcessor(basecall_data, reference, mapq, motif, index, window)
+    resegmentation_data = processor.process()
+    if not resegmentation_data:
+        return
+
+    read, called = basecall_data
 
-        reseg_processor = ResegmentationProcessor(read, reseg_path)
-        event_pos = reseg_processor.motif_positions(motif, window, bed_pos)
-        reseg_data = reseg_processor.get_resegmentation_data(event_pos)
+    examples = generate_data(read, resegmentation_data, norm_method)
+    if not examples:
+        return
 
-        examples = generate_data(read, reseg_data, sample_size)
-        if not examples:
-            return
+    align_data = processor.align(called.seq)
+    strand = Strand.strand_from_str('+' if align_data.strand == 1 else '-')
 
-        align_data = reseg_processor.get_alignment_data()
-        return FeaturesData(align_data.chromosome, align_data.strand, read.get_read_id(), examples)
+    return FeaturesData(align_data.ctg, strand, read.read_id, examples)
 
 
 def error_callback(path, exception):
@@ -127,46 +161,48 @@ def error_callback(path, exception):
 
 def init_workers(
         info_args: Dict[str, Any],
-        reseg_path: Optional[str]=None,
         norm_method: Optional[str]='standardization',
+        mapq: int=10,
         motif: str='CG',
-        sample_size: int=20,
+        index: int=0,
         window: int=8,
         bed_data: Optional[BEDData]=None) -> None:
     # Initializing workers with constant arguments
-    global _INFO_ARGS, _RESEG_PATH, _NORM_METHOD, _MOTIF, _SAMPLE_SIZE, _WINDOW, _BED_DATA
+    global _INFO_ARGS, _NORM_METHOD, _MAPQ, _MOTIF, _INDEX, _WINDOW, _BED_DATA
     _INFO_ARGS = info_args
-    _RESEG_PATH = reseg_path
     _NORM_METHOD = norm_method
+    _MAPQ = mapq
     _MOTIF = motif
-    _SAMPLE_SIZE = sample_size
+    _INDEX = index
     _WINDOW = window
     _BED_DATA = bed_data
 
 
-def worker_process_reads(paths: List[Path], out_path: Path) -> Tuple[Path, int]:
-    """ Function that process input file list and stores generated data
+def worker_process_reads(paths: List[Path], reference: str, data_path: Path, header_path: Path) -> Tuple[Path, int]:
+    """ Function that processes input file list and stores generated data
 
     :param paths: List of input files that will be processed
-    :param out_path: Path to the generated output
+    :param reference: Path to the reference file
+    :param data_path: Path to the generated output data
+    :param header_path: Path to the generated header
     :return: Path and number of failed files
     """
     error_files = 0
 
-    with BinaryWriter(str(out_path)) as writer:
+    with BinaryWriter(str(data_path), str(header_path)) as writer:
         bed_pos = set(_BED_DATA.keys()) if _BED_DATA is not None else None
 
-        for path in paths:
+        for path in tqdm(basecall(paths)):
             try:
-                result = process_read(path, _RESEG_PATH, _NORM_METHOD, _MOTIF, _SAMPLE_SIZE, _WINDOW, bed_pos)
+                result = process_read(path, reference, _NORM_METHOD, _MAPQ, _MOTIF, _INDEX, _WINDOW, bed_pos)
 
                 if result is not None:
                     writer.write_data(result, _BED_DATA, _INFO_ARGS['label'])
             except Exception as e:
-                # error_callback(path, e)
-                error_files += 1
+                error_callback(path, e)
+                # error_files += 1
 
-        return out_path, error_files
+        return data_path, error_files
 
 
 def tqdm_with_time(msg, last_action_time):
@@ -204,7 +240,7 @@ def process_data(args: argparse.Namespace) -> None:
     info_path = Path(args.output_path, 'info.txt')
     BinaryWriter.write_extraction_info(info_path, info_args)
 
-    workers_args = (info_args, args.reseg_path, args.norm_method, args.motif, args.sample_size, args.window, bed_info)
+    workers_args = (info_args, args.norm_method, args.mapq, args.motif, args.index, args.window, bed_info)
     with get_executor(args.workers, initializer=init_workers, initargs=workers_args) as executor:
         futures = []
 
@@ -216,9 +252,10 @@ def process_data(args: argparse.Namespace) -> None:
             start = i * BATCH_SIZE
             end = min(start+BATCH_SIZE, len(files))
 
-            out_file_path = Path(args.output_path, f'{i+1}.bin.tmp')
+            data_path = Path(args.output_path, f'{i+1}.data.bin.tmp')
+            header_path = Path(args.output_path, f'{i+1}.header.bin.tmp')
 
-            future = executor.submit(worker_process_reads, files[start:end], out_file_path)
+            future = executor.submit(worker_process_reads, files[start:end], args.reference, data_path, header_path)
             futures.append(future)
 
         tqdm_with_time('Extracting features', last_action_time)
@@ -238,30 +275,31 @@ def create_arguments() -> argparse.Namespace:
     # Input and output arguments
     parser.add_argument('input_path', type=Path,
                         help='Path to the input file or folder containing FAST5 files')
+
     parser.add_argument('-r', '--recursive', action='store_true',
-                        help='''Flag to indicate if folder will be searched recursively
-                (default: False)''')
+                        help='Flag to indicate if folder will be searched recursively (default: False)')
+
     parser.add_argument('output_path', type=Path,
                         help='Path to the desired output folder')
 
-    # Resegmentation path in FAST5
-    parser.add_argument('--reseg_path', type=str, default=DEFAULT_RESEGMENTATION_PATH,
-                        help='''Path to resegmentation group in FAST5 file
-                (default: Analyses/RawGenomeCorrected_000/BaseCalled_template)''')
+    # Other arguments
+    parser.add_argument('--reference', type=str, required=True,
+                        help='Path to the reference file')
 
     parser.add_argument('--norm_method', type=str, default='standardization',
                         help='Function name to use for signal normalization (default: standardization)')
 
+    parser.add_argument('--mapq', type=int, default=10,
+                        help='Mapping quality threshold (default: 10)')
+
     parser.add_argument('--motif', type=str, default='CG',
-                        help='''Motif to be searched for in the sequences. 
-                Regular expressions can be used. (default: CG)''')
+                        help='Motif to be searched for in the sequences. Regular expressions can be used (default: CG)')
 
-    parser.add_argument('--sample_size', type=int, default=20,
-                        help='Sample size for every base in the given k-mer. (default: 20)')
+    parser.add_argument('--index', type=int, default=0,
+                        help='Index of the central position in the motif (default: 0)')
 
     parser.add_argument('--window', type=int, default=8,
-                        help='''Window size around central position. 
-                Total k-mer size is: K = 2*W + 1. (default: 8)''')
+                        help='Window size around central position. Total k-mer size is: K = 2*W + 1 (default: 8)')
 
     parser.add_argument('--label', type=int, default=None,
                         help='Label to store for the given examples (default: None)')
@@ -272,9 +310,9 @@ def create_arguments() -> argparse.Namespace:
     # Bisulfite BED file arguments
     parser.add_argument('--bed_path', type=Path, default=None,
                         help='Path to BED file containing modification information (default: None)')
+
     parser.add_argument('--bed_filter', type=str, default=None,
-                        help='''BED filter method (e.g. high_confidence 
-                finds only high-confidence positions) (default: None)''')
+                        help='BED filter method (e.g. high_confidence finds only high-confidence positions) (default: None)')
 
     return parser.parse_args()
 
diff --git a/src/inference.py b/src/inference.py
index ff059df..5a76a73 100644
--- a/src/inference.py
+++ b/src/inference.py
@@ -163,6 +163,7 @@ def test(args):
                 out.write(tmp_f.read())
             os.remove(worker_path)
 
+
 def get_arguments():
     parser = argparse.ArgumentParser()
     parser.add_argument('checkpoint', type=str)
@@ -192,10 +193,12 @@ def get_arguments():
 
     return parser.parse_args()
 
+
 def main():
     args = get_arguments()
 
     test(args)
 
+
 if __name__ == "__main__":
     main()
diff --git a/src/processor/alignment.py b/src/processor/alignment.py
new file mode 100644
index 0000000..7811c82
--- /dev/null
+++ b/src/processor/alignment.py
@@ -0,0 +1,39 @@
+import mappy
+from Bio import SeqIO
+import re
+
+from typing import Tuple, Set, Dict
+
+
+def make_aligner(reference_file: str) -> mappy.Aligner:
+    aligner = mappy.Aligner(reference_file, preset='map-ont')  # Load or build index
+    if not aligner:
+        raise Exception("ERROR: failed to load/build index")
+
+    return aligner
+
+
+def get_motif_positions(reference_file: str, motif: str, index: int) -> Dict[str, Tuple[Set[int], Set[int]]]:
+    chromosomes = SeqIO.to_dict(SeqIO.parse(reference_file, 'fasta'))
+    motif_positions = dict()
+
+    for chromosome, record in chromosomes.items():
+        reference = str(record.seq)
+
+        # Forward strand
+        fwd_matches = re.finditer(motif, reference, re.I)
+        fwd_pos = set(m.start() + index for m in fwd_matches)
+
+        # Reverse strand
+        rev_matches = re.finditer(motif, mappy.revcomp(reference), re.I)
+        rev_pos = set(len(reference) - (m.start() + index) - 1 for m in rev_matches)
+
+        motif_positions[chromosome] = fwd_pos, rev_pos
+
+    return motif_positions
+
+
+def get_reference(reference_file: str, contig: str) -> str:
+    chromosomes = SeqIO.to_dict(SeqIO.parse(reference_file, 'fasta'))
+
+    return str(chromosomes[contig].seq)
diff --git a/src/processor/basecall.py b/src/processor/basecall.py
new file mode 100644
index 0000000..a605111
--- /dev/null
+++ b/src/processor/basecall.py
@@ -0,0 +1,27 @@
+from pyguppyclient import GuppyBasecallerClient, yield_reads
+from pyguppyclient.decode import ReadData, CalledReadData
+import numpy as np
+
+from typing import Tuple, List
+
+from .util import Interval
+
+
+# config='dna_r9.4.1_450bps_hac' for more precise basecalling
+def basecall(files: List[str], config: str = 'dna_r9.4.1_450bps_fast') -> Tuple[ReadData, CalledReadData]:
+    with GuppyBasecallerClient(config_name=config, trace=True) as client:
+        for file in files:
+            for read in yield_reads(file):
+                called = client.basecall(read)[1]
+                yield read, called
+
+
+def sequence_to_raw(read: ReadData, called: CalledReadData) -> List[Interval]:
+    first_signal_id = len(read.signal) - called.trimmed_samples
+    move_index = np.nonzero(called.move)[0]
+
+    seq_to_raw_start = first_signal_id + move_index * called.model_stride
+    seq_to_raw_len = np.diff(seq_to_raw_start, append=len(read.signal))
+    seq_to_raw_end = seq_to_raw_start + seq_to_raw_len
+
+    return [Interval(st, en) for st, en in zip(seq_to_raw_start, seq_to_raw_end)]
diff --git a/src/processor/custom_processor.py b/src/processor/custom_processor.py
new file mode 100644
index 0000000..49866f4
--- /dev/null
+++ b/src/processor/custom_processor.py
@@ -0,0 +1,161 @@
+from pyguppyclient.decode import ReadData, CalledReadData
+import mappy
+import numpy as np
+
+from typing import Tuple, Set, List, Optional
+
+from .alignment import make_aligner, get_reference, get_motif_positions
+from .basecall import sequence_to_raw
+from .util import Interval, ResegmentationData
+
+
+class CustomProcessor:
+    def __init__(self,
+                 basecall_data: Tuple[ReadData, CalledReadData],
+                 reference_file: str,
+                 mapq: int = 0,
+                 motif: str = 'CG',
+                 index: int = 0,
+                 window: int = 8):
+        self.basecall_data = basecall_data
+        self.reference_file = reference_file
+
+        self.mapq = mapq
+        self.window = window
+
+        self.aligner = make_aligner(reference_file)
+        self.motif_positions = get_motif_positions(reference_file, motif, index)
+
+    def align(self, query: str) -> Optional[mappy.Alignment]:
+        for hit in self.aligner.map(query):  # Traverse alignments
+            if hit.is_primary:  # Check if the alignment is primary
+                if hit.mapq < self.mapq:  # Check if the mapping quality is below set threshold
+                    return None
+                return hit
+        return None
+
+    def _get_relevant_motif_positions(self, alignment: mappy.Alignment) -> Set[int]:
+        contig_positions = self.motif_positions[alignment.ctg]
+        strand_positions = contig_positions[0] if alignment.strand == 1 else contig_positions[1]
+
+        relevant_positions = strand_positions & set(range(alignment.r_st, alignment.r_en))
+
+        if alignment.strand == 1:
+            return {pos - alignment.r_st for pos in relevant_positions}
+        else:
+            return {alignment.r_en - 1 - pos for pos in relevant_positions}
+
+    @staticmethod
+    def resolve_insertions(alignment: mappy.Alignment, seq_to_raw: List[Interval]) -> Tuple[List[Interval], List[Interval]]:
+        cigar = alignment.cigar if alignment.strand == 1 else reversed(alignment.cigar)
+
+        r_pos, q_pos = 0, alignment.q_st
+        r_len = alignment.r_en - alignment.r_st
+
+        signal_intervals = [None] * r_len
+        insertion = False
+        deletion_idx = []
+
+        for length, operation in cigar:
+            if operation in {0, 7, 8}:  # Match or mismatch
+                if insertion:
+                    signal_intervals[r_pos] = Interval(center, seq_to_raw[q_pos].end)  # Base to the right
+                    insertion = False
+                    length -= 1
+                    r_pos += 1
+                    q_pos += 1
+
+                for i in range(length):
+                    signal_intervals[r_pos + i] = seq_to_raw[q_pos + i]
+
+                r_pos += length
+                q_pos += length
+
+            elif operation == 1:  # Insertion
+                insertion_interval = Interval(seq_to_raw[q_pos].start, seq_to_raw[q_pos + length].start)
+                center = int(np.mean(insertion_interval))
+
+                signal_intervals[r_pos - 1] = Interval(signal_intervals[r_pos - 1].start, center)  # Base to the left
+                insertion = True
+
+                q_pos += length
+
+            elif operation in {2, 3}:  # Deletion or skip
+                deletion_idx.append(Interval(r_pos, r_pos + length))
+
+                if insertion:
+                    signal_intervals[r_pos] = Interval(center, seq_to_raw[q_pos].start)  # Base to the right
+                    insertion = False
+                    length -= 1
+                    r_pos += 1
+
+                for i in range(length):
+                    signal_intervals[r_pos + i] = Interval(seq_to_raw[q_pos].start, seq_to_raw[q_pos].start)
+
+                r_pos += length
+
+            else:
+                raise ValueError('Invalid CIGAR operation')
+
+        return signal_intervals, deletion_idx
+
+    @staticmethod
+    def resolve_deletions(signal_intervals: List[Interval], deletion_idx: List[Interval]) -> List[Interval]:
+        for idx_st, idx_en in deletion_idx:
+            sig_st, sig_en = signal_intervals[idx_st - 1].start, signal_intervals[idx_en].end
+            intervals = np.array_split(range(sig_st, sig_en), idx_en - idx_st + 2)
+
+            if len(intervals[-1]) == 0:  # If there is not enough signal points to divide among bases
+                while len(intervals[-1]) == 0:
+                    intervals.pop(-1)
+                interval = intervals.pop(-1)
+                signal_intervals[idx_en] = Interval(interval[0], interval[-1] + 1)  # Base to the right must have > 0 signal points
+
+            for i in range(idx_st - 1, idx_en + 1):
+                if len(intervals) == 0:  # Bases in the middle (deletions) which have 0 signal points
+                    if i < idx_en:
+                        signal_intervals[i] = Interval(signal_intervals[idx_en].start, signal_intervals[idx_en].start)
+                    continue
+
+                interval = intervals.pop(0)
+                signal_intervals[i] = Interval(interval[0], interval[-1] + 1)
+
+        return signal_intervals
+
+    def process(self) -> Optional[List[ResegmentationData]]:
+        read, called = self.basecall_data
+
+        alignment = self.align(called.seq)
+        if not alignment:
+            return None
+
+        relevant_motif_positions = self._get_relevant_motif_positions(alignment)
+        if not relevant_motif_positions:
+            return None
+
+        seq_to_raw = sequence_to_raw(read, called)
+
+        signal_intervals, deletion_idx = CustomProcessor.resolve_insertions(alignment, seq_to_raw)
+        signal_intervals = CustomProcessor.resolve_deletions(signal_intervals, deletion_idx)
+
+        resegmentation_data = []
+
+        for motif_position in relevant_motif_positions:
+            r_len = alignment.r_en - alignment.r_st
+            if motif_position - self.window < 0 or motif_position + self.window >= r_len:
+                continue
+
+            position = alignment.r_st + motif_position if alignment.strand == 1 else alignment.r_en - 1 - motif_position
+
+            event_intervals = signal_intervals[motif_position - self.window: motif_position + self.window + 1]
+            event_lens = np.array([interval.end - interval.start for interval in event_intervals])
+
+            reference = get_reference(self.reference_file, alignment.ctg)
+            region = reference[position - self.window: position + self.window + 1]
+            bases = region if alignment.strand == 1 else mappy.revcomp(region)
+
+            assert len(event_intervals) == len(event_lens) == len(bases)
+
+            resegmentation_data.append(ResegmentationData(position, event_intervals, event_lens, bases))
+
+        return resegmentation_data
diff --git a/src/processor/util.py b/src/processor/util.py
new file mode 100644
index 0000000..637823b
--- /dev/null
+++ b/src/processor/util.py
@@ -0,0 +1,16 @@
+from collections import namedtuple
+from dataclasses import dataclass
+import numpy as np
+
+from typing import List
+
+
+Interval = namedtuple('Interval', ['start', 'end'])  # Start - inclusive, end - exclusive
+
+
+@dataclass
+class ResegmentationData:
+    position: int
+    event_intervals: List[Interval]  # Intervals of signal points
+    event_lens: np.ndarray  # Lengths of intervals of signal points
+    bases: str
diff --git a/src/utils/bed_processing.py b/src/utils/bed_processing.py
index 69b3d4d..2206d9a 100644
--- a/src/utils/bed_processing.py
+++ b/src/utils/bed_processing.py
@@ -47,7 +47,7 @@ def high_confidence_filter(
     that has coverage of at least 10 reads and having all reads modified or unmodified
     (modification frequency of 0% or 100%).
 
-    :param genomic_pos: Genomic positions that is tested. Not used
+    :param genomic_pos: Genomic position that is tested. Not used
     :param mod_info: Modification information for the given positions
     :return: True if position is high confidence position, False otherwise
     """
diff --git a/src/utils/datasets.py b/src/utils/datasets.py
index ab8dc15..1a285a4 100644
--- a/src/utils/datasets.py
+++ b/src/utils/datasets.py
@@ -12,14 +12,16 @@
 from tqdm import tqdm
 
 
-DATA_DTYPE = np.dtype([('signal', np.float16, (340,)), ('kmer', np.uint8, (17,)), ('label', np.uint8)])
+# DATA_DTYPE = np.dtype([('signal', np.float16, (340,)), ('kmer', np.uint8, (17,)), ('label', np.uint8)])
+NUM_16 = 2  # 2 bytes
+NUM_8 = 1  # 1 byte
 
 
 def dataset_size(path):
-    length, r = divmod(os.path.getsize(path), DATA_DTYPE.itemsize)
-    assert r == 0, 'Cannot calculate number of examples. Possibly wrong data file.'
+    with io.open(path, 'rb') as f:
+        number_of_examples = np.frombuffer(f.read(NUM_16), dtype=np.uint16)[0]
 
-    return length
+    return number_of_examples
 
 
 def different_length_error(filename, read_name):
@@ -29,49 +31,64 @@ def different_length_error(filename, read_name):
 
 class MemoryDataset(Dataset):
     def __init__(self, path):
-        with io.open(path, 'rb') as f:
-            self.data = np.fromfile(f, dtype=DATA_DTYPE)
-
-    def __len__(self):
-        return len(self.data)
-
-    def __getitem__(self, idx):
-        example = self.data[idx]
-
-        signal = torch.from_numpy(example['signal'])
-        bases = torch.from_numpy(np.repeat(example['kmer'], 20).astype(int))
-        label = example['label']
-
-        return signal, bases, label
+        self.data = io.open(path, 'rb')
+        self.len = dataset_size(path)  # Number of examples
 
+        with open(path.replace('data.bin', 'info.txt')) as f:
+            window = int(f.readlines()[4].split()[1])
+            self.kmer = 2 * window + 1  # Length of kmer
 
-def iter_worker_init(worker_id):
-    worker_info = torch.utils.data.get_worker_info()
-    dataset = worker_info.dataset
-
-    dataset.f = io.open(dataset.path, 'rb')
-
-
-class IterDataset(Dataset):
-    def __init__(self, path):
-        self.len = dataset_size(path)
-
-        self.path = path
+        self.signal = np.frombuffer(self.data.read(NUM_16 * self.len), dtype=np.uint16)  # Lengths of signals
+        example_bytes = NUM_16 * self.signal + (NUM_16 + NUM_8) * self.kmer + NUM_8  # Lengths of examples in bytes
+        self.offset = np.concatenate(([0], example_bytes)).cumsum()[:-1]  # Offsets in bytes
 
     def __len__(self):
         return self.len
 
     def __getitem__(self, idx):
-        self.f.seek(idx * DATA_DTYPE.itemsize)
-        example = np.frombuffer(self.f.read(DATA_DTYPE.itemsize), dtype=DATA_DTYPE)[0]
+        data_dtype = np.dtype([('signal', np.float16, (self.signal[idx],)),
+                               ('lens', np.uint16, (self.kmer,)),
+                               ('kmer', np.uint8, (self.kmer,)),
+                               ('label', np.uint8)])
+
+        self.data.seek(NUM_16 + NUM_16 * self.len + self.offset[idx])
+        example = np.frombuffer(self.data.read(data_dtype.itemsize), data_dtype)[0]
 
-        signal = torch.from_numpy(example['signal'])
-        bases = torch.from_numpy(np.repeat(example['kmer'], 20).astype(int))
+        signal = torch.from_numpy(example['signal'].astype(np.float16))
+        lens = example['lens']
+        bases = torch.from_numpy(np.repeat(example['kmer'], lens).astype(int))
         label = example['label']
 
         return signal, bases, label
 
 
+# def iter_worker_init(worker_id):
+#     worker_info = torch.utils.data.get_worker_info()
+#     dataset = worker_info.dataset
+#
+#     dataset.f = io.open(dataset.path, 'rb')
+#
+#
+# class IterDataset(Dataset):
+#     def __init__(self, path):
+#         self.len = dataset_size(path)
+#
+#         self.path = path
+#
+#     def __len__(self):
+#         return self.len
+#
+#     def __getitem__(self, idx):
+#         self.f.seek(idx * DATA_DTYPE.itemsize)
+#         example = np.frombuffer(self.f.read(DATA_DTYPE.itemsize), dtype=DATA_DTYPE)[0]
+#
+#         signal = torch.from_numpy(example['signal'])
+#         bases = torch.from_numpy(np.repeat(example['kmer'], 20).astype(int))
+#         label = example['label']
+#
+#         return signal, bases, label
+
+
 class RockfishDataModule(LightningDataModule):
     def __init__(self, train_path, val_path, train_batch_size, val_batch_size, iterable):
         super().__init__()
@@ -88,28 +105,28 @@ def __init__(self, train_path, val_path, train_batch_size, val_batch_size, itera
 
     def train_dataloader(self):
         worker_init_fn = None
-        if self.iterable:
-            train_ds = IterDataset(self.train_path)
-            worker_init_fn = iter_worker_init
-        else:
-            train_ds = MemoryDataset(self.train_path)
+        # if self.iterable:
+        #     train_ds = IterDataset(self.train_path)
+        #     worker_init_fn = iter_worker_init
+        # else:
+        train_ds = MemoryDataset(self.train_path)
 
         return DataLoader(train_ds, self.train_batch_size, True,
-                        num_workers=4, pin_memory=True, drop_last=True, worker_init_fn=worker_init_fn)
+                          num_workers=4, pin_memory=True, drop_last=True, worker_init_fn=worker_init_fn)
 
     def val_dataloader(self):
         worker_init_fn = None
-        if self.iterable:
-            val_ds = IterDataset(self.val_path)
-            worker_init_fn = iter_worker_init
-        else:
-            val_ds = MemoryDataset(self.val_path)
+        # if self.iterable:
+        #     val_ds = IterDataset(self.val_path)
+        #     worker_init_fn = iter_worker_init
+        # else:
+        val_ds = MemoryDataset(self.val_path)
 
         return DataLoader(val_ds, self.val_batch_size, num_workers=4,
-                        pin_memory=True, worker_init_fn=worker_init_fn)
+                          pin_memory=True, worker_init_fn=worker_init_fn)
 
     @classmethod
-    def add_argparse_args(cls, parent_parser) :
+    def add_argparse_args(cls, parent_parser):
         parser = argparse.ArgumentParser(parents=[parent_parser], add_help=False)
 
         parser.add_argument('train_path', type=str)
@@ -124,4 +141,4 @@ def add_argparse_args(cls, parent_parser) :
     @classmethod
     def from_argparse_args(cls, args):
         return RockfishDataModule(args.train_path, args.val_path,
-                                args.train_batch_size, args.val_batch_size, args.iterable)
+                                  args.train_batch_size, args.val_batch_size, args.iterable)
diff --git a/src/utils/models.py b/src/utils/models.py
index 26e079c..6c795c3 100644
--- a/src/utils/models.py
+++ b/src/utils/models.py
@@ -43,7 +43,7 @@ def strand_from_str(string: str) -> Strand:
 
 @dataclass(eq=True, frozen=True)
 class GenomicPos:
-    """ Dataclass that describes genomic position.
+    """ Data class that describes genomic position.
 
     Genomic position is uniquely defined with chromosome, position on the chromosome and strand.
     """
@@ -54,9 +54,9 @@ class GenomicPos:
 
 @dataclass(frozen=True)
 class ModInfo:
-    """ Dataclass that describes modification information.
+    """ Data class that describes modification information.
 
-    Dataclass contains information about coverage and modification frequency (usually for genomic position).
+    Data class contains information about coverage and modification frequency (usually for genomic position).
     """
     n_reads: int
     mod_freq: int
@@ -153,7 +153,7 @@ def __init__(self, fd: h5py.File, normalization: Optional[str]=None) -> None:
     def get_read_id(self) -> str:
         """ Returns read id
 
-        :return: read id for given single FAST5 file
+        :return: read id for the given single FAST5 file
         """
         return self.fd[self.read_group_path].attrs['read_id'].decode()
 
@@ -174,7 +174,7 @@ def get_raw_signal(self, continuous: bool=True) -> np.ndarray:
 
         Returns the raw signal for the given read. Optionally, it converts discrete signal to continuous signal.
 
-        :param continuous: True if returned signal should be continuous, otherwise false
+        :param continuous: True if returned signal should be continuous, otherwise False
         :return: Raw signal for the given read
         """
 
@@ -190,7 +190,7 @@ def get_raw_signal(self, continuous: bool=True) -> np.ndarray:
     def signal_for_interval(self, interval: Interval, samples: int=20) -> Tuple[np.ndarray, np.ndarray]:
         """ Function that samples signal points from the given interval.
 
-        This function is used for sampling singal points from the given interval. If desired number of points is lower
+        This function is used for sampling signal points from the given interval. If desired number of points is lower
         than number of points in the given interval, some points are discarded. If desired number of points is higher,
         some of the points are repeated. Sampling is done by finding point indices using numpy linspace function.
 
@@ -264,7 +264,7 @@ def map_signal_to_bases(self, points_indices: Set[int]=None) -> Dict[int, Baseca
 
         points_to_data = {}
 
-        start, base_index = self.relative_start, 2  # we take quality for central base
+        start, base_index = self.relative_start, 2  # We take quality for central base
         for event_data in self.event_table:
             event_length = int(event_data['length'] * self.read.sampling_rate)
             end = start + event_length
@@ -353,9 +353,9 @@ def get_relative_to_raw(self) -> int:
         return self.read.fd[self.events_path].attrs['read_start_rel_to_raw']
 
     def motif_positions(self, motif: str='CG', window: int=8, bed_pos: Set[GenomicPos]=None) -> Set[Interval]:
-        """ Function returns intervals that correspond to given motif.
+        """ Function returns intervals that correspond to the given motif.
 
-        This function extracts intervals that contain given motif. Start of the motif is the central
+        This function extracts intervals that contain the given motif. Start of the motif is the central
         index in the interval. If genomic positions are provided, only positions included in the given set will be
         returned.
 
@@ -446,11 +446,12 @@ class Example:
     """ Data class that contains information about one example.
 
     This data class stores information about example's position on the reference genome, reference k-mer around the
-    position and sampled signal points.
+    position, sampled signal points and lengths of intervals of signal points.
     """
     position: int
     ref_kmer: str
     signal_points: np.ndarray
+    event_lens: np.ndarray
 
 
 """@dataclass
diff --git a/src/utils/writer.py b/src/utils/writer.py
index e150f5f..9b4e0a1 100644
--- a/src/utils/writer.py
+++ b/src/utils/writer.py
@@ -47,7 +47,6 @@ def __init__(self, filename: str, generation_info: Dict[str, Any]) -> None:
         self.filename = filename
         self.generation_info = generation_info
 
-
     def __enter__(self):
         self.fd = h5py.File(self.filename, 'w', libver='latest')
 
@@ -111,20 +110,22 @@ def write_data(self, data: FeaturesData, bed_info: Optional[BEDData]=None) -> No
 class BinaryWriter(DataWriter):
     """ Implementation of DataWriter that stores data in binary file. """
 
-    TRAIN_DTYPE = np.dtype([('signal', np.float16, (340,)), ('kmer', np.uint8, (17,)), ('label', np.uint8)])
-
-    def __init__(self, filename: str) -> None:
+    def __init__(self, data_filename: str, header_filename: str) -> None:
         super().__init__()
 
-        self.filename = filename
+        self.data_filename = data_filename
+        self.header_filename = header_filename
 
     def __enter__(self):
-        self.fd = io.open(self.filename, 'wb')
+        self.data_fd = io.open(self.data_filename, 'wb')
+        self.header_fd = io.open(self.header_filename, 'wb')
         return self
 
     def __exit__(self, type, value, traceback):
-        self.fd.flush()
-        self.fd.close()
+        self.data_fd.flush()
+        self.data_fd.close()
+        self.header_fd.flush()
+        self.header_fd.close()
 
     def write_data(self, data: FeaturesData, bed_info: Optional[BEDData]=None, label: Optional[int]=None) -> None:
         """ Function that writes data into binary file.
@@ -133,33 +134,65 @@ def write_data(self, data: FeaturesData, bed_info: Optional[BEDData]=None, label
         :param bed_info: Optional BED modification information used for labeling
         :param label: Optional integer present if label is explicitly given
         """
-        array = np.empty((len(data.examples),), dtype=BinaryWriter.TRAIN_DTYPE)
+        signal_lengths = []
+
+        for example in data.examples:
+            data_dtype = np.dtype([('signal', np.float16, (len(example.signal_points),)),
+                                   ('lens', np.uint16, (len(example.event_lens),)),
+                                   ('kmer', np.uint8, (len(example.ref_kmer),)),
+                                   ('label', np.uint8)])
 
-        for i, example in enumerate(data.examples):
-            array[i]['signal'] = example.signal_points
-            array[i]['kmer'] = [base_encoding[base] for base in example.ref_kmer]
+            array = np.empty(1, dtype=data_dtype)
+
+            array[0]['signal'] = example.signal_points
+            array[0]['lens'] = example.event_lens
+            array[0]['kmer'] = [base_encoding[base] for base in example.ref_kmer]
 
             if bed_info is not None:
                 mod_info = bed_info[GenomicPos(data.chromosome, example.position, data.strand)]
                 bed_label = 1 if mod_info.mod_freq > 50 else 0
-                array[i]['label'] = bed_label
+                array[0]['label'] = bed_label
             elif label is not None:
-                array[i]['label'] = label
+                array[0]['label'] = label
             else:
                 raise ValueError('No label was provided.')
 
-        array_bytes = array.tobytes()
-        self.fd.write(array_bytes)
+            array_bytes = array.tobytes()
+            signal_lengths.append(len(array[0]['signal']))
+
+            self.data_fd.write(array_bytes)
+
+        self.header_fd.write(np.array(signal_lengths, dtype=np.uint16).tobytes())
 
     @staticmethod
     def on_extraction_finish(*args, **kwargs) -> None:
         """Function that concatenates temporary files into a single output and removes temporary files."""
 
-        src_path = Path(kwargs['path'], '*.bin.tmp')
-        dest_path = Path(kwargs['path'], 'data.bin')
-        cat_command = f'cat {src_path} > {dest_path}'
+        # Concatenate temporary data files
+        src_data_path = Path(kwargs['path'], '*.data.bin.tmp')
+        dest_data_path = Path(kwargs['path'], 'data_no_header.bin')
+        cat_command = f'cat {src_data_path} > {dest_data_path}'
+        subprocess.run(cat_command, shell=True)
+
+        # Concatenate temporary header files
+        src_header_path = Path(kwargs['path'], '*.header.bin.tmp')
+        dest_header_path = Path(kwargs['path'], 'header.bin')
+        cat_command = f'cat {src_header_path} > {dest_header_path}'
+        subprocess.run(cat_command, shell=True)
 
+        # Add number of examples to the beginning of header file
+        with io.open(dest_header_path, 'rb+') as f:
+            header = np.fromfile(f, dtype=np.uint16)
+            no_of_examples = len(header)
+            header = np.concatenate(([no_of_examples], header))
+            f.seek(0)
+            f.write(header.astype('uint16').tobytes())
+
+        # Concatenate header and data file to a final data.bin file
+        final_data_path = Path(kwargs['path'], 'data.bin')
+        cat_command = f'cat {dest_header_path} {dest_data_path} > {final_data_path}'
         subprocess.run(cat_command, shell=True)
 
-        rm_command = f'rm {src_path}'
+        # Delete unnecessary files
+        rm_command = f'rm {src_data_path} {src_header_path} {dest_data_path} {dest_header_path}'
         subprocess.run(rm_command, shell=True)