squigualiser 0.3.0b0

Visualise ONT raw signals

squigualiser

A simple tool to Visualise nanopore raw signal-base alignment.

Google Chrome is the recommended web browser to visualise these plots.

signals (squiggles) + visualiser = squigualiser

1. The first read is a signal-read alignment using guppy_v.6.3.7 move table annotation (link).
2. The second read is a signal-read alignment using f5c resquiggle output (link).
3. The third read is a signal-read alignment using the squigulator's simulated output (link).
4. The fourth read (RNA) is a signal-read alignment using f5c resquiggle output (link).

• This signal-reference alignment aligns a signal to the region chr1:4270161-4271160.
• This is the same plot with a fixed base width.

Table of Contents

1. Installation
2. Signal to read visualisation
   1. Option 1 - Using basecaller move table
   2. Option 2 - Using f5c resquiggle
   3. Option 3 - Using squigulator signal simulation
3. Signal to reference visualisation
   1. Option 1 - Using basecaller move table
   2. Option 2 - Using f5c eventalign
   3. Option 3 - Using squigulator signal simulation
4. Pileup view
5. Plot multiple tracks
6. BED annotations
7. Squigualiser GUI
8. Notes
9. Guppy move table explanation
10. Base shift
11. Signal scaling
12. Plot conventions
13. Example

Installation

using python environment (tested with python 3.8.0, should work with anything higher as well)

git clone https://github.com/hiruna72/squigualiser.git
cd squigualiser
python3.8 -m venv venv3
source venv3/bin/activate
pip install --upgrade pip
pip install --upgrade setuptools wheel

export PYSLOW5_ZSTD=1 # if your slow5 file uses zstd compression and you have zstd installed, set
python setup.py install
squigualiser --help

using conda environment

git clone https://github.com/hiruna72/squigualiser.git
cd squigualiser
conda create -n squig python=3.8.0 -y
conda activate squig

export PYSLOW5_ZSTD=1 # if your slow5 file uses zstd compression and you have zstd installed, set

python setup.py install
squigualiser --help

Signal to read visualisation

Option 1 - basecaller move table

steps for using move table generated by the basecaller

1. Run basecaller (slow5-dorado, buttery-eel or ont-Guppy)

# buttery-eel (tested with v0.2.2)
buttery-eel -g [GUPPY exe path] --config [DNA model] -i [INPUT] -o [OUTPUT] --port 5558 --use_tcp -x "cuda:all" --moves_out
e.g buttery-eel -g [GUPPY exe path] --config dna_r10.4.1_e8.2_400bps_sup.cfg -i input_reads.blow5 -o out.sam --port 5558 --use_tcp -x "cuda:all" --moves_out 

# slow5-dorado (tested with v0.2.1)
slow5-dorado basecaller [DNA model] [INPUT] --emit-moves > [OUTPUT]
e.g. slow5-dorado basecaller dna_r10.4.1_e8.2_400bps_sup@v4.0.0 input_reads.blow5 --emit-moves > out.sam

# ont-guppy (tested with v6.3.7)
guppy_basecaller -c [DNA model] -i [INPUT] --moves_out --bam_out --save_path [OUTPUT]
samtools merge pass/*.bam -o pass_bam.bam # merge passed BAM files to create a single BAM file

2. Reformat move table (more info).

# PAF output for plotting
ALIGNMENT=reform_output.paf
squigualiser reform --sig_move_offset 0 --kmer_length 1 -c --bam out.sam -o ${ALIGNMENT}

# For human readability you may prefer the tsv output (not supported for plotting)
squigualiser reform --sig_move_offset 0 --kmer_length 1 --bam out.sam -o reform_output.tsv

• Refer Note(4) for more information on the paf output.
• Refer Note(5) for a description about sig_move_offset.
• Refer Note(6) for handling a potential SAM/BAM error.

3. Plot signal to read alignment

FASTA_FILE=read.fasta
SIGNAL_FILE=read.blow5
OUTPUT_DIR=output_dir

# use samtools fasta command to create .fasta file from SAM/BAM file
samtools fasta out.sam > ${FASTA_FILE}
# plot
squigualiser plot --file ${FASTA_FILE} --slow5 ${SIGNAL_FILE} --alignment ${ALIGNMENT} --output_dir ${OUTPUT_DIR}

Option 2 - f5c resquiggle

Steps for using f5c resquiggle signal-read alignment

1. Install f5c v1.3-beta or higher as explained in f5c binaries.

2. Run f5c resquiggle

FASTQ=reads.fastq
SIGNAL_FILE=reads.blow5
ALIGNMENT=resquiggle.paf

f5c resquiggle --kmer-model [KMER_MODEL] -c ${FASTQ} ${SIGNAL_FILE} -o ${ALIGNMENT} 

• Refer Note(2) for more information about KMER_MODEL.
• Refer Note(3) for more information about RNA.

3. Plot signal to read alignment

OUTPUT_DIR=output_dir
squigualiser plot -f ${FASTQ} -s ${SIGNAL_FILE} -a ${ALIGNMENT} -o ${OUTPUT_DIR} # to plot a selected read ID, you can provide -r 'READ_ID'.

Option 3 - Squigulator signal simulation

Steps for using Squigulator signal simulation software

1. Setup squigulator v0.2 or higher as explained in the documentation.

2. Simulate a signal (remember to provide -q and -c options).

REF=ref.fasta          #reference
READ=sim.fasta         #simulated reads
ALIGNMENT=sim.paf      #contains signal-read alignment
SIGNAL_FILE=sim.blow5  #simultated raw signal data

squigulator -x dna-r10-prom ${REF} -n 1 -o ${SIGNAL_FILE} -q ${READ} -c ${ALIGNMENT} # instead of dna-r10-prom, you can specify any other profile

3. Plot signal to read alignment.

OUTPUT_DIR=output_dir
squigualiser plot -f ${READ} -s ${SIGNAL_FILE} -a ${ALIGNMENT} -o ${OUTPUT_DIR} # to plot a selected read ID, you can provide -r 'READ_ID'.

Signal to reference visualisation

Option 1 - basecaller move table

Steps for using move table generated by the basecaller

1. Run basecaller (slow5-dorado, buttery-eel or ont-Guppy)

# buttery-eel (tested with v0.2.2)
buttery-eel -g [GUPPY exe path] --config [DNA model] -i [INPUT] -o [OUTPUT] --port 5558 --use_tcp -x "cuda:all" --moves_out
e.g buttery-eel -g [GUPPY exe path] --config dna_r10.4.1_e8.2_400bps_sup.cfg -i input_reads.blow5 -o out.sam --port 5558 --use_tcp -x "cuda:all" --moves_out 

# slow5-dorado (tested with v0.2.1)
slow5-dorado basecaller [DNA model] [INPUT] --emit-moves > [OUTPUT]
e.g. slow5-dorado basecaller dna_r10.4.1_e8.2_400bps_sup@v4.0.0 input_reads.blow5 --emit-moves > out.sam

# ont-guppy (tested with v6.3.7)
guppy_basecaller -c [DNA model] -i [INPUT] --moves_out --bam_out --save_path [OUTPUT]
samtools merge pass/*.bam -o pass_bam.bam # merge passed BAM files to create a single BAM file

2. Reformat move table (more info).

# PAF output for plotting
ALIGNMENT=reform_output.paf
squigualiser reform --sig_move_offset 0 --kmer_length 1 -c --bam out.sam -o ${ALIGNMENT}

# For human readability you may prefer the tsv output (not supported for plotting)
squigualiser reform --sig_move_offset 0 --kmer_length 1 --bam out.sam -o reform_output.tsv

• Refer Note(4) for more information on the paf output.
• Refer Note(5) for a description about sig_move_offset.
• Refer Note(6) for handling a potential SAM/BAM error.

3. Align reads to reference genome

REF=genome.fa #reference
MAPP_SAM=map_output.sam
samtools fastq out.sam | minimap2 -ax map-ont ${REF} -t8 --secondary=no -o ${MAPP_SAM} -

For RNA
samtools fastq out.sam | minimap2 -ax splice -uf -k14 ${REF} -t8 --secondary=no -o ${MAPP_SAM} -

4. Realign move array to reference (more info).

REALIGN_BAM=realign_output.bam
squigualiser realign --bam ${MAPP_SAM} --paf ${REFORMAT_PAF} -o ${REALIGN_BAM}

5. Plot signal to reference alignment

REGION=chr1:6811404-6811443
SIGNAL_FILE=read.blow5
OUTPUT_DIR=output_dir

# plot
squigualiser plot --file ${REF} --slow5 ${SIGNAL_FILE} --alignment ${ALIGNMENT} --output_dir ${OUTPUT_DIR} --region ${REGION} --tag_name "optionA"

Option 2: f5c eventalign

Steps for using f5c eventalign

1. Install f5c v1.3-beta or higher as explained in f5c binaries.
2. Align reads to reference genome

REF=genome.fa #reference
MAP_SAM=mapped.sam
FASTQ=read.fastq
samtools fastq basecaller_out.sam > ${FASTQ}
minimap2 -ax map-ont ${REF} ${FASTQ} -t8 --secondary=no -o ${MAP_SAM}

For RNA
minimap2 -ax splice -uf -k14 ${REF} ${FASTQ} -t8 --secondary=no -o ${MAPP_SAM}

3. create f5c index

SIGNAL=reads.blow5
f5c index ${FASTQ} --slow5 ${SIGNAL}

4. f5c eventalign

ALIGNMENT=eventalign.bam
f5c eventalign -b ${MAP_SAM} -r ${FASTQ} -g ${REF} --slow5 ${SIGNAL} -a -o eventalign.sam
samtools sort eventalign.sam -o ${ALIGNMENT}
samtools index ${ALIGNMENT}

5. Plot signal to reference alignment.

OUTPUT_DIR=output_dir
REGION=chr1:6811404-6811443
squigualiser plot -f ${REF} -s ${SIGNAL_FILE} -a ${ALIGNMENT} -o ${OUTPUT_DIR} --region ${REGION} --tag_name "eventalgin"

Option 3 - Squigulator signal simulation

Steps for using the signal simulation software (SAM output, recommended)

1. Setup squigulator v0.2 or higher as explained in the documentation.

2. Simulate a signal (remember to provide -a).

REF=ref.fasta #reference
READ=sim.fasta
ALIGNMENT=sorted_sim.bam
SIGNAL_FILE=sim.blow5
NUM_READS=50 #number of reads to simulate
squigulator -x dna-r10-prom ${REF} -o ${SIGNAL_FILE} -a sim.sam -n ${NUM_READS} && samtools sort sim.sam -o ${ALIGNMENT} && samtools index ${ALIGNMENT}

3. Plot signal to reference alignment.

OUTPUT_DIR=output_dir
REGION=chr1:6811404-6811443
squigualiser plot -f ${REF} -s ${SIGNAL_FILE} -a ${ALIGNMENT} -o ${OUTPUT_DIR} --region ${REGION} --tag_name "optionB"

Steps for using the signal simulation software (PAF output)

2. Simulate a signal (remember to provide -c and --paf-ref).

REF=ref.fasta                 #reference
ALIGNMENT=sorted_sim.paf.gz   #sorted bgzip compressed PAF file containing signal to reference alignment
SIGNAL_FILE=sim.blow5         #simulated raw signals   
NUM_READS=50                  #number of reads to simulate

For DNA
squigulator -x dna-r10-prom ${REF} -o ${SIGNAL_FILE} --paf-ref -c sim.paf -n ${NUM_READS}
sort -k6,6 -k8,8n sim.paf -o sorted_sim.paf
bgzip sorted_sim.paf 
tabix -0 -b 8 -e 9 -s 6 ${ALIGNMENT}

For RNA
squigulator -x rna-r9-prom ${REF} -o ${SIGNAL_FILE} --paf-ref -c sim.paf -n ${NUM_READS}
sort -k6,6 -k9,9n sim.paf -o sorted_sim.paf
bgzip sorted_sim.paf 
tabix -0 -b 9 -e 8 -s 6 ${ALIGNMENT}

Pileup view

Similar to IGV pileup view now you can view the signal pileup view. To create a pileup view the following conditions should be met.

1. The plot is a signal to reference visualisation, not a signal to read.
2. A genomic region should be specified using the argument --region

First, create an alignment file by following the steps mentioned in Signal to reference visualisation

REGION=chr1:6811011-6811198
squigualiser plot_pileup -f ${REF} -s ${SIGNAL_FILE} -a ${ALIGNMENT} -o ${OUTPUT_DIR} --region ${REGION} --tag_name "pileup"

Here is an example DNA pileup plot created using the testcase 20.1. Here is an example RNA pileup plot created using the testcase 43.1.

Plot multiple tracks

For in depth analysis the user can visualize multiple plots in the same web page.

For example, this plot is visualizing forward and reverse mapped reads on two separate tracks on the same webpage.

The command plot_tracks only supports pileup views and takes a command_file.txt file as the input.

The input file describes the number of commands, the dimension of each track, and the pileup commands.

The following input command_file.txt file describes two pileup tracks with 900 and 200 heights for the first and second track respectively.

Setting plot_heights=* in the command_file.txt or providing the argument --auto_height will automatically adjust the track height depending on the number of plots in each track.

Note that the only difference between the two commands is that the second command has the additional --plot_reverse argument to plot reverse mapped reads. And -o or --output_dir argument is not necessary (ignored).

num_commands=2
plot_heights=900,200
squigualiser plot_pileup --region chr1:6,811,011-6,811,198 -f genome/hg38noAlt.fa -s reads.blow5 -a eventalign.bam --tag_name "forward_mapped"
squigualiser plot_pileup --region chr1:6,811,011-6,811,198 -f genome/hg38noAlt.fa -s reads.blow5 -a eventalign.bam --tag_name "reverse_mapped" --plot_reverse

Then use the plot_tracks command as below (remember to provide -o),

COMMAND_FILE="command_file.txt"
squigualiser plot_tracks --shared_x -f ${COMMAND_FILE} -o output_dir

BED annotations

Plots support BED file annotations. Use argument --bed [BED FILE] to provide the bed file to the plot command.

Here is an example RNA pileup plot created using the testcase 43.1 that supports bed annotations.

Squigualiser GUI

For GUI lovers, plots can be generated using a web application running on localhost (http://localhost:8000/home)

python src/server.py

Notes

1. If your FASTQ file is a multi-line file (not to confuse with multi-read), then install seqtk and use seqtk seq -l0 in.fastq > out.fastq to convert multi-line FASTQ to 4-line FASTQ.
2. The argument KMER_MODEL is optional..
3. To plot RNA signal-read alignment use the alignment file created using f5c resquiggle --rna -c ${FASTQ} ${SIGNAL_FILE} -o ${ALIGNMENT}. Also provide the argument --rna to the visualising command. Currently, there exists no RNA kmer model for r10.4.1 chemistry.
4. The input alignment format accepted by squigualiser plot is explained here. This standard format made plotting a lot easier.
5. The argument sig_move_offset is the number of moves n to skip in the signal to correct the start of the alignment. This will not skip bases in the fastq sequence. For example, to align the first move with the first kmer --sig_move_offset 0 should be passed. To align from the second move onwards, --sig_move_offset 1 should be used.
6. Pysam does not allow reading SAM/BAM files without a @SQ line in the header. Hence, squigualiser reform script might error out with NotImplementedError: can not iterate over samfile without header. Add a fake @SQ header line with a zero length reference as follows,

echo -e fake_reference'\t'0 > fake_reference.fa.fai
samtools view out.sam -h -t fake_reference.fa.fai -o sq_added_out.sam

Base shift

User can shift the base sequence to the left by n number of bases by providing the argument --base_shift -n to plot and plot_pileup commands. This is helpful to correct the signal level to the base. A negative n value will shift the base sequence to the left. However, the user is adviced to use --profile which automatically sets the --base_shift. For more information please refer base_shift_and_eventalignment.

Signal scaling

The commands plot and plot_pileup can take the argument --sig_scale. By providing the argument --sig_scale znorm or --sig_scale medmad the signals will be zscore or median MAD normalized respectively.

Guppy move table explanation

Please refer here

Example

The figures on the top of the document were generated using the testcases - 1.1, 2.1, 1.11, and 3.2 respectively in test_plot_signal_to_read.sh.

Conventions

• A is a descriptive tag name to identify the plot.
• B indicates whether the positive or negative strand was used as the reference to align the signals. For RNA this will be RNA 3'->5'. Squigualiser only supports RNA reads mapped to the transcriptome.
• C always indicates the region using the positive strand coordinates, regardless of the forward and reverse mapped plots.
• D indicates the true sequencing direction of the signals.