NanoSim is a fast and scalable read simulator that captures the technology-specific features of ONT data, and allows for adjustments upon improvement of nanopore sequencing technology.
The second version of NanoSim (v2.0.0) uses minimap2 as default aligner to align long genomic ONT reads to reference genome. It leads to much faster alignment step and reduces the overall runtime of NanoSim. We also utilize HTSeq, a python package, to read SAM alignment files efficiently.
NanoSim (v2.5) is able to simulate ONT transcriptome reads (cDNA / direct RNA) as well as genomic reads. It also models features of the library preparation protocols used, including intron retention (IR) events in cDNA and directRNA reads. Further, it has stand-alone modes which profiles transcript expression patterns and detects IR events in custom datasets. Additionally, we improved the homopolymer simulation option which simulates homopolymer expansion and contraction events with respect to chosen basecaller. Multiprocessing option allows for faster runtime for large library simulation.
NanoSim (v2.6) is able to simulate ONT reads in fastq format. The base quality information is simulated with truncated log-normal distributions, learnt separately from match bases, mismatch bases, insertion bases, deletion bases, and unaligned bases, each from different basecaller and read type.
NanoSim (v3.0) is able to simulate ONT metagenome reads. It quantifies metagenome abundance in the characterization stage, and accomodates for chimeric reads. In the simulation stage, it simulates both features as well. In addition, the simulation of chimeric reads is available in genome mode too. Some pre-trained models are re-trained for compatibility issues.
NOTE: We usually add new pre-trained models and make them available through the latest releases! Users can choose to download the whole package or only scripts without models to speed it up. Please check the downloads section for more information regarding the pre-trained models.
If you use NanoSim to simulate genomic reads, please cite the following paper:
NanoSim
Yang C, Chu J, Warren RL, Birol I. NanoSim: nanopore sequence read simulator based on statistical characterization. Gigascience. 2017 Apr 1;6(4):1-6. doi: 10.1093/gigascience/gix010. PMID: 28327957; PMCID: PMC5530317.
If you use NanoSim to simulate transcriptomic reads, please cite the following paper:
Trans-NanoSim
Hafezqorani S, Yang C, Lo T, Nip KM, Warren RL, Birol I. Trans-NanoSim characterizes and simulates nanopore RNA-sequencing data. Gigascience. 2020 Jun 1;9(6):giaa061. doi: 10.1093/gigascience/giaa061. PMID: 32520350; PMCID: PMC7285873.
If you use NanoSim to simulate metagenomic reads, please cite the following paper:
Meta-NanoSim
Yang C, Lo T, Nip KM, Hafezqorani S, Warren RL, Birol I. Characterization and simulation of metagenomic nanopore sequencing data with Meta-NanoSim. Gigascience. 2023 Mar 20;12:giad013. doi: 10.1093/gigascience/giad013. PMID: 36939007; PMCID: PMC10025935.
NOTE: Please kindly note that the pretrained models in NanoSim (v3.0.2) were made using an older version of
scikit-learn(e.g. <=0.22.1). If you have to use these models (instead of creating your own models), then you must usescikit-learn=0.22.1but not the newer versions. If you have a newer version ofscikit-learninstalled, then you will get the error forNo module named 'sklearn.neighbors.kde'. If you would like to create your own models (instead of using the pretrained models), then NanoSim should work just fine withscikit-learn=1.0.2from our experience. For future releases of NanoSim, we will try to include newly pre-trained models with the updated versions of required packages in order to solve the incompatibility issues.
Python packages:
- HTSeq (Tested with version 0.11.2)
- joblib (Tested with version 0.14.1)
- numpy (Tested with version 1.17.2)
- pybedtools (Tested with version 0.8.2)
- pysam (Tested with version 0.13 or above)
- scikit-learn (Tested with version 0.21.3)
- scipy (Tested with verson 1.4.1)
- six (Tested with version 1.16.0)
- piecewise-regression (Tested with version 1.1.2)
External programs:
- minimap2 (Tested with versions 2.10, 2.17, 2.18)
- LAST (Tested with versions 581 and 916)
- samtools (Tested with version 1.12)
- GenomeTools (Tested with version 1.6.1)
- sam2pairwise (Tested with version 1.0.0)
Before you begin, make sure that your conda channels are configured properly for bioconda.
Option 1. Install the latest release through bioconda:
conda install -c bioconda nanosim
Option 2. Install the development code in our repo:
git clone https://github.com/bcgsc/NanoSim.gitconda create --name nanosim python=3.7conda activate nanosimconda install --file requirements.txt -c conda-forge -c bioconda
NOTE: If you have difficulty with installing all the dependent packages with
conda. We strongly recommend that you create a dedicated environment for running NanoSim. If you have issues withconda installbeing eternally stuck, use mamba instead ofcondato install your conda packages: https://github.com/mamba-org/mamba .So, integrating all these together:
conda create -n nanosim_env conda activate nanosim_env mamba install scikit-learn=0.22.1 six samtools pysam pybedtools minimap2 joblib htseq genometools-genometoolsNote that here we only specified the version for
scikit-learnbut not for the other packages.mambashould be able to pick the appropriate versions for the specified packages, python, and numpy, etc.
Some ONT read profiles are ready to use for users. With the profiles, users can run the simulation tool directly.
For releases before v2.2.0, we provide profiles trained for E. coli or S. cerevisiae datasets. Flowcell chemistry is R7.3 and R9, and they were basecalled by Metrichor. They can be downloaded from our ftp site
For release v2.5.0 and onwards, we provide profiles trained for H. sapiens NA12878 gDNA, cDNA 1D2, and directRNA datasets, Mus. musculus cDNA dataset, and the ZymoBIOMICS mock community datasets with 10 species and two abundance levels. Flowcell chemistry is R9.4 for all datasets. NA12878 gDNA and directRNA were basecalled by Guppy 3.1.5; NA12878 cDNA 1D2 was basecalled by Albacore 2.3.1; mouse cDNA was basecalled by Metrichor. These models are available within pre-trained_models folder.
For release v3.1.1, we provide a trained model for H. sapiens NA24385 - AshkenazimTrio - Son (hg002) which is sequenced using Kit v14 (R10 chemistry) and basecalled by dorado. You may find the trained model on 1 Million subsampled reads on the GitHub page (available along with the other models at pre-trained_models folder). If you are interested in the trained model based on the whole dataset, you can get it through Zenodo - DOI: 10.5281/zenodo.10064740. The model is trained using NanoSim v3.0.2 with scikit-learn v0.23.2 and python v3.7.10.
For release v3.2.1, we provide a trained model for H. sapiens NA24385 - AshkenazimTrio - Son (hg002) which is sequenced using Kit v14 (R10 chemistry) and basecalled by dorado (human_giab_hg002_sub1M_kitv14_dorado_v3.2.1.tar.gz). The model is trained using NanoSim v3.2.1 with python 3.7 and scikit-learn v0.23.2.
For release v3.2.2, we provide trained models for the ZymoBIOMICS mock community datasets with 10 species and two abundance levels. We also provide a trained model for H. sapiens NA12878 cDNA (rel2 release, base-called using guppy v4.2.2) ONT data on Zenodo. The models are trained using NanoSim v3.2.2 with python 3.7 and scikit-learn v0.23.2.
If you have any issues using the pre-trained models, check the dependencies section for some information and tips.
NanoSim is implemented using Python for error model fitting, read length analysis, and simulation. The first step of NanoSim is read characterization, which provides a comprehensive alignment-based analysis, and generates a set of read profiles serving as the input to the next step, the simulation stage. The simulation tool uses the model built in the previous step to produce in silico reads for a given reference genome/transcriptome. It also outputs a list of introduced errors, consisting of the position on each read, error type and reference bases.
Characterization stage runs in five mode: genome, transcriptome, metagenome, quantify, and detect_ir. Below you may see the general usage of this code. We will explain each mode separately as well.
Characterization step general usage:
usage: read_analysis.py [-h] [-v]
{genome,transcriptome,metagenome,quantify,detect_ir} ...
Read characterization step
-----------------------------------------------------------
Given raw ONT reads, reference genome, metagenome, and/or
transcriptome, learn read features and output error profiles
optional arguments:
-h, --help show this help message and exit
-v, --version show program's version number and exit
subcommands:
There are five modes in read_analysis.
For detailed usage of each mode:
read_analysis.py mode -h
-------------------------------------------------------
{genome,transcriptome,metagenome,quantify,detect_ir}
genome Run the simulator on genome mode
transcriptome Run the simulator on transcriptome mode
metagenome Run the simulator on metagenome mode
quantify Quantify transcriptome expression or metagenome abundance
detect_ir Detect Intron Retention events using the alignment file
genome mode
If you are interested in simulating ONT genomic reads, you need to run the characterization stage in "genome" mode with following options. It takes a reference genome and a training read set in FASTA or FASTQ format as input and aligns these reads to the reference using minimap2 (default) or LAST aligner. User can also provide their own alignment file in SAM/BAM formats. If a SAM/BAM file is provided, make sure the alignments contain the cs tag (See minimap2) and MD flag. The output of this is a bunch of profiles which you should use in simulation stage.
genome mode usage:
usage: read_analysis.py genome [-h] -i READ [-rg REF_G] [-a {minimap2,LAST}]
[-ga G_ALNM] [-o OUTPUT] [-c] [-hp]
[--min_homopolymer_len MIN_HOMOPOLYMER_LEN]
[--fastq] [--no_model_fit] [-t NUM_THREADS]
optional arguments:
-h, --help show this help message and exit
-i READ, --read READ Input read for training
-rg REF_G, --ref_g REF_G
Reference genome, not required if genome alignment
file is provided
-a {minimap2,LAST}, --aligner {minimap2,LAST}
The aligner to be used, minimap2 or LAST (Default =
minimap2)
-ga G_ALNM, --g_alnm G_ALNM
Genome alignment file in SAM/BAM format (optional)
-o OUTPUT, --output OUTPUT
The location and prefix of outputting profiles
(Default = training)
-c, --chimeric Detect chimeric and split reads (Default = False)
-hp, --homopolymer Analyze homopolymer lengths (Default = False)
--min_homopolymer_len MIN_HOMOPOLYMER_LEN
Minimum length of homopolymers to analyze (Default = 5
bp)
--fastq Analyze base qualities (Default = False)
--no_model_fit Disable model fitting step
-t NUM_THREADS, --num_threads NUM_THREADS
Number of threads for alignment and model fitting
(Default = 1)
transcriptome mode
If you are interested in simulating ONT transcriptome reads (cDNA / directRNA), you need to run the characterization stage in "transcriptome" mode with following options. It takes a reference transcriptome, a reference genome, and a training read set in FASTA or FASTQ format as input and aligns these reads to the reference using minimap2 (default) or LAST aligner. User can also provide their own alignment file in SAM/BAM formats. If a SAM/BAM file is provided, make sure the alignments contain the cs tag (See minimap2) and MD flag. The output of this is a bunch of profiles which you should use in simulation stage.
transcriptome mode usage:
usage: read_analysis.py transcriptome [-h] -i READ -rg REF_G -rt REF_T
[-annot ANNOTATION] [-a {minimap2,LAST}]
[-ga G_ALNM] [-ta T_ALNM] [-o OUTPUT]
[--no_model_fit] [--no_intron_retention]
[-t NUM_THREADS] [-q] [-n] [-hp]
[--min_homopolymer_len MIN_HOMOPOLYMER_LEN]
[--fastq]
optional arguments:
-h, --help show this help message and exit
-i READ, --read READ Input read for training
-rg REF_G, --ref_g REF_G
Reference genome
-rt REF_T, --ref_t REF_T
Reference Transcriptome
-annot ANNOTATION, --annotation ANNOTATION
Annotation file in ensemble GTF/GFF formats, required
for intron retention detection
-a {minimap2,LAST}, --aligner {minimap2,LAST}
The aligner to be used: minimap2 or LAST (Default =
minimap2)
-ga G_ALNM, --g_alnm G_ALNM
Genome alignment file in SAM/BAM format (optional)
-ta T_ALNM, --t_alnm T_ALNM
Transcriptome alignment file in SAM/BAM format
(optional)
-o OUTPUT, --output OUTPUT
The location and prefix of outputting profiles
(Default = training)
--no_model_fit Disable model fitting step
--no_intron_retention
Disable Intron Retention analysis
-t NUM_THREADS, --num_threads NUM_THREADS
Number of threads for alignment and model fitting
(Default = 1)
-q, --quantification Perform abundance quantification (Default = False)
-n, --normalize Normalize by transcript length
-hp, --homopolymer Analyze homopolymer lengths (Default = False
--min_homopolymer_len MIN_HOMOPOLYMER_LEN
Minimum length of homopolymers to analyze (Default = 5
bp)
--fastq Analyze base qualities (Default = False)
metagenome mode
If you are interested in simulating ONT metagenome reads, you need to run the characterization stage in "metagenome" mode with following options. It takes a metagenome list with paths pointing to each genome and a training read set in FASTA or FASTQ format as input and aligns these reads to the reference using minimap2. User can also provide their own alignment file in SAM formats. If a SAM/BAM file is provided, make sure the alignments contain the cs tag (See minimap2) and MD flag. The output of this is a bunch of profiles which you should use in simulation stage.
metagenome mode usage:
usage: read_analysis.py metagenome [-h] -i READ -gl GENOME_LIST [-ga G_ALNM]
[-o OUTPUT] [-c] [-q] [--fastq]
[--no_model_fit] [-t NUM_THREADS]
optional arguments:
-h, --help show this help message and exit
-i READ, --read READ Input read for training
-gl GENOME_LIST, --genome_list GENOME_LIST
Reference metagenome list, tsv file, the first column
is species/strain name, the second column is the
reference genome fasta/fastq file directory, the third
column is optional, if provided, it contains the
expected abundance (sum up to 100)
-ga G_ALNM, --g_alnm G_ALNM
Genome alignment file in sam format, the header of
each species should match the metagenome list provided
above (optional)
-o OUTPUT, --output OUTPUT
The location and prefix of outputting profiles
(Default = training)
-c, --chimeric Detect chimeric and split reads (Default = False)
-q, --quantification Perform abundance quantification and compute the
deviation between calculated abundance and expected
values (Default = False)
--fastq Analyze base qualities (Default = False)
--no_model_fit Disable model fitting step
-t NUM_THREADS, --num_threads NUM_THREADS
Number of threads for alignment and model fitting
(Default = 1)
quantify mode
If you are interested in quantifying the expression levels of transcriptome or abundance of metagenome, you can run this mode with following options. This mode is independent from the other features in characterization stage, and the output is not used for simulation stage.
quantifty mode usage:
usage: read_analysis.py quantify [-h] -e E -i READ [-rt REF_T] [-gl GENOME_LIST] [-ga G_ALNM]
[-ta T_ALNM] [-o OUTPUT] [-t NUM_THREADS] [-n]
optional arguments:
-h, --help show this help message and exit
-e E Select from (trans, meta)
-i READ, --read READ Input reads for quantification
-rt REF_T, --ref_t REF_T
Reference Transcriptome
-gl GENOME_LIST, --genome_list GENOME_LIST
Reference metagenome list, tsv file, the first column is species/strain
name, the second column is the reference genome fasta/fastq file
directory, the third column is optional, if provided, it contains the
expected abundance (sum up to 100)
-ga G_ALNM, --g_alnm G_ALNM
Genome alignment file in sam format, the header of each species should
match the metagenome list provided above (optional)
-ta T_ALNM, --t_alnm T_ALNM
Transcriptome alignment file in sam format(optional)
-o OUTPUT, --output OUTPUT
The location and prefix of outputting profile (Default = expression)
-t NUM_THREADS, --num_threads NUM_THREADS
Number of threads for alignment (Default = 1)
-n, --normalize Normalize by transcript length
If -e trans is used, users need to provide the reference transcriptome through -rt parameter; if -e meta is used, users need to provide a genome list containing all reference genome and the path to them through -gl parameter OR provide genome alignment file through -a parameter.
detect_ir mode usage
The "transcriptome" mode of the NanoSim is able to model features of the library preparation protocols used, including intron retention (IR) events in cDNA and directRNA reads. Further, it optionally profiles transcript expression patterns. However, if you are interested in only detecting Intron Retention events without running other analysis in the characterization stage, you may use the "detect_ir" mode. Details are as follows:
detect_ir mode usage:
usage: read_analysis.py detect_ir [-h] -annot ANNOTATION [-i READ] [-rg REF_G] [-rt REF_T]
[-a {minimap2,LAST}] [-o OUTPUT] [-ga G_ALNM] [-ta T_ALNM]
[-t NUM_THREADS]
optional arguments:
-h, --help show this help message and exit
-annot ANNOTATION, --annotation ANNOTATION
Annotation file in ensemble GTF/GFF formats
-i READ, --read READ Input read for training, not required if alignment files are provided
-rg REF_G, --ref_g REF_G
Reference genome, not required if genome alignment file is provided
-rt REF_T, --ref_t REF_T
Reference Transcriptome, not required if transcriptome alignment file is
provided
-a {minimap2,LAST}, --aligner {minimap2,LAST}
The aligner to be used: minimap2 or LAST (Default = minimap2)
-o OUTPUT, --output OUTPUT
The output name and location
-ga G_ALNM, --g_alnm G_ALNM
Genome alignment file in SAM/BAM format (optional)
-ta T_ALNM, --t_alnm T_ALNM
Transcriptome alignment file in SAM/BAM format (optional)
-t NUM_THREADS, --num_threads NUM_THREADS
Number of threads for alignment (Default = 1)
* NOTICE: -ga/-ta option allows users to provide their own alignment file. Make sure that the name of query sequences are the same as appears in the FASTA files. For FASTA files, some headers have spaces in them and most aligners only take part of the header (before the first white space/tab) as the query name. However, the truncated headers may not be unique if using the output of poretools. We suggest users to pre-process the fasta files by concatenating all elements in the header via '_' before alignment and feed the processed FASTA file as input of NanoSim.
Simulation stage takes reference genome/transcriptome and read profiles as input and outputs simulated reads in FASTA format. Simulation stage runs in two modes: "genome" and "transcriptome" and you may use either of them based on your needs.
Simulation stage general usage:
usage: simulator.py [-h] [-v] {genome,transcriptome,metagenome} ...
Simulation step
-----------------------------------------------------------
Given error profiles, reference genome, metagenome,
and/or transcriptome, simulate ONT DNA or RNA reads
optional arguments:
-h, --help show this help message and exit
-v, --version show program's version number and exit
subcommands:
There are two modes in read_analysis.
For detailed usage of each mode:
simulator.py mode -h
-------------------------------------------------------
{genome,transcriptome}
You may run the simulator on genome, transcriptome,
or metagenome mode.
genome Run the simulator on genome mode
transcriptome Run the simulator on transcriptome mode
metagenome Run the simulator on metagenome mode
genome mode
If you are interested in simulating ONT genomic reads, you need to run the simulation stage in "genome" mode with following options.
genome mode usage:
usage: simulator.py genome [-h] -rg REF_G [-c MODEL_PREFIX] [-o OUTPUT]
[-n NUMBER] [-max MAX_LEN] [-min MIN_LEN]
[-med MEDIAN_LEN] [-sd SD_LEN] [--seed SEED] [-hp]
[-k KMERBIAS] [-s STRANDNESS]
[-dna_type {linear,circular}] [--perfect] [--fastq]
[--chimeric] [-t NUM_THREADS]
optional arguments:
-h, --help show this help message and exit
-rg REF_G, --ref_g REF_G
Input reference genome
-c MODEL_PREFIX, --model_prefix MODEL_PREFIX
Location and prefix of error profiles generated from
characterization step (Default = training)
-o OUTPUT, --output OUTPUT
Output location and prefix for simulated reads
(Default = simulated)
-n NUMBER, --number NUMBER
Number of reads to be simulated (Default = 20000)
-max MAX_LEN, --max_len MAX_LEN
The maximum length for simulated reads (Default =
Infinity)
-min MIN_LEN, --min_len MIN_LEN
The minimum length for simulated reads (Default = 50)
-med MEDIAN_LEN, --median_len MEDIAN_LEN
The median read length (Default = None), Note: this
simulation is not compatible with chimeric reads
simulation
-sd SD_LEN, --sd_len SD_LEN
The standard deviation of read length in log scale
(Default = None), Note: this simulation is not
compatible with chimeric reads simulation
--seed SEED Manually seeds the pseudo-random number generator
-hp, --homopolymer Simulate homopolymer lengths (Default = False)
-k KMERBIAS, --KmerBias KMERBIAS
Minimum homopolymer length to simulate homopolymer
contraction and expansion events in, a typical k is 5
-s STRANDNESS, --strandness STRANDNESS
Proportion of sense sequences. Overrides the value
profiled in characterization stage. Should be between
0 and 1
-dna_type {linear,circular}
Specify the dna type: circular OR linear (Default =
linear)
--perfect Ignore error profiles and simulate perfect reads
--fastq Output fastq files instead of fasta files
--chimeric Simulate chimeric reads
-t NUM_THREADS, --num_threads NUM_THREADS
Number of threads for simulation (Default = 1)
transcriptome mode
If you are interested in simulating ONT transcriptome reads, you need to run the simulation stage in "transcriptome" mode with following options.
transcriptome mode usage:
usage: simulator.py transcriptome [-h] -rt REF_T [-rg REF_G] -e EXP
[-c MODEL_PREFIX] [-o OUTPUT] [-n NUMBER]
[-max MAX_LEN] [-min MIN_LEN] [--seed SEED]
[-hp] [-k KMERBIAS] [-b {albacore,guppy}]
[-s STRANDNESS] [--no_model_ir] [--perfect]
[--polya POLYA] [--fastq] [-t NUM_THREADS]
[--uracil]
optional arguments:
-h, --help show this help message and exit
-rt REF_T, --ref_t REF_T
Input reference transcriptome
-rg REF_G, --ref_g REF_G
Input reference genome, required if intron retention
simulation is on
-e EXP, --exp EXP Expression profile in the specified format as
described in README
-c MODEL_PREFIX, --model_prefix MODEL_PREFIX
Location and prefix of error profiles generated from
characterization step (Default = training)
-o OUTPUT, --output OUTPUT
Output location and prefix for simulated reads
(Default = simulated)
-n NUMBER, --number NUMBER
Number of reads to be simulated (Default = 20000)
-max MAX_LEN, --max_len MAX_LEN
The maximum length for simulated unaligned reads. Note
that this is not used for simulating aligned reads.
(Default = Infinity)
-min MIN_LEN, --min_len MIN_LEN
The minimum length for simulated unaligned reads. Note
that this is not used for simulating aligned reads.
(Default = 50)
--seed SEED Manually seeds the pseudo-random number generator
-hp, --homopolymer Simulate homopolymer lengths (Default = False)
-k KMERBIAS, --KmerBias KMERBIAS
Minimum homopolymer length to simulate homopolymer
contraction and expansion events in, a typical k is 6
-b {albacore,guppy}, --basecaller {albacore,guppy}
Simulate polyA tails with respect to chosen
basecaller: albacore or guppy
-s STRANDNESS, --strandness STRANDNESS
Proportion of sense sequences. Overrides the value
profiled in characterization stage. Should be between
0 and 1
--no_model_ir Ignore simulating intron retention events
--perfect Ignore profiles and simulate perfect reads
--polya POLYA Simulate polyA tails for given list of transcripts
--fastq Output fastq files instead of fasta files
-t NUM_THREADS, --num_threads NUM_THREADS
Number of threads for simulation (Default = 1)
--uracil Converts the thymine (T) bases to uracil (U) in the
output fasta format
sample expression file for transcriptome simulation
The expression profile is a tsv file containing expression levels of each isoform to be simulated. Users can use the output of quantify mode as template for modify or use the following format for constructing a new expression profile.
The first row is header row specifying the format of the file. target_id is the id of each transcript. The tpm column is used for simulating, while the est_count is just a placeholder to be compatible with the output of the quantify mode and other quantification tools such as Salmon.
The following rows are entries for each transcript isoform and the id of which needs to exist in the provided reference transcriptome. The id should start with ENS.
Example:
| target_id | est_counts | tpm |
|---|---|---|
| ENST00000222247.9 | 2307.2992 | 3145.3749 |
| ENST00000274065.8 | 2641.9534 | 3601.5848 |
| ENST00000400259.5 | 623.6130 | 850.1268 |
| ENST00000344548.7 | 1828.3466 | 2492.4533 |
| ENST00000484610.5 | 766.3528 | 1044.7137 |
metagenome mode
If you are interested in simulating ONT metagenome reads, you need to run the simulation stage in "metagenome" mode with following options. We have provided sample config files for users to construct their own -gl, -a, and -dl config files correctly.
metagenome mode usage:
usage: simulator.py metagenome [-h] -gl GENOME_LIST -a ABUN
[-dl DNA_TYPE_LIST] [-c MODEL_PREFIX]
[-o OUTPUT] [-max MAX_LEN] [-min MIN_LEN]
[-med MEDIAN_LEN] [-sd SD_LEN] [--seed SEED]
[-s STRANDNESS] [--perfect]
[--abun_var ABUN_VAR [ABUN_VAR ...]] [--fastq]
[--chimeric] [-t NUM_THREADS]
optional arguments:
-h, --help show this help message and exit
-gl GENOME_LIST, --genome_list GENOME_LIST
Reference metagenome list, tsv file, the first column
is species/strain name, the second column is the
reference genome fasta/fastq file directory
-a ABUN, --abun ABUN Abundance list, tsv file with header, the abundance of
all species in each sample need to sum up to 100. See
example in README and provided config files
-dl DNA_TYPE_LIST, --dna_type_list DNA_TYPE_LIST
DNA type list, tsv file, the first column is
species/strain, the second column is the chromosome
name, the third column is the DNA type: circular OR
linear
-c MODEL_PREFIX, --model_prefix MODEL_PREFIX
Location and prefix of error profiles generated from
characterization step (Default = training)
-o OUTPUT, --output OUTPUT
Output location and prefix for simulated reads
(Default = simulated)
-max MAX_LEN, --max_len MAX_LEN
The maximum length for simulated reads (Default =
Infinity)
-min MIN_LEN, --min_len MIN_LEN
The minimum length for simulated reads (Default = 50)
-med MEDIAN_LEN, --median_len MEDIAN_LEN
The median read length (Default = None), Note: this
simulation is not compatible with chimeric reads
simulation
-sd SD_LEN, --sd_len SD_LEN
The standard deviation of read length in log scale
(Default = None), Note: this simulation is not
compatible with chimeric reads simulation
--seed SEED Manually seeds the pseudo-random number generator
-s STRANDNESS, --strandness STRANDNESS
Percentage of antisense sequences. Overrides the value
profiled in characterization stage. Should be between
0 and 1
--perfect Ignore error profiles and simulate perfect reads
--abun_var ABUN_VAR [ABUN_VAR ...]
Simulate random variation in abundance values, takes
in two values, format: relative_var_low,
relative_var_high, Example: -0.5 0.5)
--fastq Output fastq files instead of fasta files
--chimeric Simulate chimeric reads
-t NUM_THREADS, --num_threads NUM_THREADS
Number of threads for simulation (Default = 1)
sample abundance file for metagenome simulation
The abundance file is a tsv file, with rows representing the abundance of each sample and columns representing each sample. Each column (except for the first row) needs to sum up to 100, because the total abundance of each sample needs to be 100.
The first row is header row to specify the number of reads in each sample. The format of the first row is:
Size total_reads_in_sample1 total_reads_in_sample2 ...
The following rows are in the format as:
Species abundance_in_sample1 abundance_in_sample2 ...
| Size | 200000 | 100 |
|---|---|---|
| Bacillus subtilis | 12 | 0.89 |
| Cryptococcus neoformans | 2 | 0.00089 |
| Enterococcus faecalis | 12 | 0.00089 |
| Escherichia coli | 12 | 0.089 |
| Lactobacillus fermentum | 12 | 0.0089 |
| Listeria monocytogenes | 12 | 89.1 |
| Pseudomonas aeruginosa | 12 | 8.9 |
| Saccharomyces cerevisiae | 2 | 0.89 |
| Salmonella enterica | 12 | 0.089 |
| Staphylococcus aureus | 12 | 0.000089 |
In the above example, there are two samples. The first sample will contain 20,0000 reads, while the second sample will contain 100 reads. The abundances in sample 1 and 2 are as shown in th table, and both of them add up to 100.
* Notice: the use of max_len and min_len in genome mode will affect the read length distributions. If the range between max_len and min_len is too small, the program will run slowlier accordingly.
* Notice: the transcript name in the expression tsv file and the ones in th polyadenylated transcript list has to be consistent with the ones in the reference transcripts, otherwise the tool won't recognize them and don't know where to find them to extract reads for simulation.
* Notice: the species name in the genome list file, dna type file, and abundance file has to be consistent. The chromosome names in the dna type file has to match the ones in the reference genomes.
Example runs:
1 If you want to simulate E. coli genome, then circular command must be chosen because it's a circular genome
./simulator.py genome -dna_type circular -rg Ecoli_ref.fasta -c ecoli
2 If you want to simulate only perfect reads, i.e. no snps, or indels, just simulate the read length distribution
./simulator.py genome -dna_type circular -rg Ecoli_ref.fasta -c ecoli --perfect
3 If you want to simulate S. cerevisiae genome with kmer bias, then linear command must be chosen because it's a linear genome
./simulator.py genome -dna_type linear -rg yeast_ref.fasta -c yeast --KmerBias
4 If you want to simulate human genome with length limits between 1000nt to 10000nt
./simulator.py genome -dna_type linear -rg human_ref.fasta -c human -max 10000 -min 1000
5 If you want to simulate human genome with controlled median read length and standard deviation, NanoSim will use log-normal distribution to approximate the read length distribution
./simulator.py genome -dna_type linear -rg human_ref.fasta -c human -med 5000 -sd 1.05
6 If you want to simulate ten thousands cDNA/directRNA reads from mouse reference transcriptome
./simulator.py transcriptome -rt Mus_musculus.GRCm38.cdna.all.fa -rg Mus_musculus.GRCm38.dna.primary_assembly.fa -c mouse_cdna -e abundance.tsv -n 10000
7 If you want to simulate five thousands cDNA/directRNA reads from mouse reference transcriptome without modeling intron retention
./simulator.py transcriptome -rt Mus_musculus.GRCm38.cdna.all.fa -c mouse_cdna -e abundance.tsv -n 5000 --no_model_ir
8 If you want to simulate two thousands cDNA/directRNA reads from human reference transcriptome with polya tails, mimicking homopolymer bias (starting from homopolymer length >= 6) and reads in fastq format
./simulator.py transcriptome -rt Homo_sapiens.GRCh38.cdna.all.fa -c Homo_sapiens_model -e abundance.tsv -rg Homo_sapiens.GRCh38.dna.primary.assembly.fa --polya transcripts_with_polya_tails --fastq -k 6 --basecaller guppy -r dRNA
9 If you want to simulate two metagenome samples with abundance variation, and chimeric reads
.simulator.py metagenome -gl sample_config_file/metagenome_list_for_simulation -a sample_config_file/abundance_for_simulation_multi_sample.tsv -dl sample_config_file/dna_type_list.tsv -c pre-trained_models/metagenome_ERR3152364_Even/training --abun_var -0.5 0.5 --chimeric
training_aligned_region.pklKernel density function of aligned regions on aligned readstraining_aligned_reads.pklKernel density function of aligned readstraining_ht_length.pklKernel density function of unaligned regions on aligned readstraining_besthit.bamThe best alignment of each read based on lengthtraining_match.hist/training_mis.hist/training_del.hist/training_ins.histHistogram of match, mismatch, and indelstraining_first_match.histHistogram of the first match length of each alignmenttraining_error_markov_modelMarkov model of error typestraining_ht_ratio.pklKernel density function of head/(head + tail) on aligned readstraining.bamThe alignment outputtraining_match_markov_modelMarkov model of the length of matches (stretches of correct base calls)training_model_profileFitted model for errorstraining_processed.bamA re-formatted BAM file for user-provided alignment filetraining_unaligned_length.pklKernel density function of unaligned readstraining_error_rate.tsvMismatch rate, insertion rate and deletion ratetraining_strandness_rateStrandness rate in input reads
training_aligned_region.pklKernel density function of aligned regions on aligned readstraining_aligned_region_2d.pklTwo-dimensional kernel density function of aligned regions over the length of reference transcript they alignedtraining_aligned_reads.pklKernel density function of aligned readstraining_ht_length.pklKernel density function of unaligned regions on aligned readstraining_besthit.bamThe best alignment of each read based on lengthtraining_match.hist/training_mis.hist/training_del.hist/training_ins.histHistogram of match, mismatch, and indelstraining_first_match.histHistogram of the first match length of each alignmenttraining_error_markov_modelMarkov model of error typestraining_ht_ratio.pklKernel density function of head/(head + tail) on aligned readstraining.bamThe alignment outputtraining_match_markov_modelMarkov model of the length of matches (stretches of correct base calls)training_model_profileFitted model for errorstraining_processed.bamA re-formatted BAM file for user-provided alignment filetraining_unaligned_length.pklKernel density function of unaligned readstraining_error_rate.tsvMismatch rate, insertion rate and deletion ratetraining_strandness_rateStrandness rate in input readstraining_addedintron_final.gff3gff3 file format containing the intron coordinate informationtraining_IR_infoList of transcripts in which there is a retained intron based on IR modeling steptraining_IR_markov_modelMarkov model of Intron Retention events
training_aligned_region.pklKernel density function of aligned regions on aligned readstraining_aligned_reads.pklKernel density function of aligned readstraining_ht_length.pklKernel density function of unaligned regions on aligned readstraining_besthit.bamThe best alignment of each read based on lengthtraining_match.hist/training_mis.hist/training_del.hist/training_ins.histHistogram of match, mismatch, and indelstraining_first_match.histHistogram of the first match length of each alignmenttraining_error_markov_modelMarkov model of error typestraining_ht_ratio.pklKernel density function of head/(head + tail) on aligned readstraining.bamThe alignment outputtraining_match_markov_modelMarkov model of the length of matches (stretches of correct base calls)training_model_profileFitted model for errorstraining_processed.bamA re-formatted BAM file for user-provided alignment filetraining_unaligned_length.pklKernel density function of unaligned readstraining_error_rate.tsvMismatch rate, insertion rate and deletion ratetraining_strandness_rateStrandness rate in input readstraining_chimeric_infoInformation for chimeric readstraining_gap_length.pklThe gap size between suplementary alignments
simulated_reads.fastaFASTA file of simulated reads. Each reads has "unaligned", "aligned", or "perfect" in the header determining their error rate. "unaligned" means that the reads have an error rate over 90% and cannot be aligned. "aligned" reads have the same error rate as training reads. "perfect" reads have no errors.
To explain the information in the header, we have two examples:
>ref|NC-001137|-[chromosome=V]_468529_unaligned_0_F_0_3236_0
All information before the first_are chromosome information.468529is the start position andunalignedsuggesting it should be unaligned to the reference. The first0is the sequence index.Frepresents a forward strand.0_3236_0means that sequence length extracted from the reference is 3236 bases.>ref|NC-001143|-[chromosome=XI]_115406_aligned_16565_R_92_12710_2
This is an aligned read coming from chromosome XI at position 115406.16565is the sequence index.Rrepresents a reverse complement strand.92_12710_2means that this read has 92-base head region (cannot be aligned), followed by 12710 bases of middle region, and then 2-base tail region.
The information in the header can help users to locate the read easily.
Specific to transcriptome simulation: for reads that include retained introns, the header contains the information starting from Retained_intron, each genomic interval is separated by ;.
Specific to chimeric reads simulation: for chimeric reads, different source chromosome and locations are separated by ;, and there's a chimeric in the header to indicate.
simulated_error_profileContains all the information of errors introduced into each reads, including error type, position, original bases and current bases.
Sincere thanks to our labmates and all contributors and users of this tool.