Welcome to the NEAT project, the NExt-generation sequencing Analysis Toolkit, version 4.3.5. This release of NEAT 4.3.5 includes several fixes and a little bit of restructuring, including a parallel process for running neat read-simulator. Our tests show much improved performance. If the logs seem excessive, you might try using the --log-level ERROR to reduce the output from the logs. See the ChangeLog for notes. NEAT 4.3.5 is the official release of NEAT 4.0. It represents a lot of hard work from several contributors at NCSA and beyond. With the addition of parallel processing, we feel that the code is ready for production, and future releases will focus on compatibility, bug fixes, and testing. Future releases for the time being will be enumerations of 4.3.X.

Neat 4.3.5 marked the officially 'complete' version of NEAT 4.3, implementing parallelization. To add parallelization to you run, simply add the "threads" parameter in your configuration and run read-simulator as normal. NEAT will take care of the rest. You can customize the parameters in you configuration file, as needed.

We have completed major revisions on NEAT since 3.4 and consider NEAT 4.3.5 to be a stable release, in that we will continue to update and provide bug fixes and support. We will consider new features and pull requests. Please include justification for major changes. See contribute for more information. If you'd like to use some of our code in your own, no problem! Just review the license, first.

We've deprecated NEAT's command-line interface options for the most part, opting to simplify things with configuration files. If you require the CLI for legacy purposes, NEAT 3.4 was our last release to be fully command-line interface. Please convert your CLI commands to the corresponding yaml configuration for future runs.

Developing and validating bioinformatics pipelines depends on access to genomic data with known ground truth. As a result, many research groups rely on simulated reads, and it can be useful to vary the parameters of the sequencing process itself. NEAT addresses this need as an open-source Python package that can integrate seamlessly with existing bioinformatics workflows—its simulations account for a wide range of sequencing parameters (e.g., coverage, fragment length, sequencing error models, mutational frequencies, ploidy, etc.) and allow users to customize their sequencing data.

NEAT is a fine-grained read simulator that simulates real-looking data using models learned from specific datasets. It was originally designed to simulate short reads, but it handles long-read simulation as well and is adaptable to any machine, with custom error models and the capability to handle single-base substitutions and indel errors. Unlike many simulators that rely solely on fixed error profiles, NEAT can learn empirical mutation and sequencing models from real datasets and use these models to generate realistic sequencing data, providing outputs in several common file formats (e.g., FASTQ, BAM, and VCF). There are several supporting utilities for generating models used for simulation and for comparing the outputs of alignment and variant calling to the golden BAM and golden VCF produced by NEAT.

To cite this work, please use:

Stephens, Z. D., Hudson, M. E., Mainzer, L. S., Taschuk, M., Weber, M. R., & Iyer, R. K. (2016). Simulating Next-Generation Sequencing Datasets from Empirical Mutation and Sequencing Models. PLOS ONE, 11(11), e0167047. https://doi.org/10.1371/journal.pone.0167047

• The NEAT Project v4.3.5
• NEAT v4.3.5
• Table of Contents
  • Prerequisites
  • Installation
  • Usage
  • Functionality
    • Estimated runtimes
  • Examples
    • Whole genome simulation
    • Targeted region simulation
    • Insert specific variants
    • Single-end reads
    • Large single-end reads
    • Parallelizing simulation
  • Utilities
    • neat model-fraglen
    • neat gen-mut-model
    • neat model-seq-err
    • neat vcf_compare
    • Note on Sensitive Patient Data

The most up-to-date requirements are found in environment.yml.

• Some version of Anaconda to set up the environment
• python == 3.10.*
• poetry == 1.3.*
• biopython == 1.85
• pkginfo
• matplotlib
• numpy
• pyyaml
• scipy
• pysam
• frozendict

NEAT assumes a Linux environment and was tested primarily on Ubuntu Linux. It should work on most Linux systems. If you use another operating system, please install WSL or a similar tool to create a Linux environment to operate NEAT from. For setting up NEAT, you will need Anaconda (or miniconda). The method described here installs NEAT as a base package in the active conda environment, so whenever you want to run NEAT, you can first activate the environment, then run from any place on your system. If you desire VCF files, please install also bcftools. For your convenience, we have added bcftools to the environment file, as it is available from conda. You may remove this line if you do not want or need VCF files with the variants NEAT added.

To install NEAT, you must create a virtual environment using a tool such as conda.

First, clone the environment and move to the NEAT directory:

$ git clone git@github.com:ncsa/NEAT.git 
$ cd NEAT

A quick form of installation uses bioconda. Once conda is installed, the following command can be run for easy setup:

(base) $ conda create -n neat -c conda-forge -c bioconda neat
(base) $ conda activate neat
(neat) $ neat --help # tests that NEAT has installed correctly

Alternatively, instead of the bioconda method, you can use the poetry module in build a wheel file, which can then be pip installed.

You will need to run these commands from within the NEAT directory:

(base) $ conda env create -f environment.yml
(base) $ conda activate neat
(neat) $ poetry build
(neat) $ pip install dist/neat*whl
(neat) $ neat --help # tests that NEAT has installed correctly

Assuming you have installed conda, run source activate or conda activate.

Please note that these installation instructions support MacOS, Windows, and Linux. However, if you are on MacOS, you need to remove the line libgcc=14 from environment.yml. A solution for some non-Linux users is simple to remove the version specification (e.g., libgcc).

Alternatively, if you wish to work with NEAT in the development-only environment, you can use poetry install within the NEAT repo, after creating the conda environment:

$ conda env create -f environment.yml
$ conda activate neat
$ poetry install

Any updates to the NEAT code will be instantly reflected with a poetry install version.

Notes: If any packages are struggling to resolve, check the channels and try to manually pip install the package to see if that helps (but note that NEAT is not tested on the pip versions). If poetry hangs for you, try the following fix (from python-poetry/poetry#8623):

export PYTHON_KEYRING_BACKEND=keyring.backends.null.Keyring

Then, re-run poetry install.

Test your install by running:

$ neat --help

You can also try running it using the Python command directly:

$ python -m neat --help

NEAT's core functionality is invoked using the read-simulator command. Here's the simplest invocation of read-simulator using default parameters. This command produces a single-ended FASTQ file with reads of length 151, ploidy 4, coverage 15x, default sequencing substitution, and default mutation rate models.

Contents of neat_config.yml:

reference: /path/to/my/genome.fa
read_len: 151
ploidy: 4
coverage: 15

neat read-simulator -c neat_config.yml -o simulated_data

The --output (-o) option sets the folder to place output data. If the folder does not exist, Python will attempt to create it. To specify a common filename prefix, you can additionally add the --prefix (-p), e.g., -p ecoli_20x will result in output files ecoli_20x.fastq.gz, ecoil_20x.vcf.gz, etc.

A config file is required. The config is a .yml file specifying the input parameters. The following is a brief description of the potential inputs in the config file. See NEAT/config_template/template_neat_config.yml for a template config file to copy and use for your runs. It provides a detailed description of all the parameters that NEAT uses.

To run the simulator in multithreaded mode, set the threads value in the config to something greater than 1.

reference: full path to a fasta file to generate reads from.
read_len: The length of the reads for the fastq (if using). Integer value, default 101.
coverage: desired coverage value. Float or integer, default = 10.
ploidy: Desired value for ploidy (# of copies of each chromosome in the organism, where if ploidy > 2, "heterozygous" mutates floor(ploidy / 2) chromosomes). Default is 2.
paired_ended: If paired-ended reads are desired, set this to True. Setting this to true requires either entering values for fragment_mean and fragment_st_dev or entering the path to a valid fragment_model.
fragment_mean: Use with paired-ended reads, set a fragment length mean manually
fragment_st_dev: Use with paired-ended reads, set the standard deviation of the fragment length dataset

The following values can be set to true or omitted to use defaults. If True, NEAT will produce the file type. The default is given:

produce_bam: False
produce_vcf: False
produce_fastq: True

The command line options for NEAT are as follows:

Universal options can be applied to any subfunction. The commands should come before the function name (e.g., neat --log-level DEBUG read-simulator ...), except -h or --help, which can appear anywhere in the command.

read-simulator command line options

NEAT produces simulated sequencing datasets. It creates FASTQ files with reads sampled from a provided reference genome, using sequencing error rates and mutation rates learned from real sequencing data. The strength of NEAT lies in the ability for the user to customize many sequencing parameters, produce 'golden,' true-positive datasets. We are working on expanding the functionality even further to model more species, generate larger variants, model tumor/normal data, and more!

Features:

• Simulate single-end and paired-end reads
• Custom read length
• Can introduce random mutations and/or mutations from a VCF file
  • Supported mutation types include SNPs, indels (of any length), inversions, translocations, duplications
  • Can emulate multi-ploid heterozygosity for SNPs and small indels
• Can simulate targeted sequencing via BED input specifying regions to sample from
• Can accurately simulate large, single-end reads with high indel error rates (PacBio-like) given a model
• Specify simple fragment length model with mean and standard deviation or an empirically learned fragment distribution
• Simulates quality scores using either the default model or empirically learned quality scores using neat gen_mut_model
• Introduces sequencing substitution errors using either the default model or empirically learned from utilities/
• Output a VCF file with the 'golden' set of true positive variants. These can be compared to bioinformatics workflow output (includes coverage and allele balance information)
• Output a BAM file with the 'golden' set of aligned reads. These indicate where each read originated and how it should be aligned with the reference
• Create paired tumour/normal datasets using characteristics learned from real tumour data

To give users a sense of how long neat read-simulator runs may take, we benchmarked NEAT 4.3.5 on several reference genomes. All runs were paired-end, with read length of 150 bp, coverage of 10, fragment mean of 300 bp, and fragment standard deviation of 50 bp. Runtimes are reported as the average across three unique runs (Avg. time (ms)) and the corresponding runtime in minutes. Cells marked with N/A indicate that NEAT was not able to run to completion.

Benchmarks were run on a System76 Meerkat with a 13th Gen Intel Core i3-1315U (8 logical cores, up to 4.50 GHz) and 16 GiB RAM, using a 512 GB SSD and Ubuntu 24.04.3 LTS (Linux kernel 6.14). Actual runtimes will vary depending on your hardware.

The inputs in single-threaded, contig-based mode most closely replicate the behavior of legacy versions of NEAT.

The configuration used:

• mode: contig
• threads: 1
• cleanup_splits: True

For small genomes, single-threaded performance is already fast (on the order of seconds). Larger genomes could not be fully benchmarked under this configuration.

Here we enabled NEAT’s parallelized mode (“small filtering”), which splits contigs into size-based blocks and processes them concurrently. The configuration used:

• mode: size
• size: 500000
• produce_bam: false
• threads: 7
• cleanup_splits: True

For mid-sized genomes (e.g., E. coli and S. cerevisiae), enabling parallelization reduced runtimes by roughly a factor of two to three compared to the base configuration. For larger genomes (honeybee and rice), the parallel configuration may make multi-hour simulations feasible.

Access to high-performance computing systems may improve runtimes.

The following commands are examples for common types of data to be generated. The simulation uses a reference genome in fasta format to generate reads of 126 bases with default 10X coverage. Outputs paired fastq files, a BAM file and a VCF file. The random variants inserted into the sequence will be present in the VCF and the reads will show their proper alignment in the BAM. Unless specified, the simulator will also insert some "sequencing error"—random variants in some reads that represents false positive results from sequencing.

Simulate whole genome dataset with random variants inserted according to the default model:

[contents of neat_config.yml]
reference: hg19.fa
read_len: 126
produce_bam: True
produce_vcf: True
paired_ended: True
fragment_mean: 300
fragment_st_dev: 30

neat read-simulator                 \
        -c neat_config.yml          \
        -o /home/me/simulated_reads/

Simulate a targeted region of a genome (e.g., an exome) with a targeted bed:

[contents of neat_config.yml]
reference: hg19.fa
read_len: 126
produce_bam: True
produce_vcf: True
paired_ended: True
fragment_mean: 300
fragment_st_dev: 30
targed_bed: hg19_exome.bed

neat read-simulator                 \
        -c neat_config              \
        -o /home/me/simulated_reads/

In this case, you would want to split the contig into blocks, rather than reading by contig. Even in single-threaded mode, this is likely to be faster. The default block size of 500,000 yields results quickly on a variety of datasets and can be easily modified to meet your requirements.

Also, we demonstrate the situation where you do not want any logs produced:

neat_config.yml:

reference: giant_bacterium.fa
read_len: 150
produce_bam: True
paired_ended: True
fragment_mean: 350
fragment_st_dev: 50
threads: 12
parallel_mode: size
parallel_block_size: 500000

Then run with the command:

neat read-simulator                 \
        --no-log                    \
        -c neat_config.yml          \
        -o /home/me/simulated_reads/

Simulate a whole genome dataset with only the variants in the provided VCF file using -v and setting mutation rate to 0 with -M.

[contents of neat_config.yml]
reference: hg19.fa
read_len: 126
produce_bam: True
produce_vcf: True
paired_ended: True
fragment_mean: 300
fragment_st_dev: 30
include_vcf: NA12878.vcf
mutation_rate: 0

neat read-simulator                 \
        -c neat_config.yml          \
        -o /home/me/simulated_reads/

Simulate single-end reads by omitting paired-ended options:

[contents of neat_config.yml]
reference: hg18.fa
read_len: 126
produce_bam: True
produce_vcf: True

neat read-simulator                 \
        -c neat_config.yml          \
        -o /home/me/simulated_read/
        -p 126_frags

Simulate PacBio-like reads by providing an error model (note that this is not yet implemented in NEAT 4.0):

[contents of neat-config.yml]
reference: hg19.fa
read_len: 5000
error_model: errorModel_pacbio.pickle.gz
avg_seq_error: 0.1

neat read-simulator                 \
        -c neat_config.yml          \
        -o /home/me/simulated_reads

Several scripts are distributed with gen_reads that are used to generate the models used for simulation.

Computes empirical fragment length distribution from sample paired-end data. NEAT uses the template length (tlen) attribute calculated from paired-ended alignments to generate summary statistics for fragment lengths, which can be input into NEAT.

    neat model-fraglen    \
        -i input.bam      \
        -o /path/to/prefix

and creates fraglen.pickle.gz model in working directory.

Takes reference genome and VCF file to generate mutation models:

neat gen-mut-model reference.fa input_variants.vcf   \
        -o /home/me/models

Trinucleotides are identified in the reference genome and the variant file. The mutation model uses trinucleotide context and selects mutation sites and alternate alleles with a transition matrix. Frequencies of each trinucleotide transition are calculated and output as a pickle file. Mutations are simulated to reflect the same context-dependent biases as the training data. In NEAT 4.3.5, we have only made minor optimizations to improve the speed, and the underlying statistical models are similar to those described in the original NEAT manuscript.

Generates sequencing error model for NEAT.

The sequencing error model uses real FASTQ/BAM data, summarizing position-specific substitution, insertion, and deletion frequencies. These are stored as discrete transition matrices that are sampled during read generation to reproduce empirical error profiles. In this way, it is similar to a simple version of gen-mut-model.

This script needs revision to improve the quality-score model and to include code to learn sequencing errors from pileup data.

Note that model-seq-err does not allow for SAM inputs. If you would like to use a BAM/SAM file, please use samtools to convert to a FASTQ, then use the FASTQ as input.

Note additionally that the file must either be unzipped or bgzipped. If your file is currently gzipped, you can use samtools' built-in bgzip utility to perform this conversion.

gzip -d my_file.fastq.gz
bgzip my_file.fastq

The blocked-zip format allows for indexing of the file.

For quality scores, note that using a single number will check quality scores for every number. As this could potentially slow down model creation, binned quality scores are advisable.

Soon, we will take a samtools mpileup output as input and have some plotting features.

neat model-seq-err                                    \
        -i input_read.fq (.gz)                        \
        -o /path/to/prefix                            \
        -q quality score offset [33]                  \
        -Q maximum quality score [2, 11, 24, 37]      \
        -m maximum number of reads to process [all]   \

Please note that -i2 can be used in place of -i to produce paired data.

Tool for comparing VCF files (Not yet implemented in NEAT 4.3.5).

neat vcf_compare
        -r <ref.fa>        * Reference Fasta                           \
        -g <golden.vcf>    * Golden VCF                                \
        -w <workflow.vcf>  * Workflow VCF                              \
        -o <prefix>        * Output Prefix                             \
        -m <track.bed>     Mappability Track                           \
        -M <int>           Maptrack Min Len                            \
        -t <regions.bed>   Targetted Regions                           \
        -T <int>           Min Region Len                              \
        -c <int>           Coverage Filter Threshold [15]              \
        -a <float>         Allele Freq Filter Threshold [0.3]          \
        --vcf-out          Output Match/FN/FP variants [False]         \
        --no-plot          No plotting [False]                         \
        --incl-homs        Include homozygous ref calls [False]        \
        --incl-fail        Include calls that failed filters [False]   \
        --fast             No equivalent variant detection [False]

We provide unit tests (e.g., mutation and sequencing error models) and basic integration tests for the CLI.

conda env create -f environment.yml
conda activate neat
poetry install --with dev
pytest -q tests

Please see CONTRIBUTING.md for more information and further instructions.

ICGC's "Access Controlled Data" documentation can be found at https://docs.icgc.org/portal/access/. To have access to controlled germline data, a DACO must be submitted. Open tier data can be obtained without a DACO, but germline alleles that do not match the reference genome are masked and replaced with the reference allele. Controlled data includes unmasked germline alleles.