Slow5tools is a simple toolkit for converting (FAST5 <-> SLOW5), compressing, viewing, indexing and manipulating data in SLOW5 format.
About SLOW5 format:
SLOW5 is a new file format for storing signal data from Oxford Nanopore Technologies (ONT) devices. SLOW5 was developed to overcome inherent limitations in the standard FAST5 signal data format that prevent efficient, scalable analysis and cause many headaches for developers. SLOW5 can be encoded in human-readable ASCII format, or a more compact and efficient binary format (BLOW5) - this is analogous to the seminal SAM/BAM format for storing DNA sequence alignments. The BLOW5 binary format supports zlib (DEFLATE) compression, or other compression methods (see notes), thereby minimising the data storage footprint while still permitting efficient parallel access. Detailed benchmarking experiments have shown that SLOW5 format is an order of magnitude faster and significantly smaller than FAST5.
Full documentation: https://hasindu2008.github.io/slow5tools
Publication (SLOW5 format): https://www.nature.com/articles/s41587-021-01147-4
Publication (slow5tools): https://genomebiology.biomedcentral.com/articles/10.1186/s13059-023-02910-3
SLOW5 specification: https://hasindu2008.github.io/slow5specs
slow5 ecosystem: https://hasindu2008.github.io/slow5
To convert to and from ONT's new POD5 format, you use blue_crab. If POD5 format and the associated POD5 C/C++ API reaches maturity/stability and adheres to C++11 standard, capabilities for POD5 <-> SLOW5 conversion will be added to slow5tools. slow5tools is strictly adhering to C++11 standard for wider compatibility.
If you are a Linux user on x86_64 architecture and want to quickly try slow5tools out, download the compiled binaries from the latest release. For example:
VERSION=v1.3.0
wget "https://github.com/hasindu2008/slow5tools/releases/download/$VERSION/slow5tools-$VERSION-x86_64-linux-binaries.tar.gz" && tar xvf slow5tools-$VERSION-x86_64-linux-binaries.tar.gz && cd slow5tools-$VERSION/
./slow5toolsBinaries should work on most Linux distributions as the only dependency is zlib which is available by default on most distributions. For compiled binaries to work, your processor must support SSSE3 instructions or higher (processors after 2007 have these) and your operating system must have GLIBC 2.17 or higher (Linux distributions from 2014 onwards typically have this).
You can also use conda to install slow5tools as conda install slow5tools -c bioconda -c conda-forge. For converting latest vbz compressed FAST5, you will need to setup the ONT vbz plugin as explained in faq:Q3.
Users are recommended to build from the latest release tar ball. A compiler that supports C++11 is needed to build slow5tools.
Quick example for Ubuntu :
sudo apt-get install libhdf5-dev zlib1g-dev #install HDF5 and zlib development libraries
VERSION=v1.3.0
wget "https://github.com/hasindu2008/slow5tools/releases/download/$VERSION/slow5tools-$VERSION-release.tar.gz" && tar xvf slow5tools-$VERSION-release.tar.gz && cd slow5tools-$VERSION/
./configure
makeThe commands to install hdf5 (and zlib) development libraries on some popular distributions :
On Debian/Ubuntu : sudo apt-get install libhdf5-dev zlib1g-dev
On Fedora/CentOS : sudo dnf/yum install hdf5-devel zlib-devel
On Arch Linux: sudo pacman -S hdf5
On OS X : brew install hdf5 zlibWARNING: Building from GitHub is meant for advanced users to test latest features. For production purposes, use the latest release version that is thoroughly tested.
Building from the Github repository additionally requires autoreconf which can be installed on Ubuntu using sudo apt-get install autoconf automake (brew install autoconf automake on macOS).
To build from GitHub:
sudo apt-get install libhdf5-dev zlib1g-dev autoconf automake #install HDF5 and zlib development libraries and autotools
git clone --recursive https://github.com/hasindu2008/slow5tools
cd slow5tools
autoreconf # autoreconf --install for macos
./configure
make
-
If you only want to manipulate S/BLOW5 files, you can disable FAST5/HDF5 for even easier compilation. Call
./configure --disable-hdf5 && makeor completely bypass the configure step and just callmake disable_hdf5=1. -
You can optionally enable zstd compression support when building slow5lib by invoking
make zstd=1. This requires zstd 1.3 or higher development libraries installed on your system (libzstd1-dev package for apt, libzstd-devel for yum/dnf and zstd for homebrew). SLOW5 files compressed with zstd offer smaller file size and better performance compared to the default zlib. However, zlib runtime library is available by default on almost all distributions unlike zstd and thus files compressed with zlib will be more 'portable' (also see notes). For enabling zstd on Apple Silicon (e.g., Mac M1) also see faq. If you cannot install the zstd library system wide you can locally build zstd and build slow5tools against that:scripts/install-zstd.sh # download and compiles zstd in the current folder ./configure --enable-localzstd make # don't run make zstd=1. libzstd.a is statically linked this time. -
slow5tools from version 0.3.0 onwards by default requires vector instructions (SSSE3 or higher for Intel/AMD and neon for ARM). If your processor is an ancient processor with no such vector instructions, invoke make as
make no_simd=1. -
If you cannot install the hdf5 library system wide you can locally build HDF5 (takes ages) and build slow5tools against that:
scripts/install-hdf5.sh # download and compiles HDF5 in the current folder ./configure --enable-localhdf5 make -
On Mac M1 or in any system if
./configurecannot find the hdf5 libraries installed through the package manager, you can specify the location as LDFLAGS=-L/path/to/shared/lib/ CPPFLAGS=-I/path/to/headers/. For an example for Apple Silicon (e.g., Mac M1) see faq. -
You can build a docker image as follows.
git clone https://github.com/hasindu2008/slow5tools && cd slow5tools docker build . docker run -v /path/to/local/data/data/:/data/ -it :image_id ./slow5tools -
To support large files on 32-bit systems use:
CFLAGS="-D_FILE_OFFSET_BITS=64" make.
- Visit the man page for all the commands and options.
- A guide on using BLOW5 for archiving and steps to verify if data integrity is preserved is here.
- A script for performing real-time FAST5 to BLOW5 conversion during sequencing is provided here.
# convert a directory of fast5 files into BLOW5 files (default compression: zlib+svb-zd)
slow5tools f2s fast5_dir -d blow5_dir
# convert a single fast5 file into a SLOW5 ASCII
slow5tools f2s file.fast5 -o file.slow5
# convert a directory of fast5 files into BLOW5 files with zstd+svb-zd compression (similar to ONT's vbz compression)
slow5tools f2s fast5_dir -d blow5_dir -c zstd -s svb-zd
# concatenate all BLOW5 fils in a directory into a single BLOW5 file (works only if all the BLOW5 files have the same header, otherwise use merge)
slow5tools cat blow5_dir -o file.blow5
# merge all BLOW5 files in a directory into a single BLOW5 file (default compression: zlib+svb-zd)
slow5tools merge blow5_dir -o file.blow5
# merge all BLOW5 files in a directory into a single BLOW5 file with zstd+svb-zd compression (similar to ONT's vbz compression)
slow5tools merge blow5_dir -o file.blow5 -c zstd -s svb-zd
# to view a BLOW5 file in SLOW5 ASCII on standard out
slow5tools view file.blow5
# Convert a BLOW5 file into SLOW5 ASCII
slow5tools view file.blow5 -o file.slow5
# convert a SLOW5 file to BLOW5 (default compression)
slow5tools view file.slow5 -o file.blow5
# index a slow5/blow5 file
slow5tools index file.blow5
# extract records from a slow5/blow5 file corresponding to given read ids
slow5tools get file.blow5 readid1 readid2 -o output.slow5
# extract records from a slow5/blow5 file based on a list of read ids
slow5tools get file.blow5 -l readids_list.txt -o output.slow5
# split a BLOW5 file into separate BLOW5 files based on the read groups
slow5tools split file.blow5 -d blow5_dir -g
# split a BLOW5 file (single read group) into separate BLOW5 files such that there are 4000 reads in one file
slow5tools split file.blow5 -d blow5_dir -r 4000
# split a BLOW5 file into separate BLOW5 files by barcode given the buttery-eel barcode summary file
slow5tools split file.blow5 -d blow5_dir -x barcode_summary.txt
# split a BLOW5 file into separate BLOW5 files based on a custom TSV file
slow5tools split file.blow5 -d blow5_dir -x custom.tsv --demux-rid readid --demux-code category
# convert a directory of blow5 files to fast5
slow5tools s2f blow5_dir -d fast5
# print summary statistics (e.g., number of read groups, compression method, number of records, etc)
slow5tools stats file.blow5
# quickly check if a blow5 file is intact
slow5tools quickcheck file.blow5
# print all per-read metadata (except the raw signal)
slow5tools skim file.blow5
#print the list of read IDs
slow5tools skim --rid file.blow5
#print the SLOW5 header
slow5tools skim --hdr file.blow5Visit here for example workflows. See here for example bash one-liners with slow5tools.
Visit the frequently asked questions or open an issue.
Following are some features and optimisations in our todo list which will be implemented based on the need. If anyone is interested please request here. Contributions are welcome.
- pipelining input, processing and output in merge, get, etc. (improved runtime upto 2X, please find the implementation here)
- reading from stdin for view
- binary releases for ARM64 processors on Linux and for MacOS
- any other useful features
slow5lib from version 0.3.0 onwards has built in StreamVByte compression support to enable even smaller file sizes, which is applied to the raw signal by default when producing BLOW5 files. zlib compression is then applied by default to each record. If zstd is used instead of zlib on top of StreamVByte, it is similar to ONT's latest vbz compression. BLOW5 files compressed with zstd+StreamVByte are still significantly smaller than vbz compressed FAST5 files.
slow5tools uses klib. Some code snippets have been taken from Minimap2 and Samtools.
Please cite the following in your publications when using SLOW5 file format:
Gamaarachchi, H., Samarakoon, H., Jenner, S.P. et al. Fast nanopore sequencing data analysis with SLOW5. Nat Biotechnol 40, 1026-1029 (2022). https://doi.org/10.1038/s41587-021-01147-4
@article{gamaarachchi2022fast,
title={Fast nanopore sequencing data analysis with SLOW5},
author={Gamaarachchi, Hasindu and Samarakoon, Hiruna and Jenner, Sasha P and Ferguson, James M and Amos, Timothy G and Hammond, Jillian M and Saadat, Hassaan and Smith, Martin A and Parameswaran, Sri and Deveson, Ira W},
journal={Nature biotechnology},
pages={1--4},
year={2022},
publisher={Nature Publishing Group}
}
Please cite the following in your publications when using slow5tools:
Samarakoon, H., Ferguson, J.M., Jenner, S.P. et al. Flexible and efficient handling of nanopore sequencing signal data with slow5tools. Genome Biol 24, 69 (2023). https://doi.org/10.1186/s13059-023-02910-3
@article{samarakoon2023flexible,
title={Flexible and efficient handling of nanopore sequencing signal data with slow5tools},
author={Samarakoon, Hiruna and Ferguson, James M and Jenner, Sasha P and Amos, Timothy G and Parameswaran, Sri and Gamaarachchi, Hasindu and Deveson, Ira W},
journal={Genome Biology},
volume={24},
number={1},
pages={69},
year={2023},
publisher={Springer}
}