Randomly subsample sequencing reads to a specified coverage.
I couldn't find a tool for subsampling reads that met my requirements. All the
strategies I could find fell short as they either just wanted a number or percentage of
reads to subsample to or, if they did subsample to a coverage, they assume all reads are
the same size (i.e Illumina). As I mostly work with long-read data this posed a problem
if I wanted to subsample a file to certain coverage, as length of reads was never taken
into account. rasusa addresses this shortcoming.
A workaround I had been using for a while was using filtlong. It was
simple enough, I just figure out the number of bases I need to achieve a (theoretical)
coverage for my sample. Say I have a fastq from an E. coli sample with 5 million reads
and I want to subset it to 50x coverage. I just need to multiply the expected size of
the sample's genome, 4.6 million base pairs, by the coverage I want and I have my target
bases - 230 million base pairs. In filtlong, I can do the following
sh
target=230000000
filtlong --target_bases "$target" reads.fq > reads.50x.fq
However, this is technically not the intended function of filtlong; it's a quality
filtering tool. What you get in the end is a subset of the "highest scoring"
reads at a (theoretical) coverage of 50x. Depending on your circumstances, this might be
what you want. However, you bias yourself towards the best/longest reads in the dataset
- not a fair representation of your dataset as a whole. There is also the possibility
of favouring regions of the genome that produce longer/higher quality reads. De Maio
et al. even found that by randomly subsampling nanopore reads you achieve
better genome assemblies than if you had filtered.
So, depending on your circumstances, an unbiased subsample of your reads might be what
you need. And if this is the case, rasusa has you covered.
Some of these installation options require the rust toolchain, which is
extremely easy to set up. However, if you do not wish to install rust then there are a
number of options available.
cargo
Prerequisite: rust toolchain (min. v1.53.0)
sh
cargo install rasusa
conda
Prerequisite: conda (and bioconda channel correctly set up)
sh
conda install rasusa
Thank you to Devon Ryan (@dpryan79) for help debugging the bioconda recipe.
Docker images are hosted at [quay.io]. For versions 0.3.0 and earlier, the images were hosted on Dockerhub.
singularityPrerequisite: singularity
sh
URI="docker://quay.io/mbhall88/rasusa"
singularity exec "$URI" rasusa --help
The above will use the latest version. If you want to specify a version then use a tag (or commit) like so.
sh
VERSION="0.5.0"
URI="docker://quay.io/mbhall88/rasusa:${VERSION}"
dockerPrerequisite: docker
sh
docker pull quay.io/mbhall88/rasusa
docker run quay.io/mbhall88/rasusa rasusa --help
You can find all the available tags on the quay.io repository. Note: versions prior to 0.4.0 were housed on Docker Hub.
homebrewPrerequisite: homebrew
The homebrew installation is done via the homebrew-bio tap.
sh
brew tap brewsci/bio
brew install rasusa
or
sh
brew install brewsci/bio/rasusa
tl;dr: Run the following snippet to download the binary for your system to the current directory and show the help menu.
shell
OS=$(uname -s)
if [ "$OS" = "Linux" ]; then
triple="x86_64-unknown-linux-musl"
elif [ "$OS" = "Darwin" ]; then
triple="x86_64-apple-darwin"
else
echo "ERROR: $OS not a recognised operating system"
fi
if [ -n "$triple" ]; then
URL="https://github.com/mbhall88/rasusa/releases/download/0.5.0/rasusa-0.5.0-${triple}.tar.gz"
wget "$URL" -O - | tar -xzf -
./rasusa --help
fi
These binaries do not require that you have the rust toolchain installed.
Currently, there are two pre-compiled binaries available:
- Linux kernel x86_64-unknown-linux-musl (works on most Linux distributions I tested)
- OSX kernel x86_64-apple-darwin (works for any post-2007 Mac)
An example of downloading one of these binaries using wget
sh
URL="https://github.com/mbhall88/rasusa/releases/download/0.5.0/rasusa-0.5.0-x86_64-unknown-linux-musl.tar.gz"
wget "$URL" -O - | tar -xzf -
./rasusa --help
If these binaries do not work on your system please raise an issue and I will potentially add some additional target triples.
Prerequisite: rust toolchain
```sh git clone https://github.com/mbhall88/rasusa.git cd rasusa cargo build --release target/release/rasusa --help
cargo test --all ```
rasusa --input in.fq --coverage 30 --genome-size 4.6mb
The above command will output the subsampled file to stdout.
Or, if you have paired Illumina
rasusa -i r1.fq -i r2.fq --coverage 30 --genome-size 4g -o out.r1.fq -o out.r2.fq
For more details on the above options, and additional options, see below.
There are three required options to run rasusa.
-i, --inputThis option specifies the file(s) containing the reads you would like to subsample. The
file(s) must be valid fasta or fastq format and can be compressed (with a tool such as
gzip).
Illumina paired files can be passed in two ways.
1. Using --input twice -i r1.fq -i r2.fq
2. Using --input once, but passing both files immediately after -i r1.fq r2.fq
Bash wizard tip 🧙: Let globs do the work for you
-i r*.fq
Note: The file format is (lazily) determined from the file name. File suffixes that are deemed valid are:
.fa, .fasta, .fa.gz, .fasta.gz.fq, .fastq, .fq.gz, .fastq.gzIf there is a naming convention you feel is missing, please raise an issue.
-c, --coverageThis option is used to determine the minimum coverage to subsample the reads to. It can be specified as an integer (100), a decimal/float (100.7), or either of the previous suffixed with an 'x' (100x).
Note: Due to the method for determining how many bases are required to achieve the desired coverage, the actual coverage, in the end, could be slightly higher than requested. For example, if the last included read is very long. The log messages should inform you of the actual coverage in the end.
-g, --genome-sizeThe genome size of the input is also required. It is used to determine how many bases
are necessary to achieve the desired coverage. This can, of course, be as precise or
rough as you like.
Genome size can be passed in many ways. As a plain old integer (1600), or with a metric
suffix (1.6kb). All metric suffixes can have an optional 'b' suffix and be lower, upper,
or mixed case. So 'Kb', 'kb' and 'k' would all be inferred as 'kilo'. Valid metric
suffixes include:
-o, --outputNOTE: This parameter is required if passing paired Illumina data.
By default, rasusa will output the subsampled file to stdout (if one file is given).
If you would prefer to specify an output file path, then use this option.
Output for Illumina paired files can be specified in the same manner as
--input
1. Using --output twice -o out.r1.fq -o out.r2.fq
2. Using --output once, but passing both files immediately after -o out.r1.fq
out.r2.fq
The ordering of the output files is assumed to be the same as the input.
Note: The output will always be in the same format as the input. You cannot pass fastq
as input and ask for fasta as output.
rasusa will also attempt to automatically infer whether comression of the output
file(s) is required. It does this by detecting any of the supported extensions:
- .gz: will compress the output with gzip
- .bz or .bz2: will compress the output with bzip2
- .lzma: will compress the output with the xz LZMA algorithm
-O, --output-typeUse this option to manually set the compression algoritm to use for the output file(s). It will override any format automatically detected from the output path.
Valid options are:
- g: gzip
- b: bzip2
- l: xz LZMA algorithm
- u: no compression
Note: these options are case insensitive.
-l, --compress-levelCompression level to use if compressing the output. 1 is for fastest/least compression and 9 is for slowest/best. By default this is set to 6, which is also the default for most compression programs.
-s, --seedThis option allows you to specify the random seed used by the random subsampler. By explicitly setting this parameter, you make the subsample for the input reproducible. The seed is an integer, and by default it is not set, meaning the operating system will seed the random subsampler. You should only pass this parameter if you are likely to want to subsample the same input file again in the future and want the same subset of reads.
-vAdding this optional flag will make the logging more verbose. By default, logging will produce messages considered "info" or above (see here for more details). If verbosity is switched on, you will additionally get "debug" level logging messages.
```text $ rasusa --help
rasusa 0.5.0 Randomly subsample reads to a specified coverage.
USAGE:
rasusa [FLAGS] [OPTIONS] --coverage
FLAGS: -h, --help Prints help information
-V, --version
Prints version information
-v
Switch on verbosity.
OPTIONS: -l, --compress-level <1-9> Compression level to use if compressing output [default: 6]
-c, --coverage <FLOAT>
The desired coverage to sub-sample the reads to.
-g, --genome-size <genome-size>
Genome size to calculate coverage with respect to. e.g., 4.3kb, 7Tb, 9000, 4.1MB
-i, --input <input>...
The fast{a,q} file(s) to subsample.
For paired Illumina you may either pass this flag twice `-i r1.fq -i r2.fq` or give two files consecutively
`-i r1.fq r2.fq`.
-o, --output <output>...
Output filepath(s); stdout if not present.
For paired Illumina you may either pass this flag twice `-o o1.fq -o o2.fq` or give two files consecutively
`-o o1.fq o2.fq`. NOTE: The order of the pairs is assumed to be the same as that given for --input. This
option is required for paired input.
-O, --output-type <u|b|g|l>
u: uncompressed; b: Bzip2; g: Gzip; l: Lzma
Rasusa will attempt to infer the output compression format automatically from the filename extension. This
option is used to override that. If writing to stdout, the default is uncompressed
-s, --seed <INT>
Random seed to use.
```
If you want to use rasusa in a snakemake pipeline, it is advised to use
the wrapper.
py
rule subsample:
input:
r1="{sample}.r1.fq",
r2="{sample}.r2.fq",
output:
r1="{sample}.subsampled.r1.fq",
r2="{sample}.subsampled.r2.fq",
params:
options="--seed 15", # optional
genome_size="3mb", # required
coverage=20, # required
log:
"logs/subsample/{sample}.log",
wrapper:
"0.70.0/bio/rasusa"
See the latest wrapper documentation for the most up-to-date version number.
“Time flies like an arrow; fruit flies like a banana.”
― Anthony G. Oettinger
The real question is: will rasusa just needlessly eat away at your precious time on
earth?
To do this benchmark, I am going to use hyperfine.
The data I used comes from
Download and rename the fastq
shell
URL="ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR649/008/SRR6490088/SRR6490088_1.fastq.gz"
wget "$URL" -O - | gzip -d -c > tb.fq
The file size is 2.9G, and it has 379,547 reads.
We benchmark against filtlong using the same strategy outlined in
Motivation.
shell
TB_GENOME_SIZE=4411532
COVG=50
TARGET_BASES=$(( TB_GENOME_SIZE * COVG ))
FILTLONG_CMD="filtlong --target_bases $TARGET_BASES tb.fq"
RASUSA_CMD="rasusa -i tb.fq -c $COVG -g $TB_GENOME_SIZE -s 1"
hyperfine --warmup 3 --runs 10 --export-markdown results-single.md \
"$FILTLONG_CMD" "$RASUSA_CMD"
| Command | Mean [s] | Min [s] | Max [s] | Relative |
|:------------------------------------------|---------------:|--------:|--------:|-------------:|
| filtlong --target_bases 220576600 tb.fq | 21.685 ± 0.055 | 21.622 | 21.787 | 21.77 ± 0.29 |
| rasusa -i tb.fq -c 50 -g 4411532 -s 1 | 0.996 ± 0.013 | 0.983 | 1.023 | 1.00 |
Summary: rasusa ran 21.77 ± 0.29 times faster than filtlong.
Download and then deinterleave the fastq with pyfastaq
shell
URL="ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR648/008/SRR6488968/SRR6488968.fastq.gz"
wget "$URL" -O - | gzip -d -c - | fastaq deinterleave - r1.fq r2.fq
Each file's size is 179M and has 283,590 reads.
For this benchmark, we will use seqtk. As seqtk requires a fixed number of
reads to subsample to, I ran rasusa initially and got the number of reads it was using
for its subsample. We will also test seqtk's 2-pass mode as this is analogous to
rasusa.
We use a lower coverage for the Illumina as the samples only have ~38x coverage.
shell
TB_GENOME_SIZE=4411532
COVG=20
NUM_READS=147052
SEQTK_CMD_1="seqtk sample -s 1 r1.fq $NUM_READS > /tmp/r1.fq; seqtk sample -s 1 r2.fq $NUM_READS > /tmp/r2.fq;"
SEQTK_CMD_2="seqtk sample -2 -s 1 r1.fq $NUM_READS > /tmp/r1.fq; seqtk sample -2 -s 1 r2.fq $NUM_READS > /tmp/r2.fq;"
RASUSA_CMD="rasusa -i r1.fq r2.fq -c $COVG -g $TB_GENOME_SIZE -s 1 -o /tmp/r1.fq -o /tmp/r2.fq"
hyperfine --warmup 5 --runs 20 --export-markdown results-paired.md \
"$SEQTK_CMD_1" "$SEQTK_CMD_2" "$RASUSA_CMD"
| Command | Mean [ms] | Min [ms] | Max [ms] | Relative |
|:--------------------------------------------------------------------------------------------------|-------------:|---------:|---------:|------------:|
| seqtk sample -s 1 r1.fq 147052 > /tmp/r1.fq; seqtk sample -s 1 r2.fq 147052 > /tmp/r2.fq; | 693.8 ± 17.4 | 678.8 | 780.5 | 1.21 ± 0.19 |
| seqtk sample -2 -s 1 r1.fq 147052 > /tmp/r1.fq; seqtk sample -2 -s 1 r2.fq 147052 > /tmp/r2.fq; | 739.5 ± 28.4 | 695.6 | 811.8 | 1.29 ± 0.20 |
| ./rasusa -i r1.fq r2.fq -c 20 -g 4411532 -s 1 -o /tmp/r1.fq -o /tmp/r2.fq | 574.7 ± 88.1 | 432.3 | 784.4 | 1.00 |
Summary: rasusa ran 1.21 times faster than seqtk (1-pass) and 1.29 times faster
than seqtk (2-pass)
So, rasusa is faster than seqtk but doesn't require a fixed number of reads -
allowing you to avoid doing maths to determine how many reads you need to downsample to
a specific coverage. 🤓
If you would like to help improve rasusa you are very welcome!
For changes to be accepted, they must pass the CI and coverage checks. These include:
rustfmt. This can be done by running cargo fmt in the
project directory.cargo clippy
--all-features --all-targets -- -D warningskcov.If you use rasusa in your research, it would be very much appreciated if you could
cite it.
Hall, Michael B. Rasusa: Randomly subsample sequencing reads to a specified coverage. (2019). doi:10.5281/zenodo.3731394
Bibtex
@article{
rasusa2019,
title={Rasusa: Randomly subsample sequencing reads to a specified coverage},
DOI={10.5281/zenodo.3731394},
publisher={Zenodo},
author={Hall, Michael B.},
year={2019},
month={Nov}
}