The Dense Depth Data Dump (D4) format and tool suite provide an alternative to BigWig for fast analysis and compact storage of quantitative genomics datasets (e.g., RNA-seq, ChIP-seq, WGS depths, etc.). It supports random access, multiple tracks (e.g., RNA-seq, ChiP-seq, etc. from the same sample), HTTP range requests, and statistics on arbitrary genome intervals. The D4tools software is built on a Rust crate. We provide both a C-API and a Python API with an Jupyter notebook providing examples of how to to read, query, and create single-track and multi-track D4 files.
Usage examples are provided below. Also, check out the slide deck that describes the motivation, performance and toolkits for D4
Modern DNA sequencing is used as a readout for diverse assays, with the count of aligned sequences, or "read depth", serving as the quantitative signal for many underlying cellular phenomena. Despite wide use and thousands of datasets, existing formats used for the storage and analysis of read depths are limited with respect to both size and speed. For example, it is faster to recalculate sequencing depth from an alignment file than it is to analyze the text output from that calculation. We sought to improve on existing formats such as BigWig and compressed BED files by creating the Dense Depth Data Dump (D4) format and tool suite. The D4 format is adaptive in that it profiles a random sample of aligned sequence depth from the input BAM or CRAM file to determine an optimal encoding that minimizes file size, while also enabling fast data access. We show that D4 uses less disk space for both RNA-Seq and whole-genome sequencing and offers 3 to 440 fold speed improvements over existing formats for random access, aggregation and summarization for scalable downstream analyses that would be otherwise intractable.
To learn more, please read the publication: https://www.nature.com/articles/s43588-021-00085-0. Note We ran the experiments described in the manuscript on a server with following hardward and software
- Processor: Intel(R) Xeon(R) Gold 6230 CPU @ 2.10GHz
- RAM: 376GB
- OS: CentOS 7.6.180 w/ Linux Kernel 3.0.10
- Rust Version: 1.47.0-nightly
The d4tools create subcommand is used to convert BAM,CRAM,BigWig and BedGraph file to D4 file.
USAGE:
create [FLAGS] [OPTIONS] <input-file> [output-file]
FLAGS:
-z, --deflate Enable the deflate compression
-A, --dict-auto Automatically determine the dictionary type by random sampling
--dump-dict Do not profile the BAM file, only dump the dictionary
-h, --help Prints help information
-S, --sparse Sparse mode, this is same as '-zR0-1', which enable secondary table compression and disable
primary table
-V, --version Prints version information
OPTIONS:
--deflate-level <level> Configure the deflate algorithm, default 5
-d, --dict-file <dict_spec_file> Provide a file that defines the values of the dictionary
-R, --dict-range <dict_spec> Dictionary specification, use "a-b" to specify the dictionary is encoding
values from A to B(exclusively)
-f, --filter <regex> A regex that matches the genome name should present in the output file
-g, --genome <genome_file> The genome description file (Used by BED inputs)
-q, --mapping-qual <mapping-qual> The minimal mapping quality (Only valid with CRAM/BAM inputs)
-r, --ref <fai_file_path> Reference genome file (Used by CRAM inputs)
-t, --threads <num_of_threads> Specify the number of threads D4 can use for encoding
ARGS:
<input-file> Path to the input file
<output-file> Path to the output file
- From CRAM/BAM file
d4tools create -Azr hg19.fa.gz.fai hg002.cram hg002.d4- From BigWig file
d4tools create -z input.bw output.d4- From a BedGraph file (extension must be ".bedgraph")
d4tools create -z -g hg19.genome input.bedgraph output.d4USAGE:
view [FLAGS] <input-file> [chr:start-end]...
FLAGS:
-h, --help Prints help information
-g, --show-genome Show the genome file instead of the file content
-V, --version Prints version information
ARGS:
<input-file> Path to the input file
<chr:start-end>... Regions to be viewed
- Convert a d4 file to a bedgraph file
$ d4tools view hg002.d4 | head -n 10
chr1 0 9998 0
chr1 9998 9999 6
chr1 9999 10000 9
chr1 10000 10001 37
chr1 10001 10002 59
chr1 10002 10003 78
chr1 10003 10004 100
chr1 10004 10005 116
chr1 10005 10006 130
chr1 10006 10007 135
- Print given regions
$ d4tools view hg002.d4 1:1234560-1234580 X:1234560-1234580
1 1234559 1234562 28
1 1234562 1234565 29
1 1234565 1234566 30
1 1234566 1234572 31
1 1234572 1234573 29
1 1234573 1234576 28
1 1234576 1234578 27
1 1234578 1234579 26
X 1234559 1234562 26
X 1234562 1234563 25
X 1234563 1234565 26
X 1234565 1234574 25
X 1234574 1234575 26
X 1234575 1234576 25
X 1234576 1234578 26
X 1234578 1234579 25
- Print the genome layout
$ d4tools view -g hg002.d4 | head -n 10
1 249250621
2 243199373
3 198022430
4 191154276
5 180915260
6 171115067
7 159138663
8 146364022
9 141213431
10 135534747
USAGE:
stat [OPTIONS] <input_d4_file>
FLAGS:
-h, --help Prints help information
-V, --version Prints version information
OPTIONS:
-r, --region <bed_file_path> A bed file that describes the region we want to run the stat
-s, --stat <stat_type> The type of statistics we want to perform, by default average. You can specify
statistic methods: mean, median, hist, percentile=X% (If this is not speficied
d4tools will use mean by default)
-t, --threads <num_of_threads> Number of threads
ARGS:
<input_d4_file>
- Mean cov for each Chrom
$ d4tools stat hg002.d4
chr1 0 249250621 27.075065016588262
chr10 0 135534747 31.59483947684648
chr11 0 135006516 25.970025943044114
chr11_gl000202_random 0 40103 14.47213425429519
chr12 0 133851895 25.80992053194316
chr13 0 115169878 24.18613685602758
chr14 0 107349540 24.25194093053403
chr15 0 102531392 23.04176524785697
chr16 0 90354753 28.106620932271266
chr17 0 81195210 25.58382477242192
...
- Median cov for each Chrom
$ d4tools stat -s median hg002.d4 | head -n 10
1 0 249250621 25
10 0 135534747 26
11 0 135006516 26
12 0 133851895 26
13 0 115169878 26
14 0 107349540 25
15 0 102531392 24
16 0 90354753 24
17 0 81195210 25
18 0 78077248 26
- Top 5% for the given region defined in a bed file
$ d4tools stat -s percentile=95 -r region.bed hg002.d4
1 2000000 3000000 33
2 0 150000000 38
D4 now supports showing and run statistics for D4 files that is served on a HTTP server without downloading the file to local. For printing the file content, simple use the following command:
$ d4tools show https://d4-format-testing.s3.us-west-1.amazonaws.com/hg002.d4 | head -n 10
1 0 9998 0
1 9998 9999 6
1 9999 10000 10
1 10000 10001 38
1 10001 10002 55
1 10002 10003 72
1 10003 10004 93
1 10004 10005 110
1 10005 10006 126
1 10006 10007 131
To run statistics on a D4 file on network, we required the D4 file contains the data index to avoid full file accessing.
- (On the server side) Prepare the D4 file that need to be accessed on web
d4tools index build --sum hg002.d4- (On the client side) Run mean depth statistics on this file
$ d4tools stat https://d4-format-testing.s3.us-west-1.amazonaws.com/hg002.d4
1 0 249250621 23.848327146193952
2 0 243199373 25.02162749408075
3 0 198022430 23.086504175309837
4 0 191154276 23.18471121200553
5 0 180915260 23.2536419094774
6 0 171115067 24.515156108374722
7 0 159138663 24.398102314080646
8 0 146364022 26.425789139628865
9 0 141213431 19.780247114029827
10 0 135534747 25.475887087464
....
To build d4, Rust toolchain is required. To install Rust toolchain,
please run the following command and follow the prompt to complete the
Rust installation.
curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | shgcc or clang is required to build htslib embeded with the d4 library.
For details, please check the htslib repository.
Normally, the build step is quite easy. Just
# For Debug Build
cargo build
# For Release Build
cargo build --releaseAnd it will produce the d4tools binary which you can find at either
target/debug/d4tools or target/release/d4tools depending on which build mode
you choose.
- Compiling error: asking for -fPIC or -fPIE option
For some environment, the Rust toolchain will ask compile the -fPIC or -fPIE to build the d4tools binary.
In this case, you should be able to use the following workaround:
# To build a debug build :
cd d4tools && cargo rustc --bin d4tools -- -C relocation-model=static
# To build a release build :
cd d4tools && cargo rustc --bin d4tools --release -- -C relocation-model=static- Install bioconda
Assuming you have bioconda environment installed and configured, you can simply install d4tools and d4binding from bioconda repository
conda install d4tools- Install from crates.io: Assuming you have Rust compiler toolchain, you can install it from crate.io as well.
cargo install d4tools
- Install from source code: The following steps allows you to install d4tools from source code. You can choose to install the d4tools binary by running
cargo install --path .D4 provides a C binding that allows the D4 library used in C and C++. Here's the steps to build D4 binding.
- Install or build the binding library
- The easist way to install d4binding library is using bioconda.
conda install d4binding
Then the header file will be installed under <conda-dir>/include. And libd4binding.so or libd4binding.dylib will be installed under <conda-dir>/lib.
- Alternatively, you can choose install from the source code as well:
# Build the D4 binding library, for debug build, remove "--release" argument
cargo build --package=d4binding --releaseAfter running this command, you should be able to find the library "target/release/libd4binding.so".
- Use D4 in C
Here's a small example that prints all chromosome name and size defined in a D4 file.
#include <stdio.h>
#include <d4.h>
int main(int argc, char** argv)
{
d4_file_t* fp = d4_open("input.d4", "r");
d4_file_metadata_t mt = {};
d4_file_load_metadata(fp, &mt);
int i;
for(i = 0; i < mt.chrom_count; i ++)
printf("# %s %d\n", mt.chrom_name[i], mt.chrom_size[i]);
d4_close(fp);
return 0;
}- Compile C++ code against D4 binding library
gcc print-chrom-info.c -o print-chrom-info -I d4binding/include -L target/release -ld4binding
For more examples, see d4binding/examples/