#bed

bin+lib gxf2bed

fastest GTF/GFF-to-BED converter chilling around

5 releases

0.2.3 Sep 29, 2024
0.2.2 Mar 27, 2024
0.2.1 Feb 27, 2024
0.2.0 Feb 20, 2024
0.1.0 Feb 6, 2024

#195 in Science

Download history 8/week @ 2024-07-29 136/week @ 2024-09-23 59/week @ 2024-09-30 13/week @ 2024-10-07 18/week @ 2024-10-14

226 downloads per month

MIT license

155KB
680 lines

gxf2bed

Version Badge Crates.io Version GitHub License Crates.io Total Downloads Conda Platform

The fastest G{F,T}F-to-BED converter around the block!

translates

```
chr27 gxf2bed gene 17266470 17285418 . + . gene_id "ENSG00000151743";

chr27 gxf2bed transcript 17266470 17281218 . + . gene_id "ENSG00000151743"; transcript_id "ENST00000541931.8";

chr27 gxf2bed exon 17266470 17266572 . + . gene_id "ENSG00000151743"; transcript_id "ENST00000541931.8"; exon_number "1"; exon_id "ENST00000541931.8.1";

...
```
into

```
chr27 17266469 17281218 ENST00000541931.8 1000 + 17266469 17281218 0,0,200 2 103,74, 0,14675,
```
before your eyes blink!

check out the metrics below:

Command Mean [s] Min [s] Max [s] Relative
target/release/gxf2bed -i ./test/Homo_sapiens.GRCh38.112.gtf -o ./test/output.bed 1.592 ± 0.007 1.585 1.603 1.00
target/release/gxf2bed -i ./test/Homo_sapiens.GRCh38.112.gtf -o ./test/output.bed.gz 2.575 ± 0.033 2.551 2.631 1.62 ± 0.02
target/release/gxf2bed -i ./test/Homo_sapiens.GRCh38.112.gtf.gz -o ./test/output.bed 2.984 ± 0.011 2.965 2.993 1.87 ± 0.01
target/release/gxf2bed -i ./test/Homo_sapiens.GRCh38.112.gtf.gz -o ./test/output.bed.gz 3.946 ± 0.009 3.938 3.958 2.48 ± 0.01
target/release/gxf2bed -i ./test/gencode.v46.chr_patch_hapl_scaff.annotation.gff3.gz -o ./test/output.bed 2.046 ± 0.003 2.042 2.050 1.28 ± 0.01
target/release/gxf2bed -i ./test/gencode.v46.chr_patch_hapl_scaff.annotation.gff3.gz -o ./test/output.bed.gz 2.050 ± 0.014 2.038 2.072 1.29 ± 0.01
target/release/gxf2bed -i ./test/gencode.v46.chr_patch_hapl_scaff.annotation.gff3 -o ./test/output.bed 1.861 ± 0.014 1.847 1.879 1.17 ± 0.01
target/release/gxf2bed -i ./test/gencode.v46.chr_patch_hapl_scaff.annotation.gff3 -o ./test/output.bed.gz 2.914 ± 0.062 2.866 3.019 1.83 ± 0.04

Converts

  • Homo sapiens GRCh38 Ensembl 112 GTF (254,129 transcripts) in 1.592 seconds.
  • Mus musculus GRCm39 GENCODE 44 (149,547 transcritps) in 0.977 seconds.
  • Canis lupus familiaris ROS_Cfam_1.0 Ensembl 112 (54,144 transcripts) in 0.501 seconds.
  • Gallus galus bGalGal1 Ensembl 112 (72,689 transcripts) in 0.529 seconds.

What's new on v.0.2.3

  • gxf2bed now is even x2 faster and can convert the human annotation in 1.5 seconds!
  • including bench to support results in README and update benchamrk results + metrics.
  • implements free compressed conversion between formats: gtf/gtf.gz/gff/gff.gz to bed/bed.gz!

Usage

Usage: gxf2bed[EXE] --input/-i <GTF/GFF> --output/-o <BED> [--parent/-p <PARENT>] [--child/-c <CHILD>] [--feature/-f <FEATURE>]

Arguments:
    --input/-i <GTF/GFF>: a .gtf/.gff file
    --output/-o <BED>: path to output .bed file
    --parent/-p <PARENT>: parent node [default: "transcript"]
    --child/-c <CHILD>: child node [default: "exon"]
    --feature/-f <FEATURE>: feature to extract from the attribute line [default: "transcript_id"]

Options:
    --help: print help
    --version: print version
    --threads/-t: number of threads (default: max ncpus)

Installation

to install gxf2bed on your system follow this steps:

  1. get rust: curl https://sh.rustup.rs -sSf | sh on unix, or go here for other options
  2. run cargo install gxf2bed (make sure ~/.cargo/bin is in your $PATH before running it)
  3. use gxf2bed with the required arguments
  4. enjoy!

Build

to build gxf2bed from this repo, do:

  1. get rust (as described above)
  2. run git clone https://github.com/alejandrogzi/gxf2bed.git && cd gxf2bed
  3. run cargo run --release -- -i <GTF/GFF> -o <BED>

Container image

to build the development container image:

  1. run git clone https://github.com/alejandrogzi/gxf2bed.git && cd gxf2bed
  2. initialize docker with start docker or systemctl start docker
  3. build the image docker image build --tag gxf2bed .
  4. run docker run --rm -v "[dir_where_your_gtf_is]:/dir" gxf2bed -i /dir/<GTF/GFF> -o /dir/<BED>

Conda

to use gxf2bed through Conda just:

  1. conda install gxf2bed -c bioconda or conda create -n gxf2bed -c bioconda gxf2bed

Benchmark + FAQ

File Format Tool Language Time [s] Fold-change Size n_transcripts
Homo_sapiens.GRCh38.112.gtf GTF gxf2bed Rust 1.592 1.00 1.4 GB 254,129
BEDOPS C 13.06 8.20
ea Perl 15.69 9.86
Signal & Brown Python 7.58 4.77
UCSC's utils C++ 10.97 6.89
gencode.v46.chr_patch_hapl_scaff.annotation.gff3 GFF gxf2bed Rust 1.861 1.00 1.6 GB 278,220
BEDOPS C 18.24 9.80
ea Perl 22.28 11.98
Signal & Brown Python 47.02 25.24
UCSC's utils C++ 17.42 9.35

If you google "gtf to bed" or "gff to bed" you'll find some posts about recommended tools. Part of these tools are already deprecated or too difficult to run (even in Linux), due to poor maintenance. This project was conceived to provide an easy way to convert GTF/GFF files to BED structures and finish a set of high-performance converters between gene model formats in Rust (bed2gtf, bed2gff and now gxf2bed).

To test the efficiency of gxf2bed, I took 4 GTF/GFF-to-BED converters and ran them with the current GRCh38 GENCODE annotation (v.44), that has ~250,000 transcripts [the values showed here are the mean of 5 consecutive runs]. Briefly, the competitors are:

  • UCSC's utils: gtfToGenePred | genePredToBed & gffToGenePred | genePredToBed (taken from here)
  • Signal & Brown: gtf2bed.py & gff32gtf.py && gtf2bed.py (taken from here)
  • ea-utils: gtf2bed.pl & gff2gtf.pl | gtf2bed.pl (taken from here)
  • BEDOPS: gtf2bed & gtf2bed (taken from here)

Besides the easy way to make gxf2bed run and the GFF-GTF channel that allows centralize both in a single converter, this tool showed a significant difference in computation time against the other tools in each one of the two formats. Even using a combination of different number of threads, times were practically the same 1.592s +/- 0.007s and the differences were maintained. Is important to note that GFF3 files took a lot more time than its GTF counterpart. This could be due to the need of chaining two different programs 1) GFF-to-GTF and 2) GTF-to-BED.

Taken together, gxf2bed offers an easier and faster way to convert GTF/GFF files to BED files. This tool could save you at least x2-3 times the time you used to spend using other tools to convert GTF files and x5 times if you want to convert GFF3 files!

Dependencies

~3–14MB
~121K SLoC