#dna #protein #gene

bin+lib frag_gene_scan_rs

Rust implementation of the gene prediction model for short and error-prone reads

2 stable releases

1.1.0 Oct 8, 2021
1.0.1 Aug 25, 2021

#137 in Science

GPL-3.0-or-later

2MB
2.5K SLoC

FragGeneScanRs

Installation

From release

Download the build of the latest release for your platform and extract it somewhere in your path.

From source

FragGeneScanRs is written in Rust, so first head over to their installation instructions. Afterwards, you can install the crate from crates.io with cargo install frag_gene_scan_rs, or you can install from here. Clone this repository or download the source code of the latest release. In this directory, run cargo install --path . to install. The installation progress may prompt you to add a directory to your path so you can easily execute it.

Usage

You can use FragGeneScanRs with the short options of FragGeneScan but it also provides long-form options and some additional options. It reads from standard input and writes to and standard output by default, allowing shorter calls in case you only need the predicted proteins.

# get predictions for 454 pyrosequencing reads with about 1% error rate
FragGeneScanRs -t 454_10 < example/NC_000913-454.fna > example/NC_000913-454.faa

# get predictions for complete reads
FragGeneScanRs -t complete -w 1 < example/NC_000913.fna > example/NC_000913.faa

Backwards compatible mode

FragGeneScanRs -s seq_file_name -o output_file_name -w [0 or 1] -t train_file_name -p num_threads

where:

  • seq_file_name is the absolute path for the FASTA file containing DNA sequences that need to undergo gene prediction

  • output_file_name is the absolute path and prefix for the three output files. Files with extensions .out, .faa and .ffn will be created, respectively containing the gene prediction metadata, protein translations of predicted genes, and the DNA sequences of predicted genes.

  • 0 or 1 for short sequence reads or complete genomic sequences.

  • train_file_name is used to select the training file for one of the following types:

    • complete for complete genomic sequences or short sequence reads without sequencing error
    • sanger_5 for Sanger sequencing reads with about 0.5% error rate
    • sanger_10 for Sanger sequencing reads with about 1% error rate
    • 454_5 for 454 pyrosequencing reads with about 0.5% error rate
    • 454_10 for 454 pyrosequencing reads with about 1% error rate
    • 454_30 for 454 pyrosequencing reads with about 3% error rate
    • illumina_5 for Illumina sequencing reads with about 0.5% error rate
    • illumina_10 for Illumina sequencing reads with about 1% error rate

    The corresponding file should be in the subdirectory train of the working directory. Other files can be added and selected here.

  • num_threads is the number of threads to be used. Defaults to 1.

Additional options

  • -m meta_file, -n nucleotide_file, -a aa_file and -g gff_file can be used to write output to specific files, instead of having the program create filenames with predetermined extentions. These take precedence over the -o option.

  • Leaving out the -o option or using the name stdout causes FragGeneScanRs to only write the predicted proteins to standard output. The other files can still be requested with the specific options above.

  • Leaving out the -s options causes FragGeneScanRs to read sequences from standard input.

  • -r train_file_dir allows to explicitly specify the pathname of the directory containing the training files, so you can execute the command anywhere on your system.

  • The option -u can be used for some additional speed and reduced memory when using multithreading. The output will no longer be in the same order as the input (as in FGS and FGS+).

The complete list of options will be printed when running FragGeneScanRs --help.

Execution time (version 1.0.0)

Benchmarks were done using the meta/benchmark.sh script on a 16-core Intel(R) Xeon(R) CPU E5-2650 v2 @ 2.60GHz with 195GB RAM. The datasets used are the example datasets provided by FragGeneScan. The table below shows the average execution time of 5 runs. Detailed results may be found in meta/benchmark.csv. For the short reads (80bp), FragGeneScanRs is about 22 times faster than FragGeneScan and 1.2 times as fast as FragGeneScanPlus. For the long reads (1328bp) and the complete genome (Escherichia coli str. K-12 substr. MG1655, 4639675bp), FragGeneScanRs is 4.3 and 2.2 times faster than FragGeneScan and 1.6 and 233.6 times faster than FGS+.

single threaded absolute execution times

parallelization efficiency

Short reads 1 thread 2 threads 4 threads 8 threads 16 threads
FragGeneScan 731 r/s 1257 r/s 2158 r/s 3408 r/s 3371 r/s
FragGeneScanPlus 13830 r/s 23997 r/s 37882 r/s 54610 r/s /
FragGeneScanRs 16119 r/s 29326 r/s 48593 r/s 73965 r/s 99885 r/s
Long reads 1 thread 2 threads 4 threads 8 threads 16 threads
FragGeneScan 317 r/s 545 r/s 1053 r/s 1715 r/s 1968 r/s
FragGeneScanPlus 863 r/s 1596 r/s 2910 r/s 5573 r/s /
FragGeneScanRs 1358 r/s 2674 r/s 5051 r/s 8803 r/s 14343 r/s
Complete genome 1 thread
FragGeneScan 6.668 s
FragGeneScanPlus 712.265 s
FragGeneScanRs 3.049 s

The commands and arguments used for this benchmarks were:

./FragGeneScan -t 454_10 -s example/NC_000913-454.fna -o example/NC_000913-454 -w 0
./FGS+ -t 454_10 -s example/NC_000913-454.fna -o example/NC_000913-454 -w 0
./FragGeneScanRs -t 454_10 -s example/NC_000913-454.fna -o example/NC_000913-454 -w 0

./FragGeneScan -t complete -s example/contigs.fna -o example/contigs -w 1
./FGS+ -t complete -s example/contigs.fna -o example/contigs -w 1
./FragGeneScanRs -t complete -s example/contigs.fna -o example/contigs -w 1

./FragGeneScan -t complete -s example/NC_000913.fna -o example/NC_000913 -w 1
./FGS+ -t complete -s example/NC_000913.fna -o example/NC_000913 -w 1
./FragGeneScanRs -t complete -s example/NC_000913.fna -o example/NC_000913 -w 1

By default, FragGeneScanPlus outputs only the predicted genes, not the metadata and DNA files. Below are measurements taken when those files aren't generated by FragGeneScanRs either.

Short reads 1 thread 2 threads 4 threads 8 threads
FragGeneScanPlus 13765 r/s 24500 r/s 39548 r/s 57147 r/s
FragGeneScanRs 16815 r/s 28784 r/s 50157 r/s 75397 r/s

The commands used here are:

./FGS+ -t 454_10 -s example/NC_000913-454.fna -o stdout -w 0 > /dev/null
./FragGeneScanRs -t 454_10 -s example/NC_000913-454.fna -o stdout -w 0 > /dev/null

Memory usage (version 1.0.0)

The figure below shows the memory footprint for multithreaded execution of FGS, FGS+ and FGSrs on long reads (1328 bp). Total memory footprint (heap, stack and memory-mapped file I/O) is measured using the Massif heap profiler of Valgrind with the --pages-as-heap option. Race conditions consistently halt the execution of FGS+ above 10 threads. FGS and FGSrs generate DNA sequences, protein translations and metadata, whereas FGS+ only generates protein translations because the software crashes when other output is generated. FGS and FGS+ report gene predictions out-of-order, where default in-order reporting was used for FGSrs.

memory usage

Dependencies

~4.5MB
~88K SLoC

_a