GHOSTX protocols

View GHOSTX computational protocol

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GHOSTX specifications

Information


Unique identifier OMICS_08012
Name GHOSTX
Software type Package/Module
Interface Command line interface
Restrictions to use None
Input data Some metagenome sequences produced from a DNA sequencer.
Input format FASTA
Output format GHOSTX outputs search results in the format similar to BLAST-tabular format.
Operating system Unix/Linux
Programming languages C++
Parallelization CUDA
License BSD 2-clause “Simplified” License
Computer skills Advanced
Version 1.3.7
Stability Stable
Requirements
gcc
Maintained Yes

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Maintainer


  • person_outline Yutaka Akiyama <>

Publications for GHOSTX

GHOSTX in pipelines

 (2)
2018
PMCID: 5852087
PMID: 29540736
DOI: 10.1038/s41598-018-22617-x

[…] v2.0.6 using default settings. bam files were converted with samtools v0.1.18, and gene coverage was calculated with subread version 1.4.6., the proteins were annotated with kaas, with sbh and ghostx as settings and with interproscan 5.6-48.0. the annotation was further enhanced by adding ec numbers via priam version march 06, 2013. further ec numbers were derived by text mining […]

2017
PMCID: 5440902
PMID: 28532419
DOI: 10.1186/s12864-017-3757-8

[…] and plotted by wego []. kyoto encyclopedia of genes and genomes (kegg) pathway mapping was done on the kegg automatic annotation server (kaas) v2.0 [], taking all plant species as references, ghostx and bi-directional best hit method., transcripts were aligned to sorghum genome v2.0 [] using gmap (genome mapping and alignment program) [] with 80% identity and 90% coverage threshold […]


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GHOSTX in publications

 (11)
PMCID: 5937802
PMID: 29690922
DOI: 10.1186/s40168-018-0460-1

[…] method becomes available: blast [], for example, has been one of the most popular tools for database searches, despite its rather large computational overhead. recently developed alternatives like ghostx [] or diamond [] offer considerable acceleration compared to the original blast algorithm while retaining similar sensitivity. employing a workflow engine, these alternatives can effortlessly […]

PMCID: 5852087
PMID: 29540736
DOI: 10.1038/s41598-018-22617-x

[…] v2.0.6 using default settings. bam files were converted with samtools v0.1.18, and gene coverage was calculated with subread version 1.4.6., the proteins were annotated with kaas, with sbh and ghostx as settings and with interproscan 5.6-48.0. the annotation was further enhanced by adding ec numbers via priam version march 06, 2013. further ec numbers were derived by text mining […]

PMCID: 5812651
PMID: 29444186
DOI: 10.1371/journal.pone.0192898

[…] fungal amplicon reads [–]., the second approach identifies species from shotgun metagenomes. most tools use custom-built databases, together with search algorithms such as blast, usearch and ublast, ghostx, and diamond [–]. these tools identify the database sequence most similar to a read from a metagenome. alternatively, algorithms such as kaiju and kraken assign reads to a lowest common […]

PMCID: 5666806
PMID: 29019934
DOI: 10.3390/ijms18102124

[…] similarity search sections at various scales, in which computational resources were efficiently used. the acceleration of ghost-mp compared with mpiblast should arise from the difference between the ghostx and blastx algorithms. furthermore, similar accelerations were observed in experiments with a compute node []., we further evaluated the scalability of ghost-mp on the k computer […]

PMCID: 5569505
PMID: 28835260
DOI: 10.1186/s40168-017-0322-2

[…] [, ]. for the kaas- and cazy-based analysis, concatenated protein fasta sequences of each mag were uploaded to the kaas and dbcan webservers using the metagenome setting for kaas (parameters: ghostx, genes dataset, sbh assignment method) and default settings for dbcan., additionally, we searched for key metabolic genes using custom blast and hmmer databases using previously defined […]


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GHOSTX institution(s)
Department of Computer Science, School of Computing, Tokyo Institute of Technology, Tokyo, Japan; Education Academy of Computational Life Sciences (ACLS), Tokyo Institute of Technology, Yokohama, Japan; Department of Computer Science, Graduate School of Information Science and Engineering, Tokyo Institute of Technology, Tokyo, Japan
GHOSTX funding source(s)
Partly supported by the Strategic Programs for Innovative Research (SPIRE) Field 1 Supercomputational Life Science of the Ministry of Education, Culture, Sports, Science and Technology (MEXT) of Japan and Core Research for Evolutional Science and Technology (CREST) “Extreme Big Data” of the Japan Science and Technology Agency (JST).

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