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DFAST and DAGA: web-based integrated genome annotation tools and resources

Overview of attention for article published in Bioscience of Microbiota, Food and Health, July 2016
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  • In the top 25% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#40 of 269)
  • Good Attention Score compared to outputs of the same age (79th percentile)

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1 blog
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Title
DFAST and DAGA: web-based integrated genome annotation tools and resources
Published in
Bioscience of Microbiota, Food and Health, July 2016
DOI 10.12938/bmfh.16-003
Pubmed ID
Authors

Yasuhiro TANIZAWA, Takatomo FUJISAWA, Eli KAMINUMA, Yasukazu NAKAMURA, Masanori ARITA

Abstract

Quality assurance and correct taxonomic affiliation of data submitted to public sequence databases have been an everlasting problem. The DDBJ Fast Annotation and Submission Tool (DFAST) is a newly developed genome annotation pipeline with quality and taxonomy assessment tools. To enable annotation of ready-to-submit quality, we also constructed curated reference protein databases tailored for lactic acid bacteria. DFAST was developed so that all the procedures required for DDBJ submission could be done seamlessly online. The online workspace would be especially useful for users not familiar with bioinformatics skills. In addition, we have developed a genome repository, DFAST Archive of Genome Annotation (DAGA), which currently includes 1,421 genomes covering 179 species and 18 subspecies of two genera, Lactobacillus and Pediococcus, obtained from both DDBJ/ENA/GenBank and Sequence Read Archive (SRA). All the genomes deposited in DAGA were annotated consistently and assessed using DFAST. To assess the taxonomic position based on genomic sequence information, we used the average nucleotide identity (ANI), which showed high discriminative power to determine whether two given genomes belong to the same species. We corrected mislabeled or misidentified genomes in the public database and deposited the curated information in DAGA. The repository will improve the accessibility and reusability of genome resources for lactic acid bacteria. By exploiting the data deposited in DAGA, we found intraspecific subgroups in Lactobacillus gasseri and Lactobacillus jensenii, whose variation between subgroups is larger than the well-accepted ANI threshold of 95% to differentiate species. DFAST and DAGA are freely accessible at https://dfast.nig.ac.jp.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 131 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 131 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 22 17%
Student > Master 20 15%
Student > Ph. D. Student 14 11%
Student > Bachelor 10 8%
Student > Doctoral Student 9 7%
Other 16 12%
Unknown 40 31%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 26 20%
Agricultural and Biological Sciences 25 19%
Immunology and Microbiology 13 10%
Computer Science 4 3%
Engineering 3 2%
Other 13 10%
Unknown 47 36%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 8. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 12 December 2016.
All research outputs
#4,737,856
of 25,850,671 outputs
Outputs from Bioscience of Microbiota, Food and Health
#40
of 269 outputs
Outputs of similar age
#77,192
of 372,687 outputs
Outputs of similar age from Bioscience of Microbiota, Food and Health
#1
of 3 outputs
Altmetric has tracked 25,850,671 research outputs across all sources so far. Compared to these this one has done well and is in the 81st percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 269 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.1. This one has done well, scoring higher than 85% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 372,687 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 79% of its contemporaries.
We're also able to compare this research output to 3 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them