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BioMaS: a modular pipeline for Bioinformatic analysis of Metagenomic AmpliconS

Overview of attention for article published in BMC Bioinformatics, July 2015
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (79th percentile)

Mentioned by

twitter
14 tweeters
googleplus
1 Google+ user

Citations

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25 Dimensions

Readers on

mendeley
180 Mendeley
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2 CiteULike
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Title
BioMaS: a modular pipeline for Bioinformatic analysis of Metagenomic AmpliconS
Published in
BMC Bioinformatics, July 2015
DOI 10.1186/s12859-015-0595-z
Pubmed ID
Authors

Bruno Fosso, Monica Santamaria, Marinella Marzano, Daniel Alonso-Alemany, Gabriel Valiente, Giacinto Donvito, Alfonso Monaco, Pasquale Notarangelo, Graziano Pesole

Abstract

Substantial advances in microbiology, molecular evolution and biodiversity have been carried out in recent years thanks to Metagenomics, which allows to unveil the composition and functions of mixed microbial communities in any environmental niche. If the investigation is aimed only at the microbiome taxonomic structure, a target-based metagenomic approach, here also referred as Meta-barcoding, is generally applied. This approach commonly involves the selective amplification of a species-specific genetic marker (DNA meta-barcode) in the whole taxonomic range of interest and the exploration of its taxon-related variants through High-Throughput Sequencing (HTS) technologies. The accessibility to proper computational systems for the large-scale bioinformatic analysis of HTS data represents, currently, one of the major challenges in advanced Meta-barcoding projects. BioMaS (Bioinformatic analysis of Metagenomic AmpliconS) is a new bioinformatic pipeline designed to support biomolecular researchers involved in taxonomic studies of environmental microbial communities by a completely automated workflow, comprehensive of all the fundamental steps, from raw sequence data upload and cleaning to final taxonomic identification, that are absolutely required in an appropriately designed Meta-barcoding HTS-based experiment. In its current version, BioMaS allows the analysis of both bacterial and fungal environments starting directly from the raw sequencing data from either Roche 454 or Illumina HTS platforms, following two alternative paths, respectively. BioMaS is implemented into a public web service available at https://recasgateway.ba.infn.it/ and is also available in Galaxy at http://galaxy.cloud.ba.infn.it:8080 (only for Illumina data). BioMaS is a friendly pipeline for Meta-barcoding HTS data analysis specifically designed for users without particular computing skills. A comparative benchmark, carried out by using a simulated dataset suitably designed to broadly represent the currently known bacterial and fungal world, showed that BioMaS outperforms QIIME and MOTHUR in terms of extent and accuracy of deep taxonomic sequence assignments.

Twitter Demographics

The data shown below were collected from the profiles of 14 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 180 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Brazil 4 2%
Italy 2 1%
France 2 1%
United Kingdom 2 1%
India 1 <1%
Canada 1 <1%
Argentina 1 <1%
Belgium 1 <1%
Korea, Republic of 1 <1%
Other 1 <1%
Unknown 164 91%

Demographic breakdown

Readers by professional status Count As %
Researcher 40 22%
Student > Ph. D. Student 37 21%
Student > Master 31 17%
Student > Bachelor 26 14%
Student > Doctoral Student 9 5%
Other 30 17%
Unknown 7 4%
Readers by discipline Count As %
Agricultural and Biological Sciences 77 43%
Biochemistry, Genetics and Molecular Biology 50 28%
Computer Science 11 6%
Environmental Science 9 5%
Immunology and Microbiology 5 3%
Other 17 9%
Unknown 11 6%

Attention Score in Context

This research output has an Altmetric Attention Score of 7. 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 19 October 2015.
All research outputs
#2,654,631
of 14,579,947 outputs
Outputs from BMC Bioinformatics
#1,156
of 5,450 outputs
Outputs of similar age
#46,800
of 230,465 outputs
Outputs of similar age from BMC Bioinformatics
#1
of 1 outputs
Altmetric has tracked 14,579,947 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 5,450 research outputs from this source. They receive a mean Attention Score of 4.9. This one has done well, scoring higher than 78% 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 230,465 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 1 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