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TaxMapper: an analysis tool, reference database and workflow for metatranscriptome analysis of eukaryotic microorganisms

Overview of attention for article published in BMC Genomics, October 2017
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Title
TaxMapper: an analysis tool, reference database and workflow for metatranscriptome analysis of eukaryotic microorganisms
Published in
BMC Genomics, October 2017
DOI 10.1186/s12864-017-4168-6
Pubmed ID
Authors

Daniela Beisser, Nadine Graupner, Lars Grossmann, Henning Timm, Jens Boenigk, Sven Rahmann

Abstract

High-throughput sequencing (HTS) technologies are increasingly applied to analyse complex microbial ecosystems by mRNA sequencing of whole communities, also known as metatranscriptome sequencing. This approach is at the moment largely limited to prokaryotic communities and communities of few eukaryotic species with sequenced genomes. For eukaryotes the analysis is hindered mainly by a low and fragmented coverage of the reference databases to infer the community composition, but also by lack of automated workflows for the task. From the databases of the National Center for Biotechnology Information and Marine Microbial Eukaryote Transcriptome Sequencing Project, 142 references were selected in such a way that the taxa represent the main lineages within each of the seven supergroups of eukaryotes and possess predominantly complete transcriptomes or genomes. From these references, we created an annotated microeukaryotic reference database. We developed a tool called TaxMapper for a reliably mapping of sequencing reads against this database and filtering of unreliable assignments. For filtering, a classifier was trained and tested on each of the following: sequences of taxa in the database, sequences of taxa related to those in the database, and random sequences. Additionally, TaxMapper is part of a metatranscriptomic Snakemake workflow developed to perform quality assessment, functional and taxonomic annotation and (multivariate) statistical analysis including environmental data. The workflow is provided and described in detail to empower researchers to apply it for metatranscriptome analysis of any environmental sample. TaxMapper shows superior performance compared to standard approaches, resulting in a higher number of true positive taxonomic assignments. Both the TaxMapper tool and the workflow are available as open-source code at Bitbucket under the MIT license: https://bitbucket.org/dbeisser/taxmapper and as a Bioconda package: https://bioconda.github.io/recipes/taxmapper/README.html .

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 70 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 70 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 15 21%
Researcher 12 17%
Student > Bachelor 8 11%
Student > Doctoral Student 7 10%
Student > Master 5 7%
Other 10 14%
Unknown 13 19%
Readers by discipline Count As %
Agricultural and Biological Sciences 29 41%
Biochemistry, Genetics and Molecular Biology 8 11%
Environmental Science 4 6%
Computer Science 4 6%
Immunology and Microbiology 2 3%
Other 9 13%
Unknown 14 20%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 May 2018.
All research outputs
#15,329,366
of 23,577,761 outputs
Outputs from BMC Genomics
#6,277
of 10,800 outputs
Outputs of similar age
#194,433
of 327,154 outputs
Outputs of similar age from BMC Genomics
#111
of 195 outputs
Altmetric has tracked 23,577,761 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% of other outputs scored the same or lower than it.
So far Altmetric has tracked 10,800 research outputs from this source. They receive a mean Attention Score of 4.7. This one is in the 37th percentile – i.e., 37% of its peers scored the same or lower than it.
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 327,154 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 37th percentile – i.e., 37% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 195 others from the same source and published within six weeks on either side of this one. This one is in the 37th percentile – i.e., 37% of its contemporaries scored the same or lower than it.