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MEGAN-LR: new algorithms allow accurate binning and easy interactive exploration of metagenomic long reads and contigs

Overview of attention for article published in Biology Direct, April 2018
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#44 of 597)
  • High Attention Score compared to outputs of the same age (89th percentile)

Mentioned by

blogs
1 blog
twitter
34 tweeters

Citations

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85 Dimensions

Readers on

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151 Mendeley
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Title
MEGAN-LR: new algorithms allow accurate binning and easy interactive exploration of metagenomic long reads and contigs
Published in
Biology Direct, April 2018
DOI 10.1186/s13062-018-0208-7
Pubmed ID
Authors

Daniel H. Huson, Benjamin Albrecht, Caner Bağcı, Irina Bessarab, Anna Górska, Dino Jolic, Rohan B. H. Williams

Abstract

There are numerous computational tools for taxonomic or functional analysis of microbiome samples, optimized to run on hundreds of millions of short, high quality sequencing reads. Programs such as MEGAN allow the user to interactively navigate these large datasets. Long read sequencing technologies continue to improve and produce increasing numbers of longer reads (of varying lengths in the range of 10k-1M bps, say), but of low quality. There is an increasing interest in using long reads in microbiome sequencing, and there is a need to adapt short read tools to long read datasets. We describe a new LCA-based algorithm for taxonomic binning, and an interval-tree based algorithm for functional binning, that are explicitly designed for long reads and assembled contigs. We provide a new interactive tool for investigating the alignment of long reads against reference sequences. For taxonomic and functional binning, we propose to use LAST to compare long reads against the NCBI-nr protein reference database so as to obtain frame-shift aware alignments, and then to process the results using our new methods. All presented methods are implemented in the open source edition of MEGAN, and we refer to this new extension as MEGAN-LR (MEGAN long read). We evaluate the LAST+MEGAN-LR approach in a simulation study, and on a number of mock community datasets consisting of Nanopore reads, PacBio reads and assembled PacBio reads. We also illustrate the practical application on a Nanopore dataset that we sequenced from an anammox bio-rector community. This article was reviewed by Nicola Segata together with Moreno Zolfo, Pete James Lockhart and Serghei Mangul. This work extends the applicability of the widely-used metagenomic analysis software MEGAN to long reads. Our study suggests that the presented LAST+MEGAN-LR pipeline is sufficiently fast and accurate.

Twitter Demographics

The data shown below were collected from the profiles of 34 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 151 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 151 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 46 30%
Researcher 26 17%
Student > Master 23 15%
Student > Bachelor 12 8%
Other 7 5%
Other 17 11%
Unknown 20 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 46 30%
Biochemistry, Genetics and Molecular Biology 43 28%
Environmental Science 10 7%
Immunology and Microbiology 8 5%
Computer Science 7 5%
Other 12 8%
Unknown 25 17%

Attention Score in Context

This research output has an Altmetric Attention Score of 23. 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 April 2020.
All research outputs
#1,019,585
of 17,421,713 outputs
Outputs from Biology Direct
#44
of 597 outputs
Outputs of similar age
#28,614
of 285,534 outputs
Outputs of similar age from Biology Direct
#1
of 1 outputs
Altmetric has tracked 17,421,713 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 94th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 597 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.1. This one has done particularly well, scoring higher than 92% 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 285,534 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 89% 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