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BrownieAligner: accurate alignment of Illumina sequencing data to de Bruijn graphs

Overview of attention for article published in BMC Bioinformatics, September 2018
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  • Good Attention Score compared to outputs of the same age (66th percentile)
  • Good Attention Score compared to outputs of the same age and source (69th percentile)

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8 X users

Citations

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26 Dimensions

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Title
BrownieAligner: accurate alignment of Illumina sequencing data to de Bruijn graphs
Published in
BMC Bioinformatics, September 2018
DOI 10.1186/s12859-018-2319-7
Pubmed ID
Authors

Mahdi Heydari, Giles Miclotte, Yves Van de Peer, Jan Fostier

Abstract

Aligning short reads to a reference genome is an important task in many genome analysis pipelines. This task is computationally more complex when the reference genome is provided in the form of a de Bruijn graph instead of a linear sequence string. We present a branch and bound alignment algorithm that uses the seed-and-extend paradigm to accurately align short Illumina reads to a graph. Given a seed, the algorithm greedily explores all branches of the tree until the optimal alignment path is found. To reduce the search space we compute upper bounds to the alignment score for each branch and discard the branch if it cannot improve the best solution found so far. Additionally, by using a two-pass alignment strategy and a higher-order Markov model, paths in the de Bruijn graph that do not represent a subsequence in the original reference genome are discarded from the search procedure. BrownieAligner is applied to both synthetic and real datasets. It generally outperforms other state-of-the-art tools in terms of accuracy, while having similar runtime and memory requirements. Our results show that using the higher-order Markov model in BrownieAligner improves the accuracy, while the branch and bound algorithm reduces runtime. BrownieAligner is written in standard C++11 and released under GPL license. BrownieAligner relies on multithreading to take advantage of multi-core/multi-CPU systems. The source code is available at: https://github.com/biointec/browniealigner.

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X Demographics

The data shown below were collected from the profiles of 8 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 40 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 40 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 13 33%
Student > Ph. D. Student 10 25%
Researcher 4 10%
Student > Postgraduate 2 5%
Professor > Associate Professor 1 3%
Other 1 3%
Unknown 9 23%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 11 28%
Agricultural and Biological Sciences 9 23%
Computer Science 8 20%
Immunology and Microbiology 1 3%
Medicine and Dentistry 1 3%
Other 1 3%
Unknown 9 23%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 09 January 2019.
All research outputs
#6,534,944
of 23,577,761 outputs
Outputs from BMC Bioinformatics
#2,455
of 7,418 outputs
Outputs of similar age
#113,232
of 336,570 outputs
Outputs of similar age from BMC Bioinformatics
#29
of 94 outputs
Altmetric has tracked 23,577,761 research outputs across all sources so far. This one has received more attention than most of these and is in the 72nd percentile.
So far Altmetric has tracked 7,418 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has gotten more attention than average, scoring higher than 66% 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 336,570 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 66% of its contemporaries.
We're also able to compare this research output to 94 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 69% of its contemporaries.