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Fast, accurate, and lightweight analysis of BS-treated reads with ERNE 2

Overview of attention for article published in BMC Bioinformatics, March 2016
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (84th percentile)
  • High Attention Score compared to outputs of the same age and source (82nd percentile)

Mentioned by

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1 news outlet
twitter
5 tweeters

Citations

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10 Dimensions

Readers on

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31 Mendeley
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Title
Fast, accurate, and lightweight analysis of BS-treated reads with ERNE 2
Published in
BMC Bioinformatics, March 2016
DOI 10.1186/s12859-016-0910-3
Pubmed ID
Authors

Nicola Prezza, Francesco Vezzi, Max Käller, Alberto Policriti

Abstract

Bisulfite treatment of DNA followed by sequencing (BS-seq) has become a standard technique in epigenetic studies, providing researchers with tools for generating single-base resolution maps of whole methylomes. Aligning bisulfite-treated reads, however, is a computationally difficult task: bisulfite treatment decreases the (lexical) complexity of low-methylated genomic regions, and C-to-T mismatches may reflect cytosine unmethylation rather than SNPs or sequencing errors. Further challenges arise both during and after the alignment phase: data structures used by the aligner should be fast and should fit into main memory, and the methylation-caller output should be somehow compressed, due to its significant size. As far as data structures employed to align bisulfite-treated reads are concerned, solutions proposed in the literature can be roughly grouped into two main categories: those storing pointers at each text position (e.g. hash tables, suffix trees/arrays), and those using the information-theoretic minimum number of bits (e.g. FM indexes and compressed suffix arrays). The former are fast and memory consuming. The latter are much slower and light. In this paper, we try to close this gap proposing a data structure for aligning bisulfite-treated reads which is at the same time fast, light, and very accurate. We reach this objective by combining a recent theoretical result on succinct hashing with a bisulfite-aware hash function. Furthermore, the new versions of the tools implementing our ideas|the aligner ERNE-BS5 2 and the caller ERNE-METH 2|have been extended with increased downstream compatibility (EPP/Bismark cov output formats), output compression, and support for target enrichment protocols. Experimental results on public and simulated WGBS libraries show that our algorithmic solution is a competitive tradeoff between hash-based and BWT-based indexes, being as fast and accurate as the former, and as memory-efficient as the latter. The new functionalities of our bisulfite aligner and caller make it a fast and memory efficient tool, useful to analyze big datasets with little computational resources, to easily process target enrichment data, and produce statistics such as protocol efficiency and coverage as a function of the distance from target regions.

Twitter Demographics

The data shown below were collected from the profiles of 5 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 31 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 3%
France 1 3%
Unknown 29 94%

Demographic breakdown

Readers by professional status Count As %
Researcher 11 35%
Student > Ph. D. Student 9 29%
Student > Master 2 6%
Student > Bachelor 2 6%
Other 1 3%
Other 4 13%
Unknown 2 6%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 10 32%
Agricultural and Biological Sciences 8 26%
Computer Science 5 16%
Unspecified 1 3%
Nursing and Health Professions 1 3%
Other 3 10%
Unknown 3 10%

Attention Score in Context

This research output has an Altmetric Attention Score of 11. 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 11 March 2016.
All research outputs
#2,709,165
of 22,852,911 outputs
Outputs from BMC Bioinformatics
#891
of 7,292 outputs
Outputs of similar age
#44,611
of 298,624 outputs
Outputs of similar age from BMC Bioinformatics
#23
of 129 outputs
Altmetric has tracked 22,852,911 research outputs across all sources so far. Compared to these this one has done well and is in the 87th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,292 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 done well, scoring higher than 87% 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 298,624 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 84% of its contemporaries.
We're also able to compare this research output to 129 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 82% of its contemporaries.