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Breaking the computational barriers of pairwise genome comparison

Overview of attention for article published in BMC Bioinformatics, August 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

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15 tweeters

Citations

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14 Dimensions

Readers on

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51 Mendeley
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1 CiteULike
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Title
Breaking the computational barriers of pairwise genome comparison
Published in
BMC Bioinformatics, August 2015
DOI 10.1186/s12859-015-0679-9
Pubmed ID
Authors

Oscar Torreno, Oswaldo Trelles

Abstract

Conventional pairwise sequence comparison software algorithms are being used to process much larger datasets than they were originally designed for. This can result in processing bottlenecks that limit software capabilities or prevent full use of the available hardware resources. Overcoming the barriers that limit the efficient computational analysis of large biological sequence datasets by retrofitting existing algorithms or by creating new applications represents a major challenge for the bioinformatics community. We have developed C libraries for pairwise sequence comparison within diverse architectures, ranging from commodity systems to high performance and cloud computing environments. Exhaustive tests were performed using different datasets of closely- and distantly-related sequences that span from small viral genomes to large mammalian chromosomes. The tests demonstrated that our solution is capable of generating high quality results with a linear-time response and controlled memory consumption, being comparable or faster than the current state-of-the-art methods. We have addressed the problem of pairwise and all-versus-all comparison of large sequences in general, greatly increasing the limits on input data size. The approach described here is based on a modular out-of-core strategy that uses secondary storage to avoid reaching memory limits during the identification of High-scoring Segment Pairs (HSPs) between the sequences under comparison. Software engineering concepts were applied to avoid intermediate result re-calculation, to minimise the performance impact of input/output (I/O) operations and to modularise the process, thus enhancing application flexibility and extendibility. Our computationally-efficient approach allows tasks such as the massive comparison of complete genomes, evolutionary event detection, the identification of conserved synteny blocks and inter-genome distance calculations to be performed more effectively.

Twitter Demographics

The data shown below were collected from the profiles of 15 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 51 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Brazil 3 6%
Philippines 1 2%
Sweden 1 2%
United States 1 2%
Unknown 45 88%

Demographic breakdown

Readers by professional status Count As %
Researcher 14 27%
Student > Master 10 20%
Student > Ph. D. Student 8 16%
Student > Bachelor 6 12%
Professor 3 6%
Other 7 14%
Unknown 3 6%
Readers by discipline Count As %
Agricultural and Biological Sciences 16 31%
Computer Science 12 24%
Biochemistry, Genetics and Molecular Biology 6 12%
Engineering 3 6%
Immunology and Microbiology 2 4%
Other 5 10%
Unknown 7 14%

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 03 February 2016.
All research outputs
#2,638,573
of 14,674,506 outputs
Outputs from BMC Bioinformatics
#1,124
of 5,466 outputs
Outputs of similar age
#47,497
of 233,882 outputs
Outputs of similar age from BMC Bioinformatics
#1
of 2 outputs
Altmetric has tracked 14,674,506 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 5,466 research outputs from this source. They receive a mean Attention Score of 4.9. This one has done well, scoring higher than 79% 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 233,882 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 2 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