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Parasail: SIMD C library for global, semi-global, and local pairwise sequence alignments

Overview of attention for article published in BMC Bioinformatics, February 2016
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  • Above-average Attention Score compared to outputs of the same age (57th percentile)

Mentioned by

4 tweeters


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Readers on

73 Mendeley
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Parasail: SIMD C library for global, semi-global, and local pairwise sequence alignments
Published in
BMC Bioinformatics, February 2016
DOI 10.1186/s12859-016-0930-z
Pubmed ID

Jeff Daily


Sequence alignment algorithms are a key component of many bioinformatics applications. Though various fast Smith-Waterman local sequence alignment implementations have been developed for x86 CPUs, most are embedded into larger database search tools. In addition, fast implementations of Needleman-Wunsch global sequence alignment and its semi-global variants are not as widespread. This article presents the first software library for local, global, and semi-global pairwise intra-sequence alignments and improves the performance of previous intra-sequence implementations. A faster intra-sequence local pairwise alignment implementation is described and benchmarked, including new global and semi-global variants. Using a 375 residue query sequence a speed of 136 billion cell updates per second (GCUPS) was achieved on a dual Intel Xeon E5-2670 24-core processor system, the highest reported for an implementation based on Farrar's 'striped' approach. Rognes's SWIPE optimal database search application is still generally the fastest available at 1.2 to at best 2.4 times faster than Parasail for sequences shorter than 500 amino acids. However, Parasail was faster for longer sequences. For global alignments, Parasail's prefix scan implementation is generally the fastest, faster even than Farrar's 'striped' approach, however the opal library is faster for single-threaded applications. The software library is designed for 64 bit Linux, OS X, or Windows on processors with SSE2, SSE41, or AVX2. Source code is available from https://github.com/jeffdaily/parasail under the Battelle BSD-style license. Applications that require optimal alignment scores could benefit from the improved performance. For the first time, SIMD global, semi-global, and local alignments are available in a stand-alone C library.

Twitter Demographics

The data shown below were collected from the profiles of 4 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 73 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Korea, Republic of 1 1%
France 1 1%
Belgium 1 1%
Unknown 70 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 19 26%
Researcher 17 23%
Student > Master 12 16%
Professor > Associate Professor 3 4%
Student > Bachelor 3 4%
Other 6 8%
Unknown 13 18%
Readers by discipline Count As %
Computer Science 22 30%
Biochemistry, Genetics and Molecular Biology 19 26%
Agricultural and Biological Sciences 10 14%
Engineering 3 4%
Chemistry 2 3%
Other 1 1%
Unknown 16 22%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 March 2017.
All research outputs
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Outputs from BMC Bioinformatics
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Outputs of similar age
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Outputs of similar age from BMC Bioinformatics
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Altmetric has tracked 15,920,152 research outputs across all sources so far. This one is in the 43rd percentile – i.e., 43% of other outputs scored the same or lower than it.
So far Altmetric has tracked 5,768 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.0. This one is in the 42nd percentile – i.e., 42% 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 346,317 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 57% of its contemporaries.
We're also able to compare this research output to 8 others from the same source and published within six weeks on either side of this one. This one has scored higher than 2 of them.