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Reconstructing evolutionary trees in parallel for massive sequences

Overview of attention for article published in BMC Systems Biology, December 2017
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5 tweeters

Citations

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15 Dimensions

Readers on

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17 Mendeley
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Title
Reconstructing evolutionary trees in parallel for massive sequences
Published in
BMC Systems Biology, December 2017
DOI 10.1186/s12918-017-0476-3
Pubmed ID
Authors

Quan Zou, Shixiang Wan, Xiangxiang Zeng, Zhanshan Sam Ma

Abstract

Building the evolutionary trees for massive unaligned DNA sequences is challenging and crucial. However, reconstructing evolutionary tree for ultra-large sequences is hard. Massive multiple sequence alignment is also challenging and time/space consuming. Hadoop and Spark are developed recently, which bring spring light for the classical computational biology problems. In this paper, we tried to solve the multiple sequence alignment and evolutionary reconstruction in parallel. HPTree, which is developed in this paper, can deal with big DNA sequence files quickly. It works well on the >1GB files, and gets better performance than other evolutionary reconstruction tools. Users could use HPTree for reonstructing evolutioanry trees on the computer clusters or cloud platform (eg. Amazon Cloud). HPTree could help on population evolution research and metagenomics analysis. In this paper, we employ the Hadoop and Spark platform and design an evolutionary tree reconstruction software tool for unaligned massive DNA sequences. Clustering and multiple sequence alignment are done in parallel. Neighbour-joining model was employed for the evolutionary tree building. We opened our software together with source codes via http://lab.malab.cn/soft/HPtree/ .

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 17 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 17 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 2 12%
Student > Postgraduate 2 12%
Student > Ph. D. Student 2 12%
Student > Bachelor 1 6%
Researcher 1 6%
Other 1 6%
Unknown 8 47%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 5 29%
Computer Science 3 18%
Engineering 1 6%
Unknown 8 47%

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 05 January 2018.
All research outputs
#7,433,997
of 12,380,517 outputs
Outputs from BMC Systems Biology
#527
of 1,041 outputs
Outputs of similar age
#181,817
of 352,921 outputs
Outputs of similar age from BMC Systems Biology
#26
of 53 outputs
Altmetric has tracked 12,380,517 research outputs across all sources so far. This one is in the 37th percentile – i.e., 37% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,041 research outputs from this source. They receive a mean Attention Score of 3.4. This one is in the 44th percentile – i.e., 44% 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 352,921 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 44th percentile – i.e., 44% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 53 others from the same source and published within six weeks on either side of this one. This one is in the 43rd percentile – i.e., 43% of its contemporaries scored the same or lower than it.