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Optimal choice of word length when comparing two Markov sequences using a χ 2-statistic

Overview of attention for article published in BMC Genomics, October 2017
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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 (82nd percentile)
  • High Attention Score compared to outputs of the same age and source (90th percentile)

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21 tweeters

Citations

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5 Dimensions

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5 Mendeley
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Title
Optimal choice of word length when comparing two Markov sequences using a χ 2-statistic
Published in
BMC Genomics, October 2017
DOI 10.1186/s12864-017-4020-z
Pubmed ID
Authors

Xin Bai, Kujin Tang, Jie Ren, Michael Waterman, Fengzhu Sun

Abstract

Alignment-free sequence comparison using counts of word patterns (grams, k-tuples) has become an active research topic due to the large amount of sequence data from the new sequencing technologies. Genome sequences are frequently modelled by Markov chains and the likelihood ratio test or the corresponding approximate χ (2)-statistic has been suggested to compare two sequences. However, it is not known how to best choose the word length k in such studies. We develop an optimal strategy to choose k by maximizing the statistical power of detecting differences between two sequences. Let the orders of the Markov chains for the two sequences be r 1 and r 2, respectively. We show through both simulations and theoretical studies that the optimal k= max(r 1,r 2)+1 for both long sequences and next generation sequencing (NGS) read data. The orders of the Markov chains may be unknown and several methods have been developed to estimate the orders of Markov chains based on both long sequences and NGS reads. We study the power loss of the statistics when the estimated orders are used. It is shown that the power loss is minimal for some of the estimators of the orders of Markov chains. Our studies provide guidelines on choosing the optimal word length for the comparison of Markov sequences.

Twitter Demographics

The data shown below were collected from the profiles of 21 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 5 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 5 100%

Demographic breakdown

Readers by professional status Count As %
Other 3 60%
Researcher 1 20%
Student > Ph. D. Student 1 20%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 2 40%
Medicine and Dentistry 2 40%
Agricultural and Biological Sciences 1 20%

Attention Score in Context

This research output has an Altmetric Attention Score of 12. 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 19 August 2018.
All research outputs
#2,170,452
of 19,243,709 outputs
Outputs from BMC Genomics
#780
of 9,741 outputs
Outputs of similar age
#51,132
of 293,380 outputs
Outputs of similar age from BMC Genomics
#3
of 22 outputs
Altmetric has tracked 19,243,709 research outputs across all sources so far. Compared to these this one has done well and is in the 88th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 9,741 research outputs from this source. They receive a mean Attention Score of 4.5. This one has done particularly well, scoring higher than 92% 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 293,380 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 82% of its contemporaries.
We're also able to compare this research output to 22 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 90% of its contemporaries.