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NPBSS: a new PacBio sequencing simulator for generating the continuous long reads with an empirical model

Overview of attention for article published in BMC Bioinformatics, May 2018
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (86th percentile)
  • High Attention Score compared to outputs of the same age and source (93rd percentile)

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1 news outlet
blogs
1 blog
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4 X users

Citations

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28 Dimensions

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64 Mendeley
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Title
NPBSS: a new PacBio sequencing simulator for generating the continuous long reads with an empirical model
Published in
BMC Bioinformatics, May 2018
DOI 10.1186/s12859-018-2208-0
Pubmed ID
Authors

Ze-Gang Wei, Shao-Wu Zhang

Abstract

PacBio sequencing platform offers longer read lengths than the second-generation sequencing technologies. It has revolutionized de novo genome assembly and enabled the automated reconstruction of reference-quality genomes. Due to its extremely wide range of application areas, fast sequencing simulation systems with high fidelity are in great demand to facilitate the development and comparison of subsequent analysis tools. Although there are several available simulators (e.g., PBSIM, SimLoRD and FASTQSim) that target the specific generation of PacBio libraries, the error rate of simulated sequences is not well matched to the quality value of raw PacBio datasets, especially for PacBio's continuous long reads (CLR). By analyzing the characteristic features of CLR data from PacBio SMRT (single molecule real time) sequencing, we developed a new PacBio sequencing simulator (called NPBSS) for producing CLR reads. NPBSS simulator firstly samples the read sequences according to the read length logarithmic normal distribution, and choses different base quality values with different proportions. Then, NPBSS computes the overall error probability of each base in the read sequence with an empirical model, and calculates the deletion, substitution and insertion probabilities with the overall error probability to generate the PacBio CLR reads. Alignment results demonstrate that NPBSS fits the error rate of the PacBio CLR reads better than PBSIM and FASTQSim. In addition, the assembly results also show that simulated sequences of NPBSS are more like real PacBio CLR data. NPBSS simulator is convenient to use with efficient computation and flexible parameters setting. Its generating PacBio CLR reads are more like real PacBio datasets.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 64 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 64 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 15 23%
Student > Ph. D. Student 13 20%
Researcher 8 13%
Student > Bachelor 6 9%
Professor 5 8%
Other 6 9%
Unknown 11 17%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 25 39%
Agricultural and Biological Sciences 10 16%
Computer Science 7 11%
Engineering 4 6%
Medicine and Dentistry 2 3%
Other 5 8%
Unknown 11 17%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 17. 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 13 August 2020.
All research outputs
#1,923,605
of 23,344,526 outputs
Outputs from BMC Bioinformatics
#465
of 7,387 outputs
Outputs of similar age
#42,900
of 330,848 outputs
Outputs of similar age from BMC Bioinformatics
#8
of 113 outputs
Altmetric has tracked 23,344,526 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,387 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one has done particularly well, scoring higher than 93% 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 330,848 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 86% of its contemporaries.
We're also able to compare this research output to 113 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 93% of its contemporaries.