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Distinguishing highly similar gene isoforms with a clustering-based bioinformatics analysis of PacBio single-molecule long reads

Overview of attention for article published in BioData Mining, April 2016
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#44 of 288)
  • High Attention Score compared to outputs of the same age (87th percentile)

Mentioned by

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1 news outlet
twitter
10 tweeters

Citations

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20 Dimensions

Readers on

mendeley
56 Mendeley
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Title
Distinguishing highly similar gene isoforms with a clustering-based bioinformatics analysis of PacBio single-molecule long reads
Published in
BioData Mining, April 2016
DOI 10.1186/s13040-016-0090-8
Pubmed ID
Authors

Ma Liang, Castle Raley, Xin Zheng, Geetha Kutty, Emile Gogineni, Brad T. Sherman, Qiang Sun, Xiongfong Chen, Thomas Skelly, Kristine Jones, Robert Stephens, Bin Zhou, William Lau, Calvin Johnson, Tomozumi Imamichi, Minkang Jiang, Robin Dewar, Richard A. Lempicki, Bao Tran, Joseph A. Kovacs, Da Wei Huang

Abstract

Gene isoforms are commonly found in both prokaryotes and eukaryotes. Since each isoform may perform a specific function in response to changing environmental conditions, studying the dynamics of gene isoforms is important in understanding biological processes and disease conditions. However, genome-wide identification of gene isoforms is technically challenging due to the high degree of sequence identity among isoforms. Traditional targeted sequencing approach, involving Sanger sequencing of plasmid-cloned PCR products, has low throughput and is very tedious and time-consuming. Next-generation sequencing technologies such as Illumina and 454 achieve high throughput but their short read lengths are a critical barrier to accurate assembly of highly similar gene isoforms, and may result in ambiguities and false joining during sequence assembly. More recently, the third generation sequencer represented by the PacBio platform offers sufficient throughput and long reads covering the full length of typical genes, thus providing a potential to reliably profile gene isoforms. However, the PacBio long reads are error-prone and cannot be effectively analyzed by traditional assembly programs. We present a clustering-based analysis pipeline integrated with PacBio sequencing data for profiling highly similar gene isoforms. This approach was first evaluated in comparison to de novo assembly of 454 reads using a benchmark admixture containing 10 known, cloned msg genes encoding the major surface glycoprotein of Pneumocystis jirovecii. All 10 msg isoforms were successfully reconstructed with the expected length (~1.5 kb) and correct sequence by the new approach, while 454 reads could not be correctly assembled using various assembly programs. When using an additional benchmark admixture containing 22 known P. jirovecii msg isoforms, this approach accurately reconstructed all but 4 these isoforms in their full-length (~3 kb); these 4 isoforms were present in low concentrations in the admixture. Finally, when applied to the original clinical sample from which the 22 known msg isoforms were cloned, this approach successfully identified not only all known isoforms accurately (~3 kb each) but also 48 novel isoforms. PacBio sequencing integrated with the clustering-based analysis pipeline achieves high-throughput and high-resolution discrimination of highly similar sequences, and can serve as a new approach for genome-wide characterization of gene isoforms and other highly repetitive sequences.

Twitter Demographics

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Mendeley readers

The data shown below were compiled from readership statistics for 56 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 2 4%
United States 2 4%
Japan 1 2%
Unknown 51 91%

Demographic breakdown

Readers by professional status Count As %
Researcher 12 21%
Student > Ph. D. Student 8 14%
Other 7 13%
Student > Bachelor 6 11%
Student > Postgraduate 3 5%
Other 8 14%
Unknown 12 21%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 16 29%
Agricultural and Biological Sciences 16 29%
Immunology and Microbiology 5 9%
Veterinary Science and Veterinary Medicine 2 4%
Medicine and Dentistry 2 4%
Other 2 4%
Unknown 13 23%

Attention Score in Context

This research output has an Altmetric Attention Score of 15. 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 01 December 2019.
All research outputs
#1,774,535
of 19,854,211 outputs
Outputs from BioData Mining
#44
of 288 outputs
Outputs of similar age
#33,218
of 276,500 outputs
Outputs of similar age from BioData Mining
#1
of 1 outputs
Altmetric has tracked 19,854,211 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 288 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.1. This one has done well, scoring higher than 85% 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 276,500 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 87% of its contemporaries.
We're also able to compare this research output to 1 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