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Improving RNA-Seq expression estimation by modeling isoform- and exon-specific read sequencing rate

Overview of attention for article published in BMC Bioinformatics, October 2015
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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 (92nd percentile)
  • High Attention Score compared to outputs of the same age and source (97th percentile)

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1 blog
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31 X users
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2 Facebook pages
googleplus
2 Google+ users

Citations

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15 Dimensions

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62 Mendeley
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1 CiteULike
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Title
Improving RNA-Seq expression estimation by modeling isoform- and exon-specific read sequencing rate
Published in
BMC Bioinformatics, October 2015
DOI 10.1186/s12859-015-0750-6
Pubmed ID
Authors

Xuejun Liu, Xinxin Shi, Chunlin Chen, Li Zhang

Abstract

The high-throughput sequencing technology, RNA-Seq, has been widely used to quantify gene and isoform expression in the study of transcriptome in recent years. Accurate expression measurement from the millions or billions of short generated reads is obstructed by difficulties. One is ambiguous mapping of reads to reference transcriptome caused by alternative splicing. This increases the uncertainty in estimating isoform expression. The other is non-uniformity of read distribution along the reference transcriptome due to positional, sequencing, mappability and other undiscovered sources of biases. This violates the uniform assumption of read distribution for many expression calculation approaches, such as the direct RPKM calculation and Poisson-based models. Many methods have been proposed to address these difficulties. Some approaches employ latent variable models to discover the underlying pattern of read sequencing. However, most of these methods make bias correction based on surrounding sequence contents and share the bias models by all genes. They therefore cannot estimate gene- and isoform-specific biases as revealed by recent studies. We propose a latent variable model, NLDMseq, to estimate gene and isoform expression. Our method adopts latent variables to model the unknown isoforms, from which reads originate, and the underlying percentage of multiple spliced variants. The isoform- and exon-specific read sequencing biases are modeled to account for the non-uniformity of read distribution, and are identified by utilizing the replicate information of multiple lanes of a single library run. We employ simulation and real data to verify the performance of our method in terms of accuracy in the calculation of gene and isoform expression. Results show that NLDMseq obtains competitive gene and isoform expression compared to popular alternatives. Finally, the proposed method is applied to the detection of differential expression (DE) to show its usefulness in the downstream analysis. The proposed NLDMseq method provides an approach to accurately estimate gene and isoform expression from RNA-Seq data by modeling the isoform- and exon-specific read sequencing biases. It makes use of a latent variable model to discover the hidden pattern of read sequencing. We have shown that it works well in both simulations and real datasets, and has competitive performance compared to popular methods. The method has been implemented as a freely available software which can be found at https://github.com/PUGEA/NLDMseq .

X Demographics

X Demographics

The data shown below were collected from the profiles of 31 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 62 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 5%
Portugal 1 2%
Sweden 1 2%
Germany 1 2%
Taiwan 1 2%
United Kingdom 1 2%
Unknown 54 87%

Demographic breakdown

Readers by professional status Count As %
Researcher 21 34%
Student > Ph. D. Student 11 18%
Student > Master 9 15%
Other 5 8%
Student > Postgraduate 4 6%
Other 8 13%
Unknown 4 6%
Readers by discipline Count As %
Agricultural and Biological Sciences 28 45%
Biochemistry, Genetics and Molecular Biology 13 21%
Computer Science 6 10%
Engineering 2 3%
Unspecified 1 2%
Other 7 11%
Unknown 5 8%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 26. 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 28 October 2015.
All research outputs
#1,347,287
of 23,881,329 outputs
Outputs from BMC Bioinformatics
#186
of 7,454 outputs
Outputs of similar age
#20,409
of 282,561 outputs
Outputs of similar age from BMC Bioinformatics
#4
of 134 outputs
Altmetric has tracked 23,881,329 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 94th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,454 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 97% 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 282,561 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 92% of its contemporaries.
We're also able to compare this research output to 134 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 97% of its contemporaries.