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Sequence-specific bias correction for RNA-seq data using recurrent neural networks

Overview of attention for article published in BMC Genomics, January 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 (89th percentile)
  • High Attention Score compared to outputs of the same age and source (92nd percentile)

Mentioned by

blogs
2 blogs
twitter
8 tweeters

Citations

dimensions_citation
12 Dimensions

Readers on

mendeley
56 Mendeley
citeulike
1 CiteULike
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Title
Sequence-specific bias correction for RNA-seq data using recurrent neural networks
Published in
BMC Genomics, January 2017
DOI 10.1186/s12864-016-3262-5
Pubmed ID
Authors

Yao-zhong Zhang, Rui Yamaguchi, Seiya Imoto, Satoru Miyano

Abstract

The recent success of deep learning techniques in machine learning and artificial intelligence has stimulated a great deal of interest among bioinformaticians, who now wish to bring the power of deep learning to bare on a host of bioinformatical problems. Deep learning is ideally suited for biological problems that require automatic or hierarchical feature representation for biological data when prior knowledge is limited. In this work, we address the sequence-specific bias correction problem for RNA-seq data redusing Recurrent Neural Networks (RNNs) to model nucleotide sequences without pre-determining sequence structures. The sequence-specific bias of a read is then calculated based on the sequence probabilities estimated by RNNs, and used in the estimation of gene abundance. We explore the application of two popular RNN recurrent units for this task and demonstrate that RNN-based approaches provide a flexible way to model nucleotide sequences without knowledge of predetermined sequence structures. Our experiments show that training a RNN-based nucleotide sequence model is efficient and RNN-based bias correction methods compare well with the-state-of-the-art sequence-specific bias correction method on the commonly used MAQC-III data set. RNNs provides an alternative and flexible way to calculate sequence-specific bias without explicitly pre-determining sequence structures.

Twitter Demographics

The data shown below were collected from the profiles of 8 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 56 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 4%
Spain 1 2%
Belgium 1 2%
Taiwan 1 2%
Unknown 51 91%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 17 30%
Researcher 10 18%
Student > Bachelor 9 16%
Student > Master 7 13%
Other 3 5%
Other 5 9%
Unknown 5 9%
Readers by discipline Count As %
Computer Science 16 29%
Agricultural and Biological Sciences 13 23%
Biochemistry, Genetics and Molecular Biology 12 21%
Mathematics 2 4%
Philosophy 1 2%
Other 7 13%
Unknown 5 9%

Attention Score in Context

This research output has an Altmetric Attention Score of 16. 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 03 March 2017.
All research outputs
#848,467
of 12,533,815 outputs
Outputs from BMC Genomics
#322
of 7,408 outputs
Outputs of similar age
#35,082
of 338,490 outputs
Outputs of similar age from BMC Genomics
#2
of 27 outputs
Altmetric has tracked 12,533,815 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 93rd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,408 research outputs from this source. They receive a mean Attention Score of 4.3. This one has done particularly well, scoring higher than 95% 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 338,490 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 89% of its contemporaries.
We're also able to compare this research output to 27 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 92% of its contemporaries.