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Research Article
Yu Hamaguchi
Waseda University
Corresponding Author
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This work is licensed under a CC BY 4.0 License. Read Full License
Background: Differential expression (DE) analysis of RNA-seq data typically depends on gene annotations. Different sets of gene annotations are available for the human genome and are continually updated–a process complicated with the development and application of high-throughput sequencing technologies. However, the impact of the complexity of gene annotations on DE analysis remains unclear.
Results: Using “mappability”, a metric of the complexity of gene annotation, we compared three distinct human gene annotations, GENCODE, RefSeq, and NONCODE, and evaluated how mappability affected DE analysis. We found that mappability was significantly different among the human gene annotations. We also found that increasing mappability improved the performance of DE analysis, and the impact of mappability mainly evident in the quantification step and propagated downstream of DE analysis systematically.
Conclusions: We assessed how the complexity of gene annotations affects DE analysis using mappability. Our findings indicate that the growth and complexity of gene annotations negatively impact the performance of DE analysis, suggesting that an approach that excludes unnecessary gene models from gene annotations improves the performance of DE analysis.
Keywords
RNA-seq, Differential expression analysis, Benchmarking, Gene annotation
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The full text of this article is available to read as a PDF.
No competing interests reported.
This is a list of supplementary files associated with this preprint. Click to download.
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CURRENT STATUS: Under Review
Version 1
Posted 08 Mar, 2021
No community comments so far
Editorial decision: Major revision
On 30 Mar, 2021
Reviews received
Received 28 Mar, 2021
Reviewers agreed
On 15 Mar, 2021
Reviewers invited
Invitations sent on 15 Mar, 2021
Editor assigned
On 09 Mar, 2021
Editor invited
On 09 Mar, 2021
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On 09 Mar, 2021
First submitted
On 06 Mar, 2021
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Subject Areas
Epigenetics & Genomics
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A new preprint design is coming!
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This preprint is under consideration at BMC Genomics. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research Article
Yu Hamaguchi
Waseda University
Corresponding Author
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Under Review
Version 1
Posted 08 Mar, 2021
No community comments so far
Editorial decision: Major revision
On 30 Mar, 2021
Reviews received
Received 28 Mar, 2021
Reviewers agreed
On 15 Mar, 2021
Reviewers invited
Invitations sent on 15 Mar, 2021
Editor assigned
On 09 Mar, 2021
Editor invited
On 09 Mar, 2021
Submission checks complete
On 09 Mar, 2021
First submitted
On 06 Mar, 2021
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Epigenetics & Genomics
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background: Differential expression (DE) analysis of RNA-seq data typically depends on gene annotations. Different sets of gene annotations are available for the human genome and are continually updated–a process complicated with the development and application of high-throughput sequencing technologies. However, the impact of the complexity of gene annotations on DE analysis remains unclear.
Results: Using “mappability”, a metric of the complexity of gene annotation, we compared three distinct human gene annotations, GENCODE, RefSeq, and NONCODE, and evaluated how mappability affected DE analysis. We found that mappability was significantly different among the human gene annotations. We also found that increasing mappability improved the performance of DE analysis, and the impact of mappability mainly evident in the quantification step and propagated downstream of DE analysis systematically.
Conclusions: We assessed how the complexity of gene annotations affects DE analysis using mappability. Our findings indicate that the growth and complexity of gene annotations negatively impact the performance of DE analysis, suggesting that an approach that excludes unnecessary gene models from gene annotations improves the performance of DE analysis.
Figure 1
Figure 2
Figure 3
Figure 4
The full text of this article is available to read as a PDF.
No competing interests reported.
This is a list of supplementary files associated with this preprint. Click to download.
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