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  A dual transcript-discovery approach to improve the delimitation of gene features from RNA-seq data in the chicken model

Orgeur, M., Martens, M., Börno, S. T., Timmermann, B., Duprez, D., & Stricker, S. (2018). A dual transcript-discovery approach to improve the delimitation of gene features from RNA-seq data in the chicken model. Biology Open, 7(1): bio.028498. doi:10.1242/bio.028498.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-002E-A53E-0 Version Permalink: http://hdl.handle.net/21.11116/0000-0000-FDB7-5
Genre: Journal Article

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© 2018. Published by The Company of Biologists Ltd

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 Creators:
Orgeur, Mickael, Author
Martens, Marvin , Author
Börno, Stefan T.1, Author              
Timmermann, Bernd1, Author              
Duprez, Delphine , Author
Stricker, Sigmar2, Author              
Affiliations:
1Sequencing (Head: Bernd Timmermann), Scientific Service (Head: Christoph Krukenkamp), Max Planck Institute for Molecular Genetics, Max Planck Society, ou_1479670              
2Research Group Development & Disease (Head: Stefan Mundlos), Max Planck Institute for Molecular Genetics, Max Planck Society, ou_1433557              

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Free keywords: Chicken genome annotation, Gallus gallus, gene prediction, genome-guided transcript discovery, RNA-seq, transcriptome reconstruction
 Abstract: The sequence of the chicken genome, like several other draft genome sequences, is presently not fully covered. Gaps, contigs assigned with low confidence and uncharacterized chromosomes result in gene fragmentation and imprecise gene annotation. Transcript abundance estimation from RNA sequencing (RNA-seq) data relies on read quality, library complexity and expression normalization. In addition, the quality of the genome sequence used to map sequencing reads and the gene annotation that defines gene features must also be taken into account. Partially covered genome sequence causes the loss of sequencing reads from the mapping step, while an inaccurate definition of gene features induces imprecise read counts from the assignment step. Both steps can significantly bias interpretation of RNA-seq data. Here, we describe a dual transcript-discovery approach combining a genome-guided gene prediction and a de novo transcriptome assembly. This dual approach enabled us to increase the assignment rate of RNA-seq data by nearly 20% as compared to when using only the chicken reference annotation, contributing therefore to a more accurate estimation of transcript abundance. More generally, this strategy could be applied to any organism with partial genome sequence and/or lacking a manually-curated reference annotation in order to improve the accuracy of gene expression studies.

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Language(s): eng - English
 Dates: 2017-11-222018-01-17
 Publication Status: Published online
 Pages: -
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 Table of Contents: -
 Rev. Method: -
 Identifiers: DOI: 10.1242/bio.028498
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Title: Biology Open
Source Genre: Journal
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Publ. Info: Cambridge : The Company of Biologists
Pages: - Volume / Issue: 7 (1) Sequence Number: bio.028498 Start / End Page: - Identifier: ISSN: 2046-6390