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  Dedicated transcriptomics combined with power analysis lead to functional understanding of genes with weak phenotypic changes in knockout lines

Xie, C., Bekpen, C., Künzel, S., Keshavarz, M., Krebs-Wheaton, R., Skrabar, N., et al. (2020). Dedicated transcriptomics combined with power analysis lead to functional understanding of genes with weak phenotypic changes in knockout lines. PLoS Computational Biology, 16(11): e1008354. doi:10.1371/journal.pcbi.1008354.

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Xie, Chen1, Author              
Bekpen, Cemalettin1, Author              
Künzel, Sven1, Author              
Keshavarz, Maryam1, Author              
Krebs-Wheaton, Rebecca1, Author              
Skrabar, Neva1, Author              
Ullrich, Kristian K.1, Author              
Zhang, Wenyu1, Author              
Tautz, Diethard1, Author              
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1Department Evolutionary Genetics, Max Planck Institute for Evolutionary Biology, Max Planck Society, ou_1445635              

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Free keywords: Transcriptome analysis; RNA sequencing; Genetically modified animals; Gene expression; Mammalian genomics; Gene prediction; Body limbs; Phenotypes
 Abstract: Author summary Knockout mice benefit the understanding of gene functions in mammals. However, it has proven difficult for many genes to identify clear phenotypes, related due to lack of sufficient assays. As Lewis Wolpert put it in a famous quote “But did you take them to the opera?”, thus metaphorically alluding to the need to extend phenotyping efforts. This insight led to the establishment of phenotyping pipelines that are nowadays routinely used to characterize knock-out lines. However, transcriptomic approaches based on RNA-Seq have been much less explored for such deep-level studies. We conducted here both, a theoretical power analysis and practical RNA-Seq experiments on two knockout lines with small phenotypic effects to investigate the parameters including sample size, sequencing depth, fold change, and dispersion. Our dedicated RNA-Seq studies discovered thousands of genes with small transcriptional changes and enriched in specific functions in both knockout lines. We find that it is more important to increase the number of samples than to increase the sequencing depth. Our work shows that a deep RNA-Seq study on knockouts is powerful for understanding gene functions in cases of weak phenotypic effects, and provides a guideline for the experimental design of such studies.

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Language(s): eng - English
 Dates: 2020-01-202020-09-202020-11-122020-11
 Publication Status: Published in print
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 Identifiers: DOI: 10.1371/journal.pcbi.1008354
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Title: PLoS Computational Biology
Source Genre: Journal
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Publ. Info: San Francisco, CA : Public Library of Science
Pages: - Volume / Issue: 16 (11) Sequence Number: e1008354 Start / End Page: - Identifier: ISSN: 1553-734X
CoNE: https://pure.mpg.de/cone/journals/resource/1000000000017180_1